DETAILED ACTION
This is office action is in response to the RCE filed 12/15/2025. Claims 1-20 are examined and pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/15/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1, 19 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 2, 5, 6, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Moresmau et al. (U.S. 2021/0357503 A1, hereinafter “Moresmau”) in further view of Bouillet et al. (U.S. 2018/0293511, hereinafter “Bouillet”).
As to claims 1, 19 and 20, A method comprising using at least one hardware 1. processor to: during a building phase,
collect data from a plurality of integration platforms managed through an integration platform as a service (iPaaS) platform, wherein the data comprise representations of a plurality of integration processes, and wherein each of the plurality of integration processes comprises at least one lineage including a sequence of steps that represents at least one path through the integration process (para. [0035]-[0036]; discloses receiving data regarding source and targets and includes customer identification number and the flow of data could be transmitted to one or more databases for tracking the customers purchasing activity),
generate a dataset comprising representations of the lineages in the plurality of integration processes (para. [0078]; discloses path of data flows in the direction from target to source for a given start data element contains all the data elements forming the data lineage for the starting data element), wherein generating the dataset comprises flattening each of the plurality of integration processes, comprising multiple paths, in the collected data, into a plurality of lineages that each consists of a single path through the integration process (para. [0089]-[0092]; discloses data field can be traced throughout an entire enterprise IT from start to the end),
However, Moresmau does not explicitly discloses based on the dataset, build a model that receives a lineage as an input and predicts at least one next step to be added to the input lineage as an output, wherein the at least one next step transforms integration data flowing through an integration process represented by the input lineage.
In an analogous art, Bouillet discloses based on the dataset, build a model that receives a lineage as an input and predicts at least one next step to be added to the input lineage as an output, wherein the at least one next step transforms integration data flowing through an integration process represented by the input lineage.(para. [0038]; discloses retrieving model lineages, model classes and data used to create a model and para. [0082]; discloses using adaptable model system, the output may be a publication of a new data feed (prediction) containing an adaptable model applied to a new data source. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moresmau by incorporating the function to retrieving lineages and an output is received from the model that generates prediction data as taught by Bouillet in order to automatically train and/or re-train one or more adaptable models and maintain the model versions when new data/data streams become available.(see Bouillet, para. [0083])
As to claim 2, Moresmau -Bouillet discloses the method of Claim 1, wherein each of the plurality of integration platforms is managed by a different organizational account than one or more other ones of the plurality of integration platforms (Moresmau, para. [0020], [0038]; discloses the enterprise data intelligence solution can manage metadata from different systems)
As to claim 5, Moresmau-Bouillet discloses the method of Claim 1, wherein the model comprises a prediction tree that comprises branches representing all of the lineages in the dataset (Bouillet, para. [0042]; discloses For the incoming data flow/data stream (e.g., reacting to indexing of the new data sources into the system), a list of recipes that may apply to the data source may be automatically listed/provided. The applicable recipes may be deployed on the data source. Each of the new adaptive models, determined values and/or KPIs linked with the applicable recipes to the incoming data flow/data stream may be stored and indexed.)
As to claim 6, Moresmau- Bouillet discloses the method of Claim 5, wherein the prediction tree is stored as a tree data structure (Moresmau, para. [0051]; discloses building a tree data structure such as an abstract syntax tree that can show the flow of possible traffic from source to end).
As to claim 14, Moresmau-Bouillet discloses the method of Claim 1, wherein the model predicts a plurality of potential next steps, and wherein each of the plurality of potential next steps is associated with a confidence value (Bouillet, para.[0050]; discloses predictive results of each model may be browsed while a result lineage of the models may be retrieved (e.g., retrieve the model and data and use the model and data to create the predictive results). ).
As to claim 15, Moresmau-Bouillet discloses the method of Claim 1, further comprising using the at least one hardware processor to, during a subsequent building phase, update the model based on collected feedback (Bouillet, para. [0091]; discloses updating the adaptable models based on new prediction stream (predictor variable) is detected as they become available).
As to claim 16, Moresmau - Bouillet discloses the method of Claim 1, further comprising using the at least one hardware processor to, after the building phase, deploy the model (Bouillet, para. [0098]; discloses the model is deployed with the new input values)
As to claim 17, Moresmau -Bouillet discloses the method of Claim 16, wherein the model is deployed as a microservice within the iPaaS platform (Bouillet, para. [0054],[0072]; discloses Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid Clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. ).
As to claim 18, Moresmau-Bouillet discloses the method of Claim 1, wherein the graphical user interface comprises a virtual canvas on which steps are dragged and dropped to construct the integration process (Moresmau, para. [0104]; discloses displaying the alteration of the data lineage for the selected data element on the graphical user interface accessible to the user, the alteration of the data lineage being the change in the at least one source-target pair over the time period comprising a change in the graphical data flow for the selected data element from each source-target pair across the plurality of disparate software platforms in the enterprise software system during the time period.).
Claims 4, 7-13 are rejected under 35 U.S.C. 103 as being unpatentable over Moresmau in view of Bouillet in further view of Iyer et al. (U.S. 2022/0075605 A1, hereinafter “Iyer”).
As to claim 4, Moresmau -Bouillet discloses the method of Claim 1, however - Moresmau-Bouillet does not disclose the method wherein the model comprises a Markov chain.
In an analogous art, Iyer discloses the method wherein the model comprises a Markov chain. (para.[0082]; discloses model includes a Markov chain and other machine learning and neural network models)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Moresmau -Bouillet by incorporating well-known machine learning models such as Markov chain and other variations as taught by Iyer in order to tailor the model based on the user and environments optimal preference.
As to claim 7, Moresmau-Bouillet-Iyer discloses the method of Claim 1, wherein the model comprises an artificial neural network (Iyer, para. [0085]; discloses adaptable model system and machine learning based models that includes artificial neural network).
As to claim 8, Moresmau -Bouillet-Iyer discloses the method of Claim 7, wherein the dataset comprises, for each lineage represented in the dataset, a feature set that comprises an adjacency matrix, representing steps and connections within a first portion of the lineage, and is labeled with at least one next step in a second portion of the lineage (Iyer, para. [0099]; discloses data is combined into one large synaptic matrix where it is assumed that the input vector has appended ones and extra columns representing the b synapses are subsumed to W).
As to claim 9, Moresmau -Bouillet -Iyer discloses the method of Claim 8, wherein, for each lineage represented in the dataset, the feature set further comprises one or more other features associated with the lineage (Iyer, para.[0045]; discloses each feature that is extracted is associated with business process) .
As to claim 10, Moresmau -Bouillet -Iyer discloses the method of Claim 8, wherein, for each lineage represented in the dataset, the feature set further comprises configuration properties for each step represented in the first portion of the lineage and for the at least one next step in the second portion of the lineage (Roy, para.[0071]; discloses automatically building a business process corpus based on the cloud integration contents, automatically extracting a set of features from the cloud integration contents, automatically assigning, using an ensemble classifier, each feature of the set of features to a business process category, and automatically generating the business process map based on output from the ensemble classifier. ).
As to claim 11, Moresmau -Bouillet -Iyer discloses the method of Claim 7, wherein the artificial neural network is a graph neural network (Iyer, para.[0082]; discloses neural network used can be convolutional inverse graphics networks) .
As to claim 12, Moresmau -Bouillet -Iyer discloses the method of Claim 11, wherein the graph neural network is a graph convolutional network (Iyer, para.[0082]).
As to claim 13, Moresmau -Bouillet -Iyer discloses the method of Claim 7, wherein the artificial neural network is a recurrent neural network (Iyer, para.[0082]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. (U.S. 2020/0279180 A1) discloses a device wherein the processing circuitry: receives a request to predict an outcome of an unresolved customer support case; extracts a set of features from a case record for the unresolved case, including a set of categorical features and a set of textual features; encodes the set of categorical and textual features into numerical representations; predicts the outcome of the unresolved customer support case using a trained case prediction model, wherein the trained case prediction model generates a predicted outcome based on the encoded categorical and textual features; and performs a corresponding customer support action based on the predicted outcome of the unresolved customer support case.
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/JOE CHACKO/Primary Examiner, Art Unit 2457