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 .
Status of Claims
Claims 1-4, 6-12, and 14-20 are pending of which claims 1, 10 and 17 are in independent form.
Claims 1-4, 6-12, and 14-20 are rejected under 35 U.S.C. 101.
Claims 1-4, 6-12, and 14-20 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-4, 6-12, and 14-20 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.
Regarding 35 USC 101 (Abstract Idea)
Applicant’s arguments have been fully considered but are not persuasive. Although Applicant asserts that the claimed operations cannot practically be performed in the human mind and improve computer simulation efficiency, the rejection is maintained because independent claims expressly recite mathematical algorithm/concept, including partial delay differential equations, local derivatives, and searching along a pathway of maximal gradient change regardless of whether such calculations can practically be performed mentally. Furthermore, the asserted reduction in CPU, GPU, Ram, or simulation time described in the specification does not establish integration into a practical application where the claim itself does not recite an improvement to computer hardware, a particulate PDE solver, memory management techniques, or other improvement to computer functionality; rather, the claim uses mathematical model simplification and optimization to obtain and execute a desired result. Accordingly, the claimed mathematical concept are not integrated into a practical application simply because their computer implementation mat permit calculations to be performed more efficiently, and the 35 USC 101 rejection is therefore maintained.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) database management device using database graphs and hierarchical layering.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claim 1 are directed to a method, which is process (statutory).
Independent claim 10, is directed to a non-transitory computer-readable medium (statutory).
Independent claim 17 is directed to a system comprising one or more processors, one or memories (statutory).
Independent All other claims depend on claims 1, 10 and 17. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 10 and 17
With respect to step 2A, prong one (Judicial Exception), it is noted that the independent claims recite an abstract idea falling within the Mental Process grouping of abstract ideas. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
“simplifying the model by removing relationships between the hierarchy of nodes that affect parent nodes less than a threshold amount” the limitation as drafted recites a mathematical algorithm involving evaluation and filtering of relationship based on a threshold, which can be performed through observation and judgement;
“assigning partial delay differential equations to relationships between the hierarchy of nodes” the limitation as drafted recites a mathematical algorithm, namely the use of differential equations to model relationships.
“simulating the simplified model” the limitation as drafted recites a mathematical algorithm involving computation of model behavior based on equation.
“identify a node … that risks missing a predefined target value” the limitation as drafted recites a mathematical algorithm involving evaluation, prediction, and judgement based on analyzed data.
“generating a pathway of actions comprising changes to driver nodes of the node that cause the time series of data to move within a threshold distance of the predefined target value; by searching along a pathway of maximal gradient change defined by local derivatives of the simplified model” the limitation as drafted recites a mathematical algorithm including calculating or utilizing derivatives and gradients of the a mathematical model and perform a gradient based search to identify a direction or change
“causing the pathway of actions to be executed” the limitation as drafted recites implementation of the planned action, which is a result oriented step following the abstract decision making process.
These limitations correspond to concept that can be performed in the human mind (Mental Process) and therefore fall within the mental process category of abstract idea (see MPEP 2016.04(a)(2)).
The claims fall within:
Mathematical Algorithm/Concept
Thus, the claims recite an abstract idea (mathematical concepts/information processing).
With respect to step 2A, Prong Two (Particular Application), the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite the use of:
“a model of a system, … is represented by a hierarchy of nodes … comprise time series of data” as drafted recites generic data representation/methodical model, which amounts to an abstract idea used to organize and analyze information, does not impose any meaningful limit on the abstract idea.
“simplifying the model by removing relationships … less than a threshold amount” as drafted recites insignificant extra-solution activity, including data analysis and filtering, which constitutes mere data manipulation prior to performing the abstract idea (see MPEP 2106.05(g)).
“assigning partial delay differential equations to relationships between the hierarchy of nodes” as drafted recites the mathematical concept itself, and do not impose any meaningful limit on how the abstract idea is performed, but instead reflect the use of mathematical tool to carry out the abstract idea.
“simulating the model” as drafted recites the mathematical processing and analysis, which amounts to mere instructions to apply the abstract idea using mathematical techniques (see MPEP 2106.05(f)).
“identify a node … that risks missing a predefined target value” as drafted recites a mental process involving evaluation and prediction, which is part of the abstract idea itself.
“generating a pathway of actions … that cause the time series of data to move within a threshold distance of the predefined target value…by searching along a pathway of maximal gradient change defined by local derivatives of the simplified model” as drafted recites result oriented functional language describing planning and optimization, without specifying any particular technical mechanism for implementing such actions.
“ causing the pathway of actions to be executed” as drafted recites generic implementation of the abstract idea, without reciting and specific technical means for execution, and therefore amount to mere instructions to apply the abstract idea.
The additional elements mentioned above fail to integrate the abstract idea into a practical application because the additional elements, individually and in combination, amount to no more than:
Mathematical Modeling and analysis of data; and
Mental Process including evaluation, prediction and planning
Result Oriented instructions to apply the abstract idea.
The claims do not:
Improve the functioning of a computer or processor;
Improve simulation technology or differential equation modeling techniques;
Provide a specific technical implementation of solving or applying partial delay differential equation;
Defining and particular technical mechanism for executing the generated actions on a system.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract mathematical encoding, data analysis, and decision making, with the result merely being applied in a generic manner.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the computer components are used as tools to perform the abstract idea of collecting, organizing, and associating information about nodes and their relationships.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims are merely generic computing components, routine and, conventional data processing and storage. Courts have consistently helped such high-level information management operations are conventional.
The claims recite only functional, result oriented language (“detecting”, “propagating”, “transferring”,…), without specifying any technical mechanism for performing these operations in a non-conventional manner.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 2-4, (Structural/Hierarchical Organization),
The claim recites:
Non-cyclical patent-child hierarchy (claim 2)
Removing parameters below a threshold (claim 3)
Removing non-driver nodes (claim 4)
This merely refines: organizing nodes into hierarchies, selecting/removing elements based on criteria. No additional technical implementation is recited for: how hierarchy is constructed, how relationships are evaluated. These fall under: Mental Process (classification, filtering, prioritization).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 6-7, (Model Simplification/Parameter Tuning),
The claim recites:
Initializing equation using domain specific/default values (claim 6)
Limiting boundary conditions to reduce search space (claim 7)
This merely recites: parameter selection and constraint setting, and rile based optimization inputs. No additional technical improvement to: computing efficiency, model execution mechanism. These fall under: Mental Process (rule setting/constraint selection) and Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 8-9, (Mathematical Optimization Techniques),
The claim recites:
Simulated annealing optimization (claim 8)
Selecting best fit model from multiple models (claim 9)
These explicitly recite well-known mathematical algorithms and optimization techniques. No implementation detail is provided beyond applying the algorithm. These fall under: Mental Process (evaluation and selection) and Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 11-12, (Mathematical Optimization Techniques),
The claim recites:
Identifying local derivatives (claim 11)
Defining local solution space (claim 12)
These explicitly recite well-known/pure mathematical operations and gradient/local optimization concepts. No technical mechanism for: computing derivatives, applying them in a specific system architecture. These fall under: Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 13-16, (Pathway Optimization/Gradient based Search),
The claim recites:
Searching along maximal gradient pathway (claim 13)
Largest observed change (claim 14)
Actions affecting time series (claim 15)
Achieving target within standard deviation (claim 16)
These merely recite: optimization and evaluation of outcomes, and mathematical comparison (standard deviation). No technical improvement in: simulation engine, data processing mechanism. These fall under: Mental Process (evaluation and decision making) and Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 19-20, (Pathway Optimization/Gradient based Search),
The claim recites:
Calculating cost equation outputs (claim 18)
Displaying summarized results (claim 19)
Including time delay in outputs (claim 20)
These merely recite: mathematical calculation, insignificant post-solution activity, mathematical parameters in evaluation. No technical improvement in: simulation engine, data processing mechanism. These fall under: Insignificant Post-Solution Activity (display/output) and Mathematical Algorithm.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
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) 1-4, 7, 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ni; Kang-Yu et al. (US 20180332256 A1) [Ni] in view of Verma; Mayank et al. (US 20190132224 A1) [Verma] in view of Raghavendra; Ramya et al. (US 20200287923 A1) [Raghavendra] in view of Morton; Kirsty et al. (US 20110246161 A1) [Morton].
Regarding Claims 1, 10 and 17, Ni discloses, a method of creating and executing action pathways for time series data, the method comprising: accessing a model of a system, wherein the system is represented by a hierarchy of nodes in a data structure, nodes in the hierarchy of nodes comprise time series of data (multi-scale multilayer graph framework…based on a time series of observable ¶ [0051], multilayer graph representation…detects dependencies ¶ [0055], signals…between nodes….these are time series for each pair of nodes ¶ [0057]);
and assigning partial delay differential equations to relationships between the hierarchy of nodes (partial differential equation…depends on…graph Laplacian operator… graph gradient operator…with time delay ¶ [0058]. Examiner hereby specifies that, partial delay differential equations has been interpreted as PDE with time delay);
simulating the [simplified] model (the vessel flow is simulated with the partial differential equation described…¶ [0077]);
However, Ni does not explicitly facilitate simplifying the model by removing relationships between the hierarchy of nodes that affect parent nodes less than a threshold amount; simplified.
Verma discloses, simplifying the model by removing relationships between the hierarchy of nodes that affect parent nodes less than a threshold amount; simplified (prune the graph…threshold-based pruning rule…keep neighbors…larger than a threshold ¶ [0069]-[0070]. Pruning rules…configuring to remove…edges…based on threshold ¶ [0156]).
It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the cited references because Verma’s system would have allowed Ni to facilitate simplifying the model by removing relationships between the hierarchy of nodes that affect parent nodes less than a threshold amount. The motivation to combine is apparent in the Ni’s reference, because there is a need to improve systems and methods for analyzing network activity and, more particularly, to systems and methods for identifying outlier network activity.
However, neither Ni nor Verma explicitly facilitate to identify a node comprising a time series of data that risks missing a predefined target value; generating a pathway of actions comprising changes to driver nodes of the node that cause the time series of data to move within a threshold distance of the predefined target value in the future; and causing the pathway of actions to be executed.
Raghavendra discloses, to identify a node comprising a time series of data that risks missing a predefined target value (deviation from a forecasted value can signal anomaly ¶ [0034]; forecasting and detecting problems via deviation [0044]);
generating a pathway of actions comprising changes to driver nodes of the node that cause the time series of data to move within a threshold distance of the predefined target value in the future (system may instantiate a change…automatically or autonomously; resources may be auto-scaled ¶ [0034], [0044]; anomaly detection and auto-scaling decisions ¶ [0021]. Examiner has interpreted: controllable system components as “driver nodes”; sequence of system changes as “pathway of actions”; and correcting deviations as “move towards target”); and
causing the pathway of actions to be executed (system may instantiate a change automatically ¶ [0034]).
It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the cited references because Raghavendra’s system would have allowed Ni and Verma to facilitate to identify a node comprising a time series of data that risks missing a predefined target value; generating a pathway of actions comprising changes to driver nodes of the node that cause the time series of data to move within a threshold distance of the predefined target value in the future; and causing the pathway of actions to be executed. The motivation to combine is apparent in the Ni and Verma’s reference, because there is a need to improve accurately predict system anomalies or determine root cause of performance issues.
However, neither one of Ni, Raghavendra, or Verma explicitly facilitates by searching along a pathway of maximal gradient change defined by local derivatives of the simplified model.
Morton discloses, by searching along a pathway of maximal gradient change defined by local derivatives of the simplified model (With all of the gradients calculated for each parameter of interest using the adjoint method, we present a new algorithm for finding an optimal solution of the inverse problem. First one starts with an initial state for the parameters. The following steps are then carried out: 1. Solve the forward problem with respect to the initial and boundary conditions. 2. Solve the adjoint problem with respect to the boundary conditions. 3. Calculate the gradient of the objective function with respect to each parameter assuming that the local Gaussian field model is applicable. 4. Determine the search direction using the gradient (steepest descent method) and previous search directions (conjugate-gradient and quasi-Newton methods). 5. Apply a one-dimensional numerical minimization, such as a line search or the secant method to minimize the objective function in the search direction. 6. Return to step 1 using the new state obtained from step 5. ¶ [0067]-[0073]).
It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the cited references because Morton’s system would have allowed Ni, Verma and Raghavendra to facilitates by searching along a pathway of maximal gradient change defined by local derivatives of the simplified model. The motivation to combine is apparent in Ni, Verma, and Raghavendra’s reference, because there is a need to improve integrating low resolution pressure transient test analysis with higher resolution petrophysical.
Regarding claim 2, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the hierarchy of nodes in the data structure comprises a plurality of non-cyclical, linear parent-child relationships (Ni: multilayer/multiscale hierarchical graph ¶ [0051], [0055]; node relationships ¶ [0057]. Also see Figs. 4-7).
Regarding claim 3, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein simplifying the model further comprises removing parameters from the model that affect simulated values less than a threshold amount (Verma: threshold based pruning of graph elements ¶ [0069], [0156]).
Regarding claim 4, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein simplifying the model further comprises removing non-driver notes from the hierarchy of nodes (Verma: pruning rules…configured to remove...nodes and edges from the graph ¶ [0156]. Also see ¶ [0067], [0083]).
Regarding claim 7, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein simplifying the model further comprises limiting boundary conditions of the partial delay equations to real-world limits to minimize a search space (Ni: PDE-based modeling of real-world system dynamics ¶ [0058], [0077]. Before deploying drones to contested water, the system can generate the multi-scale vessel graph by applying the algorithm on data collected via satellites, determine regions of interest with thresholds (e.g., a significant deviation/changes in flow-dependency within a priori-determined time window), and send drones to the regions of interest to collect finer-grained data, or perform monitoring and tracking with desired level of coverage (e.g., zone size) for given constraints (e.g., # of drones available, processing powers, etc.) ¶ [0089]. Examiner specifies that, limiting boundary conditions are standard in PDE modeling and reduces computation/search space).
Regarding claim 11, the combination of Ni, Raghavendra, Verma, and Morton discloses, simulating the model to identify local derivatives for the node with respect to the driver nodes of the node (Ni: modeling node relationship using PDE and system is simulated using PDE ¶ [0058], [0077]. Examiner specifies that PDE based modeling inherently computes rates of change (derivatives) across nodes).
Regarding claim 12, the combination of Ni, Raghavendra, Verma, and Morton discloses, defining a local space for solution exploration with respect to each of the driver nodes using the local derivatives (Raghavendra: model training/forecasting using local dependencies ¶ [0022]-[0025], [0033], evaluating system behavior using learned representation ¶ [0044]).
Regarding claim 13, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein generating the pathway of actions comprises searching along a pathway of a maximal gradient change from among a plurality of pathways (Raghavendra: models update based on improving accuracy/outputs ¶ [0022], [0033], evaluating system behavior using learned representation ¶ [0044]).
Regarding claim 14, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the maximal gradient change generate a largest observed change in simulated future values for the node (Raghavendra: forecasting future values ¶ [0022], improving prediction accuracy ¶ [0033]).
Regarding claim 15, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the pathway of actions comprises actions that cause changes to time series associated with the driver nodes for the node (Raghavendra: system may instantiate a change…automatically or autonomously; resources may be auto-scaled ¶ [0034], [0044]; anomaly detection and auto-scaling decisions ¶ [0021]. Examiner has interpreted: controllable system components as “driver nodes”; sequence of system changes as “pathway of actions”; and correcting deviations as “move towards target”).
Regarding claim 16, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein generating the pathway of actions comprises changing a plurality of time series associated with the driver nodes until a resulting simulated future value of the node is within one standard deviation of the predefined target value (Verma: a classification threshold may be calculated by adding the mean of class_0 and the standard deviation of class_0, and then nodes having PPR scores below the classification threshold may be assigned a zero ranking (e.g., a ranking associated with one of the classes) and all other nodes may be assigned a 1 ranking (e.g., ranking associated with the other class) ¶ [0080]).
Regarding claim 18, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the operations further comprise calculating cost equation outputs of actions in the pathway of actions (Verma: a cost function is formed….optimization problem…solve…output ¶ [0098]).
Regarding claim 19, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the operations further comprise generating a display summarizing actions of the pathway of actions and corresponding cost equation outputs (Verma: cost outputs ¶ [0098], presentation layer … summarize the available account level and customer level variables ¶ [0053]).
Regarding claim 20, the combination of Ni, Raghavendra, Verma, and Morton discloses, wherein the cost equation outputs comprise [a time delay until] the time series of data moves within the threshold distance of the predefined target (Verma: cost outputs ¶ [0098]);
time delay (Ni: time delay in PDE ¶ [0058]).
Claim(s) 6, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ni in view of Verma in view of Raghavendra in view of Morton in view of Joyce; Karen E. et al. (US 20140222738 A1) [Joyce].
Regarding claim 6, the combination of Ni, Raghavendra, Verma, and Morton discloses, the hierarchy of nodes (Verma: Hierarchical tree representing nodes ¶ [0127]-[0128]. Pruning rules…configured to remove...nodes and edges from the graph ¶ [0156]. Also see ¶ [0067], [0083]).
However, neither one of Ni, Raghavendra, Verma, or Morton explicitly facilitates wherein simplifying the model further comprises removing non-driver notes from.
Joyce discloses, wherein simplifying the model further comprises removing non-driver notes from (removal of nodes to analyze impact on network structure; highly central nodes maybe removed ¶ [0120], iterative removal of nodes to evaluate network metrics [0121], [0126]; identifying nodes based on: centrality, leverage, influence on information flow ¶ [0120]-[0122]).
It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the cited references because Joyce’s system would have allowed Ni, Raghavendra, Verma, and Morton to facilitate wherein simplifying the model further comprises removing non-driver notes from. The motivation to combine is apparent in Ni, Raghavendra, Verma, and Morton’s reference, because there is a need to reduce complexity, improve interpretability and improve computational efficiency.
Regarding claim 8, the combination of Ni, Raghavendra, Verma, Morton, and Joyce discloses, wherein simplifying the model further comprises performing a simulated annealing algorithm on the model that optimizes based on an error function (Joyce: The model parameters may be determined by optimization calculations including evolutionary algorithms, simulated annealing and/or hill climbing calculations ¶ [0007]).
Regarding claim 9, the combination of Ni, Raghavendra, Verma, Morton, and Joyce discloses, wherein simplifying the model further comprises identifying a best-fitting model from a plurality of models (Joyce: null models…generated…for each network ¶ [0160], identifying a best performing model from plurality of models, as evidenced by the generation of multiple null models and determining that a fully connected model “perform the best out of all null models” ¶ [0184]) using different partial delay equations for relationships between the hierarchy of nodes (Ni: partial differential equation…depends on…graph Laplacian operator… graph gradient operator…with time delay ¶ [0058], [0077]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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9/29/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
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