DETAILED ACTION
This action is responsive to the Applicant’s response filed 12/03/25.
As indicated in Applicant’s response, claims 1, 4-6, 13, 18-19, 22, 26, 33-35, 48-49, 52 have been amended, and claims 2-3, 8-12, 14-17, 20-21, 23-25, 28-32, 36, 38-41, 43-44, 46-47, 50-51 cancelled. Claims 1, 4-7, 13, 18-19, 22, 26-27, 33-35, 37, 42, 45, 48-49, 52 are pending a next office action.
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, 4-7, 18-19, 22, 34-35, 37, 49, 52 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson) in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain)
As per claim 1, Watson discloses an apparatus comprising: at least one memory; machine-readable instructions; and processor circuitry to at least one of instantiate or execute the machine-readable instructions to:
identify a software application deployed (time series pattern, analytic models - para 0031; Fourier series, probabilistic mapping of the global responses - para 0186; probabilistic modeling, classifying the source - para 0027; statistical and causality model with supporting algorithms and software methods … pattern recognition numerical methods to estimate process parameters or optimize process operation as well as those to monitor, test, and classify observations … Probabilistic Graphical Model Bayesian Net - para 0049; an algorithm may more expeditiously search for corrective values - para 0139; learning techniques, process responses numerical search optimal solution for the operating parameters - para 0186; numerical search to converge - para 0034; other agents, algorithms control modes to resolve the deviation - para 0138; software firmware, software components - para 0193, 0196) for access and execution by multiple different process control systems (integration of … multiple process models – para 0040; multiple applications … can be made to work collaboratively – para 0041; software … herein … deployed on one or more computing device 1210 – para 0197) including a first process control system (AI manager, modeling agent, Pump controller, well equipment - Figs. 4; AIMC - para 0047-0048; statistical and causality model, PGM – para 0049);
identify UI provided feedback (e.g. operator input and manual tests … requested – para 0179; unacceptable levels of stress are flagged and returned to the AI … so that specific control action may be … implemented – para 0125; deviation … identified and flagged as to their severity – para 0094; user to … identify where … the rod string is being overstressed – para 0020, 0123 – see Note1 from below) regarding a feature (e.g. rod string cycle – Fig. 7A,B; para 0123; probable causes for the deviation - claim 2, pg. 12; probabilistic mapping of the global responses - para 0186; predicted operational envelope - para 0027; real time process data - para 0027; para 0068, 0070; Test results - Fig. 10; results - para 0027-0028; results of the tests - para 0040; probable causes for the deviation - para 0063; results of tests from which to identify underlying conditions - para 0117; communicating the results - para 0198, 0199; deviation from normal or predicted operation - para 0019; identify deviation from normal operation - para 0027; operational variance identifying deviations, drift from the operational envelope - para 0063; potential deviations, simulations and/or numerical methods, process response, represented as Fourier series - para 0033-0034) of the software application (e.g. Probabilistic Graphical Model Bayesian Net - para 0049; controller programmed with appropriate software firmware – para 0193; software components … for supporting the execution of application programs - para 0196; software deployed on one or more computing device among at least controller 1202 – para 0197);
cause a change to the software application (levels of stress are flagged and returned to the AI … so that specific control action may be … implemented – para 0125; flagged for … change of control mode or action depending on the nature of the condition – para 0134; recommended control action – para 0198) based on the UI-provided feedback (para 0020 – Note1: observation from an envelope graph for an user to visually detect a overstress condition – Fig. 7A, B and para 0123 – and flagging it – para 0125, 0134 - for eliciting a corrective control action reads on control action being recommended or implemented in response to UI-provided feedback/identification of a deviation condition or faulty response ; e.g. from graphical representation of pump behavior); and
redeploy the software application (iterating the predictive model with actual results to converge on revised process parameters - para 0151; deviation from nominal values parameters or metrics as continuous distributions and determined through an exhaustive iteration of the model for all values - para 0148; para 0156-0179; tuning – para 0113, 0126-0127, 0132, 0140 ) for access by the multiple process control systems (multiple applications … can be made to work collaboratively – para 0041) based on software application (para 0139; algorithm and software methods – para 0049; controller … programmed with appropriate software – para 0193; software herein - para 0197) to include the change.
A) Watson does not explicitly disclose
identifying of UI provided feedback as identifying user-provided feedback, and
causing a change to the software application based on the user-provided feedback and recompiling of the software application to include the change.
Watson discloses implementing software change via input and test actions (para 0179) requested from operators, the change responsive to flagging of fault or conditions (para 0125, 0134) for a corresponding control action to be taken; e.g. responsive to identification by an user (para 0020) of overstressed conditions of a pump on a graphical envelope (para 0123), where process control is basically provided by algorithms and software (para 0139; algorithm and software methods – para 0049; controller … programmed with appropriate software – para 0193; software herein - para 0197). Hence, input and interactive identification by a user/operator based on graphical response discloses a form of feedback; that is, user-feedback being indication/source for change of a software, control action to be undertaken is either disclosed or would have been obvious.
Jain discloses automation AI (para 0395-0397) in form of reinforcement machine learning
associated with agent managing process/circuitry (Fig. ID5-A, ID5-C, ID5-D) using optimized graph
provided as candidates resources (Fig. 1D5-E) to a hyperparameter tuning (hyper parameter tuning -
para 0231, 0240) and benchmark for generating embedded metrics as part of rendering inference
decision on a runtime workload (para 0227), using computing resources of Edge Cloud directed to
provide services and offered solutions to consumer/producer endpoints, autonomous vehicles,
business and industrial equipment and data centers to mitigate energy cost and NW usage excess
(para 0089) using processing devices from cloud applications, IoT, gateways and process control
(para 0160) such as power distribution, pipeline control, plant control light switches (para 0161)
Systems, using machine instructions in compiled format (para 0767); where outcome by quantization or permutations by a neural network reinforcement is monitored for precision, reward value which can be redefined as a repeating action by user input, by which the user can assign a predetermined value to boost a higher performance and reward.
Hence, user-provided action or feedback responsive to outcome by software executing a machine learning in repeating action of ML based quantization within a system using graph resources for rendering decision in regard to optimization of executing resources, performance boosting of network endpoints operations related to a control process or industrial environment is recognized.
Tran discloses HMI applications for managing and configuring elements of a process control
system or manufacturing application (para 0015, 0035), via representation of graphical elements
subjected to visualization and user edits and definition ( para 0013; Fig. 1, 8) with automated alarm
notifications in form of condition flag or quality/impact report, associated with contained objects,
enabling the user to make attribute modification thereto via a Model View or graphical symbols in
relevance with degree/status of the alarms (para 0095-0107), the HMI allowing user configuration/
input made to graphic/displayed elements expressing state of a process; e.g. in response to an alarm
status as visualized by the user (para 0118-0119), to reshape graphic element (para 011-0112; Fig. 6)
associated with reconfiguring the underlying function (para 0108, 0144) as part of executable
software that implements alarm animation (para 109) and behavior of the visualized shapes/objects
on the graphical wizard (Fig. 7-8). Hence, user-provided feedback (in response to received
report/status on a process control) identified as manual input to cause a change to the software
application, with changes (para 0375) automatically propagated solely based on the user-provided
feedback is recognized.
Therefore, based on use of operator intervention in Watson to flag faulty responses, provide retest and recommend process tuning or change, in the context that the control process in Watson is basically carried with software implementation, it would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to implement collection of process data, and software execution results in Watson system so that
responsive intervention thereto would include human intervention provided both as user type feedback - as shown in user input/redefining by Jain and user flagging in Tran - in form of user-provided feedback coupled to acknowledging thereof via a process control interface, the control process in Watson would be subjected to change, or reconfiguration of the software code thereof, the change in software based on the user-provided adjustment or interactive feedback so to cause reconfiguration, modification or additional tuning to the corresponding software thereof via recompiling its source code – per machine code compilation of process actions as in Jain; because
automated capture and collection of process execution output and quantization responses
coupled to automated detection of deviation, performance degradation or potential failures or
conditions entails one or more response type actions that rely on assessment or evaluation of the
impact or risk for critical state of failure to further degrade or propagate to the rest of the system, in
which case, risk or a criticality of an alarm dictates most urgent type resolution or on-the-spot
decision making, and implementing this fault resolution aspect by augmenting SW agent diagnosis
evaluation with that provided by a human, an administrator or technical operator would further
integrate real-time feedback or assessment by the latter, as set forth in Jain and Tran, so that
prescient, immediate or supervisory level decision making by a human role can take effect with
minimal delay, including taking precedence (if need be) over existing automated diagnose result or
recommendation, resulting thereby into a most direct, consummate and rapid corrective action aimed
to replace or reconfigure a software operation deemed deficient, the corrective action driven from
this user-provided feedback having dynamic effect of stemming further degrading the system
initiated by an detrimental outcome, or subduing expansion of a damage to other parts of the
industrial control process/system in Watson.
As per claim 4, Watson discloses apparatus of claim 1, wherein the processor circuitry is to:
detect a configuration (initial model validated against configuration information provided -
para 0156; downhole model - para 0138; time series pattern, analytic models - para 0031; pump
cycle - para 0018; Fig. 5; dynamometer - para 0141; para 0156-0169; para 0190-0191; Fourier
series, probabilistic mapping of the global responses - para 0186; well parameters, Fatigue
monitoring, gas interference - Fig. 6: pump condition, pump fillage, tests, Bayesian network - Fig. 4-
1, 6-1, 6-2; pump-off, downhole model, agent tests, Fluid fatigue, energy consumption- Fig. 10.10-1,
10-2) of the first process (refer to claim 1) control system based on telemetry data (metrics or measurements that may be necessary to assist in the diagnosis and tuning of the process model - para 0131; Fig. 8; para 0021; observables or metrics - para 0117; metrics or measurements - para 0131; para 0147; real time process data generated by the process - para 0198; para 0174-0179) associated with the process control system;
execute a machine learning model (PGN evaluate the classification and diagnosis - para
0031; PGN via machine learning techniques - para 0033-0034; para 0041; PGMs via machine
learning techniques - para 0091; classification, PGM as a classifier - para 0097-0100) to classify
(classify patterns that are associated with process deviation diagnosis based on machine learning techniques - para 0097; classifying the source and likely cause of the deviation - para 0198;
classify a variation or a disturbance to a process - para 0012) the telemetry data (see above) into first data with a first data (noise and disturbances from below ) classification and second data (newer references from below ) with a second data classification;
discard the first data (to filter out noise and disturbances - para 0083) based on a policy
(reference with standard deviations - para 0083; references for comparison against incoming data -
para 0081); and
store the second data in a datastore (generated mean reference logged periodically then
replaced as newer references for historical analysis - para 0083) based on the policy (see reference
with standard deviation from above).
As per claim 5, Watson discloses apparatus of claim 1, wherein the processor circuitry is to:
detect a first configuration (initial model validated against configuration information
provided - para 0156) of the first process control system based on telemetry data (refer to claim 4)
associated with the first process control system (para 0157-0169; downhole model - para 0138);
execute a machine learning model (PGN evaluate the classification and diagnosis - para
0031; PGN via machine learning techniques - para 0033-0034; para 0041; PGMs via machine
learning techniques - para 0091; classification, PGM as a classifier - para 0097-0100) to identify one
or more data classifications (classify patterns that are associated with process deviation
diagnosis based on machine learning techniques - para 0097; receiving real time process data
comparing against objective function or predicted envelope establishing any deviation - para
0199;
classifying probable diagnostic relevant to the deviations - claim 2, pg. 12) of the telemetry
data (see above in claim 4; refer to claim 1);
identify a second process control (configuration information likely to change cyclic pump
controller context - para 0156-0169) system based on the one or more data classifications (refer to
classifying into patterns per claim 4);
identify a second configuration of the second process control system (para 0157-0169; tuning
of downhole model - para 0138; may require a different PGM than that of the fixed intra-stroke
speed as the variation of the speed can greatly affect the speed learned structure to identify
and correct process model parameters - para 0155); and
identify a change (adjustment of pump off cycle parameters - para 0181; parameters need
adjustment - para 0139) in the first process control system based on the second configuration (e.g.
tuning of downhole model - para 0138) of the second process control system..
As per claim 6, Watson discloses apparatus of claim 1, wherein the processor circuitry is to:
detect a state of operation (e.g. downhole model, streaming tests: Deviations, Diagnostic
status: Alarms, Disturbance vs Persistent - Fig. 6-1; para 0133-0137; from normal operation - para
0181; current state of the process - para 0031; identifying deviations, drift - para 0063) of the first
process control system (e.g. para 0138; Fig. 10-2, 6-1; Fig. 3-1; para 0149 and Fig. 9); and
generate an output (para 0060, 0063) to identify at least one of the software application or a
different software application (tuning of downhole model - para 0138; process simulation to map
exhaustively the process response to process variables … potential deviation, changes to a process
can be represented as a PGN parameter tuning or process optimization, process responses
could be represented in a PGN - para 0033-0034; including a parameter tuning agent, agents with causal process-specific PGNs - para 0063; pumpjack operation, may require a different PGM than that of the fixed intra-stroke speed as the variation of the speed can greatly affect the speed learned
structure to identify and correct process model parameters - para 0155) associated with a second
process control system in the state of operation,
the output to be representative of a recommendation (e.g. recommending appropriate control
action - para 0023; applying a probabilistic model for evaluating risk and recommending
initiating an appropriate control action - para 0027-0028; communicates the results of tests, diagnoses and control recommendations … via updates to the data - para 0040; assess opportunity
and/or risk of control action and recommends a control action based on the risk analysis - para
0064) to change a portion of the first process control system, the change (situations where multiple
model parameters may have changed - para 0151; AI manager compares the probabilities or
initiated additional tests if the level of uncertainty is too high - para 0203; comparing the
diagnoses if sufficiently uncertain to differentiate a cause, initiating additional tests - claim 2, pg.
12; para 0139, 0181) to the portion of the first process control system to include execution of at least
one of the software application or the different software application (see require a different PGM
from above; see additional tests from above).
As per claim 7, Watson discloses apparatus of claim 6, wherein the processor circuitry is to
detect that the state of operation is a design operation state (status Agent identification of modes,
dependencies relationships in the original design - para 0104), a fully enabled operation state
(predicted operational envelope for establishing any deviation - para 0198; operational states
tracked by the Status Agent - para 0208; taking into account operational variances - claim 2, pg. 12),
or a maintenance operation state (where the process model can be utilized from a maintenance
viewpoint - para 0042).
As per claim 18, Watson discloses a non-transitory machine-readable storage medium comprising instructions that, when executed, cause processor circuitry to at least:
identify a software application deployed for execution by multiple different process control systems including a first process control system;
identify user-provided feedback regarding a feature of the software application;
cause a change to the software application based on the user-provided feedback; and
redeploy the software application for access by the multiple process control systems
based on a recompiling of the software application to include the change.
(all of which having been addressed in claim 1)
As per claim 19, Watson discloses non-transitory machine-readable storage medium of claim 18, wherein the instructions, when executed, cause the processor circuitry to:
generate an output representative of a recommendation (e.g. Control Agent assesses opportunity and/or risk based on diagnosis and recommends a change of model or control action - para 0204) to change a portion of the first process control system (refer to claim 1); and
cause the change of the portion of the first process control system after a determination
based on a policy that the recommendation is to be implemented (e.g. computer-executable instructions for controlling a process to perform actions receiving comparing applying probabilistic
modeling prioritizing actuation of agents related to causality modifying the probabilistic
recommending appropriate control action - para 0199; cause the processor to model
and perform comprising receiving comparing applying probabilistic modeling
communicating the results modifying the model from the results applying a model at
a control agent and recommending appropriate control action - para 0198) without intervention from a user (see recommendation action per actuation by SW agent from above) associated with the first process control system.
As per claim 22, Watson discloses non-transitory machine-readable storage medium of claim 18, the instructions, when executed, cause the processor circuitry to:
detect a first configuration of the first process control system (refer to claim 5) based on telemetry data;
execute a machine learning model (refer to claim 4, 5) to identify one or more data classifications of the telemetry data (refer to claim 4; probabilistic modeling for classifying likely cause of the deviation - para 0027-0028; classify … patterns that are associated with process deviation … diagnosis based on machine learning techniques - para 0097; receiving real time process data … comparing against objective function or predicted envelope … establishing any deviation - para 0199; classifying probable diagnostic relevant to the deviations - claim 2, pg. 12);
identify a second process control system (refer to claim 5) based on the one or more data classifications (see above; refer to claim 5);
identify a second configuration of the second process control system (refer to claim 5); and identify the change (refer to claim 5) of the first process control system based on the second configuration (refer to claim 5) of the second process control system.
As per claim 34, Watson discloses non-transitory machine-readable storage medium of claim 18, wherein the instructions, when executed, cause the processor circuitry to determine that
telemetry data associated with the first process control system includes usage data (measurements
to assist in the tuning of the process model(s) - para 0031; measurements - para 0174-0175;
calculations for the cyclic tests cycle to optimize processor utilization - para 0087; processor
utilization - para 0040; energy consumption guidance to numerical optimization means - para
0186) for improvements of products (e.g. measurements assist in the tuning of the process once
"tuned", the need for these tests be minimized so as to not adversely affect production - para 0131;
Identify in a classifier recognition, drift from the operational envelop, metrics of the deviation
probable causes for the deviation - para 0063; metrics identify underlying conditions with an
acceptable degree of certainty - para 0117; metrics to identify which parameters have varied and
determine the corrected values - para 0140; optimal solution for the operating parameters - para 0186
) or sales funnel conversion to improve marketing strategies associated with the products to increase conversion rates.
As per claim 35, Watson discloses a method comprising:
identifying a software application executed by multiple different process control systems including a first process control system;
identifying, with at least one processor circuit, user-provided feedback regarding a
feature of the software application;
causing, with one or more of the at least one processor circuit, a change to the software
application based on the user-provided feedback; and
redeploying the software application for access by the multiple process control system
based on a recompiling of the software application to include the change.
(all of which having been addressed in claim 1)
As per claim 37, Watson discloses method of claim 35, further including
requesting authorization to cause a change (test may be requested - para 0179 ) to the first process control system from a user (refer to change based on user-provided feedback per rationale A in claim 1) associated with the first process control system after a determination based on a policy (probabilistic graphical model - para 0147; probabilistic diagnoses - claim 2, pg. 12 - Note2:
optimization, iterative tuning, exhaustive update, recommendation for reapplication of numerical
methods and ML models - para 0091, 0097 - under a probabilistic diagnose approach - para 0048-
0064 - per distributed testing by plural agents/machines - para 0088, 0197 - reads on a learning
policy to apply change according to definite AI methodology or model) that the change to the first process control system is to be implemented (supplement tests - para 0152; additional tests - para 0203; additional tests -claim 2, pg. 12; additional tests - para 0064) with intervention from the user (para 0179; refer to Rationale A in claim 1), the change to the first process control system to be based on the changed software application.
As per claim 49, Watson discloses method of claim 35, further including detecting a configuration (initial model validated against configuration information provided - para 0156; downhole model - para 0138; time series pattern, analytic models - para 0031; pump cycle - para 0018; Fig. 5; dynamometer - para 0141; para 0156-0169; para 0190-0191; Fourier series, probabilistic mapping of the global responses - para 0186; well parameters, Fatigue monitoring, gas interference - Fig. 6: pump condition, pump fillage, tests, Bayesian network - Fig. 4- 1, 6-1, 6-2; pumpoff, downhole model, agent tests, Fluid fatigue, energy consumption- Fig. 10, 10-1, 10-2) of the first process control system (refer to claim 1) and
generating an output representative of a recommendation (refer to claim 5; recommending appropriate control action - para 0023; applying a probabilistic for evaluating risk and recommending initiating an appropriate control action – para 0027-0028; communicates … results of tests, diagnoses and control recommendations …via updates to the data - para 0040; assess opportunity and/or risk of control action … and recommends a control action based on the … risk analysis - para 0064) to change a portion of the process control system based on the configuration, wherein the configuration includes at least one of an installed product or a selected product for deployment (the software disclosed herein may be deployed - para 0197; each type of IA may be deployed - para 0059; rod pump system - para 0042) in the first process control system, and
the recommendation (see above) includes a support service (server computers - para 0197; a process simulation utilized selectively response to their range of operation and deviation such as changes can be determined probabilistically and represented as PGN via machine learning techniques - para 0185; probabilistic model for evaluating and recommending control action - para 0198; probabilistic model recommending appropriate control action - para 0199; controller 1202 installed on the jobsite - para 0193; database to assist tracking by the PGM - para 0097) associated with the at least one of the installed product or the selected product (see above) for deployment in the first process control system.
As per claim 52, Watson discloses apparatus of claim 1, wherein the output is to be
processor circuitry is to generate an output representative of a recommendation (refer to claim 5) to
change a portion of the process control system, and cause the change (appropriate control action -
para 0023; recommending initiating an appropriate control action - para 0027-0028; communicates the results of tests, diagnoses and control recommendations … via updates to the data
- para 0040; assess opportunity and/or risk of control action … and recommends a control action based on the … risk analysis - para 0064) to the portion of the process control system based on the
recommendation (see above).
Claims 13, 33 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson) in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain) further in view of Cella et al, USPubN: 2022/0197306 (herein Cella) and Frazier et al, USPubN: 2008/0300901 (herein Frazier)
As per claim 13, Watson discloses apparatus of claim 1,
wherein the processor circuitry is to:
determine that the software application (para 0139; algorithm and software methods – para 0049; controller … programmed with appropriate software – para 0193; software herein - para 0197) executed by the first process control system (AI manager, modeling agent, Pump controller, well equipment - Figs. 4; AIMC - para 0047-0048; statistical and causality model, PGM – para 0049) is accessed by a client electronic device (laptops, computers, tablets, smart phones - para 0197) of the process control system.
Watson does not explicitly disclose circuitry to:
(i) decrement one or more tokens from a first token balance recorded on a ledger, the first
token balance corresponding to the client electronic device; and
(ii) increment the one or more tokens for a second token balance recorded on the ledger, the
second token balance corresponding to a developer of the software application, the increment of
the one or more tokens to effectuate payment for the access of the software application by the
client electronic device.
Cella discloses distributed manufacturing network that leverages service of a 3D printing
functionality in exposing this industrial capacity to designers or users that are seeking to fabricate
3D products (para 1922-1923), the distributed manufacturing network implemented through a
distributed ledger by which a contract may be configured to implement the ledger in association with
transactions between the manufacturing network and the users/customers of the 3D printing (para
1927); where, each 3D printing instance may be a tokenized transaction affecting the set of tokens
stored with the ledger account of that customer (para 1926) such that the selling transaction of data
and additive manufacturing data to the user provisioned under the smart contract uses only the
digital tokens (para 1928) while hiding identity of the transacting nodes.
Frazier similarly, discloses buying and selling entities of digital authentication network
provided with buying and selling transactions being synchronized (Fig. 3) via effect of ledger
"bought" or "sold" tokens from a respective balance of tokens for transacting party, according to
which a service sale is credited with decrement of the tokens (para 0024) from the ledger account
that purchases the service. Hence provisioning a service with increment of ledger tokens of the
service entity synchronized with user payment for the service by effect that decrements a number of
tokens from a ledger of the paying user is recognized.
Therefore, as user of the intelligent service operates with a electronic device through which
application transaction is electronically accomplished, it would have been obvious for one of
ordinary skill in the art before the effective filing date of the invention to implement Watson's
provisioning of the intelligent analysis (IA) service with network users so that access of the service
is supported by a contract implemented with strong authentication mechanism of a distributed ledger
as in Cella to securely bind transactions between the provider and its users, wherein completing a
payment between buying user (client electronic device) and the JA service seller is a tokenized form
of payment achieved solely with transfer of tokens from the "buyer" ledger to the "seller" ledger -
as shown in Frazier - including
a) a decrement of one or more tokens from a first token balance recorded on a ledger corresponding to the client electronic device; and
b) an increment of the one or more tokens for a second token balance recorded on the ledger corresponding to a providing entity (developer) of the IA application, the crediting or increment of the one or more tokens - as shown in Frazier - to effectuate payment (by the client/user electronic device) for the access of the application; because
use of a ledger-based contract to support application service transaction and financial
payment with an added layer of security to authenticate of ledger entities in conjunction with
tokenized form of payment effectuated solely with transfer of tokens achieved including
synchronized operation to debit balance of tokens from one ledger (buyer account) in order to
simultaneously credit the balance of tokens of the other ledger (seller account) would not only
benefit from strength of a cryptographic form to register and bind transacting parties per their registration under the ledger contract, but would also hide the identity of the account from which
tokens are being debited or into which tokens are being credited as part of a financial transaction
binding seller and buyer accounts, thereby precluding possibility of malicious interjection and
private asset tampering.
As per claim 33, Watson does not explicitly disclose non-transitory machine-readable storage medium of claim 19, wherein the instructions, when executed, cause the processor circuitry to determine that the change of the portion of the first process control system is associated with a monetary cost based on an identification of a customer of the first process control system.
Use of a registration system using distributed ledgers to structure account and financial
balance for transacting parties where credentials of the users registered under this ledger mechanism and ledger balance of tokens are part of the transaction being verified by the ledger contract is shown in Cella, whereas capability to afford or permit access of a control software by a user so that the user can manipulate a manufacturing process by dispatch control functions to the process is also shown in Cella (refer to rejection of claim 13); where a provider effecting delivery of product would be credited with tokens debited from the buyer ledger as shown in Frasier (refer to rejection of claim 13)
Thus, it would have been obvious for one of ordinary skill in the art before the effective
filing date of the invention to implement structuring of obligations between a industrial control
service delivery and a user in relation to user access for use with an industrial control process
necessitating an adjustment or modification to the regular state of operations of the process (based
on information received in a diagnosis stage) so that approval or authorization for such request of
change or adjust by the user for a intended change to portion of the process control system would
have to be correlated to monetary cost based on an identification of a customer of the process control
system determine that the change of the portion of the process control system is associated with a
monetary cost based on an identification of a customer of the process control system thereby to
redirect the request to the monetary system that is specifically configure manage the required
fulfillment by the user to obtain the control logic as requested; using the tokenized transaction as
shown in Cella and Frazier, and so, for the same reasons and benefits set forth with the rationale rendered in claim 13.
Claims 26 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson). in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain), further in view of Miller et al, USPubN: 2022/0091591 (herein Miller)
As per claim 26, Watson does not explicitly disclose non-transitory machine-readable storage medium of claim 18, wherein the instructions, when executed, cause the processor circuitry to:
obtain data representative of a request for recovery of the first process control system
after a failure of the first process control system;
identify one or more software applications corresponding to the first process control
system; and
distribute the one or more software applications to the first process control system to
cause the recovery of the first process control system.
Miller discloses industrial information hub (IIH - para 0167) with cloud interfaces for
applying analytics to industrial devices and assets (Fig. 24), using asset models or machine learning
models communicating with a gateway to coordinate distributed analytics (para 0178) between the
IIH system and the gateway, using distributed environment, processors and components to perform
tacks through a communication NW (para 0215-0216), the IIH system comprising a recovery
component (component 214 - Fig. 11) connected with a repository and interfacing with a plant
facility or floor (Fig.12-13, Fig. 32) to provide backups needed for disaster recovery procedure ( para
0065, 0111) using components of the IIH network to perform the recovery (recovery component
214 - para 0059) or distributed analytics based on the models(Fig. 33;) using a innovative
methodology (para 0207) established on implementing concurrency or inter-related states between
acts.
Hence, a cloud system capable of identifying and interacting with processing entities
(processors) and applications to support the distributed execution of tasks per Miller IIH so that
implementing of recovery component (among other IIH components) utilizes one or more
processors in accordance to a distributed environment network in accordance with a innovative
methodology that endorses multiple tasks or actions based on concurrency or inter-related states
between acts entails distribution of processing units to support IIH task such as recovery.
Therefore, based on the availability of environments and devices supporting a large scale
platform to accommodate large volume scale, complexities of environments and heterogenous
devices grid in Watson, it would have been obvious for one of ordinary skill in the art before the
effective filing date of the invention to implement the industrial control process in Watson so that
upon detection by a intelligent analytic system of a failure within the process control system, the
diagnostic system would respond by obtaining data representative of a request for recovery - see
recovery component in Miller - , and identifying one or more applications corresponding to the
configuration affected by the failure thereby to distribute the one or more applications or processing
units - per a distributed execution of the IIH components or processors available under Miller
distributed models - to the control process in which the failure has occurred, to cause the recovery of
the process control system; because
identification and proper assignment of capabilities and resources disposed by a cloud-based
NW control system as in Watson with large scale analytics support to implement corrective measure
responsive to industrial diagnosis and/or anomaly detection would assist the intelligent system in
response to identification of a failure (or diagnosed fault in an industrial control process) with proper
guidance on utilization of resources in that organization or assignment of platform tasks (to address a
failure correction) such as recovery of assets would not only exploit the most innovative aspect of
the system that is established upon implementing threaded or concurrent tasks execution - as in
Miller - with effective enlisting of host applications or computing entities identified for a distributed
rendering of such task (i.e. a recovery function), but also would increase the overall system
performance (minimized latency) and throughput in the aspect of deploying a networked service or
tasks thereof, the latter added with improved likelihood of a better quality of service achieved by the
NW in support of user requests for industrial analytics.
Claims 27 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson) in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain)further in view of Kowalski et al, USPubN: 2015/0215163 (herein Kowalski), Cella et al, USPubN: 2022/0197306 (herein Cella) and Moeller, USPubN: 2021/0216306 (herein Moeller)
As per claim 27, Watson does not explicitly disclose non-transitory machine-readable storage medium of claim 18, wherein the instructions, when executed, cause the processor circuitry to:
obtain data representative of a request to access an application; and
after a determination that the request indicates the access is for execution of the software
application by a cloud host, instantiate the software application on the cloud host for execution.
However, the IA methodology in Watson can be distributed to various forms of host devices
in the internet (LAN, Ethernet, Wifi, WAN, computers, laptops, server computers, tablets, smart
phones, may be deployed on one or more computing device 1210 - para 0197) and access for the IA
software and machine learning aspect of this IA methodology can be requested by multiple type of
host machines including tablets, smartphones, laptops and personal computers typical of cloud users,
where each such device can host the intelligent software and operate the IA software to automate
equipment controller (para 0044-0046) and/or configure industrial operations of a job site (para
0193).
Kowalski discloses a cloud-based computing NW set up as a company or public sector
organization to provide tenant services accessible via the internet to distributed set of clients per a
private single-tenant cloud implementation or multi-tenant service functionality supported by servers
so to provide software and resources that can be instantiated to different type of hosting devices to
implement a service control (e.g. control plane) and configuration such as launching, monitoring and
terminating resources instances on host platforms external to the server (para 0022), where a client
initiating a control or configuration request can cause a hosted workflow manager to invoke commands of the control software via the server to manipulate a workflow operation (para 0023), the
user-initiated control to inform the servicing entities to monitor resources regarding shutdown (para
0027) or failure of a given workflow, including use of model (para 0028) associated with recovery
operations, the model including a scale to manage and size of the failures so to determine service resources (e.g. virtual compute instances) to be deployed for that instance of recovery (para 0068), using multiple and concurrent resources to implement this service (para 0030)
Hence, operating cloud-based resources associated with tenant-accessible control software
whereby the user can provide command or invoke control type request for service software by the
cloud infrastructure to configure and dispatch operations to manipulate cloud resources, monitor
their status and configure their implementing a recovery instance as in Kowalski entails cloud
architecture effect of obtaining data from cloud-users request to access an application, and
determining that the request indicates access for executing the application by one of a cloud user, so
to instantiate the application (for execution) on the indicated host using the resources provisioned by
the cloud architecture.
Deploying manufacturing control software to subscribers using cryptographic validation and
token based delivery of the software is shown in Moeller for industrial control system (ICS), using a
secure environment for storing, executing and updating software implementations for the system
(para 0290), wherein a user device can operate in a alternative mode at stations (Fig. 15) of an
industrial park to detect critical situations, alarms and provide control transfer of emergency
functions ( para 0268, 0287) to address issues of site objects operating in the industrial park, the
control transfer including as user providing control or making adjustment based on state information
of devices of the industry park (para 0267) where communication of software is established between
cloud network (Fig. 11; para 0049) and the industrial control system using secure ICS interface
(SIID) management according to one or more cryptographic technologies such as public ledger, hash
keys configured for the PKI between gateways, servers, user devices (para 0065) where to gain
access (claim 14, pg. 39) to a product of (of SW update to) the control software system the user is to
provide credentials (Fig. 9B) and tokens (para 0176-0183; Figs. 7) in accordance to blockchain
management of a management network (TMN) for managing, validating secure transactions of registered devices under a distributed ledger (para 0068), the secure credentials (SVC) used to validate software downloaded to user devices or other components of the TMN (para 0069-0070) that implements the verification by the credential service; e.g. based on which list of software modules can be validated, stored for deployment to devices of the ICS (Fig. 8) as applications downloaded via gateways onto ICS devices or user devices (para 0080) using information received by the SIID devices or registered ledger information (para 0114)
Cella moreover discloses distributed manufacturing network that leverage service of a 3D printing functionality in exposing this industrial capacity to designers or users seeking to fabricate 3D
products (para 1922-1923) the distributed manufacturing network implemented through a distributed
ledger by which a contract may be configured to implement the ledger in association with transactions between the manufacturing network and the customers of the 3D printing (para 1927);
where, each 3D printing instance may be a tokenized transaction affecting the set of tokens stored
with the ledger account of that customer (para 1926) such that the selling transaction of data and
additive manufacturing data to the user provisioned under the smart contract uses only the digital
tokens (para 1928) while hiding identity of the transacting nodes.
Therefore, based on the benefit of using tokenized transaction enforced by a contract under
methodology of a distributed ledger as in Cella, and the basis that a diagnosis function is tightly
attached with monitoring (para 0208) state of operations represented by a process model in Watson
using status and monitoring agent, that user devices in many forms (para 0197) can be granted
access to the control software in Watson, it would have been obvious for one of ordinary skill in the
art before the effective filing date of the invention to the monitoring service in Watson so that
accessible software or components enabling a subscriber user to access, monitor, manage and
execute control functions of the control process would be consolidated around a secure layer that
validates target devices and credentials of the users for their request of one or more instances of the
industrial control software or update thereof - as shown in Moeller, or the tenant-accessible control
software in Kowalski - by which the recipient (cloud) user can configure and dispatch operations to
manipulate cloud resources, monitor their status and configure their implementing a recovery
instance; that is, the secure layer configured to obtain data representative of a request (user
credentials as in Moeller) to access an application; and after a determination that the request
indicates the access is for execution of the application (industrial control SW by Moeller and
recovery logic by Kowalski) by a cloud host (i.e. user device in the cloud infrastructure of Moeller),
instantiate the application - i.e. downloaded software in ICS devices in Moeller network, or recovery
software in Kowalski - on the cloud host for execution; because
organizing provision of services and software delivery as well as download of the SW update
using a secure credentials or ledger keys as a form of methodology enforced via means of a
authentication layer as set forth above would enable networked devices of a industrial control
infrastructure and cloud-based service subscribers to be verified according to strong cryptographic
security mechanism/techniques before permitting their use or gain of the industrial control software
(or access any downloaded update thereto) as set forth above, and would further strengthen the cloud infrastructure in providing user access to software or components associated with the industrial
control system as in Watson, and so, in the sense that a) only approved users by the authentication
layer can access and configure the SW to performs control and configuration over components
proprietary to the industrial network, b) the user in completing the delivery transaction only need to
provide registration data covered by a ledger or blockchain contract for effecting transfer of stored
ledger tokens as a form of payment for the SW access, the tokenized form of payment associated
with ledger registration hiding identity of the transacting parties - as shown in Cella, which adds
another measure of security and trust to the cloud-based industrial control infrastructure aspect of
provisioning of service or products to its subscribers.
Claims 42, 45 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson) in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain) further in view of Kowalski et al, USPubN: 2015/0215163 (herein Kowalski) and Moeller, USPubN: 2021/0216306 (herein Moeller)
As per claims 42 and 45, Watson does not explicitly disclose method of claim 35, further including:
identifying a first version of the software application; and after a detection that a second version of the software application is available, upgrading the software application from the first version to the second version based on a policy;
obtaining data representative of a request to access the software application; and after a determination that the request indicates the access is for local execution of the software application by a client electronic device, causing a download of the software application to the client electronic device for local execution on the client electronic device.
Kowalski discloses a cloud-based computing NW set up as a company or public sector
organization to provide tenant services accessible via the internet to distributed set of clients per a
private single-tenant cloud implementation or multi-tenant service functionality supported by servers
so to provide software and resources that can be instantiated to different type of hosting devices to
implement a service control (e.g. control plane) and configuration such as launching, monitoring and
terminating resources instances on host platforms external to the server (para 0022), where a client
initiating a control or configuration request can cause a hosted workflow manager to invoke
commands of the control software via the server to manipulate a workflow operation (para 0023).
Hence provision of a configuration to be performed by a single tenant using instance of
control software to implement a service control by a single tenant in a configuration such as
launching, monitoring and terminating resources instances associated with the cloud provider entails
determination that the user related request indicates that the access is for local execution of the
application by a client electronic device.
Further, Moellers’ delivery system approach that includes identifying a first version of an application based on the configuration so that after a detection that a second version (or update) of the application is available, upgrading the application from the first version to the second version based on a delivery policy, obtaining data representative of a request to access an application and causing a download of the application to the client electronic device for local execution on the client electronic device is shown in the credentials verification mode, the latter using cryptographic keys and ledger registration as part of enforcing a security policy to authorize download of industrial control software to registered subscribers, who upon proper verification and authentication, can access downloaded update of the software for execution in a single host platform, wherein the user can operate in alternative mode at a station (Fig. 15) within an industrial park to detect critical situations, alarms and provide control transfer of emergency functions ( para 0268, 0287) to address issues of site objects operating in the industrial park, the control transfer including as user providing control or making adjustment based on state information of devices of the industry park (para 0267)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing date of the invention to implement the access granting to control process software and components upon receiving request from subscribers of the control software in Watson so that upon
detection that a second version of the application is available, upgrading the application for
controlling a process by Watson cloud-based NW from the first version to the second version would
make the update accessible only via application of a policy - as in Moeller - the application of delivery policy and credentials carried out as result of the cloud-based delivery service steps of
identifying a first version of an application based on the configuration; detecting that a second
version of the application is available (see update in Moeller), and accordingly, obtaining data
representative of a user request to access an application or its latest update, and causing a download
of the application to the client electronic device for local execution on the client electronic device
from basis that requested version is for local execution of the application by a single tenant or client
electronic device as shown in Kowalski; because
of the same benefits proffered with the download in Kowalski per the rationale in claim 27 in
regard to provisioning of industrial control SW to a single-tenant or multiple-tenant for either a local
execution or concurrent/distributed manipulation of an industrial process task, under effect of
verifying security credentials of requesting users from application of a policy as well as secure
aspect payment in form tokenized transaction as part of receiving the delivery by the user.
Claims 48 is/are rejected under § 35 U.S.C. 103 as being unpatentable over Watson, USPubN: 2020/0201267 (herein Watson) in view of Tran et al, USPubN: 2015/0106753 (herein Tran) and Jain et al, USPubN: 2024/0007414 (herein Jain) further in view of Oguche et al, USPubN: 2018/0283 169 (herein Oguche)
As per claim 48, Watson discloses method of claim 35, further including generating an output representative of a recommendation to change (e.g. recommending appropriate control action - para 0023; applying a probabilistic model for evaluating risk and recommending initiating an appropriate control action - para 0027-0028; communicates the results of tests, diagnoses and control recommendations via updates to the data - para 0040; assess opportunity and/or risk of control action and recommends a control action based on the risk analysis - para 0064) a portion of the process control system.
Watson does not explicitly disclose
wherein the recommendation includes a deployment of a virtual private network, a firewall, or a software data diode in the first process control system, and the method further including
deploying at least one of the virtual private network, the firewall, or the software data diode in
the first process control system.
Watson discloses recommendation for automatic tuning (Fig. 2, para 0061-0062; Model Tuning Agents - para 0048) from use of various Agents of intelligent agent or AI manager framework included with operations such diagnostic, control, and testing and model analytics, according to which a control process model (cyclic pumping process - Fig. 3) is being redeployed via iterative tuning, probabilistic correction model (para 0130; PGM correctly tune the model, tuning algorithms, iterating the predictive model - para 0150-0151) for further analytics, optimization and improvement of control action for the process (para 0063-0064) to address status conditions such as alarms or deviations (para 105) which cause recommendations (see above) for further control action or supplemental tests; hence redeployment of software data in the process control system is recognized - herein referred to as (*).
Oguche discloses deployment of control elements in an industrial infrastructure to monitor conditions in operational environment of well and pipeline structures, where monitored conditions (para 0064-0067) data are collected (para 0006-0009) for transfer into records in storage facility
(para 0011, 0021, 0067) and validation by servers to mitigate errors and disturbances, according to which, the server makes recommendation for operation of the infrastructure based on the received condition data (para 0026-0027), which includes modifications to conditions of the infrastructure (para 0028-0029) to optimize production of the well, the recommended communicated to seek a decision process (para 0033-0034, 0054-0056) and confirmation process (para 0031-0032) in order for the system to issue the recommended control change; e.g. to control devices like downhole, chokes, safety valves, inflow devices (para 0024, 0086) whose conditions are communicated via a Virtual Private Network (para 0025) in relation to use of SW and HW in virtualized form (para 0131-0133) as alternative implementation for integrating storage facility with decision from server analytics.
Hence, recommendation paradigm effected with decision process over process state and
conditions of an infrastructure using communication with a Virtual Private Network as part of using
virtualized HW, SW components of the infrastructure that integrating storage facility with decision
from server analytics entails recommendation to affirm deployment of a change per use of a virtual
Private Network coupled with virtualized implementation of HW and SW.
Therefore, it would have been obvious for one of ordinary skill in the art before the effective
filing date of the invention to implement iterative redeployment of process model - as in (*) - from
stages of diagnoses of industrial conditions received into a integration layer or IA manager of the model-based recommendation portion of Watson IA system so that for achieving the recommendation from a decision process of the integration layer, deployment for a recommended control action includes deployment of virtualized components via a virtual private network as shown
in Oguche; because
virtualized implementation of SW and HW as part of implementing control action recommended by an integration process that weights import of the conditions/issues to address, the
feasibility of the measures to implement in relation to state and availability of processing resources
at hand to support the networked implementation would help mitigate physical enlistment of devices
and real memory devices for building the payload and processing resources required to fulfill the
recommendation associated with communication of data needed to dispatch the control over
executing host devices of industrial process infrastructure, since virtualized components implementations require less cost in physical resources, whereas
virtualized private network deployed with the above virtualization of software and hardware
as part of the recommended decision, would also cause a reduced involvement of physical resources,
a better likelihood for achieving a robust form of encryption, an excellent performance on data
verification and transfer across entities of the NW, improved commitment to privacy and impenetrable screening by the security features of the network, as well as the benefit due to the low
price aspect in creating a VPN as a secure fabric for managing communication as intended for the
deployment.
Response to Arguments
Applicant's arguments filed 12/03/25 have been fully considered but they are not persuasive. Following are the Examiner’s observations in regard thereto.
(A) Applicants have submitted that for independent claim 1, the teachings by Watson as cited with mention of operators and supervisors in regard to providing input (e.g. for guidance, input request) cannot be same as the user-provided feedback as claimed (Applicants Remarks pg. 17-18), much less a user-provided feedback in regard to “feature” of a SW application (Applicants Remarks pg. 19). The Office action has identified portion that graph representation of a pump response can be overstressed, where flagging of such issues can be used to trigger code or process operation adjust. Besides, the identification of user-provided feedback feature in claim 1 has been addressed with a obviousness rationale set forth with a combination of references, therefore, Applicant’s allegation made against Watson amounts to a mere traversal that singles out one reference instead of discussing non-obviousness of that feature based on the combination proposed via articulation by one skill in the art on obviousness of this feature, as shown in the Office Action, since non-obviousness of a feature being prosecuted with a combination of teachings cannot be established via attacking one single reference.
Thus, non-obviousness of this “user-provided feedback” is deemed inconclusive, not to mention that merits of user-provided feedback regarding a feature of the SW application cannot be given full patentability merits since this “regarding a feature” limitation belongs to a most recently added language to the claims, and this new language is only being prosecuted for the first time, rendering any proffered patentability merits thereof MOOT.
(B) Applicants have submitted that Tran, as cited, does not teach identify software application deployed for access and execution by multiple process control system, identifying user-provided feedback and causing change based thereon to redeploy the application based on recompiling (Applicants Remarks pg. 19). The rationale of obviousness using Tran was to address user input to cause a modification or additional tuning of an application code, and so, as consequence of (or under effect from) user interactively providing configuration and input. The argument as alleged by Applicant is deemed largely misplaced or insufficient for failing to show whether this user-provided feedback is absent in Tran.
( C ) Applicants have submitted that Jain, as cited, does not teach identify software application deployed for access and execution by multiple process control system, identifying user-provided feedback and causing change based thereon to redeploy the application based on recompiling (Applicants Remarks pg. 20) in view of Jain just teaching a reward assigning. The rationale of obviousness using Jain was to address user input to cause a modification or additional tuning of an application code, and so, as consequence of (or under effect from) user interactively providing configuration and input. The argument as alleged by Applicant is deemed largely misplaced or insufficient for failing to show whether this user-provided feedback is absent in Jain.
( D) Applicants have submitted that claims 18 and 35 recite the same feature as discussed above in claim 1; and based on the deficiencies by Watson, Tran and Jain as set forth above, withdrawal of these claim rejection is requested (Applicants Remarks pg. 21)
The Applicant’s alleged deficiencies directed against Watson, Tran or Jain are deemed largely insufficient, MOOT or misplaced; and the claims as currently amended will stand rejected as set forth above in this Office Action.
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 extension fee 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 Tuan A Vu whose telephone number is (571) 272-3735. The examiner can normally be reached on 8AM-4:30PM/Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Chat Do can be reached on (571)272-3721.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3735 ( for non-official correspondence - please consult Examiner before using) or 571-273-8300 ( for official correspondence) or redirected to customer service at 571-272-3609.
Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 Group receptionist: 571-272-2100.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/Tuan A Vu/
Primary Examiner, Art Unit 2193
Decembre 27, 2025
