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 .
Claims 1, 2, 4-11, 13-20 are pending.
Applicant’s replacement Drawing Fig.5 is approved by the examiner. The objection to the drawings is withdrawn.
Applicant’s amendment filed 14 May 2026 is not sufficient to overcome the previous rejections of all pending claims under 35 U.S.C. 101 and 103 discussed below.
The amendment further introduces new issue of 35 U.S.C. 112 discussed below
Response to Arguments
Applicant's arguments filed 14 May 2026 have been fully considered but they are not persuasive.
Regarding the rejection under 35 U.S.C. 101, applicant cites paragraphs 0002-0004, 0082-0083, 0090-0091 then argues at page 12 last paragraph to page 13 first paragraph:
“Applicant respectfully submits that these features integrate the claims into a practical application. First, by virtue of the continuing of a training of the model by using new data after the model has been sufficiently trained, the training of the model is continually being improved and updated. Second, by virtue of the displaying of a graphical representation that includes at least one heatmap that visually represents data for each of the at least one matching object based on predetermined criteria, the visual representation of the data including a color coded representation of magnitude and degree of change, a user can easily see this information, thereby facilitating that the generation of an operational environment is less error-prone because unwanted idiosyncratic risk and/or spurious correlations for relatively minor selection inconsistencies are reduced”.
In response the examiner is not persuaded. The amendment filed 14 May 2026 merely rolled up claim 3 now canceled into claim 1, added a wherein clause that merely recites continuing a training of the at least one model by using new data after the model has been sufficiently trained. However the specification paragraph 0082 cited by the applicant merely describes sufficiently in the following terms:
“the training model may be sufficiently trained when model assessment methods such as, for example, a holdout method, a K-fold-cross-validation method, and a bootstrap method determine that at least one of the training model's least squares error rate, true positive rate, true negative rate, false positive rate, and false negative rates are within predetermined ranges”.
The examiner points out the language “may be”, “for example”, “such as” in the cited portion indicate the recited “sufficiently trained” seem to be customary in the art, not some specific inventive mechanism that integrate an abstract idea into a practical application.
Furthermore the claimed graphical representation including “at least one heatmap that visually represents data for each matching object based on predetermined criteria, the visual representation of the data including a color coded representation of magnitude and degree of change” is merely well-known in the art as shown by Sengupta discussed in the previous Office action page 17, again not any inventive mechanism that integrate an abstract idea into a practical application.
Applicant argues at page 13 third paragraph of the response:
“Similarly as in the above-cited Decision, Applicant respectfully submits that in the instant application, the features recited in each of independent claims 1, 10, and 19 have the effect of continually improving a training of a model by using new data. Therefore, Applicant respectfully submits that as amended herein, when considered as a whole, each of independent claims 1, 10, and 19 integrates an abstract idea into a practical application”.
In response the examiner is not persuaded. Using new data in training of a model does not seem to improve any model because any model has to be trained by data intended for it to learn.
Applicant argues at page 14 second paragraph of the response:
“In this regard, Applicant respectfully submits that the displaying of a graphical representation that includes at least one heatmap that visually represents data for each of the at least one matching object based on predetermined criteria, the visual representation of the data including a color coded representation of magnitude and degree of change. as recited in the independent claims, provides an improved computer interface because such a display has the effect of providing a user with an ability to easily see this information, thereby facilitating that the generation of an operational environment is less error-prone because unwanted idiosyncratic risk and/or spurious correlations for relatively minor selection inconsistencies are reduced, and that such an ability would likely not otherwise be available. Therefore, similarly as in Core Wireless, each of these claims requires the displaying of a limited set of data to a user via a user interface to provide information that facilitates a more accurate generation of an operational environment”.
In response the examiner is not persuaded. The claimed heatmap merely displays color coded data which is customary to prior art display technology as shown by Sengupta discussed above. No claim limitation shows how “an operational environment is less error-prone because unwanted idiosyncratic risk and/or spurious correlations for relatively minor selection inconsistencies are reduced” as argued by applicant.
For all the reasons discussed above the rejection of all pending claims under 35 U.S.C. 101 is maintained.
Regarding the rejection under 35 U.S.C. 103, applicant argues at page 16, paragraph before the last paragraph of the response: “Applicant respectfully submits that although Flores discloses "a dynamic (e.g., real-time) view of an operational environment", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to a request of any kind, and that there is also no disclosure whatsoever in relation to either of a target or a corresponding parameter”.
In response the examiner is not persuaded. The claimed request, target and corresponding parameter read on the operations team 152 and/or an architect interacting with dashboard 120 to manage views 140, patterns 142 and/or states 144 associated with operational environment 122 (see Flores col.4 lines 34-43).
Applicant argues at page 17 paragraph before the last paragraph: “Applicant respectfully submits that although Flores discloses "an enterprise model" which "can include a target system", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to a listing of matching objects, and there is also no disclosure whatsoever in relation to using the disclosed enterprise model to determine anything, nor to make such a determination based on comparative analytics”.
In response the examiner is not persuaded. Applicant admitted Flores discloses and enterprise model which can include a target system. Any model with a target has to consider matching objects and using the model to determine or make a determination based on comparative analysis.
Applicant argues at page 18 of the response: “Applicant respectfully submits that although Flores discloses "an enterprise architecture model associated with an operational environment within an operational dashboard", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to retrieving data from anything, and that there is also no disclosure whatsoever in relation to a data repository that is either dynamically updated or from a historical data source”.
In response the examiner is not persuaded. Interaction with an operational dashboard clearly retrieve data the operator of the dashboard is requesting from a dynamic or static source (Flores col.4 lines 34-43). The claimed data repository is met at least by the cache of Flores (see at least col.7 lines 52-62: “That is, it is implied that access control, preferences, and the like are handled in step 305 during initialization. It should be appreciated that a persistent storage for events, configuration and cached operational data within the application environment to ensure optimal synchronization can be present. In step 350, if there is a model available, the method can continue to step 355, else proceed to step 360. In one instance, when the model lacks operational data (e.g., no model available), the data can be collected from the operational environment and can be synchronized with the dashboard”.
Applicant argues at page 19 of the response regarding claims 2, 11, 20: “Applicant respectfully submits that although Flores discloses "receiving user input", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to the input including any particular thing, and that there is also no disclosure whatsoever in relation to an action to alter the operational environment.”
In response the examiner is not persuaded. The fact that user input is received in Flores as admitted by the applicant shows the input is related to an action to alter the operational environment (Flores col.9 lines 22-26: Engine 420 functionality can include, but is not limited to, notification functionality, receiving user input, data visualization, and the like. In one instance, engine 420 can be a portion of a dashboard software).
Applicant argues at page 20 of the response regarding claims 6, 15: “Applicant respectfully submits that although Flores discloses that "The model can include an entity which can be an operational node, an architectural component node, a resource, a goal, or a constraint of an enterprise environment", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to any of corporate action information, research reporting information, supply chain information, newsfeed information, or social media information”.
In response the examiner is not persuaded. The examiner points out the excerpt of Flores was cited as “see at least”. The citation is not exhaustive of Flores teachings. The enterprise architecture model associated with an operational environment taught by Flores at col. 1 lines 45-60 is clearly related to corporate action information. Note limitations are recited in the alternatives.
Applicant argues at page 21 of the response regarding claims 7, 16: “Applicant respectfully submits that although Flores discloses "a data set permitting the correlation of entity 413 to asset 415", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to a correlation score of any kind, and there is no disclosure whatsoever in relation to how such a correlation score would be calculated”.
In response the examiner is not persuaded. As written, the claimed correlation score is broad enough to read on the correlation of entity to asset and mapping taught in Flores at the cited col.10 lines 22-36).
Applicant argues at page 22 of the response regarding claims 8, 17: “Applicant respectfully submits that although Flores discloses that "the synchronization can be performed as a daemon process within the dashboard and can be run at a configured time period or through end user interruption", a careful inspection of the above-reproduced portion of Flores reveals that there is no disclosure whatsoever in relation to an anti-correlation characteristic of any kind, and there is no disclosure whatsoever in relation to such a characteristic being usable to exclude data from further analysis”
In response the examiner is not persuaded. The excluded data including an excluded time period is clearly taught in Flores at the cited portion since it shows step 345 is performed on a timed/interrupt basis.
Applicant presents no further arguments. For all the reasons discussed above, the rejection of all pending claims is maintained using the references of record.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-11, 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10, 19 “sufficiently trained” seems merely subjective. The specification merely mention at paragraph 0082: “the training model may be sufficiently trained when model assessment methods such as, for example, a holdout method, a K-fold-cross-validation method, and a bootstrap method determine that at least one of the training model's least squares error rate, true positive rate, true negative rate, false positive rate, and false negative rates are within predetermined ranges”.
Note the language “may be”, “for example”, “such as” in the cited paragraph of the specification do not define the claimed “sufficiently trained”. Therefore it is not clear what the recited “sufficiently trained” requires. The claims are indefinite.
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 1-2, 4-11, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claim 1 for patent eligible subject matter:
Step 1: claim 1 is directed to a method thus seems to recite a statutory class of a process.
Step 2A Prong 1: claim 1 recites determining a listing determining at least one benchmark reference identifying at least one data set, including historical data" These limitations are processes that under its broadest reasonable interpretation cover performance of the limitation by a human user including observations, evaluations, judgements and opinions, but for the recitation of "at least one processor'. That is, other than reciting "at least one processor", nothing in the claim element precludes the steps from practically being performed by a human user. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation by a human user then it fails within the mental process grouping of abstract idea. Accordingly the claim recites an abstract idea.
Step 2A Prong 2: the judicial exception is not integrated into a practical application. The claim recites the additional element "receiving at least one request retrieving at last one dataset " The recitation of receiving a request and retrieving at least one dataset are mere generic data gathering activity recognized by the courts as well-understood, routine and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional "displaying a graphical representation of the listing is mere generic data presentation which is considered to be insignificant extra solution activity (MPEP 2106.05(g) because it does not impose any meaningful limit on practicing the abstract idea. The now added “wherein after the at least one model has been sufficiently trained..., wherein the graphical representation includes at least one heatmap that visually represent data....” merely describe a condition of the training and a visual representation of the matching object data at a high level of generality thus considered to be insignificant extra solution activity (MPEP 2106.05(g) because they do not impose any meaningful limit on practicing the abstract idea.
Thus claim 1 as a whole does not improve any technology or technical field, does not apply the judicial exception with or by use of a particular machine, does not add specific limitation other than what is well-understood, routine, conventional activity in the field, does not add unconventional steps that confine the claim to a particular useful application, does not include other meaningful limitations beyond linking the use of the judicial exception to a particular technological environment.
Claim 2 merely further describes he graphical representation, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 4 merely further describes querying and determining the listing, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 5 merely recites persisting the information related to the target and matching object in cache, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 6 merely further describes the at least one dataset, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 7 merely further recites determining at last one correlation characteristic for each matching object in the listing, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 8 merely further describes the correlation characteristic, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claim 9 merely further describes the at least one model, considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claims 10, 11, 13-18 essentially correspond to devices for performing the method of claims 1, 2, 4-9 thus are non-statutory for the same reasons discussed in claims 1, 2, 4-9 above.
Claims 19-20 recite limitations similar to claims 1.2 in form of non- transitory computer program product thus are non-statutory for the same reasons discussed in claims 1-2 above.
No claim is patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4-11, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Flores et al (US 10797958 B2) of record, in view of Chritoforo (WO 2015/034522 A1) of record, in view of Chauhan et al (US 20230106381 A1), further in view of Sengupta et al (US 11409549 B2) of record.
Regarding claim 1, Flores substantially teaches a method for providing customizable operational environments for automated real-time analysis (see at least col.1 lines 45-48 (4) One aspect of the present invention can include a system, an apparatus, a computer program product and a method for enabling real-time operational environment conformity to an enterprise architecture model.), the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor, at least one request from a user via an application, each of the at least one request including a target and at least one corresponding parameter for an operational environment (see at least col.4 lines 34-43: In scenario 140, an operations team 152 and/or an architect 154 can interact with dashboard 120 to manage views 140, patterns 142, and/or states 144 associated with operational environment 122. It should be appreciated that a strategic view can be a static data snapshot (e.g., enterprise architecture model 110) which can differ from a traditional operational view which can permit a dynamic (e.g., real-time) view of an operational environment (e.g., environment 122). That is, the disclosure can reconcile the static snapshot view with the dynamic view of the operational environment.);
determining, by the at least one processor using at least one model, a listing of at least one matching object for each of the at least one request based on comparative analytics, the at least one matching object corresponding to the target (see at least col.4 lie 55- col.5 line 4: (17) As used herein, an enterprise architecture model 110 can be a collection of entities representing an enterprise organizational structure, an operational node, an architecture component node, a resource, a goal, a constraint and the like. The model 110 can include, but is not limited to, a target system, a context, a model component, a model node, a functional requirement, a non-functional requirement, and the like. The operational environment 122 can be a collection of elements including, but not limited to, a computing resource and/or a computing resource state associated with the enterprise organization. Elements can include, but is not limited to, a computing device, a computing environment, a distributed computing environment, a virtual computing environment, and the like. It should be appreciated that environment 122 can include one or more computing systems, computing devices, and the like. For example, environment can be an electronic mail server system.);
the difference is Flores does not specifically show determining, by the at least one processor using the at least one model, at least one benchmark reference for the target;
however it is customary in the art to do so as shown by Christoforo (see [0047] The visualization system and method when used for the financial industry can provide an assessment of portfolio risk and performance by investment manager, industry sector, asset class, geography, or other factors in the context of time and spatial relationships to a benchmark, such as, for example, the S&P 500. The system and method of the present invention may also be used for visualization operational risk in a corporate operating environment).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of Flores in order to allow data analytics using existing norms for comparison.
Flores/Christoforo further teaches:
identifying, by the at least one processor using the at least one model, at least one data set that corresponds to each of the at least one matching object, the at least one data set including historical data (see at least Flores col.4 lines 44-48:(16) Dashboard 120 can be an interface for presenting one or more views 140 of an operational environment 122. Dashboard 120 can include, but is not limited to, key performance indicators (KPI), trends, comparisons, exceptions, historic data, and the like.);
retrieving, by the at least one processor, the at least one data set from a data repository that is dynamically updated in real-time and from at least one historical data source (Flores col.1 lines 45-50: (4) One aspect of the present invention can include a system, an apparatus, a computer program product and a method for enabling real-time operational environment conformity to an enterprise architecture model. An enterprise architecture model associated with an operational environment within an operational dashboard can be identified.); and
displaying, by the at least one processor via a graphical user interface, a graphical representation of the listing of the at least one matching object, the at least one benchmark reference, and the at least one data set (Flores col.1 lines 53-60: The environment can include elements which can be a computing resource and a computing resource state associated with the environment. An operational state change within the operational environment can be performed. A result of the change can be presented within an architectural view of the model within the dashboard. The result can include a context, a policy, a function, or a relationship affecting the model.).
Flores/Christoforo doe not specifically show the now added “wherein after the at least one model has been sufficiently trained, the method further comprises continuing a training of the at least one model by using new data,
However it is customary in the art to do so as shown by Chauhan (see at least
[0095]: In another exemplary embodiment, the model may include training models such as, for example, a machine learning model which is generated to be further trained on additional data. Once the training model has been sufficiently trained, the training model may be deployed onto various connected systems to be utilized
[0096] In another exemplary embodiment, the training model may be operable, i.e., actively utilized by an organization, while continuing to be trained using new data.
It would have been obvious to one or ordinary skill in the art to include the teachings of Chauhan while implementing the method of Flores/Christoforo for the benefit of having a fully trained model in one domain of an organization learn more features in another domain of the organization.
Flores/Christoforo/Chauhan does not specifically show:
“and wherein the graphical representation includes at least one heatmap that visually represents data for each of the at least one matching object based on predetermined criteria, the visual representation of the data including a color coded representation of magnitude and degree of change”.
However it is customary in the art to use color codes in graphical representation of data analysis as shown by Sengupta (see col.6 lines 17- 35: (27) The plot can include a visual representation of predictive model characteristics provided by the user. For example, input target accuracy can be represented by a color-coded region ("light green") 125 on the plot. The color-coded region can include the origin of the plot (e.g., representative of perfect accuracy) 115. The shape of the color-coded target region 125 can be determined by an arch tangent to the relative cost curve 135 and/or the accuracy curve 130, can include a conic section such as hyperbola, parabola, or section of ellipse, and the like. The entirety of the target area 125 can be bounded by the target accuracy, target cost curves 135, and the perfect model point (e.g., origin) 115. The size of the color-coded region 125 can be inversely proportional to the input target accuracy. Presence of the graphical object 120 in the color-coded region 125 can indicate that the performance of the model has an accuracy greater than or equal to the input target accuracy. Additional color-coded regions can be added to show accuracy bands representing an accuracy scale or the performance of random selection);
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of Flores/Christoforo/Chauhan in order to assist users visualizing the model performance as taught by Sengupta.
Regarding claim 2, Flores/Christoforo/Chauhan/Sengupta further teaches the method of claim 1, wherein the graphical representation includes at least one graphical element that is configured to receive an input from the user, the input including at least one action to alter the operational environment of the target (Flores col.9 lines 18-26: (40) Operations engine 420 can be a hardware/software element for enabling an architectural view within an operations dashboard 444. Engine 420 can include, but is not limited to, operational component 422, dashboard element 424, synchronizer 426, settings 428, and the like. Engine 420 functionality can include, but is not limited to, notification functionality, receiving user input, data visualization, and the like. In one instance, engine 420 can be a portion of a dashboard software.).
Regarding claim 4, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 1, prior to the determining of the listing of the at least one matching object, further comprises: querying, by the at least one processor, a caching layer for the operational environment; and determining, by the at least one processor using the at least one model, the listing of the at least one matching object when the operational environment is not cached in the caching layer (Flores col.7 lines 5-11: (29) In step 305, an enterprise management application can be launched and an appropriate dashboard environment can be initialized. It should be appreciated that access control, preferences, and the like can be performed during the initialization. Further, it should be appreciated that there can be persistent storage for events, configuration, and cached operational data to ensure optimal synchronization., see also col.7 lines 54-62: in step 305 during initialization. It should be appreciated that a persistent storage for events, configuration and cached operational data within the application environment to ensure optimal synchronization can be present. In step 350, if there is a model available, the method can continue to step 355, else proceed to step 360. In one instance, when the model lacks operational data (e.g., no model available), the data can be collected from the operational environment and can be synchronized with the dashboard.).
Regarding claim 5, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 1, further comprising: persisting, by the at least one processor, information that relates to the target and the corresponding at least one matching object in a caching layer, wherein the persisted information is provided to a plurality of subsequent requests that include the target without requiring subsequent calculations (Flores Fig.1 items 110, 120, 140).
Regarding claim 6, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 1, wherein the at least one data set includes information that corresponds to at least one from among corporate action information, research reporting information, supply chain information, newsfeed information, and social media information (Flores col.1 lines 45-60: One aspect of the present invention can include a system, an apparatus, a computer program product and a method for enabling real-time operational environment conformity to an enterprise architecture model. An enterprise architecture model associated with an operational environment within an operational dashboard can be identified. The model can include an entity which can be an operational node, an architecture component node, a resource, a goal, or a constraint of an enterprise environment. The environment can include elements which can be a computing resource and a computing resource state associated with the environment. An operational state change within the operational environment can be performed. A result of the change can be presented within an architectural view of the model within the dashboard. The result can include a context, a policy, a function, or a relationship affecting the model.).
Regarding claim 7, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 1, wherein at least one correlation characteristic is determined for each of the at least one matching object in the listing, the at least one correlation characteristic including a correlation score that is calculated based on the target (Flores col. 10 lines 22-36: (47) Mapping 432 can be a data set permitting the correlation of entity 413 to asset 415. Mapping 432 can be dynamically and/or statically generated from model 412 and/or environment 414. In one instance, mapping 432 can be heuristically generated based on historic models. Mapping 432 can include, but is not limited to, asset identifier, an entity identifier, a state information, and the like. For example, mapping 432 can include an entry 434 able to associated an Asset_A with an Entity_A and a State_A. State information can represent the status of the entity 413, asset 415, and the like. State information include, but is not limited to, conformity, non-conformity, and the like. In one embodiment, mapping 432 can conform to a database record, a file, and the like. For example, mapping 432 can conform to an XML file.).
Regarding claim 8, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 7, wherein the at least one correlation characteristic includes an anti-correlation characteristic that is usable to exclude data from further analysis, the excluded data including an excluded time period (Flores col.7 lines 45-52: (31) In step 345, the dashboard can be synchronized. In one embodiment, step 345 can be performed on a timed/interrupt basis via daemon processes in the dashboard. In the embodiment, the synchronization can be performed as a daemon process within the dashboard and can be run at a configured time period or through end user interruption. It should be appreciated that the daemon can leverage the application environment created in method 301.).
Regarding claim 9, Flores/Christoforo/Chauhan/Sengupta further teaches or suggests the method of claim 1, wherein the at least one model includes at least one from among a large language model, a deep learning model, a neural network model, a natural language processing model, a machine learning model, a mathematical model, and a process model (Flores col. 1 lines 53- 60: The environment can include elements which can be a computing resource and a computing resource state associated with the environment. An operational state change within the operational environment can be performed. A result of the change can be presented within an architectural view of the model within the dashboard. The result can include a context, a policy, a function, or a relationship affecting the model.).
Claims 10-11, 13-18 essentially recite limitations similar to claims 1-2, 4-9 in form of devices thus are rejected for the same reasons discussed in claims 1-2, 4-9 above.
Claims 19-20 essentially recite limitations similar to claims 1-2, in form of non-transitory computer program product, thus are rejected for the same reasons discussed in claims 1-2 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lozano et al (US 20220019496 A1) teach an error documentation system including tools to collect and analyze application error data for individual development teams and tools to share documented defects and solutions across development teams during any stage of development cycle. The system may receive and analyze event logs for error events triggered by applications on end-user devices. The system may automatically generate defect tickets and/or ticket entries for defects identified in event logs. The system may train one or more machine learning (ML) models to correlate input with identified defects from a defects database. In response to identifying correlated identified defects, the system may generate ticket entries indicating the correlated identified defects and associated solutions for the defects. The system may provide an interface for users to query the data stored in the database. A trained ML model(s) 216 can comprise a classifier that is tasked with classifying unknown input (e.g., an unknown defect) as one of multiple class labels by error types (e.g., exceptions, null pointers, etc.) and defects. In additional examples, the model(s) 216 can be retrained with additional and/or new training data labeled with one or more new defects and/or error types to teach the model(s) 216 to classify unknown input by defects that may now include the one or more new error types and defects.
Gurevich et al (US 11122009 B2) teach systems and methods to identify geographic locations of all social media content items retrieved form a social network in real time, wherein the geographic locations are physical locations from which the social media content items are originated or authored. If the latitude/longitude (geographic) coordinates of the content item are available, the geographic location of the social media content item can be identified using such geographic coordinates. For content items which geographic coordinates are not available, historical archive of content items with high-confidence of geographic locations are utilized to train a location classifier to predict geographic locations of such content items with high accuracy. Finally, the identified locations of the content items are confirmed to be accurate and are presented to a user together with the content items.
Guan (CN 114968412 B) teaches a configuration file generation method, device, electronic device and storage medium based on artificial intelligence, the configuration file generation method based on artificial intelligence comprises: counting the historical demand and the configuration file corresponding to each historical demand, wherein the historical demand and the configuration file comprise multiple vocabulary, and calculating the weight of each vocabulary; constructing a demand matrix and a configuration matrix based on the vocabulary; calculating the candidate code corresponding to each vocabulary based on the demand matrix and the configuration matrix, and calculating the product of the candidate code and the weight as the coding data; generating a model based on the code data training configuration file; inquiring the to-be-evaluated code corresponding to the to-be-evaluated requirement, inputting the to-be-evaluated code into the configuration file generation model to obtain the target configuration file. The method can generate the configuration file meeting the requirement through the configuration file generation model so as to improve the accuracy of the configuration file generation.
Lau, Henry CW, Premaratne Samaranayake, and Dilupa Nakandala. "A fuzzy-based integrated framework for monitoring stochastic demand in a supply chain environment." 2011 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2011.
Abstract:
This paper develops a Fuzzy-based Integrated Framework (FIF) that responds to the risk of fluctuating demand in a supply chain environment. It is a hybrid framework based on the fuzzy based stochastic demand model and the integrated processes of supply chain in enterprise resource planning (ERP) environment. This proposed FIF can be used to adjust the rate of change of demand based on fuzzy system reasoning, supported by real time inputs of the specific parameters and control data using integrated supply chain processes/models in ERP system environment. Based on a cost effective approach, this framework minimizes the total cost comprising inventory and stock-out costs. Using a case study, we validate the proposed integrated framework in actual industrial settings and discuss the practical importance for supply chain management in responding to uncertainties inherited in the operational environment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/UYEN T LE/Primary Examiner, Art Unit 2156 27 July 2026
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