DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 01/28/2026 has been entered.
Status of the Claims
Claims 1-19 are pending.
Claim Construction
Independent Claims recite limitation – “a reduced memory representation” of data. Such limitation is very road and open to various interpretations. The reduced memory representation can broadly be referring to – compression, simplified encoding, filtering, dimensionality reduction, data cleansing, binarization, etc..
Clarification is requested.
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 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bettaiah et al. (US 10505825) in view of Leonard et al. (US 20180260106) and in further view of Crabtree et al. (US 20210385251).
Regarding claim 1, Bettaiah teaches a cloud-based system for visualizing dynamically updated data flows, (C85L13-14) the cloud-based system comprising: one or more processors accessible over a computer network (C187L39-40); one or more memories communicatively coupled with the one or more processors, the one or more memories
storing a set of predefined graphical shapes, each predefined graphic shape defining a graphical curve type of an expected data flow (C276L28-31, C321L7-10, 16-23, C322L35-36, 60-65, F39B, 62B); and
computer executable instructions stored in the one or more memories that, when executed by the one or more processors, cause the one or more processors to:
input an expected data flow defined over a set time period (C26L11-30, F70D, 82B:7712);
generate a selection display depicting each of the predefined graphical shapes, the selection display configured for visualization on a graphic user interface (GUI) of a display screen of a client device (C275L31-38, C276L6-35);
receive a selection of a predefined graphical shape as selected from the predefined graphical shapes, generate, based on the selection of the predefined graphical shape (C321L7-10, 16-23), a default graphical display depicting a default graphical curve having by a graphical curve type (C264L52-67 – C265L1-7, 20-35, 48-559, C279L42-51, F39B, F70B:70220a-c) defined by each of:
(a) the predefined graphical shape as selected (C264L65-67, C266L10-15, C304L20-30, 65-67, F44, 45B, 55C, F70B:70220a-c), and
(b) the data flow defined over the set time period, the default display configured for visualization on the GUI of the display screen of the client device (C146L5-8, C149L45-51, C224L38-39, C255L10-16, C304L10-16),
receive a selection of a unit time period selected from within the set time period, the unit time period displayed on the default graphical display (C159L60-67 – C160L1-39),
receive an override data value for overriding the expected data flow, the override data value defined for the unit time period as selected from within the set time period (C185L33-43, 53-60, C188L21-37 – C189L5-25),
generate an overridden expected data flow defined over the set time period by updating the expected data flow with the override data value, dynamically update the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow (C325L4-52, C326L17-2760-65, C333L15-65, F70D-J, F71)(see NOTE I),
generate, based on the augmented graphical curve (C261L35-38, C320L20-23), a dynamically updated graphical display (C160L13-18) depicting the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period (C134L15-18, C136L10-15, C161L5-20, C188L52-67), and
transmit a reduced memory representation (C78L10-25, C376L40-42, “Package … may represent a form for the control module data that is compacted, compressed”, C377L16-43, C377L65 “reduce the size of this performance data”) of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device (C115L25-55, C183L14-30, C232L48-52C276L38-62), wherein the GUI of the display screen of the client device regenerates (C377L1-5 see “decompression or unpacking”), from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period (C134L15-18, C136L10-15, C161L5-20, C188L52-67) (see NOTE II).
NOTE I - Bettaiah teaches that user can override thresholds for the current and expected values (C159L60-67), wherein the display “may dynamically update the aggregate KPI value as the user adjusts the weights. This may provide near real-time feedback on how adjustments to the weights” C18318-33 and “may be dynamically updated over time, so as to provide the user with the most recent available information … KPIs are recalculated according to the defined schedule … the visual representations can be automatically updated in response to a specific user request … and then displayed and refreshed at predefined time intervals or in real time once new values are calculated based on KPI monitoring parameters” C296L45-62.
In summary, the system displays predicted / expected values displayed on a time series data flow. The user can override or change predicted / expected thresholds and weights. The visualization (graphical curve) is dynamically updated based on the user input. All of the values (expected, changed) are presented in augmented display to the graph, which is construed to be analogous to the limitation “generate an overridden expected data flow defined over the set time period by updating the expected data flow with the override data value, dynamically update the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow.”
However, to merely obviate such reasoning, Leonard discloses dynamically update the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow [0007], [0136], [0144].
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Bettaiah to dynamically update the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow as disclosed by Leonard. Doing so would an interactive graphical user-interface for analyzing and manipulating time-series projections (Leonard [0003]).
NOTE II – With respect to the limitation “transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period,” the support for such functionality is seems to be based on paragraphs [0028] and [0077], which disclose – “save and load data (e.g., of a data flow) by converting such data flow related to the data flow, a JavaScript Object Notation (JSON) data format and saving in a database (e.g., a relational database and/or a NoSQL database). This allows for quickly saving, retrieving, and then regenerating for display graphical curves.”
Bettaiah likewise discloses storing “entity definition within an RDBMS” for normalized data and saving data in a JSON-based database to facilitate storing entity definitions for denormalized data representations (C78L3-24). Bettaiah further teaches compacting and compressing data packages for efficient storing and decompressing and unpacking such packages for presentation, which provides “reduced storage burden” C283L49-50.
However, in order to obviate the functionality for the limitation in view of the specification, Crabtree discloses –
“transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve ([0054] “workflow are parsed and put into JSON format 210 before being sent to the distributed computational graph (DCG) backend”, [0055], [0057] “generated the workflow code from the JSON configuration, and convert it to binary format for data transmission, processing, and storage … processes the streaming data in an optimized way. Upon completion of the workflow, the results of which are stored to a data store 240, and a workflow report 250 displays the results back to the user via the user interface”, [0058]).
NOTE storing data in binary format generally reduces storage size and memory usage. Binary formats store information directly as bits/bytes (native format), eliminating the need for bulky character representations for numbers and reducing overhead, which leads to files being over 50% smaller.
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Bettaiah to transmit a reduced memory representation of data and regenerates such data from the reduced memory representation for the graphical display as disclosed by Crabtree. Doing so allows processes the streaming data in an optimized way and optimize the ingestion of data into cloud-based services by transforming the data prior to forwarding upstream (Crabtree [0057], [[0063]).
Regarding claim 10, Bettaiah teaches a cloud-based method for visualizing dynamically updated data flows, the cloud-based method comprising: inputting, by one or more processors accessible over a computer network, an expected data flow defined over a set time period; generating, by the one or more processors, a selection display depicting each of a set of predefined graphical shapes, the selection display configured for visualization on a graphic user interface (GUI) of a display screen of a client device, each predefined graphic shape defining a graphical curve type of an expected data flow; receiving, by the one or more processors, a selection of a predefined graphical shape as selected from the predefined graphical shapes; generating, based on the selection of the predefined graphical shape by the one or more processors, a default graphical display depicting a default graphical curve having by a graphical curve type defined by each of: (a) the predefined graphical shape as selected, and (b) the data flow defined over the set time period, the default display configured for visualization on the GUI of the display screen of the client device; receiving, by the one or more processors, a selection of a unit time period selected from within the set time period, the unit time period displayed on the default graphical display; receiving, by the one or more processors, an override data value for overriding the expected data flow, the override data value defined for the unit time period as selected from within the set time period; generating, by the one or more processors, an overridden expected data flow defined over the set time period by updating the expected data flow with the override data value; dynamically updating, by the one or more processors, the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow; generating, based on the augmented graphical curve by the one or more processors, a dynamically updated graphical display depicting the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period and transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period.
Claim 10 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 2 and 11, Bettaiah as modified teaches the cloud-based system and the method, wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
transmits each of the selection display, the default display, and the dynamically updated graphical display to the client device over the computer network (Bettaiah C278L61-64, C296L20-30, C301L11-12, Leonard [0007]); and
receive each of the selection and the override data value from the client device over the computer network (Bettaiah C325L4-52, C326L17-2760-65, C333L15-65, F70D-J, Leonard [0032], [0136]-[0138]).
Regarding claims 3 and 12, Bettaiah as modified teaches the cloud-based system and the method, wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to: set as statically defined the override data value within overridden expected data flow defined over the set time period (Bettaiah C74L55-63, C135L34-36, C149L30-35, C154L10-30, C296L57-60, C326L60-67, Leonard [0139], [0152]).
Regarding claims 4 and 13, Bettaiah as modified teaches the cloud-based system and the method, wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
generate, as part of a display, a graphical element indicating that the override data value is a statically defined value within the overridden expected data flow (Bettaiah C74L55-63, C135L34-36, C149L30-35, C154L10-30, C296L57-60, C326L60-67, Leonard [0139], [0142], [0144], [0151], F11-18).
Regarding claims 5 and 14, Bettaiah as modified teaches the cloud-based system and the method, wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
convert the overridden expected data flow of the augmented graphical curve into a data flow graph having a converted curve data format (Bettaiah C104L7-20, C361L21-25, C366L64-67, WU [0080], [0145]),
store the data flow graph into a database, load the data flow graph from the database (Bettaiah C35L49-51, C39L7-10, C55L37-42, C58L56-63, C80L31-59, C112L65-67, C174L1-23, C366L24-32, C378L1-4, Leonard [0094], [0097], [0113]), and
regenerate, based on the data flow graph from the database, the dynamically updated graphical display depicting the augmented graphical curve having the updated portion visually depicting the overridden expected data flow over the set time period (Bettaiah C49L6-61, C55L54-55, C56L7-17, 30-52, C95L58-60, C266L10-67, C285L11-32, Leonard [0129], [0133], [0136]), the dynamically updated graphical display configured for visualization on the GUI of the display screen of the client device (Bettaiah C55L10-45, C58L60-67, C266L64-67, C272L39-60, C285L1-30, 40-45, WU [0018], [0032], [0275]).
Regarding claims 6 and 15, Bettaiah as modified teaches the cloud-based system and the method, wherein the converted curve data format comprises a compressed data format stored in a single non-denormalized column of the database (Bettaiah C39L4-17, C78L10-25, C376L58-60, C377L5-42, WU [0031], [0035], [0039]-[0040] “map all or a subset of JSON fields into a column of the table for efficient use of relational query engines”; “concatenate all values of an array together as a single value of a column; and/or create column names with an index number to hold one value from the index of an array. There may be a restriction that each column of the table may only hold values of a single type”, [0104]-[0105], [0283], [0349]).
Regarding claims 7 and 16, Bettaiah as modified teaches the cloud-based system and the method, wherein one or more portions of the expected data flow are received in a plurality of different formats when analyzed by the one or more processors, and the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
convert the one or more portions of the expected data flow to a normalized format (Bettaiah C17L48-50, C38L66-67, C78L5-6, C147L48-67, Leonard [0054], [0058]).
Regarding claims 8 and 17, Bettaiah as modified teaches the cloud-based system and the method, wherein the normalized format comprises a JavaScript Object Notation (JSON) format (Bettaiah C39L5-10, C78L13, C240L10, C376L43-45).
Regarding claims 9 and 18, Bettaiah as modified teaches the cloud-based system and the method, wherein upon or as part of generation of the overridden expected data flow, the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
update one or more remaining data values other than the override data value within the overridden expected data flow defined over the set time period (Bettaiah C74L55-63, C135L34-36, C149L30-35, C154L10-30, C296L57-60, C326L60-67, Leonard [0139], [0152]),
wherein the augmented graphical curve is created to graphically depict each of the override data value and the one or more remaining data values, each as updated in the overridden expected data flow as triggered by a change in the override data value (Bettaiah F70:I-D, Leonard [0006]-[0007], [0152], F11-18).
Regarding claim 19, Bettaiah teaches a tangible, non-transitory computer-readable medium storing instructions for visualizing dynamically updated data flows, that when executed by one or more processors cause the one or more processors to: input, by one or more processors accessible over a computer network, an expected data flow defined over a set time period; generate, by the one or more processors, a selection display depicting each of a set of predefined graphical shapes, the selection display configured for visualization on a graphic user interface (GUI) of a display screen of a client device, each predefined graphic shape defining a graphical curve type of an expected data flow; receive, by the one or more processors, a selection of a predefined graphical shape as selected from the predefined graphical shapes; generate, based on the selection of the predefined graphical shape by the one or more processors, a default graphical display depicting a default graphical curve having by a graphical curve type defined by each of: (a) the predefined graphical shape as selected, and (b) the data flow defined over the set time period, the default display configured for visualization on the GUI of the display screen of the client device; receive, by the one or more processors, a selection of a unit time period selected from within the set time period, the unit time period displayed on the default graphical display; receive, by the one or more processors, an override data value for overriding the expected data flow, the override data value defined for the unit time period as selected from within the set time period; generate, by the one or more processors, an overridden expected data flow defined over the set time period by updating the expected data flow with the override data value; dynamically update, by the one or more processors, the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow; generate, based on the augmented graphical curve by the one or more processors, a dynamically updated graphical display depicting the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period; and transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period.
Claim 19 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Claims 5 and 14 is/are additionally and alternatively rejected under 35 U.S.C. 103 as being unpatentable over Bettaiah as modified in further view of Bedard et al. (US 20140149836) or Valsaraj et al. (US 10255085).
Regarding claims 5 and 14, Bettaiah as modified teaches the cloud-based system and the method, wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:
store the data flow graph into a database, load the data flow graph from the database, and
regenerate, based on the data flow graph from the database, the dynamically updated graphical display depicting the augmented graphical curve having the updated portion visually depicting the overridden expected data flow over the set time period, the dynamically updated graphical display configured for visualization on the GUI of the display screen of the client device.
Bettaiah as modified teaches the user can select a particular widget (aka curve), “a single value widget, a spark line widget, a Noel gauge widget, and a trend indicator widget”, “user can choose one or more of the available style setting(s) to replace or modify the default KPI widget. Style settings define how the KPI widget should be presented and can specify, for example, the shape of the widget, the size of the widget, the name of the widget, the metric unit of a KPI value, and/or other visual characteristics of the widget … widget that is displayed in the dashboard template can be displayed using the selected style settings”, C265L17-59.
Bettaiah further teaches the user can customize, set a time range and override the widget template and values C266L10-25, 45-50, C285L11-32. Such customized, overridden dashboard widgets is saved C266L50-61 and reused (aka regenerated) C266L64-67, C272L39-60, and is stored in JSON format (C39L7-10), which is construed to be analogous to the limitation “convert the overridden expected data flow of the augmented graphical curve into a data flow graph having a converted curve data format” and “regenerate, based on the data flow graph from the database.”
However, to further obviate such reasoning, Bedard discloses - “convert the overridden expected data flow of the augmented graphical curve into a data flow graph having a converted curve data format” ([0027], [0117]) and “regenerate, based on the data flow graph from the database” ([0055], [0063]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Bettaiah to include convert data flow of the augmented graphical curve into a data flow graph having a converted curve data format as disclosed by Bedard. Doing so would allow dashboards to be fully compatible and rendered using varying web technologies and optimized for mobile devices (Bedard Abstract, [0002]).
Valsaraj further teaches convert the overridden expected data flow (C4L55-59) of the augmented graphical curve into a data flow graph having a converted curve data format (C10L31-35, C20L53-54), store the data flow graph into a database (C15L40-67), load the data flow graph from the database, and regenerate, based on the data flow graph from the database, the dynamically updated graphical display depicting the augmented graphical curve having the updated portion visually depicting the overridden expected data flow over the set time period (C13L21-24), the dynamically updated graphical display configured for visualization on the GUI of the display screen of the client device (C16L1-10, C18L49-67, C28L1-5, 39-67).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Bettaiah to include convert data flow of the augmented graphical curve into a data flow graph having a converted curve data format as disclosed by Valsaraj. Doing so would provide override guidance that is intuitive and easily digestible for a user and an efficient, accurate, consistent and easily identification of problematic datasets (Valsaraj C4L63-67).
Claims 6 and 15 is/are additionally and alternatively rejected under 35 U.S.C. 103 as being unpatentable over Bettaiah as modified in further view of Duddleson et al. (US 20020040639).
Regarding claims 6 and 15, Bettaiah as modified teaches claims 6 and 15 as disclosed above, Duddleson additionally discloses the cloud-based system and the method, wherein the converted curve data format comprises a compressed data format stored in a single non-denormalized column of the database ([0027]-[0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Bettaiah to include non-denormalized column as disclosed by Duddleson. Doing so would provide a more efficient use of the physical resources of the computer so that more users get their results more quickly (Duddleson [0034]).
Claims 1 is additionally and alternatively rejected under 35 U.S.C. 103 as being unpatentable over Setlur et al. (US 2022/0164540) in view of either – Hunter (US 20210073647) or Harris et al. (US 11526502).
Regarding claim 1, Setlur teaches a cloud-based system for visualizing dynamically updated data flows, the cloud-based system comprising: one or more processors accessible over a computer network; one or more memories communicatively coupled with the one or more processors, the one or more memories storing a set of predefined graphical shapes, each predefined graphic shape defining a graphical curve type of an expected data flow ([0096], [0131]-[0137], [0172]); and computer executable instructions stored in the one or more memories that, when executed by the one or more processors, cause the one or more processors to:
input an expected data flow defined over a set time period ([0110], [0112]);
generate a selection display depicting each of the predefined graphical shapes, the selection display configured for visualization on a graphic user interface (GUI) of a display screen of a client device ([0112], [0128], [0141]);
receive a selection of a predefined graphical shape as selected from the predefined graphical shapes, generate, based on the selection of the predefined graphical shape, a default graphical display depicting a default graphical curve having by a graphical curve type ([0129], [0148], [0172]) defined by each of:
(a) the predefined graphical shape as selected, and (b) the data flow defined over the set time period, the default display configured for visualization on the GUI of the display screen of the client device, receive a selection of a unit time period selected from within the set time period, the unit time period displayed on the default graphical display ([0112], [0119], [0131]-[0137]),
receive an override data value for overriding the expected data flow ([0119]), the override data value defined for the unit time period as selected from within the set time period, generate an overridden expected data flow defined over the set time period by updating the expected data flow with the override data value ([0116], [0175]),
dynamically update the default graphical curve to create an augmented graphical curve defined by the overridden expected data flow, generate, based on the augmented graphical curve, a dynamically updated graphical display depicting the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period ([0116], [0149]), and
transmit
Although Setlur discloses storing data “as spreadsheet files, CSV files, XML files, flat files, or JSON files” [0061], Setlur does not explicitly teach, however Hunter discloses transmit a reduced memory representation … wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation ([0070] “an array may be decomposed into lists or dictionaries in JSON amenable to serialization. … While in serialized form, the array of norm vertices may reduce memory requirements during data storage operations and bandwidth requirements during data transfer operations”). Harris discloses the same in C9L25-42, C11L45-50.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Setlur to transmit a reduced memory representation as disclosed by Hunter or Harris. Doing so would increase storage efficiency, increase system efficiency or reduce memory use (Hunter [0045], [0106]).
Response to Arguments
Applicant's arguments filed 12/31/2025 have been fully considered but they are not persuasive.
The applicant starts arguments by initially addressing the Wu reference with respect to the limitation “transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period”.
However, the limitation is initially disclosed by the main reference of Bettaiah. It is first noted that the “reduced memory representation” is a very broad limitation, which can certainty encompass data / image compression, as disclosed by Bettaiah. Bettaiah discloses transmitting data packages in a compressed and compacted form, which reduces the size of data. Bettaiah further clearly teaches unpacking the compressed data and augmenting GUI with the overridden expected data. Analogously, to the applicant’s specification, Bettaiah teaches that the user can select various inputs to override data reflecting the expected/predicted KPI values. Bettaiah further discloses storing the data in the JSON format to facilitate storing KPI correlations search definitions C174L10-11, analogously to the applicant’s disclosure. It is well-known that storing data in JSON format can reduce storage memory compared to verbose formats like XML due to its lightweight, text-based structure. The applicant’s own specification shows analogous functionality of the JSON objects (see specification [0077] Pub Version –“ may save and load data (e.g., of a data flow) by converting such data flow related to the data flow, a JavaScript Object Notation (JSON) data format … This provides a compact form of storage allowing for storing multiple data flows without needing to save the related graphics, and thereby implement a reduced data storage footprint”). Thus, storing data in JSON format provides the “reduced memory representation”, as required. Bettaiah also discloses “reduced or enlarged if the quantity of data (e.g., KPI values or machine data) is not within a predetermined range of data, which may be based on a storage or processing capacity of a computing system” C160L15-18. Once again, given that the “reduced memory representation” is a very broad limitation, which can encompass data compression, storing and transmitting data in JSON format, Bettaiah on his own is fully capable of teachings the limitation of “transmit a reduced memory representation of the dynamically updated graphical display to the client device for visualization on the GUI of the display screen of the client device, wherein the GUI of the display screen of the client device regenerates, from the reduced memory representation, the augmented graphical curve having an updated portion visually depicting the overridden expected data flow over the set time period”, as required. The applicant only briefly argues Bettaiah’ reference and instead argues a reference of WU, which has been withdrawn .
Applicant's remaining arguments, in regard to the presently amended claims, are addressed in the updated rejections to the claims above. Please note alternative rejection to the claim 1 immediately above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/Primary Examiner, Art Unit 2165 May 2, 2026
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