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 .
In response to Applicant’s claims filed on December 17, 2025 claims 1-20 are now pending for examination in the application.
Response to Arguments
Applicant’s prior art arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Remarks, page 9 include:
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Examiners reply:
The synchronized updating features of the claims are directed to an automation of a mental comparison between related time-aligned data sets and the corresponding display of that comparison. Using generic GUI elements to implement this does not improve computer functionality and therefore does not integrate the abstract idea into a practical application.
Remarks, page 9 also say:
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Examiners reply:
Functional relationships between displayed things is not a technological improvement. The claims are silent with respect to any new rendering technique, display hardware, or improved processing mechanism. The claims merely correlate and highlight related data. This coordination of displayed information is a computer-implemented abstract mental process.
Remarks, page 10 says:
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Examiners reply:
The claims have been evaluated as a whole and when considered in their entirety they still amount to collecting, organizing, and displaying information. The additional GUI elements do not add meaningful limitations beyond the abstract idea.
Remarks, page 13 says:
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The limitation “in response to a user selection … updating the graphical user interface…” is itself part of the abstract idea of conditionally presenting correlation information based on user input. Such conditional GUI updates correspond to automated execution of mental processes (selection, comparison, highlighting, etc) rather than improvements to a computer’s functionality. The claims were evaluated as a whole and this limitation does not add a technical feature that integrates the abstract idea into a practical application under MPEP 2106.05(a).
Remarks, page 13 also says:
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Examiners reply:
Trading Techs was not decided merely because the claims has GUI elements or user selection logic; the Trading Techs claims recited a specific GUI structure that prevented a known trading error and improved the functioning of the trading system itself. It is not clear what this has to do with the dual histograms, synchronized bin selection, and data panels of the claims in this case. The claim of this case do not prevent an identified technical error or improve system operation, they are specifically and only for presenting correlated data for user inspection.
Remarks, page 14 says:
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Examiners reply:
MPEP 2106.04(a)(2) also includes “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.” Furthermore, the claims do not add a technical feature that integrates the abstract idea into a practical application under MPEP 2106.05(a).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 8, and 15 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1 recites the following limitations directed towards a Mental Processes:
identifying time bins based on the search results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying pipeline data);
generating first histogram data based on the time bins and the events, the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating second histogram data based on the time bins and the events, the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool);
a first data panel for first event data describing one or more of the first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool); and
a second data panel for second event data describing one or more of the second subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool);
in response to a user selection of one of the first set of bins, updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by using computer as a tool);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by using computer as a tool);
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Independent claims 8 recites the following limitations directed towards a Mental Processes:
identifying time bins based on the search results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying pipeline data);
generating first histogram data based on the time bins and the events, the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating second histogram data based on the time bins and the events, the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
a first data panel for first event data describing one or more of the first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events); and
a second data panel for second event data describing one or more of the second subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
in response to a user selection of one of the first set of bins, updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 8:
one or more processors (i.e., as a generic component performing a generic computer
function); and
a computer-readable medium storing instructions that are operable when executed by the one or more processors to perform operations for displaying search result data for an observability pipeline system (i.e., as a generic component performing a generic computer
function), comprising:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Independent claims 15 recites the following limitations directed towards a Mental Processes:
identifying time bins based on the search results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying pipeline data);
generating first histogram data based on the time bins and the events, the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating second histogram data based on the time bins and the events, the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
a first data panel for first event data describing one or more of the first subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events); and
a second data panel for second event data describing one or more of the second subset of the events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events);
in response to a user selection of one of the first set of bins, updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data);
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by updating histogram data).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 15:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 8, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 9, 16:
Step 2A, prong one of the 2019 PEG:
identifying a characteristic of interest (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying characteristics);
identifying the first subset of the events based on the characteristic of interest, wherein the first subset of events has a first value for the characteristic of interest (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying characteristics); and
identifying the second subset of the events based on the characteristic of interest, wherein the second subset of events has a second, distinct value for the characteristic of interest (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying characteristics).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3, 10, 17:
Step 2A, prong one of the 2019 PEG:
wherein the characteristic of interest comprises an event source, an event location, or an event destination (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by identifying characteristics).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4, 11, 18:
Step 2A, prong one of the 2019 PEG:
the first histogram and the second histogram are presented side-by-side in a first section of the graphical user interface (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data);
the first data panel and the second data panel are presented side-by-side in a second section of the graphical user interface, wherein the first section is presented above or below the second section (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5, 12, 19:
Step 2A, prong one of the 2019 PEG:
the first data panel comprises first segments corresponding to the first set of bins, each first segment comprising a text representation of one or more events in a respective one of the first set of bins and a numerical representation of the time bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events); and
the second data panel comprises second segments corresponding to the second set of bins, each second segment comprising a text representation of one or more events in a respective one of the second set of bins and a numerical representation of the time bin (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 13, 20:
Step 2A, prong one of the 2019 PEG:
wherein the each of the first segments is aligned with a respective one of the second segments, such that each pair of aligned first and second segments correspond to a respective one of the time bins (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating histogram data using time and events).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 14:
Step 2A, prong one of the 2019 PEG:
comprising, in response to a user selection of one of the first segments, expanding the selected segment to display a text representation of additional events (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by expanding histogram data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Breeden et al. (US Patent No. 11388211) in view of Khan et al. (US Pub. No. 20230022715).
With respect to claim 1, Breeden et al. teaches a method of displaying search result data for an observability pipeline system, comprising:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (Column 17 Lines 16-34 discloses the monitoring platform executes the query against the selected data set to deliver results to the user and Column 9 Lines 42-55 disclose receives the observability data collected by the collector 304 and provides critical insights into the collected trace data to a client, who may be an application owner or developer);
identifying time bins based on the search results (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity);
generating first histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating second histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a first data panel for first event data describing one or more of the first subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client); and
a second data panel for second event data describing one or more of the second subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client). Breeden et al. does not explicitly in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin.
However, Khan et al. discloses in response to a user selection of one of the first set of bins (Paragraph 31 discloses the dynamic histogram builder 198 enables the histogram displayed via the user interface to be dynamically adjusted and/or changed based on user input (e.g., a time period for the histogram displayed via the user interface) using the histogram indices without the system 190 from having to calculate a histogram based on the input stream and input from the histogram visualization client 200), updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288); and
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Breeden et al. with Khan et al. This would have facilitated displaying search results. See Khan et al. Paragraphs 1-2. In addition, all references teach features that are directed to analogous art and they are directed to the same field of endeavor: data analysis.
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 1. Regarding claim 2, Breeden et al. discloses the method of claim 1, comprising:
identifying a characteristic of interest (Column 8 Lines 62-67and Column 9 Lines 1-8 discloses identify different log entries. Each log entry can include a portion of a log file, include unstructured raw machine data, and reflect an interaction between the client-side host and another computing device within the IT environment. As such, the collector 304 can generate log entries from the log data. The log entries may be further processed by the collector 304 or another system, such as the data intake and query system 326, to generate events);
identifying the first subset of the events based on the characteristic of interest, wherein the first subset of events has a first value for the characteristic of interest (Column 8 Lines 62-67and Column 9 Lines 1-8 discloses identify different log entries. Each log entry can include a portion of a log file, include unstructured raw machine data, and reflect an interaction between the client-side host and another computing device within the IT environment. As such, the collector 304 can generate log entries from the log data. The log entries may be further processed by the collector 304 or another system, such as the data intake and query system 326, to generate events); and
identifying the second subset of the events based on the characteristic of interest, wherein the second subset of events has a second, distinct value for the characteristic of interest (Column 8 Lines 62-67and Column 9 Lines 1-8 discloses identify different log entries. Each log entry can include a portion of a log file, include unstructured raw machine data, and reflect an interaction between the client-side host and another computing device within the IT environment. As such, the collector 304 can generate log entries from the log data. The log entries may be further processed by the collector 304 or another system, such as the data intake and query system 326, to generate events).
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 2. Regarding claim 3, Breeden et al. discloses the method of claim 2, wherein the characteristic of interest comprises an event source, an event location, or an event destination (Column 47 Lines 10-30 discloses approximately 20.6k data entries 1804 can include the field value “/var/log/containers/currency . . . ” or be associated with metadata that identifies the event as being associated with the service.name “/var/log/containers/currency).
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 2. Regarding claim 4, Khan et al. discloses the method of claim 1, wherein:
the first histogram and the second histogram are presented side-by-side in a first section of the graphical user interface (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288);
the first data panel and the second data panel are presented side-by-side in a second section of the graphical user interface, wherein the first section is presented above or below the second section (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288). The motivation to combine statement previously provided in the rejection of dependent claim 2 provided above, combining the Breeden et al. reference and the Khan et al. reference is applicable to dependent claim 4.
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 4. Regarding claim 5, Khan et al. discloses the method of claim 4, wherein:
the first data panel comprises first segments corresponding to the first set of bins, each first segment comprising a text representation of one or more events in a respective one of the first set of bins and a numerical representation of the time bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288); and
the second data panel comprises second segments corresponding to the second set of bins, each second segment comprising a text representation of one or more events in a respective one of the second set of bins and a numerical representation of the time bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288). The motivation to combine statement previously provided in the rejection of dependent claim 4 provided above, combining the Breeden et al. reference and the Khan et al. reference is applicable to dependent claim 5.
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 5. Regarding claim 6, Khan et al. discloses the method of claim 5, wherein the each of the first segments is aligned with a respective one of the second segments, such that each pair of aligned first and second segments correspond to a respective one of the time bins (Paragraph 44 discloses the histogram indices obtained from storage, the dynamic histogram builder 198 may generate the updated histogram depicted in the user interface 330 of FIG. 5B without having to calculate the size of the bins based on the change in the time period of the input data in the first section 332. That is, generation of the histogram displayed in the second section 334 of the user interface 330 may not be perceptible to the user. Further, the dynamic histogram builder 198 may consume a relatively small amount of computing resources compared to a conventional histogram calculation for each view of the input data stream). The motivation to combine statement previously provided in the rejection of dependent claim 5 provided above, combining the Breeden et al. reference and the Khan et al. reference is applicable to dependent claim 6.
The Breeden et al. reference as modified by Khan et al. teaches all the limitations of claim 5. Regarding claim 7, Breeden et al. discloses the method of claim 5, comprising, in response to a user selection of one of the first segments, expanding the selected segment to display a text representation of additional events (Column 47 Lines 10-30 discloses approximately 20.6k data entries 1804 can include the field value “/var/log/containers/currency . . . ” or be associated with metadata that identifies the event as being associated with the service.name “/var/log/containers/currency).
With respect to claim 8, Breeden et al. teaches a computer system comprising:
one or more processors (Column 15 Lines 5-30 discloses hardware (non-limiting examples: processors, hard drives, solid-state memory, RAM, etc.)); and
a computer-readable medium (Column 15 Lines 5-30 discloses hardware (non-limiting examples: processors, hard drives, solid-state memory, RAM, etc.)) storing instructions that are operable when executed by the one or more processors to perform operations for displaying search result data for an observability pipeline system, comprising:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (Column 17 Lines 16-34 discloses the monitoring platform executes the query against the selected data set to deliver results to the user and Column 9 Lines 42-55 disclose receives the observability data collected by the collector 304 and provides critical insights into the collected trace data to a client, who may be an application owner or developer);
identifying time bins based on the search results (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity);
generating first histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating second histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a first data panel for first event data describing one or more of the first subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client); and
a second data panel for second event data describing one or more of the second subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client). Breeden et al. does not explicitly in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin.
However, Khan et al. discloses in response to a user selection of one of the first set of bins (Paragraph 31 discloses the dynamic histogram builder 198 enables the histogram displayed via the user interface to be dynamically adjusted and/or changed based on user input (e.g., a time period for the histogram displayed via the user interface) using the histogram indices without the system 190 from having to calculate a histogram based on the input stream and input from the histogram visualization client 200), updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288); and
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Breeden et al. with Khan et al. This would have facilitated displaying search results. See Khan et al. Paragraphs 1-2. In addition, all references teach features that are directed to analogous art and they are directed to the same field of endeavor: data analysis.
With respect to claim 9, it is rejected on grounds corresponding to above rejected claim 2, because claim 9 is substantially equivalent to claim 2.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 3, because claim 10 is substantially equivalent to claim 3.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 4, because claim 11 is substantially equivalent to claim 4.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 5, because claim 12 is substantially equivalent to claim 5.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 6, because claim 13 is substantially equivalent to claim 6.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 7, because claim 14 is substantially equivalent to claim 7.
With respect to claim 15, Breeden et al. teaches a non-transitory computer-readable medium storing instructions that are operable when executed by a data-processing apparatus to perform operations comprising:
obtaining search results, the search results identified based on searching data generated by an observability pipeline system, the search results comprising events processed by the observability pipeline system (Column 17 Lines 16-34 discloses the monitoring platform executes the query against the selected data set to deliver results to the user and Column 9 Lines 42-55 disclose receives the observability data collected by the collector 304 and provides critical insights into the collected trace data to a client, who may be an application owner or developer);
identifying time bins based on the search results (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity);
generating first histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the first histogram data comprising a first set of event clusters for the respective time bins, wherein the first set of event clusters correspond to a first subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating second histogram data based on the time bins and the events (Column 22 Lines 47-58 discloses A fixed size bin histogram is generated for each span identity to track metrics associated with the span identity), the second histogram data comprising a second set of event clusters for the respective time bins, wherein the second set of event clusters correspond to a second, distinct subset of the events (Column 13 Lines 19-35 discloses generated content can include information at an individual container or pod level or at a host or cluster level);
generating a graphical user interface comprising:
a first histogram representing the first histogram data and comprising a first set of bins graphically representing the first set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a first data panel for first event data describing one or more of the first subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client);
a second histogram representing the second histogram data and comprising a second set of bins graphically representing the second set of event clusters (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client); and
a second data panel for second event data describing one or more of the second subset of the events (Column 10 Lines 28-46 discloses generate a visualization, e.g., a histogram or an application topology graph (referred to interchangeably as a “service graph” herein) to represent information regarding the traces and spans received from a client). Breeden et al. does not explicitly in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin.
However, Khan et al. discloses in response to a user selection of one of the first set of bins (Paragraph 31 discloses the dynamic histogram builder 198 enables the histogram displayed via the user interface to be dynamically adjusted and/or changed based on user input (e.g., a time period for the histogram displayed via the user interface) using the histogram indices without the system 190 from having to calculate a histogram based on the input stream and input from the histogram visualization client 200), updating the graphical user interface to include:
in the first histogram, a visual indication of the selected bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the second histogram, a visual indication of a corresponding bin, wherein the selected bin and the corresponding bin represent event clusters for the same time bin (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288);
in the first data panel, a visual indication of first event data that corresponds to the selected bin in the first histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288); and
in the second data panel, a visual indication of second event data that corresponds to the corresponding bin in the second histogram (Paragraph 40 discloses individual histograms (e.g., histogram indices) may be generated for each time interval 284, 286, and 288. As illustrated by a plot 300 in FIG. 4B, for example, a first histogram 302 may correspond to the time interval 284, a second histogram 304 may correspond to the time interval 286, and a third histogram 306 may correspond to the time interval 288).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Breeden et al. with Khan et al. This would have facilitated displaying search results. See Khan et al. Paragraphs 1-2. In addition, all references teach features that are directed to analogous art and they are directed to the same field of endeavor: data analysis.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 2, because claim 16 is substantially equivalent to claim 2.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 3, because claim 17 is substantially equivalent to claim 3.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 4, because claim 18 is substantially equivalent to claim 4.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 5, because claim 19 is substantially equivalent to claim 5.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 6, because claim 20 is substantially equivalent to claim 6.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20210287156 is directed to SYSTEMS AND METHODS FOR GENERATING PERFORMANCE PROFILES OF NODES: [1051] The performance module 280 can identify performance goals associated with each electronic activity type. The performance module 280 can determine a second distribution of the subset of electronic activities across the electronic activity types and generate a score for a metric for the performance profile based on the second distribution. The performance module 280 can determine the second distribution by incrementing and maintaining a counter associated with each electronic activity type for each electronic activity of the subset that is of the respective electronic activity type. The performance module 280 can generate a second histogram, bar graph, or any other graph that can display group electronic activity types that displays the distributions of the electronic activity types across electronic activities.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154