DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a final office action in response to the amendment filed 17 February 2026. Claims 1, 3, 5-7, 9, 14, 16-17 and 20 have been amended. Claims 1-20 are pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 3, 5-7, 9, 14, 16-17 and 20 has been entered.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(b) rejection. The rejection is respectfully withdrawn.
Applicant’s amendment is insufficient to overcome the 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Applicant’s amendment is insufficient to overcome the 35 U.S.C. 103 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 103 rejection have been fully considered, but are moot in view of the new grounds of rejection necessitated by Applicant’s amendment to the claims because the arguments do not apply to the combination of references used in the current rejection detailed below.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the newly recited limitations for insulating a plurality of data sources from direct interactions with the product support system improve the functioning of both the product support system and the real-time mission critical data sources by avoiding downtime of the production system by using a redundant dataset in a manner that is not directed to an abstract idea, does not fall within the mental processes grouping of abstract concepts, and is patent subject matter eligible. Examiner respectfully disagrees.
While the newly amended claim language recites additional elements that involve computing technology, the underlying improvement is to the abstract idea of data management, not to an improvement to computer processing or related database/ data storage technologies in a manner that integrates the abstract idea into a practical application or amounts to significantly more than the recited abstract idea when considered as a whole. The additional elements are used as tools to implement the data management, access, and presentation abstract idea. Therefore, the 35 U.S.C. 101 rejection is proper, maintained, and updated below, as necessitated by amendment.
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 an abstract idea of collecting, storing, analyzing, updating, and presenting data, without significantly more. Independent claim 1 recites a product, independent claim 9 recites a process, and independent claim 20 recites a system to enable access to historical versions of a set of metrics for a product support system. Independent claims 1, 9, and 20 recite substantially similar limitations.
Taking independent claim 1 as representative, independent claim 1 recites the following limitations:
insulate a plurality of data sources maintained external to the product support system from direct interactions with the product support system and avoid downtime of the product support system by causing a redundant dataset to be created and maintained within a big data framework corresponding to data contained within the plurality of data sources, including synchronizing the big data framework with the data in accordance with a first predefined or configurable schedule, wherein the plurality of data sources include one or more real-time mission-critical data sources in which the data relates to a plurality of support cases including one or more levels of support data populated at least in part by product support personnel; and
enable access to historical versions of a set of metrics for the data by or on behalf of one or more of the product support personnel by enriching the data to include counts of active support cases, not-updated support cases, and aged support cases of the plurality of support cases including creating and persisting time-series data in near real-time based on snapshots captured from the big data framework in accordance with a second predefined or configurable schedule.
Under Step 1, independent claims 1, 9, and 20 recite at least one step or act, including capturing data relating to a plurality of support cases. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One the limitations of claim 1 for managing data integrity, capturing data, enabling access to data, enriching data, and creating and persisting time series in near real time data, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (collecting, analyzing, comparing, or categorizing information) because none of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. Further, capturing data is construed as insignificant extra solution activity because it provide input for the recited data processing steps. The claim limitations are directed to enabling product support personnel access to historical versions of a set of metrics data for workflow management and prioritization. Workflow management and prioritization reasonably falls within the certain methods of organizing human activities grouping of abstract concepts because prioritizing and managing workload is a form of managing personal behavior. As a result the claims are directed to an abstract idea of collecting, analyzing, and updating data for workflow management and prioritization that falls within the mental processes and certain methods of organizing human activities groupings of abstract concepts.
Under Step 2A Prong Two the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims only recite a processor and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0096] states: “The processing resource may be, for example, one or more general-purpose microprocessors or a system on a chip (SoC) integrated circuit.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h).
The limitations for insulating data sources by creating a redundant data set, synchronizing the big data framework on a schedule, and enriching the data and creating persisting time-series data in near real-time based on snapshots captured from the bid data framework, are each data management steps that improve how the data integrity is maintained, how the data is manipulated for user review, and how the data is synchronized, without technical implementation details that describe an improvement to the underlying computing technologies. The claimed improvement is to the abstract idea of data management, not to an improvement to computer processing or related database/ data storage technologies. The additional elements are used as tools to implement the data management, access, and presentation abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. See MPEP 2106.05. Regarding the limitation for “creating and persisting time-series near real-time data based on periodic snapshots of the plurality of support cases” for support personnel access to historical versions of a set of metrics, merely organizes the captured data into categories in a manner that is non-technical and does not amount to significantly more than the recited abstract idea.
Dependent claims 2-8 and 10-19 include the abstract idea of the independent claims. The limitations of the dependent claims merely narrow the mental process/certain methods of organizing human activity abstract idea by describing how the captured data is analyzed, manipulated, managed, and presented to the end user as a business decision making tool. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claims 9 and 20 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore, claims 1 -20 are ineligible under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2017/0109679), in view of Perneti et al. (US 2021/0243255), and in further view of Basu et al. (US 11,087,261).
Regarding Amended Claim 1, Zhao et al. discloses a non-transitory machine-readable medium storing instructions, which when executed by one or more computer systems of a product support system cause the one or more computer systems to: (… a method, apparatus, and system for processing data. Zhao et al. [para. 0019]. … When a computer system reads and executes the code and/or data stored on the computer-readable storage medium, the computer system performs the methods and processes embodied as data structures and code and stored within the computer-readable storage medium. Zhao et al. [para. 0070, 0075, 0079-0081, 0016-0017;l Fig. 5-6]);
Zhao et al. fails to explicitly disclose steps to insulate a plurality of data sources maintained external to the product support system from direct interactions with the product support system and avoid downtime of the product support system by causing a redundant dataset to be created and maintained within a big data framework corresponding to data contained within the plurality of data sources, including synchronizing the big data framework with the data in accordance with a first predefined or configurable schedule. Perneti et al. discloses this limitation. ( storage drive 171A-F may be one or more zoned storage devices. Perneti et al. [para. 0048]. … A storage system can consist of two storage array controllers that share a set of drives for failover purposes. Perneti et al. [para. 0054]. … Data and metadata is stored by a set of underlying storage layouts that are optimized for varying workload patterns and storage devices. These layouts incorporate multiple redundancy schemes, compression formats and index algorithms. Some of these layouts store information about authorities and authority masters, while others store file metadata and file data. Perneti et al. [para. 0087-0090]. … Such data protection techniques can include… data replication techniques through which data stored in the storage system is replicated to another storage system such that the data may be accessible via multiple storage systems, data snapshotting techniques through which the state of data within the storage system is captured at various points in time, data and database cloning techniques through which duplicate copies of data and databases may be created, and other data protection techniques. Perneti et al. [para. 0132, 0168-0170]. … The storage systems described above may also be optimized for use in big data analytics. Perneti et al. [para. 0184-0189]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. to include steps to insulate a plurality of data sources maintained external to the product support system from direct interactions with the product support system and avoid downtime of the product support system by causing a redundant dataset to be created and maintained within a big data framework corresponding to data contained within the plurality of data sources, including synchronizing the big data framework with the data in accordance with a first predefined or configurable schedule as disclosed by Perneti et al. to serve as a continuous data protection store (Perneti et al. [para. 0207]), in a manner that would have yielded predictable results at the relevant time.
wherein the plurality of data sources include one or more real-time mission-critical data sources in which the data relates to a plurality of support cases including one or more levels of support data populated at least in part by product support personnel; (… complaints, feedback, discussions, and/or other content items related to customer service issues with online professional network are stored as customer service cases (e.g., case 1 122, case y 124) in a data repository 134 … the data may include records and/or transcripts of interaction between the customer service representatives and users. Zhao et al. [para. 0022-0025]. … number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … quantitative metrics 224 and qualitative metrics 226 may be obtained from customer service tools, monitoring tools, … cloud-based data sources, offline data sources, third-party data sources, social media websites, review websites, and/or other mechanisms for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]);
enable access to historical versions of a set of metrics for the data by or on behalf of one or more of the product support personnel by enriching the data to include counts of active support cases, not-updated support cases, and aged support cases of the plurality of support cases including creating and persisting time-series data in near real-time based on snapshots captured from the big data framework (… number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]. … for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]. … The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Zhao et al. and Perneti et al. combined fail to explicitly disclose steps to creating and persisting time-series data in near real-time based on snapshots captured from the big data framework in accordance with a second predefined or configurable schedule. Basu et al. discloses this limitation. ( monitoring a set of performance indicators (PIs) or metrics, at different time intervals (from near-real-time to daily/ weekly/ monthly/quarterly/etc. Basu et al. [para. 0002]. … the present system projects performance indicators that can be used to monitor the performance of a business process. Basu et al. [para. 0053]. … The “historical KPI-related data” may describe the behavior, during a historical time period, of (i) one or more KPIs; or (ii) one or more “influencer(s)” known to or suspected to influence KPI value(s). The historical KPI-related data may be provided to digital computer 1200 in any manner. Basu et al. [para. 0201]. … performance metric analysis system 2220 may gather data from entity 2240 or a third party data source 2250 to analyze such data to perform analysis on such data and may present an interface … performance metric analysis system 2220 may, based upon one or more schedules, send out requests to each ETL collectors 2260 at each of the entity locations 2240a, 2240n and receive, in response, a set of data corresponding to that performance metric and that entity location 2240a, 2240n collected over a certain time period. Basu et al. [para. 0317-0326, 0347(visual indication of time rule violation); Fig. 10]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. and Perneti et al. combined to include creating and persisting time-series data in near real-time based on snapshots captured from the big data framework in accordance with a second predefined or configurable schedule as disclosed by Basu et al. to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which may cause any upcoming problems, optimization may determine substantially optimum solution to preempt (or benefit from) any determined upcoming problems and what-if simulation allows a user to determine the effect of prescribed solutions on performance metrics (Basu et al. [para. 0318]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 2, Zhao et al., Pernei et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the not-updated support cases represent those of the active support cases for which comments had not been updated for a predefined or configurable timeframe as of a particular date and wherein the aged support cases represent those of the active support cases that remained unresolved for a predefined or configurable timeframe as of the particular date. (… number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]. … for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]. … The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Regarding Amended Claim 3, Zhao et al., Perneti et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the historical versions of the set of metrics include a dynamically calculated target for a count of the not-updated support cases calculated during creation of the time-series data based on a number of active support cases for products that are actively shipping and a number of active support cases for products that have been retired. Basu et al. discloses this limitation. (… the calculated deviating influencer values 250 are determined by one or more computer(s) 1200 in accordance with historical data describing historical behavior of one or more KPI(s) or influencer(s). Basu et al. [para. 0239-0250]. … FIGS. 6A-6B are time lines illustrating relationships between a time period for action for deviating influencers, a deviating KPI display time frame, and a KPI-moving future time frame. ... the displayed influencers 270 (including the influencer identifier 150 and the GUI control/widget 170) may be displayed (for example, sorted or sized) in accordance with estimations of their relative ‘importance’ during a future time frame (i.e. including but not limited to deviating-KPI-viewing time frame 198, time period for action 196, the time frame specified in 120A, or any other time frame). Basu et al. [para. 0267-0274]. … The KPIs may be influenced by one or more ‘influencers.’ For the present example, the influencers may include: Number of Contacts, Product Description, Brand Description, Reason Codes (i.e., issue description), Shipping Delay, Age of the System, Dispatch Sent, Hold Time, Local Queue Time, etc. Basu et al. [para. 0153]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. and Perneti et al. combined to include the historical versions of the set of metrics include a dynamically calculated target for a count of the non-updated support cases calculated during creation of the time-series data based on a number of active support cases for products that are actively shipping and a number of active support cases for products that have been retired as disclosed by Basu et al. to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which may cause any upcoming problems, optimization may determine substantially optimum solution to preempt (or benefit from) any determined upcoming problems and what-if simulation allows a user to determine the effect of prescribed solutions on performance metrics (Basu et al. [para. 0318]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 4, Zhao et al., Perneti et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the historical versions of the set of metrics include a dynamically calculated target for a count of the aged support cases calculated during creation of the time-series data (The target for the productivity KPI may be shown as a horizontal line 314 in chart 308, and the target for the KPI represented by the x-axis may be shown as a vertical line 316 in chart 308. Lines 314-316 may thus indicate thresholds for the KPIs that divide chart 308 into quadrants representing different levels of performance for the customer service representatives. Zhao et al. [para. 0056]);
Zhao et al. fails to explicitly disclose a dynamically calculated target for a count of the aged support cases based on a number of active support cases for products that are actively shipping and a number of active support cases for products that have been retired. Basu et al. discloses this limitation. (… the calculated deviating influencer values 250 are determined by one or more computer(s) 1200 in accordance with historical data describing historical behavior of one or more KPI(s) or influencer(s). Basu et al. [para. 0239-0250]. … FIGS. 6A-6B are time lines illustrating relationships between a time period for action for deviating influencers, a deviating KPI display time frame, and a KPI-moving future time frame. ... the displayed influencers 270 (including the influencer identifier 150 and the GUI control/ widget 170) may be displayed (for example, sorted or sized) in accordance with estimations of their relative ‘importance’ during a future time frame (i.e. including but not limited to deviating-KPI-viewing time frame 198, time period for action 196, the time frame specified in 120A, or any other time frame). Basu et al. [para. 0267-0274]. … The KPIs may be influenced by one or more ‘influencers.’ For the present example, the influencers may include: Number of Contacts, Product Description, Brand Description, Reason Codes (i.e., issue description), Shipping Delay, Age of the System, Dispatch Sent, Hold Time, Local Queue Time, etc. Basu et al. [para. 0153]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. and Perneti et al. combined to include a dynamically calculated target for a count of the aged support cases based on a number of active support cases for products that are actively shipping and a number of active support cases for products that have been retired as disclosed by Basu et al. to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which may cause any upcoming problems, optimization may determine substantially optimum solution to preempt (or benefit from) any determined upcoming problems and what-if simulation allows a user to determine the effect of prescribed solutions on performance metrics (Basu et al. [para. 0318]), in a manner that would have yielded predictable results at the relevant time.
Regarding Amended Claim 5, Zhao et al., Perneti et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the instructions further cause the one or more computer systems to on a periodic basis, for each support engineer of a plurality of support engineers: identify the not-updated support cases for which the support engineer is responsible; and provide the support engineer with an electronic communication including information regarding the identified not-updated support cases to facilitate prioritization by the support engineer of a workload of the support engineer. (Different views of data in table 302 may be generated by applying one or more parameters 304 to the data. Parameters 304 may include one or more targets for the KPIs, such as an expected number of queue hours (i.e., “Expected Queue Hrs”) and/or a number of cases solved per queue hour (i.e., “Cases Solved/Queue Hr”). The GUI may update the colors of values in table 302 in response to the specified targets. For example, values in a given row of table 302 may be colored orange if one or more KPIs for the corresponding customer service representative are below the specified targets. … Parameters 304 may also include a number of filters for data in table 302. The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Regarding Amended Claim 7, Zhao et al., Perneti et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the instructions further cause the one or more computer systems to: calculate a plurality of key performance indicators (KPIs) and trends for the KPIs based on the one or more data sources and the persisted enriched data and the snapshots; (To calculate performance measurements 208, analysis apparatus 202 may obtain a set of quantitative metrics 224 and/or a set of qualitative metrics 226 for each customer service representative from data repository 134. … Performance measurements 208 may also include a quality KPI. … quality KPI 218 may be calculated as a weighted combination of different types of CSATs 216 and/or other scores or ratings of the customer service representative's performance over the period. Zhao et al. [para. 0027-0034]);
and cause to be presented to the one or more product support personnel via an interactive dashboard of a user interface graphical representations of the KPIs and trends. (After performance measurements 208 are calculated by analysis apparatus 202, management apparatus 206 may display information associated with quantitative metrics 224, qualitative metrics 226, and/or performance measurements 208 in a graphical user interface (GUI) 204. Zhao et al. [para. 0037-0041]).
Regarding Claim 8, Zhao et al., Perneti et al., and Basu et al. combined disclose the non-transitory machine-readable medium, wherein the KPIs include one or more of a Net Promotor Score (NPS), Reasonable Time (RT), Customer Effort (CE), Time-to-Response (TTR), and Mean Time To Resolution (MTTR). (Data 228 may also include aggregated metrics, performance measurements, and/or statistics related to customer service representative performance, such as overall or average solved cases, queue hours, CSATs 216, and/or performance measurements 208 for a given team, location, manager, role, hire status, queue-facing status, and/or other grouping of customer service representatives. Zhao et al. [para. 0041]).
Regarding Claim 9, Zhao et al. discloses a method comprising: (… a method, apparatus, and system for processing data. Zhao et al. [para. 0019]);
Zhao et al. fails to explicitly disclose insulating, by one or more computer systems of a product support system, a plurality of data sources maintained external to the product support system from direct interactions with the product support system and avoid downtime of the product support system by causing a redundant dataset to be created and maintained within a big data framework corresponding to data contained within the plurality of data sources, including synchronizing the big data framework with the data in accordance with a first predefined or configurable schedule. Perneti et al. discloses this limitation. ( storage drive 171A-F may be one or more zoned storage devices. Perneti et al. [para. 0048]. … A storage system can consist of two storage array controllers that share a set of drives for failover purposes. Perneti et al. [para. 0054]. … Data and metadata is stored by a set of underlying storage layouts that are optimized for varying workload patterns and storage devices. These layouts incorporate multiple redundancy schemes, compression formats and index algorithms. Some of these layouts store information about authorities and authority masters, while others store file metadata and file data. Perneti et al. [para. 0087-0090]. … Such data protection techniques can include… data replication techniques through which data stored in the storage system is replicated to another storage system such that the data may be accessible via multiple storage systems, data snapshotting techniques through which the state of data within the storage system is captured at various points in time, data and database cloning techniques through which duplicate copies of data and databases may be created, and other data protection techniques. Perneti et al. [para. 0132, 0168-0170]. … The storage systems described above may also be optimized for use in big data analytics. Perneti et al. [para. 0184-0189]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. to include steps to insulating, by one or more computer systems of a product support system, a plurality of data sources maintained external to the product support system from direct interactions with the product support system and avoid downtime of the product support system by causing a redundant dataset to be created and maintained within a big data framework corresponding to data contained within the plurality of data sources, including synchronizing the big data framework with the data in accordance with a first predefined or configurable schedule as disclosed by Perneti et al. to serve as a continuous data protection store (Perneti et al. [para. 0207]), in a manner that would have yielded predictable results at the relevant time.
wherein the plurality of data sources include one or more real-time mission-critical data sources in which the data relates to a plurality of support cases including one or more levels of support data populated at least in part by product support personnel; (… the data may include records and/or transcripts of interaction between the customer service representatives and users. Zhao et al. [para. 0022-0025]. … Analysis apparatus 202 may calculate a set of performance measurements 208 for a number of customer service representatives, such as customer service representatives for online professional network 118 of FIG. 1 and/or customer service representatives for other products or services. Zhao et al. [para. 0027]. … number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … quantitative metrics 224 and qualitative metrics 226 may be obtained from customer service tools, monitoring tools, … cloud-based data sources, offline data sources, third-party data sources, social media websites, review websites, and/or other mechanisms for tracking the productivity and/or quality of work of customer service representatives. … the GUI may be used to characterize, assess, and manage the performance of customer service representatives, such as customer service representatives for online professional network 118 of FIG. 1 and/or another product, application, or service. Zhao et al. [para. 0045-0048]);
enabling, by the one or more computer systems, access to historical versions of a set of metrics for the data by or on behalf of one or more of the product support personnel by enriching the data to include counts of the plurality of support cases associated with one or more categories including creating and persisting time-series data in near real-time based on snapshots captured from the big data framework (… number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0032-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]. … for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]. … The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
and Perneti et al. combined fail to explicitly disclose steps to creating and persisting time-series data in near real-time based on snapshots captured from the big data framework in accordance with a second predefined or configurable schedule. Basu et al. discloses this limitation. ( monitoring a set of performance indicators (PIs) or metrics, at different time intervals (from near-real-time to daily/ weekly/ monthly/quarterly/etc. Basu et al. [para. 0002]. … the present system projects performance indicators that can be used to monitor the performance of a business process. Basu et al. [para. 0053]. … The “historical KPI-related data” may describe the behavior, during a historical time period, of (i) one or more KPIs; or (ii) one or more “influencer(s)” known to or suspected to influence KPI value(s). The historical KPI-related data may be provided to digital computer 1200 in any manner. Basu et al. [para. 0201]. … performance metric analysis system 2220 may gather data from entity 2240 or a third party data source 2250 to analyze such data to perform analysis on such data and may present an interface … performance metric analysis system 2220 may, based upon one or more schedules, send out requests to each ETL collectors 2260 at each of the entity locations 2240a, 2240n and receive, in response, a set of data corresponding to that performance metric and that entity location 2240a, 2240n collected over a certain time period. Basu et al. [para. 0317-0326, 0347(visual indication of time rule violation); Fig. 10]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. and Perneti et al. combined to include creating and persisting time-series data in near real-time based on snapshots captured from the big data framework in accordance with a second predefined or configurable schedule as disclosed by Basu et al. to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which may cause any upcoming problems, optimization may determine substantially optimum solution to preempt (or benefit from) any determined upcoming problems and what-if simulation allows a user to determine the effect of prescribed solutions on performance metrics (Basu et al. [para. 0318]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 10, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the one or more categories include active support cases of the plurality of support cases, not-updated support cases of the active support cases, and aged support cases of the active support cases. (… quantitative metrics 224 may include … the number of open (e.g., unresolved) cases in the customer service representative's queue, the number of solved or closed cases in the customer service representative's queue, the number of cases handled by or assigned to the customer service representative, and/or the number of distinct solved or closed cases (e.g., in which a solved case is counted only once independently of the number of times it is reopened or subsequently resolved) in the customer service representative's queue. … number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]).
Zhao et al. and Perneti et al. combined fail to explicitly disclose capturing data in accordance with a second predefined or configurable schedule. Basu et al. discloses this limitation. ( monitoring a set of performance indicators (PIs) or metrics, at different time intervals (from near-real-time to daily/weekly/ monthly/quarterly/etc. Basu et al. [para. 0002]. … a business process can include a call center, the function being customer service or technical support. Basu et al. [para. 0031]. … Call centers are used by … computer product help desks. Basu et al. [para. 0089-0091]. … the present system projects performance indicators that can be used to monitor the performance of a business process. Basu et al. [para. 0053]. … The “historical KPI-related data” may describe the behavior, during a historical time period, of (i) one or more KPIs; or (ii) one or more “influencer(s)” known to or suspected to influence KPI value(s). The historical KPI-related data may be provided to digital computer 1200 in any manner. Basu et al. [para. 0201]. … performance metric analysis system 2220 may gather data from entity 2240 or a third party data source 2250 to analyze such data to perform analysis on such data and may present an interface … performance metric analysis system 2220 may, based upon one or more schedules, send out requests to each ETL collectors 2260 at each of the entity locations 2240a, 2240n and receive, in response, a set of data corresponding to that performance metric and that entity location 2240a, 2240n collected over a certain time period. Basu et al. [para. 0317-0326, 0347 (visual indication of time rule violation); Fig. 10]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection steps of Zhao et al. and Perneti et al. combined to include capturing data in accordance with a predefined or configurable schedule as disclosed by Basu et al. to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which may cause any upcoming problems, optimization may determine substantially optimum solution to preempt (or benefit from) any determined upcoming problems and what-if simulation allows a user to determine the effect of prescribed solutions on performance metrics (Basu et al. [para. 0318]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 11, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the not-updated support cases represent those of the active support cases for which comments had not been updated for a predefined or configurable timeframe as of a particular date. … number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]. … for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]. … The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Regarding Claim 12, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the aged support cases represent those of the active support cases that remained unresolved for a predefined or configurable timeframe as of a particular date. (… number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]. … chart 222 may provide a visualization that allows the performance of the customer service representatives to be evaluated along multiple dimensions. … management apparatus 206 may display filters 230 for … and/or timescales and/or timeframes associated with quantitative metrics 224, qualitative metrics 226, performance measurements 208, and/or data 228. Zhao et al. [para. 0040-0042; Fig. 3B]. … for tracking the productivity and/or quality of work of customer service representatives. Zhao et al. [para. 0045-0048]. … The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Regarding Claim 13, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the historical versions of the set of metrics include a count of the active support cases, a count of the not-updated support cases, and a count of the aged support cases. (… number of cases 210 may include a number of solved cases, reopened cases, handled cases, and/or routed cases in the customer service representative's queue over a pre-specified period (e.g., a week, a month, etc.). Zhao et al. [para. 0030-0036]).
Regarding Amended Claim 14, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the historical versions of the set of metrics further include a dynamically calculated target for the count of the not-updated support cases. (The target for the productivity KPI may be shown as a horizontal line 314 in chart 308, and the target for the KPI represented by the x-axis may be shown as a vertical line 316 in chart 308. Lines 314-316 may thus indicate thresholds for the KPIs that divide chart 308 into quadrants representing different levels of performance for the customer service representatives. Zhao et al. [para. 0056]).
Regarding Claim 15, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, wherein the historical versions of the set of metrics further include a dynamically calculated target for the count of the aged support cases. (The target for the productivity KPI may be shown as a horizontal line 314 in chart 308, and the target for the KPI represented by the x-axis may be shown as a vertical line 316 in chart 308. Lines 314-316 may thus indicate thresholds for the KPIs that divide chart 308 into quadrants representing different levels of performance for the customer service representatives. Zhao et al. [para. 0056]).
Regarding Amended Claim 17, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, further comprising on a periodic basis, for each support engineer of a plurality of support engineers: identifying the not-updated support cases for which the support engineer is responsible; and facilitating prioritization by the support engineer of a workload of the support engineer by providing the support engineer with an electronic communication including information regarding the identified not-updated support cases. (Different views of data in table 302 may be generated by applying one or more parameters 304 to the data. Parameters 304 may include one or more targets for the KPIs, such as an expected number of queue hours (i.e., “Expected Queue Hrs”) and/or a number of cases solved per queue hour (i.e., “Cases Solved/Queue Hr”). The GUI may update the colors of values in table 302 in response to the specified targets. For example, values in a given row of table 302 may be colored orange if one or more KPIs for the corresponding customer service representative are below the specified targets. … Parameters 304 may also include a number of filters for data in table 302. The filters may include time-based filters, such as a year, timescale, and/or timeframe associated with the data. Zhao et al. [para. 0050-0052]).
Regarding Claim 18, Zhao et al., Perneti et al., and Basu et al. combined disclose the method, further comprising: calculating, by the one or more computer systems, a plurality of key performance indicators (KPIs), trends for the KPIs, and thresholds for the KPIs based on the one or more data sources and the persisted enriched data and the snapshots; (To calculate performance measurements 208, analysis apparatus 202 may obtain a set of quantitative metrics 224 and/ or a set of qualitative metrics 226 for each customer service representative from data repository 134. … Performance measurements 208 may also include a quality KPI. … quality KPI 218 may be calculated as a weighted combination of different types of CSATs 216 and/or other scores or ratings of the customer service representative's performance over the period. Zhao et al. [para. 0027-0034]);
and causing to be presented, by the one or more computer systems to the one or more product support personnel via an interactive dashboard of a user interface, graphical representations of the trends and thresholds. (After performance measurements 208 are calculated by analysis apparatus 202, management apparatus 206 may display information associated with quantitative metrics 224, qualitative metrics 226, and/or performance measurements 208 in a graphical user interface (GUI) 204. Zhao et al. [para. 0037-0041]).
Regarding Claim 19, Zhao et al., Perneti et al, and Basu et al. combined disclose the method, wherein the KPIs include one or more of a Net Promotor Score (NPS), Reasonable Time (RT), Customer Effort (CE), Time-to-Response (TTR), and Mean Time To Resolution (MTTR). (Data 228 may also include aggregated metrics, performance measurements, and/or statistics related to customer service representative performance, such as overall or average solved cases, queue hours, CSATs 216, and/or performance measurements 208 for a given team, location, manager, role, hire status, queue-facing status, and/or other grouping of customer service representatives. Zhao et al. [para. 0041]).
Regarding Claim 20, Claim 20 recites substantially similar limitations to those of claim 1 and is therefore rejected based upon the same prior art combination, reasoning, and rationale. Claim 20 is directed to a product support system comprising: one or more computer systems; and instructions .. executed by one or more computer systems, which is disclosed by Zhao et al. (As shown in FIG. 2, the system includes an analysis apparatus 202 and a management apparatus 206. Zhao et al. [para. 0026; Fig. 2, 6]).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2017/0109679), in view of Perneti et al. (US 2021/0243255), in view of Basu et al. (US 11,087,261), and in further view of Harrison et al. (US 2018/0096000).
Regarding Claim 6, Zhao et al., Perneti et al., and Basu et al. combined fail to explicitly disclose the non-transitory machine-readable medium, wherein the plurality of data stores comprise a plurality of Hadoop data lakes and wherein the method further comprises periodically synchronizing the plurality of Hadoop data lakes with a plurality of third-party data sources fewer than five times per day to avoid impacting mission critical tasks that make use of the plurality of third-party data sources. Harrison et al. discloses this limitation. (… systems and methods for importing data from a variety of structured data sources such as relational databases into a large-scale unstructured or flexibly structured data repository and for the management of the data after import. … the data lake is implemented as a Hadoop data repository employing a Hadoop Distributed File System (HDFS) with an Apache Hive data warehousing infrastructure. … any form of data source may be used. … data that originated from differently structured data sources having different original data schemas may coexist within data lake 108 in the form of a collection of Hive tables 110. Harrison et al. [para. 0026, 0053-0059, 0158-0164; Fig. 1-4, 8, 22-24]. … The synchronisation process is illustrated in FIG. 22. In this example, a data lake (Hadoop platform) 108 is illustrated comprising multiple Hadoop/Hive databases each sourced from multiple data sources and including data corresponding to tables of those data sources. … delta load is performed on a periodic basis, e.g. daily, from each of the imported data sources, and the OPEN and CLOSED databases are updated accordingly. This periodic update is coordinated by the History Capture process. Harrison et al. [para. 0206]. … the Metadata Manager tool and is periodically synchronised with the data structures on a Hadoop platform 108. Harrison et al. [para. 0402-0410]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection and storage steps of Zhao et al., Perneti et al., and Basu et al. combined to include a plurality of Hadoop data lakes and wherein the method further comprises periodically synchronizing the plurality of Hadoop data lakes with a plurality of third-party data sources fewer than five times per day to avoid impacting mission critical tasks that make use of the plurality of third-party data sources as disclosed by Harrison et al. for importing data from a variety of structured data sources such as relational databases into a large-scale unstructured or flexibly structured data repository and for the management of the data after import (Harrison et al. [para. 0053]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 16, Zhao et al., Perneti et al., and Basu et al. combined fail to explicitly disclose the method, wherein the plurality of data stores comprise a plurality of data lakes and wherein the method further comprises periodically synchronizing the plurality of data lakes with a plurality of third-party data sources fewer than five times per day to avoid impacting mission critical tasks that make use of the plurality of third-party data sources. Harrison et al. discloses this limitation. (… systems and methods for importing data from a variety of structured data sources such as relational databases into a large-scale unstructured or flexibly structured data repository and for the management of the data after import. … the data lake is implemented as a Hadoop data repository employing a Hadoop Distributed File System (HDFS) with an Apache Hive data warehousing infrastructure. … any form of data source may be used. … data that originated from differently structured data sources having different original data schemas may coexist within data lake 108 in the form of a collection of Hive tables 110. Harrison et al. [para. 0026, 0053-0059, 0158-0164; Fig. 1-4, 8, 22-24]. … The synchronisation process is illustrated in FIG. 22. In this example, a data lake (Hadoop platform) 108 is illustrated comprising multiple Hadoop/Hive databases each sourced from multiple data sources and including data corresponding to tables of those data sources. … delta load is performed on a periodic basis, e.g. daily, from each of the imported data sources, and the OPEN and CLOSED databases are updated accordingly. This periodic update is coordinated by the History Capture process. Harrison et al. [para. 0206]. … the Metadata Manager tool and is periodically synchronised with the data structures on a Hadoop platform 108. Harrison et al. [para. 0402-0410]). It would have been obvious to one of ordinary skill in the art of workflow monitoring and performance evaluation before the effective filing date of the claimed invention to modify the data collection and storage steps of Zhao et al., Perneti et al. and Basu et al. combined to include a plurality of data lakes and wherein the method further comprises periodically synchronizing the plurality of data lakes with a plurality of third-party data sources fewer than five times per day to avoid impacting mission critical tasks that make use of the plurality of third-party data sources as disclosed by Harrison et al. for importing data from a variety of structured data sources such as relational databases into a large-scale unstructured or flexibly structured data repository and for the management of the data after import (Harrison et al. [para. 0053]), in a manner that would have yielded predictable results at the relevant time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chawla et al. (US 10,362,836) – Synchronizing snapshots between storage systems, including: receiving, from a source storage system, an identification of a snapshot to be replicated to a destination storage system; identifying, from hint information stored on the destination storage system, a most recent version of the snapshot that is stored on the destination storage system; issuing, to the source storage system, a request for an identification of the differences between the snapshot to be replicated to the destination storage system and the most recent version of the snapshot that is stored on the destination storage system; receiving, from the source storage system, the identification of the differences
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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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