DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. This Office Action is the first on the merit of the instant application filed on June 19, 2024, in which claims 1-19 are presented for examination.
3. Claims 1-19 are pending. Claims 1 and 11 are in independent form.
Drawings
4. The drawings filed on June 19, 2024 are in compliance with 37 CFR 1.121(d) and considered and accepted.
Claim Objections
5. Claim 13 objected to because of the following informalities: Claim 13 on line 1, recites: “…the memory further stores instructions “what” cause …”. The phrase “….instructions what cause…” is grammatically incorrect. Examiner suggests replacing the phrase “…the memory further stores instructions what cause…” with “…the memory further stores instructions that cause…” Appropriate correction is required.
Priority
6. Acknowledgment is made of Applicant’s claim for priority to U.S. Provisional Application No. 63/509,144 filed on June 20, 2023.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1–19 are rejected under 35 U.S.C. § 101 because the claims are directed to an abstract idea without reciting significantly more, as analyzed under the USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and October 2019 Update.
Step 1: Statutory category Claims 1 and 11 recite a “system” and therefore fall within the statutory “machine” category at Step 1, which does not end the analysis under §101 and the 2019 PEG.
Step 2A, Prong 1 – Judicial exception As drafted, the claims recite (i) methods of organizing human activity, (ii) mental processes, and (iii) mathematical concepts, which are groupings of abstract ideas under the 2019 PEG.
Representative claim 1 recites (bracketed): [a] receiving parent questions having a timeseries property and a first response based on that property (collecting and classifying information by topic and timeframe, which is a mental process/organizing human activity) ; [b] receiving child questions tied to aspects of the timeseries property and associating a transformation function (further collection and classification with rule association, i.e., mental processes/organizing human activity) ; [c] determining the parent response from child responses using transformation functions over time (e.g., SUM/AVG/MIN/MAX/STDDEV and time-window relations), which are mathematical concepts per the PEG ; [d] generating parent and child reports (presentation of information, which the Office and courts have consistently treated as abstract when not tied to a specific technological improvement).
9. The October 2019 Update confirms that a claim may still “recite a mental process” or abstract idea even if it requires a computer, and examiners must identify the abstract groupings such as mental processes, methods of organizing human activity, and mathematical concepts as set out in the 2019 PEG.
Therefore, at least claim 1 as a whole recites abstract ideas of collecting, organizing, analyzing via mathematical operations, and presenting information, which are judicial exceptions under Step 2A, Prong 1 per the 2019 PEG.
Step 2A, Prong 2 – Integration into a practical application The additional elements identified in the claims—e.g., a processor, memory, report data structure, hierarchy tree, database, and user interfaces—do not integrate the abstract ideas into a practical application under the considerations of MPEP 2106.05(a)–(c), (e)–(h) as reflected in the 2019 PEG and the PPAC slide deck summarizing Prong 2.
Mere instructions to “apply” the abstract idea on a computer, generic data collection, routine storage in a database, routing/association of data, and presenting results via a UI are not indicative of integration into a practical application and are treated as insignificant extra-solution or post-solution activity under the PEG factors and MPEP 2106.05(f)–(h).
Applicant’s reliance on “specific processors” (e.g., user device and coordination processors), “specific data structures” (report data structure, hierarchy tree, database), enumerated transformations (SUM/AVG/MIN/MAX/STDDEV), and UI figures does not show a particular machine, transformation, or improvement to computer functionality as required by the Prong 2 considerations (e.g., MPEP 2106.05(a)–(c)), but rather recites conventional computer components used as tools to perform the abstract information processing workflow, which the PEG and update explain is not integration into a practical application.
Accordingly, the claims fail Step 2A, Prong 2.
Step 2B – “Significantly more” (inventive concept) Under Step 2B, the additional elements, individually and in ordered combination, do not amount to significantly more than the judicial exceptions themselves, consistent with Alice’s directive that generic computer implementation, without improvement to computer functionality, is insufficient to transform an abstract idea into a patent-eligible invention.
The recited processors, memories, databases, message routing/association, mathematical aggregations over time windows and subgroups, and report generation/export are well-understood, routine, and conventional activities performed by generic computing components, and there is no recited improvement to the functioning of the computer or to another technology or technical field per Alice and the 2019 PEG’s Step 2B framework.
The October 2019 Update also emphasizes that examiners must identify why additional elements do not integrate the exception or provide significantly more, and here the claim elements reflect conventional computer functions—receiving, classifying, mathematically aggregating, storing, and outputting data—without any non-conventional data structure, processing pipeline, or measurable computer-functional improvement described or claimed.
Conclusion under §101 For the reasons above, claims 1–19 are directed to abstract ideas (certain methods of organizing human activity, mental processes, and mathematical concepts) and do not recite additional elements that integrate the exception into a practical application or amount to significantly more, consistent with the 2019 PEG and Alice, and are therefore rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. 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 nonobviousness.
13. Claim(s) 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Taghaddos et al. US20130111323A1 (hereinafter Taghaddos) in view of Borah US10187249B2 (hereinafter Borah) further in view of Bhatia US8,990,258B2 (hereinafter Bhatia).
Regarding claim 21, Taghaddos discloses a rules-driven survey system, comprising: a processor (Taghaddos [Figure 2, element 113] e.g., “PROCESSOR”); and a memory (Taghaddos [Figure 2, element 112]) comprising a not-transitory processor-readable medium storing processor-executable instructions that when executed by the processor (Taghaddos [claim 9] e.g., “A non-transitory computer readable medium comprising executable instructions encoded thereon operable on a computerized device to perform processing”), causes the processor to: receive a plurality of parent questions having one or more question property including [a timeseries property], the plurality of parent questions further having a first response [based on the timeseries property] (Taghaddos [0075] e.g., “The survey editor 140 provides an interface for associating at least one rule with a survey question. Adding rules enabling skip logic and branching when a survey is taken on the survey player 142. … The survey player 142 executes skip logic and branching for the rules associated with a survey question.”); and generate a [first report] based on the first response and a second report based on the second response (Taghaddos [0070] e.g., “SCs can also export the raw results data in CSV format.”). Taghaddos, however, does not explicitly teach a timeseries property on questions, nor computing a parent “first response” over a defined time window via interval-based aggregation functions. Borah discloses including a timeseries property and a first response based on the timeseries property (Borah [col. 5, lines 13-35] e.g., “e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data. For example, the raw 1-minute time series data can be rolled up into 10-minute buckets … The rolled up 10-minute buckets can be rolled up into hourly buckets … The rolled up daily buckets can be rolled up into weekly … monthly … yearly buckets.”), and having at least one associated transformation function (Borah [col. 5, lines 20-26] e.g., “by averaging the 1-minute interval data every 10 minutes … averaging every six 10-minute buckets into each hourly bucket … averaging every twenty-four 1-hour buckets into each daily bucket.”). It would have been obvious to one of ordinary skill in the art before the effective filing date to include the distributed metric data time rollup in real-time taught by Borah into Taghaddos’ survey system so that timestamped response data are summarized over defined windows (e.g., 10-minute, hourly, daily), thereby computing parent “first responses” as time-scoped aggregates of underlying responses. This predictable use of known time-series rollups on timestamped data yields known benefits (trend analysis, storage reduction, multi-resolution summaries) without changing the principles of operation of either system.
The combined teachings of Taghaddos and Borah teach: receive a plurality of child questions directed (Taghaddos [0075] e.g., “ The survey editor 140 provides an interface for associating at least one rule with a survey question… The survey player 142 executes skip logic and branching for the rules associated with a survey question”), to at least one aspect of the timeseries property and having at least one associated transformation function (Borah [col. 5, lines 19-28] e.g., “…rolled up into 10-minute buckets (e.g., by averaging the 1-minute interval data every 10 minutes). The rolled up 10-minute buckets can be rolled up into hourly buckets (e.g., by averaging every six 10-minute buckets into each hourly bucket). The rolled up hourly buckets can be rolled up into daily buckets (e.g., by averaging every twenty-four 1-hour buckets into each daily bucket”, see also [col. 10, lines 8-16] e.g., “After 10 minutes, each of the aggregators a1 (418), a3 (422), and aB (424) can aggregate the 10 metric values for m1, m2, and m3 and write the aggregated values…”. Taghaddos provides child questions conditioned by rules (directed to aspects relevant to the parent). Borah provides the “transformation function” (e.g., average; more generally, an aggregation ) that is applied per time bucket to the time-series data, satisfying “having at least one associated transformation function”); and determine the first response to the plurality of parent questions based on a second response to the plurality of child questions having at least one aspect of the timeseries property and the at least one associated transformation function (Borah [col. 5, lines 13-35] e.g., “e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data...rolled up into 10-minute buckets … hourly … weekly … monthly … yearly buckets.”), see also [col. 10, lines 8-12] e.g., “After 10 minutes…aggregate the 10 metric values f0… After 60 minutes … aggregate the 60 metric values … and write the aggregated values…”. The “second response” (child-level values over the timeseries) is aggregated by the associated transformation (e.g., average) over the defined interval. That aggregated, bucket-level value constitutes the parent’s “first response” determined “based on” the second response with the transformation function. Taghaddos supplies the parent-child dependency structure; Borah supplies the precise time-bucket aggregation mechanics.). Taghaddos teaches reporting, but not multi-format, self-service web reporting. Secondary, Bhatia reference discloses browser-based self-service reporting and multi-format outputs: generate a first report based on the first response and a second report based on the second response (Bhatia [Description] e.g., “… the present invention enables the custom report generation solely from a standard web browser or the like …” and “outputs can be in any binary file format, such as, for example, HyperText Markup Language (HTML), Portable Document Format (PDF), Microsoft Excel (XLS, XLSX), Microsoft Word (DOC, DOCX), Extensible Markup Language (XML) …”). It would have been obvious to one of ordinary skill in the art before the effective filing date to include the self-service database reporting systems and methods taught by Bhatia into the combined teachings of Taghaddos and Borah to yield the predictable result of generating multi-format reporting to present separate parent and child reports beyond CSV, a routine enhancement to Taghaddos’ reporting without altering core functionality.
Regarding claim 2, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the first response has a first response timeframe and the second response has a second response timeframe, the first response timeframe being a multiple of the second response timeframe (Borah [col. 5, lines 13-35] e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data. For example, the raw 1-minute time series data can be rolled up into 10-minute buckets … The rolled up 10-minute buckets can be rolled up into hourly buckets … The rolled up daily buckets can be rolled up into weekly … monthly … yearly buckets.”). It would have been obvious to configure parent question timeseries intervals per the conventional rollup intervals taught by Borah to yield predictable, standard time-windowed summaries.
Regarding claim 3, the rejection of claim 2 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the plurality of child questions are associated with a plurality of subgroups, and wherein the second response comprises responses from each of the plurality of subgroups (Taghaddos [0075] e.g., “The survey editor 140 provides an interface for associating at least one rule with a survey question…The survey player 142 executes skip logic and branching for the rules associated with a survey question”, see also Borah. Borah’s sub-group can be the time buckets whose values are aggregated at each interval: (Borah [col. 5, lines 19-28] e.g., “… rolled up into 10-minute buckets (e.g., by averaging the 1-minute interval data every 10 minutes). The rolled up 10-minute buckets can be rolled up into hourly buckets (e.g., by averaging every six 10-minute buckets into each hourly bucket). The rolled up hourly buckets can be rolled up into daily buckets (e.g., by averaging every twenty-four 1-hour buckets into each daily bucket).”).
Regarding claim 4, the rejection of claim 3 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the at least one associated transformation function is an operation performed on the second responses from each of the plurality of subgroups (Borah [col. 10, lines 8-16] e.g., ““After 10 minutes, each of the aggregators … can aggregate the 10 metric values … and write the aggregated values … Similarly, after 60 minutes … aggregate the 60 metric values … and write the aggregated values …” Employing continuous per-bucket operations across subgroups is a predictable application to survey-response timeseries).
Regarding claim 5, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the at least one associated transformation function is one of a summation function, an average function, a minimum function, a maximum function, and a standard deviation function (Borah [col. 5, lines 20-35] e.g., “…by averaging the 1 minute interval data every 10 minutes … averaging every six 10 minute buckets … averaging every twenty four 1 hour buckets …”. Borah teaches min/max/average per time bucket: see [col. 6, lines 11-19] e.g., “For example, when the time range specified is within 24 hours, the 1-minute resolution data … can be returned. When the time range specified is within 8 days the 10-minute resolution data … can be returned. When the time range specified is beyond 8 days, the 60-minute resolution data … can be returned.” Adapting result resolution by time horizon is a conventional optimization for time-series queries and would predictably apply to survey-response metrics).
Regarding claim 6, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the memory further stores instructions that cause the processor to receive the plurality of parent questions from a first user and to receive the plurality of child questions from a second user (Taghaddos [0037] e.g., “The account owner has the ability to add additional users to their account depending on what plan they've selected … These users then have the ability to create/manage surveys under the same account.”).
Regarding claim 7, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the plurality of parent questions are associated with a first question type and the plurality of child questions are associated with a second question type (Taghaddos [0038] e.g., “the survey editor 140 enables a survey creator (SC) to compose survey questions …” and provides question-type selection and editing UIs (e.g., FIGS. 3, 4B). Providing a GUI for composing the questions used in time-series analysis is expressly taught), see also p0051] e.g., “screen 430 enumerating some of the question types available to be selected …Multiple Choice …Rating Scale … Form… Comment… Matrix of choices … instructions…”. Different questions types for parent child are expressly supported by the editor).
Regarding claim 8, the rejection of claim 7 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the first question type and the second question type are selected from a multiple-choice question type, a numerical question type, a text question type, a free response question type, a file-upload question type, and a Likert scale question type (Taghaddos [0051] e.g., “Multiple Choice; Rating Scale (Likert 5 scale; NPS 0 10); Form (numeric/text fields); Comment (free response); Matrix of choices; Instructions…”. Thus, for “selected from” Taghaddos provides several enumerated types within the claimed set (e.g., multiple choice, Likert/numerical, text/free response).
Regarding claim 9, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the memory further stores instructions that cause the processor to generate the first report and the second report in a format selected from a PDF format, a CSV format, a TXT format, and an HTML format (Taghaddos [0070] e.g., “SCs can also export the raw results data in CSV format.”, see also (Bhatia [col. 4, lines “… the present invention enables end-users to securely create and customize their own reports… outputs can be in any binary file format, such as, for example, HyperText Markup Language (HTML), Portable Document Format (PDF), Microsoft Excel (XLS, XLSX), Microsoft Word (DOC, DOCX), Extensible Markup Language (XML) …”. Providing TXT/plain-text output is a routine variant of CSV/HTML/PDF generation in reporting systems and would have been an obvious format option).
Regarding claim 10, the rejection of claim 1 is hereby incorporated by reference, the combination of Taghaddos, Borah, and Bhatia discloses a system, wherein the timeseries property includes at least one of: a timeframe, a date range, a start date, an end date, and a timestamp (Borah [col. 8, lines 25-28] e.g., “The metrics data received from the agents is time series data where every metric data point received has a time stamp associated with it”, see also [col. 6, lines 7-11] e.g., “End users can access the collected metrics data by using queries that identifies a metric ID and a time range. Depending on the time range requested, different rollup … may need to be performed.”). It would have been obvious to include timeframe/date range/start/end selection on the timeseries property in the survey context to retrieve appropriate intervals and resolutions.
Regarding claim 11, Taghaddos discloses a system, comprising:
a processor (Taghaddos [Figure 2, element 113] e.g., “PROCESSOR”); and
a memory (Taghaddos [Figure 2, element 112] e.g., “MEMORY”) comprising a non-transitory processor-readable medium storing processor-executable instructions that when executed by the processor (Taghaddos [claim 9] e.g., “A non-transitory computer readable medium comprising executable instructions encoded thereon operable on a computerized device to perform processing”), causes the processor to:
receive [a report data structure] having plurality of parent questions (Taghaddos [0039] e.g., “Upon completing survey creation, the survey, survey settings, questions and possible answers are stored separately in the database 181”). Taghaddos, however, does not teach a report data structure. Bhatia teaches a report data structure that drives report generation (Bhatia [col. 4, lines 46-48] e.g., “…the report itself contains no data, but rather it merely includes definitions on how report outputs are generated from a database” and users select, see also [col. 2, lines 45-50] e.g., “…selecting various report elements including, for example, data sources, fields, filters, labels, charts, dashboards, and the like”). It would have been obvious to one of ordinary skill in the art before the effective filing date to include the self-service database reporting systems and methods taught by Bhatia, into Taghaddos’ survey system, to yield the predictable results of generating multi-format reporting to present separate parent and child reports beyond CSV, a routine enhancement to Taghaddos’ reporting without altering core functionality.
The combined teachings of Taghaddos and Bhatia teach: receive a plurality of child questions directed to at least one aspect of the plurality of parent questions (Taghaddos [0075] e.g., “ The survey editor 140 provides an interface for associating at least one rule with a survey question… The survey player 142 executes skip logic and branching for the rules associated with a survey question”. Taghaddos’ rules-based parent-child flow (skip-logic/branching) links child question parent questions. Thus, child questions are directed to aspects (conditional/results) if parent questions via the associated rules);
associate at least [one transformation function] with at least one child question of the plurality of child questions, [the at least one transformation function] having an output as a response to at least one parent question of the plurality of parent questions (Bhatia [col. 15, lines 7-11] e.g., “…the user selects the field (step 1550), selects the appropriate description (step 1552), selects the appropriate sorting, i.e. A-Z or Z-A (step 1554) selects the appropriate function (step 1556), and selects the appropriate format (step 1558)”. Bhatia’s report summaries/function associated to selected fields. The combined teaching of Taghaddos and Bhatia does not explicitly teach one transformation function [and the at least one transformation function having an output as a response to at least one parent question of the plurality of the parent questions]. Borah discloses one transformation function and the at least one transformation function (Borah [col. 5, lines 13-35] e.g., “e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data. For example, the raw 1-minute time series data can be rolled up into 10-minute buckets … The rolled up 10-minute buckets can be rolled up into hourly buckets … The rolled up daily buckets can be rolled up into weekly … monthly … yearly buckets.”); and generate a report based on the output of the at least one transformation function and having the report data structure (Bhatia [Description] e.g., “… the present invention enables the custom report generation solely from a standard web browser or the like …” and “outputs can be in any binary file format, such as, for example, HyperText Markup Language (HTML), Portable Document Format (PDF), Microsoft Excel (XLS, XLSX), Microsoft Word (DOC, DOCX), Extensible Markup Language (XML) …”). It would have been obvious to one of ordinary skill in the art before the effective filing date to include the distributed metric data time rollup in real-time taught by Borah into Taghaddos and Bhatia’s teaching. Taghaddos provides the survey capture model (parent/child via rules) and stored question structures. Bhatia provides the standard “report data structure” and the association of summary/aggregation functions to selected fields. Borah provides the conventional transformation mechanics (interval aggregations like average/sum) and the notion that the aggregate is the meaningful “responses” at the parent lever over child responses. Combing them yields a predictable reporting system that computes parent-level outputs from child-level data and generates reports.
Regarding claim 12, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the plurality of parent questions has a parent question timeframe and the plurality of child questions has a child question timeframe, the parent question timeframe being a multiple of the child question timeframe (Borah [col. 5, lines 13-35] e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data. For example, the raw 1-minute time series data can be rolled up into 10-minute buckets … The rolled up 10-minute buckets can be rolled up into hourly buckets … The rolled up daily buckets can be rolled up into weekly … monthly … yearly buckets.”). It would have been obvious to configure parent question timeseries intervals per the conventional rollup intervals taught by Borah to yield predictable, standard time-windowed summaries.
Regarding claim 13, the rejection of claim 12 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the memory further stores instructions what cause the processor to:
receive one or more response to the plurality of child questions, each response of the one or more response having a timestamp (Borah [col. 8, lines 25-28] e.g., “The metrics data received from the agents is time series data where every metric data point received has a time stamp associated with it”); and
wherein the at least one transformation function is an operation performed on each response to the plurality of child questions having a timestamp within the parent question timeframe (Borah [col. 5, lines 13-35] e.g., “e.g., “Time rollups are performed by rolling up and aggregating raw granular (e.g., 1-minute interval) time series data into progressively less granular data...rolled up into 10-minute buckets … hourly … weekly … monthly … yearly buckets.”), see also [col. 10, lines 8-12] e.g., “After 10 minutes…aggregate the 10 metric values f0… After 60 minutes … aggregate the 60 metric values … and write the aggregated values…”. The “second response” (child-level values over the timeseries) is aggregated by the associated transformation (e.g., average) over the defined interval. That aggregated, bucket-level value constitutes the parent’s “first response” determined “based on” the second response with the transformation function. Taghaddos supplies the parent-child dependency structure; Borah supplies the precise time-bucket aggregation mechanics.).
Regarding claim 14, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the plurality of parent questions and the plurality of child questions are associated with a plurality of question properties including at least one of a question identifier a question name, a question set identifier, a question category, a question type, a question numbering, a question tooltip, and a question short code (Taghaddos [0038] e.g., “the survey editor 140 enables a survey creator (SC) to compose survey questions …” and provides question-type selection and editing UIs (e.g., FIGS. 3, 4B). Providing a GUI for composing the questions used in time-series analysis is expressly taught), see also p0051] e.g., “screen 430 enumerating some of the question types available to be selected …Multiple Choice …Rating Scale … Form… Comment… Matrix of choices … instructions…”. Different questions types for parent child are expressly supported by the editor).
Regarding claim 15, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the memory further stores instructions that cause the processor to store the one or more response to the plurality of child questions in a database, each response being associated with a user ID and one or more response property including at least one of a response timestamp, a response location, a response update timestamp, and a response metadata (Taghaddos [0051] e.g., “Multiple Choice; Rating Scale (Likert 5 scale; NPS 0 10); Form (numeric/text fields); Comment (free response); Matrix of choices; Instructions…”. Thus, for “selected from” Taghaddos provides several enumerated types within the claimed set (e.g., multiple choice, Likert/numerical, text/free response).
Regarding claim 16, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the at least one transformation function is selected from a summation function, an average function, a minimum function, a maximum function, and a standard deviation function (Taghaddos [0067] e.g., “They can choose to be emailed each time they receive a response or once a day in a “daily digest” type of email that summarizes the response”).
Regarding claim 17, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein generating the report is triggered based on at least one of a user input, a reporting timeframe, and a threshold associated with the plurality of parent questions being met (Taghaddos [0067] e.g., “…copying a unique link that the survey system …copy an HTML link … copy a Pop-Up link …Using these options, the SC can promote the survey via Twitter and Facebook.”).
Regarding claim 18, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the memory further stores instructions that cause the processor to generate the report in a format selected from a PDF format, a CSV format, a TXT format, and an HTML format (Taghaddos [0070] e.g., “SCs can also export the raw results data in CSV format.”, see also (Bhatia [col. 4, lines “… the present invention enables end-users to securely create and customize their own reports… outputs can be in any binary file format, such as, for example, HyperText Markup Language (HTML), Portable Document Format (PDF), Microsoft Excel (XLS, XLSX), Microsoft Word (DOC, DOCX), Extensible Markup Language (XML) …”. Providing TXT/plain-text output is a routine variant of CSV/HTML/PDF generation in reporting systems and would have been an obvious format option).
Regarding claim 19, the rejection of claim 11 is hereby incorporated by reference, the combination of Taghaddos, Bhatia, and Borah Bhatia discloses a system, wherein the report data structure is provided in a predetermined data format selected from an XML format, a YAML format, and a JSON format (Borah [col. 8, line8-24] e.g., “…the HBase table …can have multiple column families —one column for each data resolution. Each column family has a different TTL configuration matching a corresponding retention period …the 1-minute resolution data can be written into the first column family … After rolling up the metrics for 10 min and 60 minutes, the respective rollups can be stored in 2nd and 3rd column families.”, see also [col. 6, lines 3-19] e.g., “the system will continuously rollup the 1-minute resolution metrics to 10-minutes … every 10 minutes, and then to 60-minutes … every hour”).
Conclusion
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERHANU MITIKU whose telephone number is (571)270-1983. The examiner can normally be reached Monday – Friday 8:30AM – 4:00PM.
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/BERHANU MITIKU/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156