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 .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on August 13, 2025, wherein claims 1-20 are currently pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, Claims 1-10 are directed to a system (i.e. machine) and claims 11-20 are directed to method (i.e., process). Accordingly, all claims are directed to one of the four statutory categories of invention.
The claimed invention is directed to an abstract idea without significantly more.
(Step 2A, Prong 1 (MPEP 2106.04(a-b))) The independent claims (1, 11) recite obtaining (collecting/receiving/extracting/etc.,) abstract information/data (e.g. topics, measurements, time, type of source, goals etc.,), data analysis and manipulation to determine more information/data (additionally using abstract steps of, for example, sampling, calculating, proportioning, comparing, predicting – as can been seen mathematical concepts are bring used in the analysis), and providing/displaying this determined data for further analysis and decision-making. The claimed invention further uses mathematical steps (as shown above) to analyze and determine further data. The limitations of independent claims (1, 11), under the broadest reasonable interpretation, covers methods of organizing human activity (fundamental economic principles or practices (commercial trend analysis using collection information from various platforms) and managing behavior and interactions between people (identifying and predicting trends (of people (decisions, interactions, behaviors, etc., on various platforms)) and providing the trending information to users/people)) and mathematical concepts (clearly using mathematical concepts in calculation, measurements, predictions, etc.,; the results of which are used in the analysis). If claim limitations, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04). If claim limitations, under its broadest reasonable interpretation, covers the performance of the limitation as mathematical relationships, mathematical formulas or equations, mathematical calculations then it falls within the Mathematical concepts grouping of abstract ideas. (MPEP 2106.04).
Accordingly, since Applicant's claims fall under organizing human activities grouping and mathematical concepts grouping, the claims recite an abstract idea.
(Step 2A, Prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of generic/general-purpose computers and generic/general-purpose computing components/elements/etc., (for example, “systems, processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in Independent claim 1); and “processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in independent claim 11)) in the context of the claims, the claim encompasses the above stated abstract idea of organizing human activity (fundamental economic principles or practices (commercial trend analysis using collection information from various platforms) and managing behavior and interactions between people (identifying and predicting trends (of people (decisions, interactions, behaviors, etc., on various platforms)) and providing the trending information to users/people)) and mathematical concepts (clearly using mathematical concepts in calculation, measurements, predictions, etc.,; the results of which are used in the analysis). As shown above, the claims and specification recite generic/general-purpose computers and generic/general-purpose computing components/elements/etc., which are recited at a high level of generality performing generic/general-purpose computer functions. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The generic/general-purpose computers and computing elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computers and/or computer components/elements/ devices, etc., (for example, “systems, processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in Independent claim 1); and “processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in independent claim 11)). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes (organizing human activity (fundamental economic principles or practices (commercial trend analysis using collection information from various platforms) and managing behavior and interactions between people (identifying and predicting trends (of people (decisions, interactions, behaviors, etc., on various platforms)) and providing the trending information to users/people)) and mathematical concepts (clearly using mathematical concepts in calculation, measurements, predictions, etc.,; the results of which are used in the analysis)) involving simple information exchange. Carrying out abstract processes (as discussed above) involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Step 2B (MPEP 2106.05)) Applicant’s claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are, for example, “systems, processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in Independent claim 1); and “processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in independent claim 11). The additional elements recited in Applicant’s claims are not sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claims recite using known and/or generic/general-purpose computers and generic/general-purpose computing components/elements/etc. For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example (very little technology or technical elements recited in the specification/disclosure), fig. 1; ¶¶ 0022-0024 [reciting only general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice, at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claim into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (for example, “systems, processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in Independent claim 1); and “processors, interfaces (displays, etc.,), networks (including social media, channels, etc., - which are only there to get abstract information from), data storage (databases), application programming interface (generically stated software/application), transmission and communication (over network), etc., (in independent claim 11)) or combination of elements in the claims other than the abstract idea per se amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to MPEP 2106.05 and also directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc.(U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the independent claims (1, 11) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-10, 12-20) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The dependent claims either individually or in combination are merely an extension of the abstract idea itself. The above rejection discussed for the independent claims fully applies to the dependent claims.
The dependent claims (2-10, 12-20) further state using obtained data/information (where the information itself is abstract in nature), data analysis and manipulation to determine more information/data (additionally using abstract steps of, for example, sampling, calculating, proportioning, comparing, predicting – as can been seen mathematical concepts are bring used in the analysis), and providing/displaying this determined data for further analysis and decision-making. The claimed invention further uses mathematical steps (as shown above) to analyze and determine further data. These dependent claims also cover methods of organizing human activity (fundamental economic principles or practices (commercial trend analysis using collection information from various platforms) and managing behavior and interactions between people (identifying and predicting trends (of people (decisions, interactions, behaviors, etc., on various platforms)) and providing the trending information to users/people)) and mathematical concepts (clearly using mathematical concepts in calculation, measurements, predictions, etc.,; the results of which are used in the analysis).
This judicial exception is not integrated into a practical application because the claims and specification recite additional elements as generic/general-purpose computing/technology components/elements/terms/limitations (for example, “system, processors, interface, application programming interface, storage, communication via network, ” (in dependent claims 2-10); “interface, application programming interface, storage, communication via network,” (in dependent claims 12-20)) performing generic computer/computing/technology functions. (MPEP 2106.04). The dependent claims merely use the same general technological environment and instructions as the independent claims above to implement the abstract idea. The generic/general-purpose computing/technology components/elements/terms/limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computing/technology components/elements/terms/limitations (for example, “system, processors, interface, application programming interface, storage, communication via network, ” (in dependent claims 2-10); “interface, application programming interface, storage, communication via network,” (in dependent claims 12-20)). Hence, the additional elements (for example, “system, processors, interface, application programming interface, storage, communication via network, ” (in dependent claims 2-10); “interface, application programming interface, storage, communication via network,” (in dependent claims 12-20)) do not integrate the abstract idea in to a practical application because they does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
Also, the dependent claims either individually or in combination are merely an extension of the abstract idea itself and the dependent claims (similar to the independent claims) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (for example, “system, processors, interface, application programming interface, storage, communication via network, ” (in dependent claims 2-10); “interface, application programming interface, storage, communication via network,” (in dependent claims 12-20)) or combination of elements in the dependent claims other than the abstract idea per se amounts to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc. (758 F.3d 1344 (2014) discussing U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the dependent claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the dependent claims (2-10, 12-20) do not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Danson et al., (US 2018/0032886) in view of Sharma et al., (US 2017/0180567).
As per claim 1, Danson discloses a system for collecting and managing trend data comprising:
(a) one or more or processors; (b) a channel extraction interface executed by the one or more processors and configured to receive data from a plurality of channels, and where the plurality of channels includes at least one social media channel; (c) a data storage (¶¶ 0006-0009 [processors]); (b) a channel extraction interface executed by the one or more processors and configured to receive data from a plurality of channels, and where the plurality of channels includes at least one social media channel; (c) a data storage (¶¶ 0003, 0006-0009 [processors…communications], 0028-0031 [shows the processors, network, and other technical components; also shows communication channels including social media channels], 0047-0049, 0057, 0068); wherein the one or more processors are configured to:
(i) receive a trend dataset from the plurality of channels via the channel extraction interface, wherein the trend dataset describes, for each of the plurality of channels, one or more topics that are trending on that channel, and a trend measurement for each of the one or more topics on that channel (see citations above and also see ¶¶ 0006, 0057 [prevalence of terms (time frames…percentage of terms…changes in occurrences – (trends))…data streams…time frame and tallies the number of occurrences…(data trends)…each separate data source, such as in email, telephone discussions, and social media feeds (each of the channels)… calculation of frequency in individual data sources (e.g., in a particular social media app, or for a particular speaker or pair of communicants) may allow a user to track the prevalence of terms across various media sources…time frame itself can be adjusted depending on the potential terminology or trends, such that this analysis and calculation can be conducted iteratively over various first time frames], 0058-0060, 0073-0076 [every media channel…communications…media channel…set of trending topics]);
(ii) determine a proportionality for each of the one or more topics based on the trend measurements for the one or more topics on that channel (¶¶ 0057-0060 [number of occurrences…frequency…percentage…calculated…frequency percentile…frequency threshold…percentile]);
(iii) determine an extraction capability for each of the plurality of channels (see citations above and also see ¶¶ 0073-0075 [e.g. of discussing “capability” – trend analysis…identify tends…every media channel…communication disruption (or other issues)…in media channel]; see also 0054-0057 [communication channels…sufficient amount of communication…abilities]);
(iv) determine an extraction goal for each of the plurality of channels based on the proportionality and the extraction capability for each of the plurality of channels (see citations above and also see, for example, ¶¶ 0057-0060 [overall percentage…calculation of frequency…calculated for time slots within time frame (proportionality)…threshold may also be calculated by a frequency percentile…most frequently-mentioned term identified during the time frame is said to be in the 100th percentile for frequency, and the least frequently-mentioned term is in the 0th percentile. In this example, the frequency threshold may be set (goal) at the 25th-50th percentile for term frequency, at the 50th-60th percentile (goal), or at the 60th-75th percentile (goal)]);
(v) for each of the plurality of channels, receive a trend content dataset from that channel via the channel extraction interface and based on the extraction goal for that channel (¶¶ 0010-0011, 0015-0017 [trend data…set of trend factors…set of trending [data]], 0023-0025, 0032-0034, 0053-0057, 0061 [complies a set…trend displayed…set…user selection…algorithms…set of trending information…adjustment to sets of information…display module]), and
store the trend content dataset in the data storage, wherein the trend content dataset comprises a representative grouping of content associated with the one or more trends on that channel (see citations above and also see ¶¶ 0012-0014 [storing information (store trend/trending information)], 0018, 0026 [library or database (storing and stored datasets of trends content) – see in conjunction with 0031-0033], 0034 [data storage…analysis control system 180 may be generally configured to provide recording, voice analysis, data storage, data relationship analysis, trend analysis, behavioral analysis, and other processing functionality to the analysis center…data…stored], 0042, 0046, 0064-0072 [set of information and information is grouped – where groups can further be broken up]);
(vi) determine a timeseries prediction for each of the one or more topics for a subsequent period of time (see citations above and also see ¶¶ 0006, 0008, 0013-0015 [time periods (first, second, etc.,)…sequential], 0049-0050 [information from subsequent time frames], 0057 [detail information over timeseries and example of topics and analysis of topics/trends/etc., over time slots in a time frame (subsequent periods of time)], 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)]; see also 0070, 0079 [(information analysis)… between the first/second and third/fourth time frames (subsequent)…topics…analysis of changes…predict…future trends…predictions]); and
(vii) provide a trend interface dataset to a user device based on the timeseries predictions and the trend content datasets, wherein the trend interface dataset is usable to cause the user device to display a trend interface describing the timeseries predictions and the trend content datasets (see citations above and also see, for example, ¶¶ 0006 [display device…display], 0029 [mobile phones…personal computing devices…smartphones and tablets, and personal digital assistants (PDAs)], 0032-0034 [information/data accessed by display module (where display module includes interface…user can view)], 0057 [detail information over timeseries and example of topics and analysis of topics/trends/etc., over time slots in a time frame (subsequent periods of time)], 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)], 0061-0063 [also showing displaying information – set of trending information (analysis control system 200 displays the set of trending terms to a user. In some cases, the set of trending terms is displayed on the display module 190 of FIG. 1 or the display component 292 of FIG. 2. The set may be displayed graphically or in text form. In some cases, additional analysis data is displayed along with the list of trending terms. This additional data may include a total number of terms analyzed, a list of newly identified terms from the first and second time frames, the frequency thresholds used in steps 340, 400, and 440, and the total number of frequent terms from sets determined at steps 370 and 470)]; see also 0070, 0079 [(information analysis)… between the first/second and third/fourth time frames (subsequent)…topics…analysis of changes…predict…future trends…predictions]; see also 0042 [interface component…display], 0047-0048 [interface component described – display…data in a graphical format…display…graph]).
Danson does not explicitly state sampling of information (although Danson does disclose grouping of data and selecting data subsets – see citations above).
Analogous art Sharma (trend analysis and using social data (social media) – ¶¶ 0048-0053) discloses sampling of content/information (for example, see ¶¶ 0184-0191 [sampling data], 0504, 0579, 0818+, 0884+ [sampling information], 1026-1032).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Danson sampling as taught by analogous art Sharma in order to take a group of information for analysis is since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (TSM/KSR-G); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Sharma (sampling information for analysis is an old-and well-known technique in data analytics) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 11, claim 11 discloses substantially similar limitations as claim 1 above; and therefore claim 11 is rejected under the same rationale and reasoning as presented above for claim 1.
As per claim 2, Danson discloses the system of claim 1, wherein the one or more processors are further configured to, for each channel of the plurality of channels: (a) generate an insight based on the trend dataset for that channel; (b) generate one or more channel queries based on the insight for that channel (see citations above for claim 1 and also see, for example, ¶¶ 0073 [establishing a topic similarity threshold. This threshold can involve quantitative comparisons of terms associated with topics (such as the number of different terms or the number of times in a communication stream that a term has occurred), as well as qualitative comparisons (which may involve weighting terms and topics differently according to their source and usage). In some embodiments, topics that are associated with similar events are compared. For example, the topic “Oscars 2015” from the first time frame may be compared with the topic “Oscars 2016” from the second time frame. The analysis of topics representing similar events can allow “micro-trending” analysis. In particular, the similarities and differences between topics associated with corresponding events (such as “Oscars 2015” and “Oscars 2016”) can give insight into the evolution of these events over time. For example, the term “#oscarssowhite” may appear in under the topic “Oscars 2016” while not appearing under “Oscars 2015.” The frequency and usage of this newly emerging term may signal a change in the attitudes of viewers, or more generally, users of a service or product. Another example of micro-trend analysis is the identification of the absence of trends. For example, during Super Bowl 50, the system 200 may identify trends associated with the game that appear in every media channel except for one. This may signal that there is a communication disruption or a lack of interest in that media channel. Furthermore, the system 200 may reach out to the media channel (for example, through the use of the routing engine 192 of FIG. 1) to notify it of the finding. At step 590, the method 500 may include determining whether matching topics are above the similarity threshold of step 580. At step 560, topics that are not similar enough to other topics may be removed from the analysis], 0076-0079); and (c) query that channel via the channel extraction interface and based on the one or more channel queries in order to receive the trend content dataset (¶¶ 0003-0004 [searches (queries)], 0037-0042, 0044).
Danson does not explicitly state usage map (although usage map is insights into different usage areas – where Danson does show (as seen above) insights and trends on data usage in media platforms).
Analogous art Sharma discloses usage map (for example, see ¶¶ 0234 [map…usage data], 0235 [map…to data/information…user interface…record], 0443, 0452, 0507 [mapping interface…data…analysis…usage], 0971 [planning…anticipated usage…usage…monitored…recognize trends], 1043 [forecast trends and patterns…analytic usage]).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Danson usage map as taught by analogous art Sharma in order to provide efficient insights for accurate analysis since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (TSM/KSR-G); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Sharma (usage maps for data analysis is an old-and well-known technique in data analytics) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 12, claim 12 discloses substantially similar limitations as claim 2 above; and therefore claim 12 is rejected under the same rationale and reasoning as presented above for claim 2.
As per claim 3, Danson discloses the system of claim 2, wherein the channel extraction interface is configured to, for at least one channel of the plurality of channels, request the trend dataset via an application programming interface provided by that channel (¶¶ 0047 [interface component…receive and transmit analysis center-related data between local and remote networked systems and communicate information via the communications link (this is an API as an API allows communication and exchange of data between applications (software (can be integrated in a hardware) with a function)) – see in conjunction with 0028, 0032-0034, 0068], 0028 [data sources… e-mails, web interactions, texts, chats…e.g., including via Skype®, Facetime®, Tango™…communication app…program], 0029-0031, 0049-0050, 0053, 0061, 0064-0068).
As per claim 13, claim 13 discloses substantially similar limitations as claim 3 above; and therefore claim 13 is rejected under the same rationale and reasoning as presented above for claim 3.
As per claim 4, Danson discloses the system of claim 1, wherein the one or more processors are further configured to: (a) determine the proportionality for each of the one or more topics based on the trend measurement for that topic and the aggregated trend measurements for all of the one or more topics for that channel (see citations above and also see, for example, ¶¶ 0057-0060 [overall percentage…calculation of frequency…calculated for time slots within time frame (proportionality)…threshold may also be calculated by a frequency percentile…most frequently-mentioned term identified during the time frame is said to be in the 100th percentile for frequency, and the least frequently-mentioned term is in the 0th percentile. In this example, the frequency threshold may be set (goal) at the 25th-50th percentile for term frequency, at the 50th-60th percentile (goal), or at the 60th-75th percentile (goal)]; see also 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)], 0061-0063 [also showing displaying information – set of trending information (analysis control system 200 displays the set of trending terms to a user. In some cases, the set of trending terms is displayed on the display module 190 of FIG. 1 or the display component 292 of FIG. 2. The set may be displayed graphically or in text form. In some cases, additional analysis data is displayed along with the list of trending terms. This additional data may include a total number of terms analyzed, a list of newly identified terms from the first and second time frames, the frequency thresholds used in steps 340, 400, and 440, and the total number of frequent terms from sets determined at steps 370 and 470));
Danson does not explicitly state (b) determine the extraction capability for each of the plurality of channels based on one or more of: (i) a pre-configured storage limitation of the data storage; (ii) a data transmission limitation for communications between the channel extraction interface and that channel; and (iii) a limitation of an application programming interface of that channel via which the trend content dataset is received.
Analogous art Sharma discloses (b) determine the extraction capability for each of the plurality of channels based on one or more of: (i) a pre-configured storage limitation of the data storage (¶¶ 0608 [database…controls and limits], 0230 [limitations regarding access]; Table 1 [storage…queues…sufficient to handle FIFO request management…transaction volumes…queue…bottleneck…database architecture…cannot faster performance…]; Table 49 [discusses various examples of limitations regarding storage/database]); (ii) a data transmission limitation for communications between the channel extraction interface and that channel; and (iii) a limitation of an application programming interface of that channel via which the trend content dataset is received (Sharma: Table 16 [connectivity (with API)…pending…(state of system and the network…API interface…validation processing…error indication (errors))]; see also Tables 17 [connectivity…API interface…error indication…processing stopped], 18, 20, 21, 40 [network…validations (failed)…error indication]; ¶¶ 1020, 1043-1050 [measure network load and resource utilization…(based on limitation making adjustments with cloud – while tracking degradation and taking action)…difficult to connect]).
Therefore, it would be obvious to one of ordinary skill in the art to include in the system/method of Danson (b) determine the extraction capability for each of the plurality of channels based on one or more of: (i) a pre-configured storage limitation of the data storage; (ii) a data transmission limitation for communications between the channel extraction interface and that channel; and (iii) a limitation of an application programming interface of that channel via which the trend content dataset is received as taught by analogous art Sharma in order to provide efficient data analysis since doing so could be performed readily by any person of ordinary skill in the art, with neither undue experimentation, nor risk of unexpected results (TSM/KSR-G); and also since one of ordinary skill in the art at the time of the invention would have recognized that applying the known technique and concepts of Sharma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such concepts and features into similar systems (KSR-D). (MPEP 2141).
As per claim 14, claim 14 discloses substantially similar limitations as claim 4 above; and therefore claim 14 is rejected under the same rationale and reasoning as presented above for claim 4.
As per claim 5, Danson discloses the system of claim 1, wherein the one or more processors are further configured to, for each of the one or more topics from all of the plurality of channels:
(a) determine a platform breadth for that topic based on the number of the plurality of channels that topic is trending on (see citations above for claims 1-2 and also see, for example ¶¶ 0003 [track…topics…variety of data sources (different media channels – news feeds, social media, etc.,)…trends…trending], 0038 [shows examples/list of media channels from which information/data is obtained], 0057 [“breath” for topic shown – “Frequency,” as used herein, refers to a comparison of the relative prevalence of terms. Although this may include a raw numerical comparison of terms within a given communication (i.e., 12 instances of the term “democrat” compared to 24 instances of the term “republican” within a single communication), “frequency” can also represent a comparison the prevalence of terms across the same time frame, a comparison of terms (or associated key words, like “donkey” or “elephant” with respect to the main U.S. political parties) across different time frames, a comparison of terms across different communication sources or channels, a comparison of terms across different clients, or a comparison of the overall percentage of terms in one or more communications], 0073 [threshold…weightings]);
(b) determine a duration for that topic based on the number of consecutive time periods for which that topic was identified as trending (see citations above and for claims 1-2 and also see, for example, ¶¶ 0006-0009 [analysis…time periods (first, second, etc., - consecutive time periods)… analyzing data relating to trends…analyze a first plurality of communications occurring over a first time period, determine a first plurality of terms based on the analyzed first plurality of communications, analyze a second plurality of communications occurring over a second time period, determine a second plurality of terms based on the analyzed second plurality of communications, identify a trending topic based on frequency of terms used in both the first and second time periods, determine at least one reason why the identified trending topic is trending], 0057 [time slots within the first time frame, in which the time slots are portions of the time frame]);
(c) determine a cultural breadth for that topic based on the number of cultural categories associated with the topic, wherein the cultural categories associated with that topic are selected from a preconfigured plurality of cultural categories; (d) determine a seasonality for that topic based on a temporal recurrence of that trend evidenced by historic trend datasets; and (e) determine the timeseries prediction for that trend based at least in part on the platform breadth, the duration, the cultural breadth, and the seasonality of that trend (see citations above and for claims 1-2 and also see, for example, ¶¶ 0073 [topics associated with corresponding events (such as “Oscars 2015” and “Oscars 2016”) can give insight into the evolution of these events over time…during Super Bowl, the system may identify trends associated with the game that appear in every media channel except for one], 0037 [e.g. of seasonality based on time/temporal], 0053-0054 [cultural and seasonal examples – elections, actor discovery (cultural entertainment) or novel topic (cultural), rad in the 80s events/topics (cultural), weather/rain/etc., (seasonal)], 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)]);
As per claim 15, claim 15 discloses substantially similar limitations as claim 5 above; and therefore claim 15 is rejected under the same rationale and reasoning as presented above for claim 5.
As per claim 6, Danson discloses the system of claim 5, wherein the trend interface, when describing that topic, further describes the platform breadth, the duration, the cultural breadth, and the seasonality for that trend (see citations above for claims 1-2 and 5 and also see, for example, ¶¶ 0006-0009 [analysis…time periods (first, second, etc., - consecutive time periods)… analyzing data relating to trends…analyze a first plurality of communications occurring over a first time period, determine a first plurality of terms based on the analyzed first plurality of communications, analyze a second plurality of communications occurring over a second time period, determine a second plurality of terms based on the analyzed second plurality of communications, identify a trending topic based on frequency of terms used in both the first and second time periods, determine at least one reason why the identified trending topic is trending], 0057 [time slots within the first time frame, in which the time slots are portions of the time frame], 0073 [topics associated with corresponding events (such as “Oscars 2015” and “Oscars 2016”) can give insight into the evolution of these events over time…during Super Bowl, the system may identify trends associated with the game that appear in every media channel except for one], 0037 [e.g. of seasonality based on time/temporal], 0053-0054 [cultural and seasonal examples – elections, actor discovery (cultural entertainment) or novel topic (cultural), rad in the 80s events/topics (cultural), weather/rain/etc., (seasonal)], 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)]).
As per claim 16, claim 16 discloses substantially similar limitations as claim 6 above; and therefore claim 16 is rejected under the same rationale and reasoning as presented above for claim 6.
As per claim 7, Danson discloses the system of claim 6, wherein the trend interface describes:(a) the platform breadth as high or low based upon a preconfigured platform breadth threshold; (b) the duration as emerging or persisting based upon a preconfigured platform duration threshold; and (c) the cultural breadth as broad or niche based upon a preconfigured cultural breadth threshold (see citations above for claims 1-2 and 5 and also see, for example, ¶¶ 0006-0009 [analysis…time periods (first, second, etc., - consecutive time periods)… analyzing data relating to trends…analyze a first plurality of communications occurring over a first time period, determine a first plurality of terms based on the analyzed first plurality of communications, analyze a second plurality of communications occurring over a second time period, determine a second plurality of terms based on the analyzed second plurality of communications, identify a trending topic based on frequency of terms used in both the first and second time periods, determine at least one reason why the identified trending topic is trending], 0057 [time slots within the first time frame, in which the time slots are portions of the time frame], 0073 [topics associated with corresponding events (such as “Oscars 2015” and “Oscars 2016”) can give insight into the evolution of these events over time…during Super Bowl, the system may identify trends associated with the game that appear in every media channel except for one], 0037 [e.g. of seasonality based on time/temporal], 0053-0054 [cultural and seasonal examples – elections, actor discovery (cultural entertainment) or novel topic (cultural), rad in the 80s events/topics (cultural), weather/rain/etc., (seasonal)], 0059-0064 [analysis…time frames…subsequent time frames…prediction (likelihood)]).
As per claim 17, claim 17 discloses substantially similar limitations as claim 7 above; and therefore claim 17 is rejected under the same rationale and reasoning as presented above for claim 7.
As per claim 8, Danson discloses the system of claim 1, wherein the one or more processors are further configured to, when determining the timeseries prediction for each of the one or more topics: (a) evaluate one or more quantitative variables associated with that topic in the trend dataset, wherein the one or more quantitative variables includes an interaction variable that describes a magnitude of user interaction with that topic (¶¶ 0010-0011 [a first plurality of terms based on the analyzed first plurality of communications; analyze a second plurality of communications occurring over a second time period based on voice data and non-voice data; determine a second plurality of terms based on the analyzed second plurality of communications; compare the terms of the first plurality of terms and the second plurality of terms based on one or more factors including a frequency of the terms in each of the first and second time periods…identify the set of trending terms further includes establishing a frequency threshold, and including terms that exceed the frequency threshold in the identified set of trending terms], 0015-0016 [analyze a first plurality of communications occurring over a first time period and determine a first plurality of terms; analyze a second plurality of communications occurring over a second time and determine a second plurality of terms; determine the frequency that each term of the first plurality of terms and the second plurality of terms respectively occurs during the first and second plurality of communications; compare the frequency of each of the terms in the first plurality of terms to the frequency of each of the terms in the second plurality of terms; identify one or more trend parameters; determine one or more trend factors based on application of the identified one or more trend parameters…trend factors further include an emergence of a trend, a length of a trend, the popularity of a trend, and the geographic spread of a trend…the popularity of a trend is based on the frequency of terms of the first and second plurality of terms that are related to the determined trend], 0030, 0037, 0045, 0047, 0057, 0073 [qualitative comparisons…weightings]);
(b) evaluate one or more qualitative variables associated with that topic, wherein the one or more qualitative variables includes a text content associated with that topic (see citations above and citations of claims 1-2, and 5; and also see ¶¶ 0028 [text], 0031-0032 [text file…text], 0035, 0053-0055 [text analysis], 0063);
(c) evaluate a historical trend dataset associated with that topic and stored by the data storage; and (d) determine the timeseries prediction based on the evaluations of the one or more quantitative variables, the one or more qualitative variables, and the historical trend dataset for that trend. (see citations above and citations of claims 1-2, and 5; and also see ¶¶ 0004 [previously0identified keywords], 0053-0055 [drawing inferences on previously-defined data/information…(comparing information from past information)], 0066, 0073-0080 [matching…different time periods…that relate to a repeating event, such as an annual meeting…analysis of changes in frequency rates can be used to predict the emergency and popularity of future trends.. annual event generated a large number of diverse trending terms last year, a similar number of terms may be expected this year…(prediction based on historical information)]).
As per claim 18, claim 18 discloses substantially similar limitations as claim 8 above; and therefore claim 18 is rejected under the same rationale and reasoning as presented above for claim 8.
As per claim 9, Danson discloses the system of claim 1, wherein the one or more processors are further configured to, when determining the timeseries prediction: determine a plurality of portions of the historical trend dataset for that topic, wherein each portion corresponds to a channel of the plurality of channels; evaluate each portion of the plurality of portions against the other portions to identify a temporal relationship for that topic that describes a period of time between a change in that topic's trend on a first channel and a corresponding change in that topic's trend on a second channel; and determine the timeseries prediction further based on the identified temporal relationship (¶¶ 0004 [previously0identified keywords], 0053-0055 [drawing inferences on previously-defined data/information…(comparing information from past information)], 0062-0066, 0070 first and second time frames may be fixed, equal, and sequential. In the example of method 500, the analysis control system 200 does not generate the set of terms identified from the communications of the first and second time frame by reference to an existing library of terms, such as pre-defined terms. An independent analysis at each time frame or set of time frames may allow for the recognition of newly emerging terms. It should be understood that a trend can only be identified after an initial second time frame, as the initial first time frame is used to identify keywords that may form a trend. After an initial first and second time frame, the second time frame may be re-purposed as a subsequent first time frame and either the initial first and second time frame or the re-purposed second time frame form the basis against which a later-determined time frame becomes the second time frame and is compared against what is now the considered a first time frame, 0073-0080 [the topic “Oscars 2015” from the first time frame may be compared with the topic “Oscars 2016” from the second time frame. The analysis of topics representing similar events can allow “micro-trending” analysis. In particular, the similarities and differences between topics associated with corresponding events (such as “Oscars 2015” and “Oscars 2016”) can give insight into the evolution of these events over time…matching…different time periods…that relate to a repeating event, such as an annual meeting…analysis of changes in frequency rates can be used to predict the emergency and popularity of future trends.. annual event generated a large number of diverse trending terms last year, a similar number of terms may be expected this year…(prediction based on historical information)]).
As per claim 19, claim 19 discloses substantially similar limitations as claim 9 above; and therefore claim 19 is rejected under the same rationale and reasoning as presented above for claim 9.
As per claim 10, Danson discloses the system of claim 1, wherein the one or more processors are further configured to: (a) while creating and providing trend interface datasets over a period of time, store a plurality of trend datasets and a plurality of trend content datasets in the data storage (see citations above for claims 1-3, 5, and 9; and also see, for example, ¶¶ 0002 [providing data to user], 0006-0007 [providing trend information to users], 0008-009 [providing trend information to users via display device]);
(b) receive a user configured trend interest from a user; (c) search the plurality of trend datasets and the plurality of trend content datasets based on the user configured trend interest to identify a custom trend dataset (see citations above and also see citations for claims 1-3, 5, and 9; and also see, for example, ¶¶ 0053-0054 [user configurations and input to get information – the trending information provided to the user], 0061-0063 [user selections on what information to provide and then providing set of information to users], 0061 [analysis control system 200 compares sets of frequent terms based on the first and second time frames. In some cases, the system 200 compiles a set of terms that appear in both sets of frequent terms. The system 200 may then determine the change in frequency between corresponding terms, preferably based on a statistically significant change in frequency. Terms that are included in the first set of frequent terms but do not appear in the second set of frequent terms may be removed from the analysis. Alternatively, this may indicate a trend itself, or the end of a trend, and may be displayed in a set of trending terms in step 480 according to the disclosure as it may be of great interest to certain users. Typically, however, terms that appear on the second set that do not appear on the first set, or that appear with greater or increasing frequency in the second set compared to the first set, may be included in a set of trending terms in step 480. Terms that appear in both sets of frequent terms may also be included in the set of trending terms. In some cases, an additional frequency threshold is applied during the determination of the set of trending terms at step 480. In this case, terms with a negative change in frequency between the first and second time frames may be excluded from the set of trending terms or as noted above may highlight a trend in itself or the end of a trend and thus be included in the trending terms. A user may select whether to have the system and methods herein evaluate only for increasing trend topics, decreasing trend topics, or both. As in the case of the frequency threshold set in steps 340 and 440, the frequency threshold set during the determination of the set of trending terms may be calculated differently during the same time frame depending on data source]); and
(d) determine a customer timeseries prediction for the user configured trend interest based on the custom trend dataset, wherein the trend interface further describes the custom trend dataset (see citations above and also see citations for claims 1-3, 5, and 9; and also see, for example, ¶¶ 0062-0066 [for example (among many) at 0064 – the set of trending terms is transmitted with an analysis of the set of trending terms and/or a recommendation of action. Based on behavioral analytics, the recommendation could be based on a prediction of a likelihood of an action or event occurring, as well. For example, the analysis center 100 may identify terms associated with customer complaints about a product line. The set of trending terms determined by the system 200 may allow the analysis center 100 to identify a trend of complaints about a specific product of the product line, as well as to identify when the complaints emerged and whether they are increasing. This may also be evaluated in connection with the behavioral analytics, such as personality type, of the customers complaining or of agents in a customer service center working with those complaining customers. The set of trending terms is then sent by the analysis center 100 to a communication distributor for distribution as desired, such as to one or more of an employee in a quality control department, to a customer service center supervisor or agent, to a sales team, to an engineering team to begin designing a workaround or future product fix or improvement, etc. The analysis center 100 may also send a recommendation along with the data. The user(s) can then take action to respond to the trend, such as a newly arising problem. In some embodiments, after viewing the set of trending terms, a user can give feedback to the system 200 in an effort to fine-tune the results], 0070-0075, 0079-0082).
As per claim 20, claim 20 discloses substantially similar limitations as claim 10 above; and therefore claim 20 is rejected under the same rationale and reasoning as presented above for claim 10.
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Hendrickson et al., (US 2016/0359993): Provides for trend detection in a messaging platform. A trend detection model is selected and a time series having a plurality of instances of social data is received, wherein the instances of social data share a countable parameter. A count is made of occurrences of countable parameters in each instance of social data assigned to that bin and a trend detected based at least in part on the trend detection model and on the count for each bin.
Jain et al., (US 10,250,547): Discusses receiving, by an information distribution system and from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, and determining, by the information distribution system and using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag. In response to determining that the trending score satisfies a threshold, the method further includes sending, by the information distribution system and to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, by the information distribution system and for display at the one or more of the one or more client devices, the targeted content.
Heath (US 2013/0073336): Illustrates calculating how the consumer or brand sentiment concerning the consumer products or services, or promotions thereof, of interest trends over time; calculating how the consumer or brand sentiment concerning the consumer products or services, or promotions thereof, of interest varies by online source or group of sources; and calculating how the consumer or brand sentiment concerning the consumer products or services, or promotions thereof, of interest concurrently trends over time and varies by online source or group of sources.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
Prosecution Insights