DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
Claims 1, 11 and 20 are presently amended.
Claims 10 and 19 are cancelled.
Claims 1-9, 11-18 and 20 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant' s arguments filed 7/11/2025 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, and are persuasive in-part for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “Step 2a - Prong One Analysis: The Claims Recite Eligible Subject Matter Because They Are Not Directed To The Abstract Idea Of Certain Methods Of Organizing Human Activity… Independent claims 1, 11, and 20 have been amended to incorporate the feature "wherein the analysis is performed based on training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model"… This is not an abstract idea under MPEP 2106.04(a)(2), as it involves a specific, technical process rooted in computer technology, analogous to the situation in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), where claims improving database functionality were not abstract…
The claimed method further involves creating user segments using regex-based and parameter-based filters (e.g., time-based, location-based, event-based) for precise categorization, involving computational processing of complex string patterns and generating downloadable segment trend plots (e.g., bar graphs, PNG format, etc.) using specific visualization algorithms and triggering marketing campaigns with synchronized communication protocols. These steps are inherently technical, requiring specialized hardware and software to process high-velocity, heterogeneous data streams in real-time, far beyond manual or mental processes… The specification notes that prior systems lacked real-time capabilities and granular segmentation, which the invention overcomes through a high-bandwidth network and specific algorithms. This aligns with the claims in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), where specific rules for automation were not abstract, as the claims here involve specific machine learning algorithms and real-time data processing protocols” [Arguments, pages 16-21].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the presently amended claims recite an abstract idea. In particular, Examiner maintains that, when considered as a whole, the present claims remain directed towards concepts relating to certain methods of organizing human activity. Similarly,
Examiner observes that in Inc. v. Bandai Namco Games Am. Inc., the claims were directed to methods of automatic lip synchronization and facial expression animation using computer-implemented rules, and thus were not directed to an abstract idea, or an abstract idea sub-category, such as marketing activities and behaviors. In Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016), the claims detailed a self-referential table for a computer database, and thus were directed to an improvement in computer capabilities and not directed to an abstract idea.
Further, with respect to the above-argument which notes the use of a visualization algorithm and the amended “supervised machine learning model or an unsupervised machine learning model”, Examiner observes that the core substance of the present claims describe steps for commercial or legal interactions, which includes agreements in the form of contracts; legal obligations; advertising, and marketing or sales activities or behaviors; business relations. Specifically, creating market segments and initializing marketing campaigns is considered to describe marketing activities and behaviors. Thus, claims 1, 11 and 20 recite concepts identified as abstract ideas. As such, Examiner remains unpersuaded.
Second, Applicant argues that “Step 2a - Prong Two Analysis: The Features Recited In The Amended Claims Are Integrated Into A Practical Application At page 18 of the Office action, regarding Step 2A, prong 2, the Examiner asserts that judicial exception is not integrated into a practical application…
Even if the claims recite an abstract idea, they integrate it into a practical application under MPEP 2106.05(a) by providing a technical solution to the challenge of processing large-scale, real-time data for granular user segmentation, improving online marketing systems. As to the above-identified features recited in amended independent claim 1, that subject matter provides a solution that overcomes the problems associated with conventional systems. For example, the present systems and methods for the segmentation of the individuals accessing the online platforms is not at granular level. In addition, the present systems and methods do not allow the online platform providers to customize the marketing campaigns according to the granularity of the segments of the individuals. Further, the present systems and methods do not allow the online platform providers to measure the effectiveness of the marketing campaigns for the segments in real- time. Furthermore, the present systems and methods do not allow the online platform providers to analyze the performance of the marketing campaigns for segments in real-time. (See paragraph [0002] of the specification as originally filed)…
Moreover, integration with diverse platforms (e.g., e-commerce, social media, fintech) requires synchronization protocols to handle heterogeneous data formats, improving scalability. Additionally, generating downloadable segment trend plots (e.g., bar graphs, PNG format) involves specific visualization algorithms, while real-time campaign triggering requires synchronized communication protocols to deliver advertisements instantly.
These technical improvements are analogous to those in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), where claims solving internet-specific problems (e.g., user retention) were eligible. The claims address the challenge of real-time, targeted marketing by enabling low-latency data processing and precise segmentation. The 2024 Al Update, Example 47, Claim 3, supports eligibility for real-time data processing solutions, similar to this invention's real-time marketing solution. Accordingly, the above- identified features recited in amended independent claim 1, show that the claim recites technical, practical features of the invention, and does not recite an abstract idea…” [Arguments, pages 21-24].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present claims recite a judicial exception without significantly more. In particular, Examiner respectfully maintains that the additional elements, when considered individually and in combination, are not recited or integrated with sufficient detail to demonstrate a practical application of the recited marketing activities.
First, Examiner observes that the claimed segmentation of users is itself an abstract idea, rather than a practical application thereof. In other words, Examiner respectfully maintains that, while the claims recite some additional elements, the claims do not recite any meaningfully activities other than the ineligible marketing behaviors. For example, with respect to the amended claim elements, Examiner maintains that training a machine learning model using a (supervised or unsupervised) machine learning model does not demonstrate any meaningful limit that could be considered more than a drafting effort designed to monopolize the judicial exception. Similarly, the claimed “…one or more communication devices…” and the access of “…one or more online platforms through the one or more communication devices…”, even when considered in the context of the claims as a whole, are recited with a level of generality that is not sufficient to demonstrate a practical application.
Further, with respect to DDR Holdings, LLC v. Hotels.com, the claims were directed to systems and methods of generating a composite webpage that combines certain visual elements of a host website with the content of a third-party merchant. The court found that the claim had additional elements that amounted to significantly more than the abstract idea, because they modified conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, which differed from the conventional operation of Internet hyperlink protocol that transported the user away from the host’s webpage to the third party’s webpage when the hyperlink was activated.
Similarly, the 2024 Al Update, Example 47, Claim 3 is eligible because the additional elements in steps (d)-(f), when considered in combination, integrate the abstract idea into a practical application because the claim improves the functioning of a computer or technical field. See MPEP 2106.04(d)(1) and 2106.05(a). The claimed invention reflects this improvement in the technical field of network intrusion detection. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f).Thus, the claim as a whole integrates the judicial exception into a practical application.
In contrast, Examiner observes that the present claims do not provide any such details for the recited “…one or more online platforms…”, or other additional elements. Examiner further observes that the present claims do not improve any technical field such as network intrusion detection, and instead focus solely on ineligible marketing activities. Thus, Examiner respectfully maintains that the present claims recite concepts identified as abstract ideas without significantly more. As such, Examiner remains unpersuaded.
Third, Applicant argues that “Step 2b: The Claims Recite "Significantly More" Than An Abstract Idea…
The above-indicated elements of amended independent claim 1 result an improvement to an existing technology. In use, the features of independent claim 1 may be incorporated into analytical software or hardware. Like the technology-based solution for filtering content on the Internet that the BASCOM Court ruled (827 F.3d 1341, 1350) met Step 2 of the Alice/Mayo test, the invention of amended independent claim 1 also implements a technology-based solution that uses an approach for creating segments of users and then filtering out a set of users for whom marketing campaigns should be initialized for. The segments of users are created, and the delivery of marketing campaigns are restricted to specific users, within the segments, who meet a criterion. By implementing this type of approach, there is an improved utilization of resources by the user segmentation system, as resources that would have been utilized for delivering the marketing campaign to non- targeted users are now avoided.
The claims provide an inventive concept under MPEP 2106.05(d) through a non- conventional combination of elements, as recited in Claim 1. The "user segmentation system with a processor" operating over a "communication network 302" described as an "optical fiber high bandwidth network" that enables a high data rate with negligible connection drops" ensures low-latency, high-throughput data transfer for "receiving ... a first set of data... in real time,""fetching... a second set of data," and "obtaining... a third set of data... in real-time." This is not a generic component, but a specialized one optimized for high-velocity data streams, unlike standard internet infrastructure, addressing prior art limitations of "batch processing delays".
The step of "creating ... one or more segments ... using a plurality of filters" including regex-based filters (e.g., "time-based,""location-based,""event-based", and the like) processes complex string patterns (e.g., URLs, event logs) with high computational efficiency, enabling precise, real-time segmentation. Regex processing is a specialized technique beyond conventional filtering.
"Generating ... a segment trend plot" in "graphical forms downloadable in one or more formats" (e.g., bar graphs, PNG) and "triggering, in real-time ... one or more marketing campaigns" to "display ... one or more advertisements" involve specific visualization algorithms and synchronized communication protocols, requiring technical coordination across systems.
This ordered combination of “receiving,""fetching,""obtaining,""analyzing,""creating,""identifying,""generating,""triggering," and "displaying" steps, implemented via a specialized "user segmentation system" and "communication devices" over an optical fiber network, is not well-understood, routine, or conventional. It aligns with Bascom Glob. Internet Servs., Inc.
v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016), where a non-conventional arrangement provided an inventive concept. The claims distinguish from Recentive Analytics, Inc. v. Fox Corp., as they specify technical improvements in machine learning and infrastructure, not generic applications…” [Arguments, pages 24-27].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and maintains that the present claims recite a judicial exception without significantly more. In particular, and as stated in response to the above argument, Examiner respectfully maintains that the present invention does not recite an improvement to any particular technology, and instead focuses solely on ineligible marketing activities without significantly more.
With respect to the argument that the claims provide an inventive concept under MPEP 2106.05(d) because each step is performed “at the user segmentation system with the processor” over a "communication network 302" described as an "optical fiber high bandwidth network", Examiner respectfully disagrees and observes that the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
In response to the assertion that the present claims recite an ordered combination of steps similar to those in BASCOM Global Internet Servs. v. AT&T Mobility, Examiner respectfully disagrees. In Bascom, the combination of additional elements, and specifically "the installation of a filtering tool at a specific location, remote from the end‐users, with customizable filtering features specific to each end user" where the filtering tool at the ISP was able to "identify individual accounts that communicate with the ISP server, and to associate a request for Internet content with a specific individual account," were held to be meaningful limitations because they confined the abstract idea of content filtering to a particular, practical application of the abstract idea.
In contrast, the present claims recites steps, devices, and a machine learning model at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these claims do not amount to significantly more than the judicial exception. As such, Examiner remains unpersuaded.
Claim Rejections - 35 USC § 101
Claims 1-9, 11-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-9, 11-18 and 20 are directed to statutory categories, namely a process (claims 1-9), a machine (claims 11-18) and an article of manufacture (claim 20).
Step 2A, Prong 1: Claims 1, 11 and 20 in part, recite the following abstract idea:
…receiving, at… a first set of data associated with a plurality of users, wherein the plurality of users is associated with… wherein the first set of data is received in real time; fetching, at…, a second set of data associated with a plurality of past events of the plurality of users on…; obtaining, at… a third set of data associated with a plurality of live events of the plurality of users on… wherein the third set of data is obtained in real-time; analyzing, at… the first set of data, the second set of data and the third set of data…, wherein the analysis is performed based on… wherein the analysis is performed to identify one or more behavioral patterns from the first set of data, the second set of data, and the third set of data for a past behavior category and a live behavior category, wherein the analysis is performed in real time, wherein the behavioral patterns comprise uniform resource locater visit pattern, webpage visit pattern, number of webpage accessed pattern, application installation pattern, application launch pattern, application uninstallation pattern, accessed content pattern, started content pattern, paused content pattern, resumed content pattern, searched content pattern, notification click pattern, notification views pattern; creating, in real time at… one or more segments of the plurality of users based on the analysis of the first set of data, the second set of data, and the third set of data, the past behavior category, and the live behavior category, wherein the past behavior category includes a plurality of first sub-categories including a past action sub-category, a past inaction sub-category, and a past action with properties sub-category, wherein the past action sub-category includes the plurality of past events, and a plurality of parameters including a day, a time, a language, a location, one or more events, an inactivity, and an online platform, wherein the past inaction sub-category includes a plurality of past inactions that are not performed by the plurality of users, wherein the past action with properties sub-category includes a plurality of properties including user properties, demographic properties, geographic properties, technographic properties, reachability, and application fields, wherein the live behavior category includes a plurality of second sub-categories including a real-time action sub-category, a real- time inaction sub-category, a real-time page visit sub-category, and a real-time page count sub- category, wherein the real-time action sub-category includes the plurality of live events, wherein the real-time inaction sub-category includes a plurality of live inactions corresponding to live events that are not performed by the plurality users within a pre-defined time, and wherein each of the past inaction sub-category, the past action with properties sub-category, the real-time action sub-category, and real-time inaction sub-category includes the plurality of parameters; identifying … one or more patterns of the one or more segments using a plurality of filters, wherein the plurality of filters is based on one or more parameters; generating, at the user segmentation system with the processor, a segment trend plot for each of the one or more segments based on the one or more identified patterns of the one or more segments, wherein the segment trend plot is generated in one or more graphical forms downloadable in one or more formats; triggering, in real time at… initialization of one or more marketing campaigns for a subset or an entirety of users in the one or more segments based on the generated segment trend plot of the one or more segments, wherein the subset or the entirety of users in the one or more segments of the plurality of users meets a criteria for initializing the one or more marketing campaigns, wherein the one or more marketing campaigns comprises one or more advertiser defined parameters, wherein the advertiser defined parameters include one or more of minimum spend, discounts, campaign duration, campaign relevancy, campaign location, a customer's patronage of the online platform, or user interaction; and displaying… one or more advertisements associated with the one or more marketing campaigns for the subset or the entirety of users in the one or more segments of the plurality of users, based on the triggering of the initialization of the one or more marketing campaigns and the one or more patterns, wherein the one or more advertisements are displayed to each user among the subset or the entirety of users in the one or more segments of the plurality of users on their associated … in real-time [Claim 1],
…perform a method for granular level segmentation of users based on activities on online platforms in real-time, the method comprising: receive, at…, a first set of data associated with a plurality of users, wherein the plurality of users is associated with …, wherein the first set of data is received in real time; fetch, at …, a second set of data associated with a plurality of past events of the plurality of users …; obtain, at …, a third set of data associated with a plurality of live events of the plurality of users …, wherein the third set of data is obtained in real-time; analyze, …, the first set of data, the second set of data and the third set of data …, wherein the analysis is performed based on …, wherein the analysis is performed to identify one or more behavioral patterns from the first set of data, the second set of data, and the third set of data for a past behavior category and a live behavior category, wherein the analysis is performed in real time, wherein the behavioral patterns comprise uniform resource locater visit pattern, webpage visit pattern, number of webpage accessed pattern, application installation pattern, application launch pattern, application uninstallation pattern, accessed content pattern, started content pattern, paused content pattern, resumed content pattern, searched content pattern, notification click pattern, notification views pattern; create, in real time at… one or more segments of the plurality of users based on the analysis of the first set of data, the second set of data, and the third set of data, the past behavior category and the live behavior category, wherein the past behavior category includes a plurality of first sub-categories including a past action sub- category, a past inaction sub-category, and a past action with properties sub- category, wherein the past action sub-category includes the plurality of past events, and a plurality of parameters including a day, a time, a language, a location, one or more events, an inactivity, and an online platform, wherein the past inaction sub-category includes a plurality of past inactions that are not performed by the plurality of users, wherein the past action with properties sub- category includes a plurality of properties including user properties, demographic properties, geographic properties, technographic properties, reachability, and application fields, wherein the live behavior category includes a plurality of second sub-categories including a real-time action sub-category, a real-time inaction sub-category, areal-time page visit sub-category, and a real-time page count sub- category, wherein the real-time action sub-category includes the plurality of live events, wherein the real-time inaction sub-category includes a plurality of live inactions corresponding to live events that are not performed by the plurality users within a pre-defined time, and wherein each of the past inaction sub-category, the past action with properties sub-category, the real-time action sub-category, and real-time inaction sub-category includes the plurality of parameters; identify, … one or more patterns of the one or more segments using a plurality of filters, wherein the plurality of filters is based on one or more parameters, wherein the one or more segments are created in real-time; generate, … a segment trend plot for each of the one or more segments based on the one or more identified patterns of the one or more segments, wherein the segment trend plot is generated in one or more graphical forms downloadable in one or more formats; trigger, in real time at… initialization of one or more marketing campaigns for a subset or an entirety of users in the one or more segments based on the generated segment trend plot of the one or more segments, wherein the subset or the entirety of users in the one or more segments of the plurality of users meets a criteria for initializing the one or more marketing campaigns, wherein the one or more marketing campaigns comprises one or more advertiser defined parameters, wherein the advertiser defined parameters include one or more of minimum spend, discounts, campaign duration, campaign relevancy, campaign location, a customer's patronage of the online platform, or user interaction; and displaying … one or more advertisements associated with the one or more marketing campaigns for the subset or the entirety of users in the one or more segments of the plurality of users, based on the triggering of the initialization of the one or more marketing campaigns and the one or more patterns, wherein the one or more advertisements are displayed to each user among the subset or the entirety of users in the one or more segments of the plurality of users on their associated … in real-time. [Claim 11],
…receiving, … a first set of data associated with a plurality of users, wherein the plurality of users is associated with… wherein the first set of data is received in real time; fetching, at… a second set of data associated with a plurality of past events of the plurality of users… ; obtaining, at…, a third set of data associated with a plurality of live events of the plurality of users on…, wherein the third set of data is obtained in real-time; analyzing, … the first set of data, the second set of data and the third set of data… wherein the analysis is performed based on wherein the analysis is performed to identify one or more behavioral patterns from the first set of data, the second set of data, and the third set of data for a past behavior category and a live behavior category, wherein the analysis is performed in real time, wherein the behavioral patterns comprise uniform resource locater visit pattern, webpage visit pattern, number of webpage accessed pattern, application installation pattern, application launch pattern, application uninstallation pattern, accessed content pattern, started content pattern, paused content pattern, resumed content pattern, searched content pattern, notification click pattern, notification views pattern; creating, in real time at… one or more segments of the plurality of users based on the analysis of the first set of data, the second set of data, and the third set of data, the past behavior category, and the live behavior category, wherein the past behavior category includes a plurality of first sub- categories including a past action sub-category, a past inaction sub-category, and a past action with properties sub-category, wherein the past action sub-category includes the plurality of past events, and a plurality of parameters including a day, a time, a language, a location, one or more events, an inactivity, and an online platform, wherein the past inaction sub-category includes a plurality of past inactions that are not performed by the plurality of users, wherein the past action with properties sub- category includes a plurality of properties including user properties, demographic properties, geographic properties, technographic properties, reachability, and application fields, wherein the live behavior category includes a plurality of second sub-categories including a real-time action sub-category, a real-time inaction sub- category, a real-time page visit sub-category, and a real-time page count sub- category, wherein the real-time action sub-category includes the plurality of live events, wherein the real-time inaction sub-category includes a plurality of live inactions corresponding to live events that are not performed by the plurality users within a pre-defined time, and wherein each of the past inaction sub-category, the past action with properties sub-category, the real-time action sub-category, and real-time inaction sub-category includes the plurality of parameters; identifying, … one or more patterns of the one or more segments using a plurality of filters, wherein the plurality of filters is based on one or more parameters; generating … a segment trend plot for each of the one or more segments based on the one or more identified patterns of the one or more segments, wherein the segment trend plot is generated in one or more graphical forms downloadable in one or more formats; triggering, in real time at… initialization of one or more marketing campaigns for a subset or an entirety of users in the one or more segments based on the generated segment trend plot of the one or more segments, wherein the fourth set of data of the subset or the entirety of users in the one or more segments of the plurality of users meets a criteria for initializing the one or more marketing campaigns, wherein the one or more marketing campaigns comprises one or more advertiser defined parameters, wherein the advertiser defined parameters include one or more of minimum spend, discounts, campaign duration, campaign relevancy, campaign location, a customer's patronage of the online platform, or user interaction; and displaying …, one or more advertisements associated with the one or more marketing campaigns for the subset or the entirety of users in the one or more segments of the plurality of users, based on the triggering of the initialization of the one or more marketing campaigns and the one or more patterns, wherein the one or more advertisements are displayed to each user among the subset or the entirety of users in the one or more segments of the plurality of users on their associated … in real-time. [Claim 20].
These concepts are not meaningfully different than the following concepts identified by the MPEP:
Concepts relating to certain methods of organizing human activity. The aforementioned limitations describe steps for commercial or legal interactions, which includes agreements in the form of contracts; legal obligations; advertising, and marketing or sales activities or behaviors; business relations). Specifically, creating market segments and initializing marketing campaigns is considered to describe marketing activities and behaviors. As such, claims 1, 11 and 20 recite concepts identified as abstract ideas.
Dependent claims 2-9 and 12-18 recite limitations relative to the independent claims, including, for example:
…wherein the first set of data corresponds to personal information of the plurality of users, wherein the first set of data comprises name data, age data, e-mail identity data, contact number data, gender data, geographic location data, angiographic data, demographic data, payment cards data, banking partners data, and relationship status data, wherein the first set of data is received from one or more online platform database, one or more communication device database, and third party database [Claim 2],
… wherein the second set of data corresponds the plurality of past events of the plurality of users, wherein the plurality of past events comprises past uniform resource locater visits, past number of visits, past number of pages accessed, past webpage visited, past application installed, past number of times application installed, past application launched, past number of times application launched, past application uninstalled, past accessed content, past started content, past paused content, past resumed content, past searched content, past notification clicks, past notification views, past products surfed, past products added to cart, past reviews for products, past favorite product category, past inactivity for products, past accounts opened, past credit card requests, past credit cards issued, past loan requests, past net- banking requests, past multimedia content surfed, past multimedia content watched, past texts exchanged, past business blogs, past live media streamed, past audio-video callings, past medicines searched, past medicines bought, past medical test kit bought, past medical tests scheduled, past bill payments, past doctor consultation scheduled, past hospital visit planned… [Claim 3],
…wherein the third set of data corresponds the plurality of live events of the plurality of users, wherein the plurality of live events comprises real-time uniform resource locater visits, real-time number of webpage visits, real-time number of webpages accessed, real-time webpage visit, real- time application installed, real-time application launch, real-time application uninstalled, real- time accessed content, real-time started content, real-time paused content, real-time resumed content, real-time searched content, real-time notification clicks, real-time notification views, real-time products surfed, real-time products added to cart, real-time reviews for products, real-time favorite product category, real-time inactivity for products, real- time accounts opened, real-time credit card requests, real-time credit cards issued, real-time loan requests, real-time net-banking requests, real-time multimedia content surfed, real-time multimedia content watched, real-time texts exchanged, real-time business blogs, real-time live media streamed, real-time audio-video callings, real-time medicines searched, real-time medicines bought, real-time medical test kit bought, real-time medical tests scheduled, real- time bill payments, real-time doctor consultation scheduled, real-time hospital visit planned, real-time dietary plan requested, real-time personal trainer hired, real-time fitness center searched, real-time educational video searched, real-time educational video watched, real- time projects submitted, real-time mock tests subscribed, real-time educational counselling requested, real-time problem solving session requested, real-time international masters interests, real-time properties searched, real-time properties watched, real-time properties… [Claim 4],
The limitations of these dependent claims are merely narrowing the abstract idea identified in the independent claims, and thus, the dependent claims also recite abstract ideas.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims 1, 11 and 20 only recite the following additional elements –
…a user segmentation system with a processor… one or more communication devices…; … the user segmentation system with the processor… one or more online platforms through the one or more communication devices; …the user segmentation system with the processor…; …the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; …the one or more online platforms through the one or more communication devices… the user segmentation system with the processor; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … the user segmentation system with the processor…; …the user segmentation system with the processor…; …at the user segmentation system with the processor… one or more communication devices… [Claim 1],
…one or more processors; and a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to…; …a user segmentation system with a processor… one or more communication devices…; … the user segmentation system with the processor… one or more online platforms through the one or more communication devices; …the user segmentation system with the processor…; the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; …the one or more online platforms through the one or more communication devices… the user segmentation system with the processor; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … the user segmentation system with the processor…; …the user segmentation system with the processor…; …at the user segmentation system with the processor… one or more communication devices… [Claim 11],
…at a computing device… one or more communication devices…; …the computing device… on one or more online platforms through the one or more communication devices…; the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; … the computing device… the one or more online platforms through the one or more communication devices…; … at the computing device… ; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … at the computing device… at the computing device…; …at the user segmentation system with the processor… one or more communication devices… [Claim 20].
The system, devices, machine learning model and executable instructions are recited at a high-level of generality (see MPEP § 2106.05(a)), like the following MPEP example:
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48
Furthermore, the computer implemented element is considered to amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)), like the following MPEP example:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Accordingly, these additional elements do not integrate the abstract idea into a practical application.
The remaining dependent claims do not recite any new additional elements, and thus do not integrate the abstract idea into a practical application.
Step 2B: Claims 1, 11 and 20 and their underlying limitations, steps, features and terms, considered both individually and as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reasons:
Independent claims 1, 11 and 20 only recite the following additional elements -
…a user segmentation system with a processor… one or more communication devices…; … the user segmentation system with the processor… one or more online platforms through the one or more communication devices; …the user segmentation system with the processor…; …the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; …the one or more online platforms through the one or more communication devices… the user segmentation system with the processor; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … the user segmentation system with the processor…; …the user segmentation system with the processor…; …at the user segmentation system with the processor… one or more communication devices… [Claim 1],
…one or more processors; and a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to…; …a user segmentation system with a processor… one or more communication devices…; … the user segmentation system with the processor… one or more online platforms through the one or more communication devices; …the user segmentation system with the processor…; the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; …the one or more online platforms through the one or more communication devices… the user segmentation system with the processor; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … the user segmentation system with the processor…; …the user segmentation system with the processor…; …at the user segmentation system with the processor… one or more communication devices… [Claim 11],
…at a computing device… one or more communication devices…; …the computing device… on one or more online platforms through the one or more communication devices…; the user segmentation system with the processor…; …an operating system of the one or more communication devices…; …the one or more communication devices…; … the computing device… the one or more online platforms through the one or more communication devices…; … at the computing device… ; … using one or more machine learning algorithms… training of a machine learning model using a supervised machine learning model or an unsupervised machine learning model…; … at the computing device… at the computing device…; …at the user segmentation system with the processor… one or more communication devices… [Claim 20].
These elements do not amount to significantly more than the abstract idea for the reasons discussed in 2A prong 2 with regard to MPEP 2106.05(a) and MPEP 2106.05(f). By the failure of the elements to integrate the abstract idea into a practical application there, the additional elements likewise fail to amount to an inventive concept that is significantly more than an abstract idea here, in Step 2B.
As such, both individually or in combination, these limitations do not add significantly more to the judicial exception.
The remaining dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims do not recite any new additional elements other than those mentioned in the independent claims, which amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). As such, these claims are not patent eligible.
Prior Art Considerations
Examiner conducted a thorough search of the body of available prior art (see attached documents regards PTO-892 Notice of Reference Cited and PE2E Search History). Notably, Examiner discovered several patent literature documents that taught aspects of the invention, but no single disclosure taught “every element required by the claims under its broadest reasonable interpretation” [MPEP § 2131] to make a 35 USC § 102 rejection. Further, Examiner considered the individual elements of the recited claims taught across the prior art cited below, but did not find it obvious to combine such disclosures [MPEP § 2142] to make a 35 USC § 103 rejection.
In particular, Ferguson et al., U.S. Publication No. 2003/0033194 [hereinafter Ferguson] discloses, a system and method for on-line training of a non-linear model for use in electronic commerce, wherein “Predictive models may be used for analysis, control, and decision making in many areas, including electronic commerce (i.e., e-commerce), e-marketplaces, financial (e.g., stocks and/or bonds) markets and systems, data analysis, data mining, process measurement, optimization (e.g., optimized decision making, real-time optimization), quality control, as well as any other field or domain where predictive or classification models may be useful and where the object being modeled may be expressed abstractly” [Ferguson, ¶ 7]. While Ferguson discloses aspects of the present invention including the personal information and database elements, Ferguson is silent with respect to the particular behavior category and sub-category elements listed in the amended claims.
Further, Zhao, U.S. Publication No. 2021/0081759 [hereinafter Zhao] discloses deep neural network based user segmentation, wherein “the method includes receiving user datasets from a database along with respective user identifiers, retention labels, static user features and interactive user features associated with an online product during a time period” [Zhao, Abstract]. While Zhao discloses aspects of the present invention including user classification data, Zhao does not disclose the particular behavior category and sub-category elements listed in the amended claims.
Further still, Bruckhaus et al., U.S. Patent No. 8,417,715 [hereinafter Bruckhaus] discloses platform independent plug-in methods and systems for data mining and analytics wherein which “comprises extracting and converting data using a data management component into a form usable by a data mining component, performing data mining to develop a model in response to a question or problem posed by a user” [Bruckhaus, Abstract]. While Bruckhaus discloses aspects of the present invention including various types of online platforms for obtaining user data, Bruckhaus is silent with respect to the particular behavior category and sub-category elements recited in the amended claims.
For the above reasons, Examiner determined the currently pending claims novel and non-obvious given the current search. Amendment to the claims and further search in reaction to such amendment may yield the claims anticipated or obvious in future prosecution, determined at that time.
Allowable Subject Matter
Claims 1-9, 11-18 and 20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jain et al., U.S. Publication No. 2021/0097384, discloses deep segment personalization.
Chang et al., U.S. Publication No. 2012/0226697, discloses a scalable engine that computes user micro-segments for offer matching.
Cavalin et al., U.S. Publication No. 2017/0177722, discloses segmenting social media users by means of life event detection and entity matching.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624