DETAILED ACTION
Status of Claims
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-Final office action in response to communication received on June 12, 2026. Claims 1-8, 10-18, and 20 are pending and examined herein.
Claim Rejections - 35 USC § 101
2. 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 1-8, 10-18, 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. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-8 and 10-11 are a method; claims 12-18 are a system; and claim 20 is a non-transitory computer readable medium. Thus, each claim 1-8, 10-18, and 20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-8, 10-18, and 20 [i.e. recitation with the exception of additional elements as noted and analyzed further under step 2A prong two and additional elements analyzed under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below in prong two) to be the abstract recitation] (II) is that of setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels and evaluating campaign performance to make adjustment to in-flight campaigns which is certain methods of organizing human activity.
The phrase "Certain methods of organizing human activity" applies to 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 social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract recitation, i.e. additional elements, as recited in claims 1-8, 10-18, and 20 at least are one or more interfaces, modules, database table, a platform, automatically executing and transmitting, and API (per claim 1), an administrative user interface displaying (claims 4-5), a computing system, interface, a platform, API, a second computing system executable by a processor, and automatically executing (claim 12), trained neural network(claim 6), transmitting (claim 7), neural network (claim 17), and computer-readable medium storing computer-executable instructions which, when executed by a processor of a computing system, cause the computing system to perform, interface, a platform, automatically executing, and API managed by the platform (claim 20). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1, 12, and their associated disclosure. Further, it appears the abstract idea is generally linked to a technical environment merely sending/receiving/publishing data over a network, note 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). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized marketing content such as ads, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)). Thus, the process is similar to collecting information, analyzing it (using models), and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels and evaluating campaign performance to make adjustment to in-flight campaigns is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-8, 10-18, and 20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels and evaluating campaign performance to make adjustment to in-flight campaigns has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant extra solution activity such as pre-solution activity e.g. data gathering or post solution activity e.g. displaying on interface, the Examiner relies on as-filed disclosure, court cases, publication(s), and/or official notice below to demonstrate that such a way to gather data and/or display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(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) [similarly here at least campaign definition data is received; and publishing]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)."; Pub. No.: US2017/0372352 note [0097] “As mentioned above, in one or more embodiments the real-time experimentation system 100 executes a placebo experiment by creating an ad placement for a media lift experiment, executing the media lift experiment, and tracking and rolling up the results of the media lift experiments in a statistics platform to make the results available in a user interface. More particularly, the real-time experimentation system 100 can define experiment parameters, including control frequency, test groups, and control groups. The real-time experimentation system 100 can utilize a user interface and API functionality to make results available for both test and control group statistics. Moreover, the real-time experimentation system 100 can suggest revisions to campaign parameters based on the results of the media lift experiments.”; [0114] “As mentioned above, in one or more embodiments, the real-time experimentation system 100 also monitors performance of test ads and control ads and generates a user interface portraying a measure of media lift. To illustrate, the real-time experimentation system 100 can build a reporting API (i.e., a set of subroutine definitions, protocols and tools for building software and applications), to make test and control stats available to a user interface while adding performance statistics (i.e. measures of media lift) to the reporting user interface. In addition, the real-time experimentation system 100 can generate the user interface dashboard such that a user can control a number of aspects of an online ad campaign and media lift experiment, including how ads are chosen within an experiment to be test ads or control ads.”; Pub. No.: US2019/0130436 note [0002] “Although conventional content distribution systems allow individuals and businesses to disseminate digital content through computer networks, they also have a number of drawbacks. For example, conventional content distribution systems are generally rigid, and provide little flexibility during execution of a digital content campaign to respond to changing conditions. To illustrate, conventional content distribution systems often require publishers to create custom pipelines that periodically query conventional content distribution systems (e.g. via an API) regarding performance of a digital content distribution campaign. Then, based on information regarding the performance of the digital content distribution campaign, conventional content distribution systems often require merchants to manually adjust various campaign parameters to effectuate a change to the content distribution campaign.”; [0023] “One or more embodiments of the present invention include a digital campaign modification system that dynamically adjusts content distribution campaigns in real-time based on custom rules created from selectable triggering conditions and corresponding actions. In particular, in one or more embodiments, the digital campaign modification system provides a user interface with selectable options that allow digital publishers to customize and automate actions and notifications based on the performance of content distribution campaigns in real-time. For instance, in one or more embodiments, the digital campaign modification system generates suggested triggering conditions and corresponding actions utilizing a machine learning model trained based on historical content distribution campaigns. Accordingly, the digital campaign modification system provides publishers with the option to anticipate and automate actions during execution of a content distribution campaign without the need for constant manual monitoring and delayed responsive action.”; [0130] “As shown in FIG. 9, the digital campaign modification system 104 may include a user interface manager 904, a user input detector 906, an auction manager 908, a custom rule generator 910, a content distribution campaign generator 912, an activity monitor 914, a content distribution campaign modifier 916, a machine learning manager 918, a storage manager 920 (including a machine learning model 922 and custom rules 924), and a social graph 926 (including node information 928 and edge information 930).”; [0131] “As just mentioned, the digital campaign modification system 104 can include a user interface manager 904. The user interface manager 904 can provide, manage, and/or control a graphical user interface (or simply “user interface”) for use with the digital campaign modification system 104 (e.g., the user interfaces 304, 314, 320, and 500). In particular, the user interface manager 702 may facilitate presentation of information by way of an external component of a client device. For example, the user interface manager 904 may display a user interface by way of a display screen associated with the client device.”; [0132] “The user interface may be composed of a plurality of graphical components, objects, and/or elements that allow a user to perform a function (e.g., a color modifier element, a message drafting element, or a message thread area as described above). The user interface manager 904 can present, via the client device, a variety of types of information, including text, images, video, audio, characters, standardized image characters, customized image characters, or other information. Moreover, the user interface manager 904 can provide a variety of user interfaces specific to any variety of functions, programs, applications, plug-ins, devices, operating systems, and/or components of the client device. Additional details with respect to various example user interface elements are described throughout with regard to various embodiments containing user interfaces.”; [0189] “In particular embodiments, networking system 1202 may be capable of linking a variety of entities. As an example and not by way of limitation, networking system 1202 may enable users to interact with each other as well as receive content from third-party systems 1208 or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels.” [similarly here interfaces and APIs are utilized to receive and transmit adjustments].
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) For Withdrawal Of Prior Art Based Rejection
3. Claims 1-3, 5-6, 9-10, 12, 18-20 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Thimmaiah et al. (Pub. No.: US 2020/0160373) referred to hereinafter as Thimmaiah, Boyd et al. (Pub. No.: US 2002/0123930) referred to hereinafter as Boyd, in view of Maga et al. (Patent No.: US 8,762,193) referred to hereinafter as Maga, and in view of Chaar et al. (Pub. No.: US 2023/0267507) referred to hereinafter as Chaar.
Claims 4, 7, and 13 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Thimmaiah, Boyd, in view of Maga, Chaar, and in view of Switzer (Pub. No.: US2012/0066065).
Claims 8 and 14-15 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Thimmaiah, in view of Boyd, Maga, Chaar, and in view of Megahed et al. (Pub. No.: US 2018/0005253) referred to hereinafter as Megahed.
Claims 16-17 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over Thimmaiah, Boyd, in view of Maga, Chaar, in view of Megahed, and in view of Liu et al. (Pub. No.: US 2011/0238486) referred to hereinafter as Liu.
Claims 11 was previously rejected under 35 U.S.C. 103(a) as being unpatentable over Thimmaiah, in view of Boyd, Maga, Chaar, and in view of Liu et al. (Pub. No.: US 2011/0238486) referred to hereinafter as Liu.
Furthermore, upon conducting an updated search the Examiner discovered the following which are relevant to the inventive concept, note as follows:
*Being noted initially
- US 20170372352 A1
[0037] Furthermore, the real-time experimentation system can suggest (or automatically implement) modifications to campaign parameters to improve the performance of an online ad campaign in a real-time bidding environment. In addition, the real-time experimentation system can allow a user to control various features of the media lift experiment including advertisements, a control frequency threshold for control groups and test groups, campaign parameters, or attribution methodology. Indeed, based on the results of the real-time experimentation, the real-time experimentation system can modify a campaign in real time, thereby increasing effectiveness and reducing wasted resources.
[0097] As mentioned above, in one or more embodiments the real-time experimentation system 100 executes a placebo experiment by creating an ad placement for a media lift experiment, executing the media lift experiment, and tracking and rolling up the results of the media lift experiments in a statistics platform to make the results available in a user interface. More particularly, the real-time experimentation system 100 can define experiment parameters, including control frequency, test groups, and control groups. The real-time experimentation system 100 can utilize a user interface and API functionality to make results available for both test and control group statistics. Moreover, the real-time experimentation system 100 can suggest revisions to campaign parameters based on the results of the media lift experiments.
[0114] As mentioned above, in one or more embodiments, the real-time experimentation system 100 also monitors performance of test ads and control ads and generates a user interface portraying a measure of media lift. To illustrate, the real-time experimentation system 100 can build a reporting API (i.e., a set of subroutine definitions, protocols and tools for building software and applications), to make test and control stats available to a user interface while adding performance statistics (i.e. measures of media lift) to the reporting user interface. In addition, the real-time experimentation system 100 can generate the user interface dashboard such that a user can control a number of aspects of an online ad campaign and media lift experiment, including how ads are chosen within an experiment to be test ads or control ads.
[0141] As mentioned above, in one or more embodiments, the real-time experimentation system 100 automatically implements the suggested modifications 1006a-1006e. In particular, rather than providing the suggested modifications 1006a-1006e via the user interface 1004, the real-time experimentation system 100 can automatically implement the modifications and adjust the online ad campaign. Thus, the real-time experimentation system 100 can improve the performance of an online ad campaign based on the results of a media lift experiment in real time.
[0142] The real-time experimentation system 100 can conduct media lift experiments and modify online ad campaigns in real-time. In particular, the real-time experimentation system 100 can operate an online ad campaign while performing and receiving results of a media lift experiment. Moreover, the real-time experimentation system 100 can modify an online ad campaign (while it is running) based on the results of a media lift to improve performance of the online ad campaign while the online ad campaign is ongoing. The real-time experimentation system 100 can then continue running a media lift experiment as the modified online ad campaign is running. Accordingly, in one or more embodiments, the real-time experimentation system 100 can constantly monitor and improve online ad campaigns.
[0143] Turning now to FIG. 11, additional detail is provided regarding components and capabilities of one or more embodiment of the real-time experimentation system 100. As shown, the real-time experimentation system 100 may include, but is not limited to a user interface manager 1102, a user input detector 1104, a media property manager 1106, a bid manager 1108, an experiment group manager 1110, an experiment ad engine 1112, an attribution facility 1114, a media lift engine 1116, a campaign manager 1118, and a storage manager 1120 (including test ad(s) 1120a, control ad(s) 1120b, group data 1120c, and experimental results 1120d).
[0144] As just mentioned, and as illustrated in FIG. 11, the real-time experimentation system 100 may include the user interface manager 1102. The user interface manager 1102 can provide, manage, and/or control a graphical user interface (or simply “user interface”) for use with the real-time experimentation system 100. In particular, the user interface manager 1102 may facilitate presentation of information by way of an external component of one or more client devices. For example, the user interface manager 1102 may display a user interface by way of a display screen associated with one or more client devices. The user interface may be composed of a plurality of graphical components, objects, and/or elements that allow a user to perform a function. The user interface manager 1102 can present a variety of types of information, including text, images, video, audio, characters, or other information. Moreover, the user interface manager 1102 can provide a variety of user interfaces specific to any variety of functions, programs, applications, plug-ins, devices, operating systems, and/or components of computing devices (e.g., the user interfaces 706, 806, 1004).
[0145] In addition to the user interface manager 1102, as shown in FIG. 11, the real-time experimentation system 100 also includes the user input detector 1104. The user input detector 1104 can detect, identify, monitor, receive, process, capture, and/or record various types of user input. For example, the user input detector 1104 may be configured to detect one or more user interactions with respect to a user interface. As referred to herein, a “user interaction” refers to conduct performed by a user (or a lack of conduct performed by a user) to control the function of a computing device. “User input,” as used herein, refers to input data generated in response to a user interaction.
- US2019/0130436
[0002] Although conventional content distribution systems allow individuals and businesses to disseminate digital content through computer networks, they also have a number of drawbacks. For example, conventional content distribution systems are generally rigid, and provide little flexibility during execution of a digital content campaign to respond to changing conditions. To illustrate, conventional content distribution systems often require publishers to create custom pipelines that periodically query conventional content distribution systems (e.g. via an API) regarding performance of a digital content distribution campaign. Then, based on information regarding the performance of the digital content distribution campaign, conventional content distribution systems often require merchants to manually adjust various campaign parameters to effectuate a change to the content distribution campaign.
[0023] One or more embodiments of the present invention include a digital campaign modification system that dynamically adjusts content distribution campaigns in real-time based on custom rules created from selectable triggering conditions and corresponding actions. In particular, in one or more embodiments, the digital campaign modification system provides a user interface with selectable options that allow digital publishers to customize and automate actions and notifications based on the performance of content distribution campaigns in real-time. For instance, in one or more embodiments, the digital campaign modification system generates suggested triggering conditions and corresponding actions utilizing a machine learning model trained based on historical content distribution campaigns. Accordingly, the digital campaign modification system provides publishers with the option to anticipate and automate actions during execution of a content distribution campaign without the need for constant manual monitoring and delayed responsive action.
[0130] As shown in FIG. 9, the digital campaign modification system 104 may include a user interface manager 904, a user input detector 906, an auction manager 908, a custom rule generator 910, a content distribution campaign generator 912, an activity monitor 914, a content distribution campaign modifier 916, a machine learning manager 918, a storage manager 920 (including a machine learning model 922 and custom rules 924), and a social graph 926 (including node information 928 and edge information 930).
[0131] As just mentioned, the digital campaign modification system 104 can include a user interface manager 904. The user interface manager 904 can provide, manage, and/or control a graphical user interface (or simply “user interface”) for use with the digital campaign modification system 104 (e.g., the user interfaces 304, 314, 320, and 500). In particular, the user interface manager 702 may facilitate presentation of information by way of an external component of a client device. For example, the user interface manager 904 may display a user interface by way of a display screen associated with the client device.
[0132] The user interface may be composed of a plurality of graphical components, objects, and/or elements that allow a user to perform a function (e.g., a color modifier element, a message drafting element, or a message thread area as described above). The user interface manager 904 can present, via the client device, a variety of types of information, including text, images, video, audio, characters, standardized image characters, customized image characters, or other information. Moreover, the user interface manager 904 can provide a variety of user interfaces specific to any variety of functions, programs, applications, plug-ins, devices, operating systems, and/or components of the client device. Additional details with respect to various example user interface elements are described throughout with regard to various embodiments containing user interfaces.
[0189] In particular embodiments, networking system 1202 may be capable of linking a variety of entities. As an example and not by way of limitation, networking system 1202 may enable users to interact with each other as well as receive content from third-party systems 1208 or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels.
[0190] In particular embodiments, a third-party system 1208 may include one or more types of servers, one or more data stores, one or more interfaces, including but not limited to APIs, one or more web services, one or more content sources, one or more networks, or any other suitable components, e.g., that servers may communicate with. A third-party system 1208 may be operated by a different entity from an entity operating networking system 1202. In particular embodiments, however, networking system 1202 and third-party systems 1208 may operate in conjunction with each other to provide social-networking services to users of networking system 1202 or third-party systems 1208. In this sense, networking system 1202 may provide a platform, or backbone, which other systems, such as third-party systems 1208, may use to provide social-networking services and functionality to users across the Internet.
[0193] In particular embodiments, networking system 1202 may include a variety of servers, sub-systems, programs, modules, logs, and data stores. In particular embodiments, networking system 1202 may include one or more of the following: a web server, action logger, API-request server, relevance-and-ranking engine, content-object classifier, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, advertisement-targeting module, user-interface module, user-profile store, connection store, third-party content store, or location store. Networking system 1202 may also include suitable components such as network interfaces, security mechanisms, load balancers, failover servers, management-and-network-operations consoles, other suitable components, or any suitable combination thereof. In particular embodiments, networking system 1202 may include one or more user-profile stores for storing user profiles. A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. Categories may be general or specific. As an example and not by way of limitation, if a user “likes” an article about a brand of shoes the category may be the brand, or the general category of “shoes” or “clothing.” A connection store may be used for storing connection information about users. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, educational history, or are in any way related or share common attributes. The connection information may also include user-defined connections between different users and content (both internal and external). A web server may be used for linking networking system 1202 to one or more client devices 1206 or one or more third-party system 1208 via network 1204. The web server may include a mail server or other messaging functionality for receiving and routing messages between networking system 1202 and one or more client devices 1206. An API-request server may allow a third-party system 1208 to access information from networking system 1202 by calling one or more APIs. An action logger may be used to receive communications from a web server about a user's actions on or off networking system 1202. In conjunction with the action log, a third-party-content-object log may be maintained of user exposures to third-party-content objects. A notification controller may provide information regarding content objects to a client device 1206. Information may be pushed to a client device 1206 as notifications, or information may be pulled from client device 1206 responsive to a request received from client device 1206. Authorization servers may be used to enforce one or more privacy settings of the users of networking system 1202. A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by networking system 1202 or shared with other systems (e.g., third-party system 1208), such as, for example, by setting appropriate privacy settings. Third-party-content-object stores may be used to store content objects received from third parties, such as a third-party system 1208. Location stores may be used for storing location information received from client devices 1206 associated with users. Advertisement-pricing modules may combine social information, the current time, location information, or other suitable information to provide relevant advertisements, in the form of notifications, to a user.
[0213] In particular embodiments, networking system 1202 may calculate a coefficient in response to a request for a coefficient from a particular system or process. To predict the likely actions a user may take (or may be the subject of) in a given situation, any process may request a calculated coefficient for a user. The request may also include a set of weights to use for various factors used to calculate the coefficient. This request may come from a process running on the online social network, from a third-party system 1208 (e.g., via an API or other communication channel), or from another suitable system. In response to the request, networking system 1202 may calculate the coefficient (or access the coefficient information if it has previously been calculated and stored). In particular embodiments, networking system 1202 may measure an affinity with respect to a particular process. Different processes (both internal and external to the online social network) may request a coefficient for a particular object or set of objects. Networking system 1202 may provide a measure of affinity that is relevant to the particular process that requested the measure of affinity. In this way, each process receives a measure of affinity that is tailored for the different context in which the process will use the measure of affinity.
*Previously noted
- US12014389 Systems and methods for collaborative offer generation
see Abstract note "a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment."; "offer system itself 960 includes an audience definition engine 963 that determines the consumers at play for a given retailer using loyalty card information, credit card data, pharmacy identifiers, and third party data (social media data, public records, etc.). For the defined consumer audience, the ROI for a given offer structure is determined using an ROI analyzer 965. The ROI analyzer utilized transaction logs and data known for each of the consumers, along with the shopper weights 983 from the retailers, to calculate a “value” for each consumer. The consumers are then aggregated into buckets of a defined size by this value. Thus, for any given promotion with a particular number of offers being issued, the total value of the offer campaign may be determined based upon the value of the consumers that receive the offer."
- US20150220999 see Abstract " targeting users with a reward, offer, or incentive may include selecting at least one reward, offer, or incentive to present to a user by applying at least one rule, restriction, or filter dictated by a merchant to the set to be provided to the user, applying at least one rule, restriction, or filter dictated by a financial institution to the set, and applying a filter to the set to obtain those rewards, offers, or incentives with the highest likelihood of being accepted by the user. At least one parameter of the at least one reward, offer, or incentive is adjusted prior to presentation to the user based on a spending trajectory, user propensity model, user profile information or segmentation criteria, or campaign goal"; [0384] " method of targeting users with a reward, offer, or incentive 8300 may include selecting at least one reward, offer, or incentive to present to a user or a segment of users 8302 and adjusting at least one of a minimum spending threshold, a discount amount, a duration of the campaign, and a category of the at least one reward, offer, or incentive prior to presentation to the user or segment of users 8304. Selecting may be done by: applying at least one rule, restriction, or filter dictated by a merchant to a set of rewards, offers, or incentives to be provided to the user or the segment of users 8308, applying at least one rule, restriction, or filter dictated by a financial institution to the set of rewards, offers, or incentives 8310, and applying a filter to the set of rewards, offers, or incentives to obtain those rewards, offers, or incentives with the highest likelihood of being accepted by the user or segment of users 8312. The filter to obtain rewards, offers, or incentives with the highest likelihood of being accepted may be based on a predictive model of user purchase behavior developed using at least one of: data on one or more past user responses to one or more savings opportunities, public or inferred data relevant to the user, preferences (including merchant category preferences, transaction category preferences, product category preferences, and merchant preferences), a geographic location, a seasonal variety, a spending level, and any recent changes from historic spending patterns. Adjusting may include calculating a spending trajectory based on a historical spending pattern and adjusting the at least one minimum spend threshold, discount amount, duration of the campaign, and category of the at least one reward, offer, or incentive in accordance with the spend trajectory. Adjusting may be done in accordance with at least one segmentation criterion applied to the user. Adjusting may be completed in a time span selected from the group consisting of: less than about 10 sec, less than about 5 sec, less than about 1 second, or substantially instantaneously. Adjusting may be done in accordance with at least one input selected from the group consisting of: a spend trajectory, a user propensity model, user profile information or segmentation criteria, and a campaign goal. A rule may be applied to determine which of the inputs is used in adjusting. A weight may be applied to each of the selected inputs in adjusting."
- WO2002015454 see Abstract " fully automatically producing optimized personalized sets of offers according to a target function, using collected customer and product or service data. First, in the data analysis stage (106), the possible offers are graded separately for each customer, taking into consideration the predicted contribution (138) of the offer to the campaign's target function (142). Second, in the choosing of personalized offers stage (108), the system chooses for each customer a set of offers that were ranked by the highest grades and which deal with all the posed constraints. This enables automatic production of optimized personalized sets of offers for customers, without the need for statisticians or database experts."; "The offer set could include regular coupons (discounts on the purchase of a product), coupons with redeeming conditions (e.g., buy two packages and get one free), advertisement offers, samples, gifts, and/or package deals."; "A product manufacturer may wish to increase the penetration rate, i.e., to maximize the number of new customers. Another manufacturer might not want to waste discounts on customers that might purchase his product without the offer. In target function terminology, the target function 142 in the first case will be to maximize the total use of offers, or to increase the number of purchases that follow a free sample. In the second case , the target function 142 will be to increase the overall purchase basket."; "The data content could be history of time spent at a certain Web site, the areas of interest shown by the customer, history of purchases at the level of products purchased by each customer, history of purchases regarding customer reaction to past offers, current time, and/or customer's current location."
- US20140180790 see "[0060] According to an embodiment, a method comprises: identifying a set of at least two offers responsive to a request; calculating universal scores for each offer in the set of offers; for each offer in the set of offers, calculating a function of: a first ratio of total activations for the offer to average total activations for all offers in a plurality of offers, a second ratio of total impressions for the offer to average total impressions for all offers in the plurality of offers, and a third ratio of total redemptions for the offer to average total redemptions for all offers in the plurality of offers; ranking the set of offers based at least in part on the universal scores; providing information about at least a subset of the ranked set of offers in response to the request."; "[0061] In an embodiment, calculating a universal score comprises comparing historical performance of the offer to average offer performance for multiple offers in a group of offers. In an embodiment, calculating the universal score for comprises calculating a function of at least activations, redemptions, and impressions for the offer. In an embodiment, calculating the universal score comprises calculating a function of at least offer inventory and offer end date. In an embodiment, calculating the universal scores comprises calculating predetermined universal scores based on a set of signals in advance of receiving the request. In an embodiment, calculating the universal scores comprises calculating delta changes to the predetermined universal scores based on changes in the signals between the time that the predetermined universal scores were originally calculated and the time that the request was received. In an embodiment, calculating the universal score for a particular item is based on a manually specified sponsorship score for the particular item."; "[0163] In one embodiment, an offer distributor may analyze various contextual transaction data, historical transaction records and/or offer data associated with two or more offers, and may decide which of those offers to distribute. In one embodiment, an offer distributor receives a set of offer data associated with each offer, such as, for example, an offer provider identity, a historical or projected impact of the offer on sales of an item, a historical or projected offer redemption rate, a historical or projected profit per offer impression, a historical or projected profit margin, a historical or projected offer volume (e.g., the total number of offer activations, offer impressions, offer redemptions and/or products sold), a historical or projected offer yield (e.g., a total profit figure associated with an offer after taking into account the total number of sold products corresponding to that offer, offer activations, offer impressions and/or offer redemptions), a historical trend in offers made available by the offer provider, a historical or projected profit margin for the offer distributor and/or for the offer provider, a historical or projected impact of the offer on other offers, a historical or projected impact of the offer on consumer behavior, etc."; "[0224] In an embodiment, in response to certain triggers like receipts, and so forth, certain offers are automatically activated for a consumer. Automatically activated offers are offers that become activated for a consumer without the consumer explicitly requesting activation of the offer. Various auto-activation rules may be defined on a global scale, and/or auto-activation may be an offer parameter definable by an offer provider. Such auto-activation rules may be associated with, among other contexts, a consumer's device, the device's location, a specific offer, items in a consumer's basket, and so forth. In an embodiment, all targeted offers are auto-activated. In an embodiment, the highest n recommended offers are pre-activated."; "[0375] Block 2580 comprises ranking the identified offers based at least in part on the universal scores and the association scores. In other words, in response to any given request for a recommendation, the offers are ranked based not only on a measure of how much revenue the recommendation is predicted to bring to a particular party, but also a measure of how much of an association there is between the request and the offers. For example, a universal ranking mechanism may comprise sorting each offer by the product of the offer's universal score and the association score, or average association score, of the item(s) based upon which the offer was identified in block 2560. Other universal ranking mechanisms may involve other functions of the universal scores and association scores."
However, the above noted references and the ones the Examiner has noted throughout the prosecution fail to teach the claim as amended on December 22, 2025 when the claim is considered as a whole. As such, a prima facie case of obviousness could no longer be established.
Therefore prior art based rejection is hereby withdrawn.
Response to Applicant’s Remarks
4. Regarding 35 U.S.C. 101, the Examiner respectfully finds the Applicants’ argument unpersuasive.
The Applicant argues “The Office Action alleges that the claims are directed to the abstract idea of organizing human activity, specifically "setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels." See Office Action at pg. 3. Applicant respectfully disagrees. In the present response, claims 1, 12, and 20 have been amended to recite a specific technical mechanism for real-time, closed-loop monitoring and in-flight modification of a live campaign capabilities that go well beyond merely "setting up" a campaign, and that represent an improvement to the campaign distribution platform itself” in view of the filed claim amendments. The Applicant is reminded that the claims must be given their broadest reasonable interpretation in light of the as-filed specification. The rejection has been updated in view of the filed, entered claim amendments. The evaluation claim recitation under prong one is based on the abstract recitation. Thus, the claims, based on abstract recitation, squarely recite certain methods of organizing human activity.
Next, the Applicant particularly argues under prong two, note “Under Step 2A Prong Two, Examiners are to evaluate whether additional elements integrate a judicial exception into a practical application, including whether those elements reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. MPEP 2106.04(d)(J). The newly added limitations collectively achieve exactly this. As described in the specification, the in-flight intervention subsystem is a dedicated technical component of the campaign distribution platform that enables an administrative user to observe live campaign performance data and to transmit adjustments specifically, modifications to the customer-offer group mapping or to offer depths-back through the campaign API to modify ongoing campaign message delivery. See “Specification at [0040]-[004 l ], FIG. 3. This is not a generic display of results, nor is it a routine sending and receiving of data over a network.
In the amended claims, information about a campaign is not merely displayed as a post-solution activity. The three newly added limitations function together as a specific technical feedback loop. As recited in the amended claim 1, the administrative user interface presents observed performance metrics-including not just redemption results but also factors correlated with those results-during the live execution of the campaign; the system receives, at that same interface, in-campaign adjustments that are technically defined as either (a) adding or removing specific customer-offer groups from the mapped list or (b) modifying the offer depth associated with specific customer-offer groups; and the in-flight intervention subsystem automatically transmits those adjustments via the campaign API to modify actual campaign message delivery in real time. The specification further describes this as a system advantage that conventional approaches lack-the ability to monitor and modify a live, distributed campaign in response to actual observed performance. See Specification at [0040]-[0041], [0084]. Accordingly, the claim does not simply output information to a human for their use; it recites an automated technical pathway by which the adjustments received at the administrative interface are transmitted to and applied by the campaign API to modify the ongoing delivery of campaign messages. This constitutes the use of a specific technical architecture integral to the claimed system, not a generic linking of the abstract idea to a computer environment.”
However, the Examiner respectfully disagrees because merely adjusting an in-flight marketing campaign by providing an interface to a marketing administrator to adjust offer depths based on evaluation of offer performance is still a similar process to collecting marketing performance, evaluate said performance, and make adjustments to optimize offer and/or audience to be targeted with offers (EPG), which is done using a campaign API to apply changes using a network based communication environment fails to integrate the abstract idea into practical application, note “claim elements in addition to the abstract recitation, i.e. additional elements, as recited in claims 1-8, 10-18, and 20 at least are one or more interfaces, modules, database table, a platform, automatically executing and transmitting, and API (per claim 1), an administrative user interface displaying (claims 4-5), a computing system, interface, a platform, API, a second computing system executable by a processor, and automatically executing (claim 12), trained neural network(claim 6), transmitting (claim 7), neural network (claim 17), and computer-readable medium storing computer-executable instructions which, when executed by a processor of a computing system, cause the computing system to perform, interface, a platform, automatically executing, and API managed by the platform (claim 20). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1, 12, and their associated disclosure. Further, it appears the abstract idea is generally linked to a technical environment merely sending/receiving/publishing data over a network, note 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). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized marketing content such as ads, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)). Thus, the process is similar to collecting information, analyzing it (using models), and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels and evaluating campaign performance to make adjustment to in-flight campaigns is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
Lastly, in their argument against step 2B analysis the Applicant is reminded that the evaluation is that of additional elements both singularly and in-combination yet without identifying any additional element(s) the Applicant only argues in view of alleged abstract idea without noting any additional element(s) with particularity and simply notes “[…] apply targeted mid-campaign” and simply notes that somehow interface and API present additional elements that are not well-understood, routine or conventional (hereinafter WURC). However, as already explained in prong two leveraging one or more additional elements at high level is simply “apply it” and further generally linking to a network based communication environment to receive/send/transmit and distribute via API is general linking the abstract idea to a technical environment. To the extent these additional elements facilitate insignificant extra solution activity, such as data gathering and displaying or updating, the Examiner has provided evidence, contrary to the Applicant’s assertion(s), the indeed use of such interface and APIs is indeed WURC, note “, per MPEP 2106.05, as it applies to claims 1-8, 10-18, and 20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of setting up a marketing campaign by evaluating past campaigns including offers and customer behavior using models to generate lists of customers to target with offers at different depth levels and evaluating campaign performance to make adjustment to in-flight campaigns has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant extra solution activity such as pre-solution activity e.g. data gathering or post solution activity e.g. displaying on interface, the Examiner relies on as-filed disclosure, court cases, publication(s), and/or official notice below to demonstrate that such a way to gather data and/or display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(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) [similarly here at least campaign definition data is received; and publishing]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)."; Pub. No.: US2017/0372352 note [0097] “As mentioned above, in one or more embodiments the real-time experimentation system 100 executes a placebo experiment by creating an ad placement for a media lift experiment, executing the media lift experiment, and tracking and rolling up the results of the media lift experiments in a statistics platform to make the results available in a user interface. More particularly, the real-time experimentation system 100 can define experiment parameters, including control frequency, test groups, and control groups. The real-time experimentation system 100 can utilize a user interface and API functionality to make results available for both test and control group statistics. Moreover, the real-time experimentation system 100 can suggest revisions to campaign parameters based on the results of the media lift experiments.”; [0114] “As mentioned above, in one or more embodiments, the real-time experimentation system 100 also monitors performance of test ads and control ads and generates a user interface portraying a measure of media lift. To illustrate, the real-time experimentation system 100 can build a reporting API (i.e., a set of subroutine definitions, protocols and tools for building software and applications), to make test and control stats available to a user interface while adding performance statistics (i.e. measures of media lift) to the reporting user interface. In addition, the real-time experimentation system 100 can generate the user interface dashboard such that a user can control a number of aspects of an online ad campaign and media lift experiment, including how ads are chosen within an experiment to be test ads or control ads.”; Pub. No.: US2019/0130436 note [0002] “Although conventional content distribution systems allow individuals and businesses to disseminate digital content through computer networks, they also have a number of drawbacks. For example, conventional content distribution systems are generally rigid, and provide little flexibility during execution of a digital content campaign to respond to changing conditions. To illustrate, conventional content distribution systems often require publishers to create custom pipelines that periodically query conventional content distribution systems (e.g. via an API) regarding performance of a digital content distribution campaign. Then, based on information regarding the performance of the digital content distribution campaign, conventional content distribution systems often require merchants to manually adjust various campaign parameters to effectuate a change to the content distribution campaign.”; [0023] “One or more embodiments of the present invention include a digital campaign modification system that dynamically adjusts content distribution campaigns in real-time based on custom rules created from selectable triggering conditions and corresponding actions. In particular, in one or more embodiments, the digital campaign modification system provides a user interface with selectable options that allow digital publishers to customize and automate actions and notifications based on the performance of content distribution campaigns in real-time. For instance, in one or more embodiments, the digital campaign modification system generates suggested triggering conditions and corresponding actions utilizing a machine learning model trained based on historical content distribution campaigns. Accordingly, the digital campaign modification system provides publishers with the option to anticipate and automate actions during execution of a content distribution campaign without the need for constant manual monitoring and delayed responsive action.”; [0130] “As shown in FIG. 9, the digital campaign modification system 104 may include a user interface manager 904, a user input detector 906, an auction manager 908, a custom rule generator 910, a content distribution campaign generator 912, an activity monitor 914, a content distribution campaign modifier 916, a machine learning manager 918, a storage manager 920 (including a machine learning model 922 and custom rules 924), and a social graph 926 (including node information 928 and edge information 930).”; [0131] “As just mentioned, the digital campaign modification system 104 can include a user interface manager 904. The user interface manager 904 can provide, manage, and/or control a graphical user interface (or simply “user interface”) for use with the digital campaign modification system 104 (e.g., the user interfaces 304, 314, 320, and 500). In particular, the user interface manager 702 may facilitate presentation of information by way of an external component of a client device. For example, the user interface manager 904 may display a user interface by way of a display screen associated with the client device.”; [0132] “The user interface may be composed of a plurality of graphical components, objects, and/or elements that allow a user to perform a function (e.g., a color modifier element, a message drafting element, or a message thread area as described above). The user interface manager 904 can present, via the client device, a variety of types of information, including text, images, video, audio, characters, standardized image characters, customized image characters, or other information. Moreover, the user interface manager 904 can provide a variety of user interfaces specific to any variety of functions, programs, applications, plug-ins, devices, operating systems, and/or components of the client device. Additional details with respect to various example user interface elements are described throughout with regard to various embodiments containing user interfaces.”; [0189] “In particular embodiments, networking system 1202 may be capable of linking a variety of entities. As an example and not by way of limitation, networking system 1202 may enable users to interact with each other as well as receive content from third-party systems 1208 or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels.” [similarly here interfaces and APIs are utilized to receive and transmit adjustments].
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.”
Therefore the Examiner respectfully finds the Applicant’s arguments against 35 U.S.C. 101 unpersuasive and maintains the rejection in view of prong one and prong two rebuttal to Applicant’s arguments as set forth above.
Regarding 35 U.S.C. 103, not the section Reason(s) For Withdrawal Of Prior Art Based Rejection as set forth above.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
*Being noted initially:
*Previously noted:
- Pub. No.: US2015/0269607 see [0013] In certain implementations, the offer intelligence 114 can contain information from a plurality of sources of offer intelligence (which may be referred to as "offer intelligence sources"), where at least one (e.g., one, two, more than two, each) of such sources can provide intelligence associated with a respective facet of promotional content. For example, at least one of the plurality of offer intelligence sources can include catalog information indicative or otherwise representative of contemporaneous and historical promotional campaigns and related offers for a group of organizations spanning a predetermined period (e.g., minutes, days, weeks, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, or 40 years). The predetermined period can be determined at least by the type of promotional campaign (e.g., direct-mail campaigns, web-based campaigns, etc.), industry vertical, a combination thereof, or the like. For another example, at least one source of the plurality of offer intelligence sources can include format information (e.g., data and/or metadata) indicative of promotional content associated with contemporaneous and/or historical offers. The promotional content for a respective offer can include the offer itself (e.g., a discount, a free-of-charge indication, a BOGO indication, a combination thereof, or the like); a headline associated with the respective offer; information representative of the offer and/or an organization associated with the offer; and/or other information associated with the respective offer. For yet another example, at least one source of the plurality of offer intelligence sources can include knowledge indicative of advisable practices (e.g., best practices) for issuance of specific offers (e.g., coupons, rewards, and/or other incentives) according to season (or other instants, such as day, week, month, or the like); location; target market (e.g., intended consumer audience) segmentation; product category; service category; and/or organization category. For a further example, at least one source of the plurality of offer intelligence sources can include intelligence that can convey satisfactory content (e.g., best content, second-best content, multiple offers, or the like) for promotional content and/or satisfactory type (e.g., best type, second-best type, or the like) of promotional content, such as a percentage discount over price point, an absolute discount (e.g., a specific dollar amount) over price point, BOGO offer, qualified free-of-charge offer, combination thereof, or the like.
[0031] Performance information associated with a promotional campaign can be utilized or otherwise leveraged to generate offer intelligence, which can be utilized by the campaign management platform 210 to update and/or augment at least one of the offer intelligence source(s) 230 and/or the promotional campaign. In certain embodiments, the analysis unit 220 can include a learning engine that can generate information, intelligence, and/or knowledge associated with promotional campaigns via various machine-learning techniques (e.g., classification, regression, clustering, ensemble learning, Bayesian inferences, combinations thereof, and so forth). Accordingly, it can be readily appreciated that, in one aspect, promotional campaigns generated in accordance with aspects of the disclosure can have higher efficacy over time, with the ensuing increased efficiency of utilization of resources (monetary or otherwise) available for promotional campaigns.
[0041] The memory 530 can comprise functionality instructions storage 534 and functionality information storage 538. The functionality instructions storage 534 can comprise computer-accessible instructions that, in response to execution by at least one of the processor(s) 514, can implement one or more of the functionalities of the disclosure. The computer-accessible instructions can embody or can comprise one or more software components illustrated as promotional campaign component(s) 536. In one scenario, execution of at least one component of the promotional campaign component(s) 536 can implement one or more of the methods described herein. For instance, such execution can cause a processor that executes the at least one component to carry out a disclosed example method. It should be appreciated that, in one aspect, a processor of the processor(s) 514 that executes at least one of the promotional campaign component(s) 536 can retrieve information from or retain information in a memory element 540 in the functionality information storage 538 in order to operate in accordance with the functionality programmed or otherwise configured by the promotional campaign component(s) 536. Such information can include at least one of code instructions, information structures, or the like. Such instructions and information structures can embody or can constitute machine-learning techniques (e.g., pattern recognition algorithms; inference algorithms; triangulation or location estimation algorithms; temporal algorithms (e.g., algorithms in which an offer is served or otherwise communicated to consumers at a relevant day and/or time); and the like) that can be utilized to implement at least certain functionality described herein. It should be appreciated that, in one example, the relevancy of time to communicate an offer can be determined or otherwise modeled by an inference technique or algorithm. At least one of the one or more interfaces 550 (e.g., application programming interface(s)) can permit or facilitate communication of information between two or more components within the functionality instructions storage 534. The information that is communicated by the at least one interface can result from implementation of one or more operations in a method of the disclosure. In certain embodiments, one or more of the functionality instructions storage 534 and the functionality information storage 538 can be embodied in or can comprise removable/non-removable, and/or volatile/non-volatile computer storage media.
- Patent No. US 7376603 see col 8 line 62 - col 9 line 2 note "The system, according to an embodiment of the present invention, randomly assigns customers to test groups so that different decisions are applied and tested on statistically similar groups of customers. Customers are randomly assigned to each of the test groups. If there are three test groups, for example, a percentage of the customers are assigned to Test Group 1, another percentage to Test Group 2, and the balance to Test Group 3."
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571)270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
