Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 2/18/2026, Applicant responded on 6/11/2026. Amended claim 1, 18, 20.
IDS filed on 3/25/2026 are acknowledged and considered by the Examiner.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 18, 20 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 18, 20 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Independent Claim 1, as amended, is not merely directed to the abstract concept of allocating resources or budgeting. Rather, Claim 1 recites a highly specific, technological method of processing distinct data sets to solve a problem unique to computer networks and digital analytics platforms: "observability gaps."….This claimed process cannot be performed in the human mind, nor is it a generic method of organizing human activity. The human mind cannot execute data-driven attribution models to calibrate fragmented, incomplete third-party datasets against directly observed, code-level first-party tracking data. The specification explicitly details that this technical solution addresses "observability gaps from policy and/or regulatory and platform limitations". As such, the amended claims effect an improvement to the functioning of the data analytics system itself by programmatically synthesizing and calibrating disparate, incomplete digital data streams into a reliable model…a claim is not directed to an abstract idea if it provides a specific improvement to the functioning of a computer, to another technology, or to a technical field. Applicant respectfully submits that the amended claims provide a specific, technical solution to a technical problem inherent in digital data tracking and cross-platform analytics...the claims do not merely recite the generic concept of "budgeting" or "allocating resources." Instead, they claim a highly specific, technological method of normalizing corrupted or incomplete digital data streams. The amended claims require "generating observed attributions from the first channel data" and "calibrating the second channel data with the observed attributions via a data-driven attribution model to fill in observability gaps in the second channel data." This is a solution to a uniquely digital problem: fragmented and incomplete third-party tracking datasets…By anchoring deficient third-party data to deterministic, first-party digital tracking code (e.g., SDKs or HTML snippets), the claimed system programmatically synthesizes and calibrates disparate data streams into a reliable model. This process cannot be performed in the human mind, nor is it a generic method of organizing human activity. The human mind cannot execute data- driven attribution models to mathematically calibrate cross-platform API data against directly observed, code-level event tracking to resolve observability gaps. Because the claims effect a specific improvement to the functioning of the data analytics system itself, they integrate the alleged abstract idea into a practical application and are therefore patent eligible. Therefore, the claims integrate the alleged abstract idea into a practical application and alternatively provide an inventive concept that amounts to significantly more than the abstract idea by claiming a specific, non-routine ordered combination of elements to resolve digital data discrepancies. For at least the following reasons, Applicant submits that the pending claims are patent eligible and requests that the rejections under § 101 be withdrawn...” The Examiner respectfully disagrees.
While Applicant’s amendments advance prosecution, the claims recite and direct to, …human observing, calibrating, evaluating advertisement data for different avenues of advertisement and recommending future resources and budget allocations for different avenues of advertisement based on observed gaps…, which is a problem directed to, a mental process, organizing human activity, as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer, website, HTML, JavaScript, API, SDK, mobile app, performing extra solution activities, gathering data and outputting data, and generally linked to a technical environment, i.e. computer, website, HTML, JavaScript, API, SDK, mobile app. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link).
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s remarks are moot in light of new grounds of rejections necessitated by Applicant’s amendments.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 18, 20) recite, “A … method comprising:
obtaining, by a … of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on the first platform;
obtaining second channel data associated with a second media channel of the entity, the second channel data being third-party data, the second media channel being on a second platform, the second platform being different than the first platform;
generating observed attributions from the first channel data;
calibrating the second channel data with the observed attributions via a data-driven attribution model to fill in observability gaps in the second channel data;
processing, using a … model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the calibrated second media channel;
generating a recommendation for future resource allocation based on the determination of the allocation of resources to the first media channel and the second media channel; and
causing, by the …, a presentation of the recommendation on …. ”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …obtaining, by a … of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on the first platform; obtaining second channel data associated with a second media channel of the entity, the second channel data being third-party data, the second media channel being on a second platform, the second platform being different than the first platform; generating observed attributions from the first channel data; calibrating the second channel data with the observed attributions via a data-driven attribution model to fill in observability gaps in the second channel data; processing, using a … model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the calibrated second media channel; generating a recommendation for future resource allocation based on the determination of the allocation of resources to the first media channel and the second media channel; and causing, by the …, a presentation of the recommendation on …., under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims recite a mental process.
Further, …obtaining, by a … of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on the first platform; obtaining second channel data associated with a second media channel of the entity, the second channel data being third-party data, the second media channel being on a second platform, the second platform being different than the first platform; generating observed attributions from the first channel data; calibrating the second channel data with the observed attributions via a data-driven attribution model to fill in observability gaps in the second channel data; processing, using a … model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the calibrated second media channel; generating a recommendation for future resource allocation based on the determination of the allocation of resources to the first media channel and the second media channel; and causing, by the …, a presentation of the recommendation on…, are human observing, calibrating, evaluating advertisement data for different avenues of advertisement and recommending future resources and budget allocations for different avenues of advertisement based on observed gaps, which are commercial interactions, managing interactions and relationship between people, therefore the claims, recite certain methods of organizing human activities.
Accordingly, the claims recite and directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 18, 20: computer-implemented, computing device, machine-learning, a user interface of a user device, A computing system, comprising: one or more processors; and one or more one or more computer-readable media storing instructions that are executable to cause the one or more processors to, One or more computer readable media storing instructions that are executable by one or more processors to
Claim 4: uploaded
Claim 5: website
Claim 6: generated using HyperText Markup Language (HTML) code
Claim 7: code is a snippet of JavaScript code
Claim 8: mobile app
Claim 9: generated using software development kit (SDK) code
Claim 12: website, mobile app
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…obtaining…”, “…receiving…”, “…uploaded…”, “…transmitted…”, “…generating…”, “…presentation…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…obtaining…”, “…receiving…”, “…uploaded…”, “…transmitted…”, data output – “…generating…”, “…presentation…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0175] Figure 11 is a block diagram of an example networked computing system that can perform aspects of example implementations of the present disclosure. The system can include a number of computing devices and systems that are communicatively coupled over a network 49. An example computing device 50 is described to provide an example of a computing device that can perform any aspect of the present disclosure (e.g., implementing model host 31, client(s) 32, or both). An example server computing system 60 is described as an example of a server computing system that can perform any aspect of the present disclosure (e.g., implementing model host 31, client(s) 32, or both). Computing device 50 and server computing system(s) 60 can cooperatively interact (e.g., over network 49) to perform any aspect of the present disclosure (e.g., implementing model host 31, client(s) 32, or both). Model development platform system 70 is an example system that can host or serve model development platform(s) 12 for development of machine-learned models. Third-party system(s) 80 are example system(s) with which any of computing device 50, server computing system(s) 60, or model development platform system(s) 70 can interact in the performance of various aspects of the present disclosure (e.g., engaging third-party tools, accessing third-party databases or other resources, etc.).
[0177] Computing device 50 can be any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop), a mobile computing device (e.g., smartphone or tablet), a gaming console or controller, a wearable computing device, an embedded computing device, a server computing device, a virtual machine operating on a host device, or any other type of computing device. Computing device 50 can be a client computing device. Computing device 50 can be an end-user computing device. Computing device 50 can be a computing device of a service provided that provides a service to an end user (who may use another computing device to interact with computing device 50).
[0180] Computing device 50 can store or include one or more machine-learned models 55. Machine-learned models 55 can include one or more machine-learned model(s) 1101 , such as a sequence processing model 4. Machine-learned models 55 can include one or multiple model instance(s) 31-1. Machine-learned model(s) 55 can be received from server computing system(s) 60, model development platform system 70, third party system(s) 80 (e.g., an application distribution platform), or developed locally on computing device 50. Machine-learned model(s) 55 can be loaded into memory 52 and used or otherwise implemented by processor(s) 51. Computing device 50 can implement multiple parallel instances of machine-learned model(s) 55.
[0183] Server computing system 60 can store or otherwise include one or more machine-learned models 65. Machine-learned model(s) 65 can be the same as or different from machine-learned model(s) 55. Machine-learned models 65 can include one or more machine-learned model(s) 1101 , such as a sequence processing model 4. Machine-learned models 65 can include one or multiple model instance(s) 31-1. Machine-learned model(s) 65 can be received from computing device 50, model development platform system 70, third party system(s) 80, or developed locally on server computing system(s) 60. Machine-learned model(s) 65 can be loaded into memory 62 and used or otherwise implemented by processor(s) 61. Server computing system(s) 60 can implement multiple parallel instances of machine-learned model(s) 65.
[0186] Third-party system(s) 80 can include one or more processors 81 and a memory 82. Processor(s) 81 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. Memory 82 can include one or more non-transitory computer-readable storage media, such as HBM, RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. Memory 82 can store data 83 and instructions 84 which can be executed by processor(s) 81 to cause third-party system(s) 80 to perform operations. The operations can implement any one or multiple features described herein. The operations can implement example methods and techniques described herein. Example operations include the functionality described herein with respect to tools and other external resources called when training or performing inference with machine-learned model(s) 1101 , 4, 16, 20, 55, 65, etc. (e.g., third-party resource(s) 85).
[0187] Figure 12 illustrates one example arrangement of computing systems that can be used to implement the present disclosure. Other computing system configurations can be used as well. For example, in some implementations, one or both of computing system 50 or server computing system(s) 60 can implement all or a portion of the operations of model development platform system 70. For example, computing system 50 or server computing system(s) 60 can implement developer tool(s) 75 (or extensions thereof) to develop, update/train, or refine machine-learned models 1, 4, 16, 20, 55, 65, etc. using one or more techniques described herein with respect to model alignment toolkit 17. In this manner, for instance, computing system 50 or server computing system(s) 60 can develop, update/train, or refine machine-learned models based on local datasets (e.g., for model personalization/customization, as permitted by user data preference selections).
[0188] Figure 13 is a block diagram of an example computing device 98 that performs according to example embodiments of the present disclosure. Computing device 98 can be a user computing device or a server computing device (e.g., computing device 50, server computing system(s) 60, etc.). Computing device 98 can implement model host 31. For instance, computing device 98 can include a number of applications (e.g., applications 1 through N). Each application can contain its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. As illustrated in Figure 24, each application can communicate with a number of other components of the computing device, such as, for example, one or more sensors, a context manager, a device state component, or additional components. In some implementations, each application can communicate with each device component using an API (e.g., a public API). In some implementations, the API used by each application is specific to that application.
[0189] Figure 14 is a block diagram of an example computing device 99 that performs according to example embodiments of the present disclosure. Computing device 99 can be the same as or different from computing device 98. Computing device 99 can be a user computing device or a server computing device (e.g., computing device 50, server computing system(s) 60, etc.). Computing device 98 can implement model host 31. For instance, computing device 99 can include a number of applications (e.g., applications 1 through N). Each application can be in communication with a central intelligence layer. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application can communicate with the central intelligence layer (and model(s) stored therein) using an API (e.g., a common API across all applications).
[0192] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.
[0193] While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the subject disclosure does not preclude inclusion of such modifications, variations or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure covers such alterations, variations, and equivalents.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to WO Patent Publication to WO2022269336A1 to Sharma et al., (hereinafter referred to as “Sharma”) in view of US Patent Publication to US20240070716A1 to Palosi et al., (hereinafter referred to as “Palosi”).
As per Claim 1, Sharma teaches: A computer-implemented method comprising: ([0019])
obtaining, by a computing device of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on the first platform; (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day.)
obtaining second channel data associated with a second media channel of the entity, the second channel data being third-party data, the second media channel being on a second platform, the second platform being different than the first platform; (in at least [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible.)
generating observed … from the first channel data; (in at least [0038] The process defined in step 212 of FIG. 2 follows a sub-routine method (as shown in FIG. 3) which comprises following steps: a) The optimizer updates a channel level KPI and a system level KPI on identification of one or more KPIs affecting a campaign performance (301). The analytical module is prompted to analyse updates; b) The analytical module also tracks performance of similar campaigns on the targeted TPPs to assess a TPP historical record with respect to concerned campaign nature (302); c) In case the historical data provides a good track record of the TPP, the recommendation engine sends a set of recommendations to optimize the campaign quality through the recommendation engine (303); d) In case the campaign quality is good and performance is better on another TPP, the recommendation engine sends a set of recommendations to redistribute the expenditure to another TPP and optimize the KPIs of the TPP with low performance (304); e) In case the campaign and TPP both are of good quality, the recommendation engine sends a bid increase recommendation and increase the analysis period (305).)
calibrating the second channel data with the observed … via a data-driven attribution model to fill in observability gaps in the second channel data; (in at least [0038] The process defined in step 212 of FIG. 2 follows a sub-routine method (as shown in FIG. 3) which comprises following steps: a) The optimizer updates a channel level KPI and a system level KPI on identification of one or more KPIs affecting a campaign performance (301). The analytical module is prompted to analyse updates; b) The analytical module also tracks performance of similar campaigns on the targeted TPPs to assess a TPP historical record with respect to concerned campaign nature (302); c) In case the historical data provides a good track record of the TPP, the recommendation engine sends a set of recommendations to optimize the campaign quality through the recommendation engine (303); d) In case the campaign quality is good and performance is better on another TPP, the recommendation engine sends a set of recommendations to redistribute (i.e. calibrate) the expenditure to another TPP and optimize the KPIs of the TPP with low performance (i.e. observability gaps) (304); e) In case the campaign and TPP both are of good quality, the recommendation engine sends a bid increase recommendation and increase the analysis period (305). [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
processing, using a machine-learning model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the calibrated second media channel; (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
generating a recommendation for future resource allocation based on the determination of the allocation of resources to the first media channel and the second media channel; and (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
causing, by the computing device, a presentation of the recommendation on a user interface of a user device. (in at least [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps. )
Although implied, Sharma does not expressly disclose the following limitations, which however, are taught by Palosi,
generating observed attributions from the first channel data; (in at least [0025] the advertiser is able to choose their goals, and the system will allocate budgets between advertising channels automatically in order to meet them. For example, if the goal is Leads and Google Ads is generating a lower cost per lead than Bing Ads, the system will shift budget toward Google Ads in a controlled state, to move towards their goals and maximize their budget. [0056] The budget module may be in communication with the machine learning engine 218 to automatically establish a budget and/or goals. The budget module 210 may receive marketing data, product data, salesperson metrics, and other information related to an enterprise's marketing campaigns, strategies, personnel availability, etc. The budget module 210 may be in operable communication with the machine learning engine 128 to autonomously or semi-autonomously determine and recommend more allocation and calibration of a budget such that the ROAS and opportunities are increased. [0057] the budget module 210 may calibrate based on capacity of the salesperson via a pre-established set of rules or artificial intelligence. The budget module allows the system to spend less advertising budget when the salesperson's capacity is full and spend more when the salesperson's capacity is not full. The budget module 210 may enact the rule set automatically, in such autonomously or semi-autonomously managing the advertising budget allocation and calibration. [0058] the budget module 210 may allocate by channel or campaigns based on goals established by the user or group of users. In one example, goal options can be more opportunity volume, or more revenue such as to allocate advertising budget away from low performing salespersons and provide higher performing salespersons with additional budget while ensuring the additional budget is allocate to salespeople who have adequate bandwidth.)
calibrating the second channel data with the observed attributions via a data-driven attribution model to fill in observability gaps in the second channel data; (in at least [0025] the advertiser is able to choose their goals, and the system will allocate budgets between advertising channels automatically in order to meet them. For example, if the goal is Leads and Google Ads is generating a lower cost per lead than Bing Ads, the system will shift budget toward Google Ads in a controlled state, to move towards their goals and maximize their budget. [0056] The budget module may be in communication with the machine learning engine 218 to automatically establish a budget and/or goals. The budget module 210 may receive marketing data, product data, salesperson metrics, and other information related to an enterprise's marketing campaigns, strategies, personnel availability, etc. The budget module 210 may be in operable communication with the machine learning engine 128 to autonomously or semi-autonomously determine and recommend more allocation and calibration of a budget such that the ROAS and opportunities are increased. [0057] the budget module 210 may calibrate based on capacity of the salesperson via a pre-established set of rules or artificial intelligence. The budget module allows the system to spend less advertising budget when the salesperson's capacity is full and spend more when the salesperson's capacity is not full. The budget module 210 may enact the rule set automatically, in such autonomously or semi-autonomously managing the advertising budget allocation and calibration. [0058] the budget module 210 may allocate by channel or campaigns based on goals established by the user or group of users. In one example, goal options can be more opportunity volume, or more revenue such as to allocate advertising budget away from low performing salespersons and provide higher performing salespersons with additional budget while ensuring the additional budget is allocate to salespeople who have adequate bandwidth.[0063] the machine learning engine 218 may receive various data including salesperson data, product data, sales cycle metrics, marketing data, KPI information (including sales KPI's and marketing KPI's), and the like. Sales and marketing KPI's may include total sales, sales opportunities, closed opportunities, closed average sales, close rate, ROAS, advertising budget, etc. [0067] FIG. 6 illustrates a screenshot of the accounts interface 600 including a CRM portion 610 wherein the user is communicating with a CRM interface (e.g., a third party). For example, the user may interact with sales and marketing platforms, social media platforms, and other platforms which may aid in budget calibration and allocation. [0069] the term “segment” and/or “segments” refers to the separation of departments (i.e., sales, service, plumbing, HVAC, etc.) within a business and their goals. Further, businesses may be segmented by region (i.e., east, west, etc.). The segment may be any type of label which differentiates individuals and groups of individuals within a business and their unique goals. [0070] FIG. 8 illustrates a screenshot of the objectives interface 800 wherein objectives and opportunities are provided to the user. The opportunities portion 810 and revenue portion 820 includes various segments, campaigns, and other parameters which are provided to the user to aid in budget calibration and allocation. As used herein, the term “objectives” is used to define goals for each segment. [0071] FIG. 9 illustrates a screenshot of the campaigns interface 900, wherein campaign information is displayed to the user. The campaign interface 900 includes account information, campaign information, segment information, and the like. Campaigns are assigned to each advertising platform for different regions or departments (e.g., segment). The campaign interface 900 allows users to define which campaign belongs to a particular segment or group of segments. [0074] FIG. 12 illustrates a screenshot of the rules interface 1200, wherein the user establishes rules utilized by the system to efficiently schedule, adjust the budget, and perform the various other features of the system.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Sharma, as taught by Palosi above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Sharma with the motivation of, …to generate high-quality leads during business development practices….to effectively market their goods and/or services to prospective buyers…to ensure that advertising budgets are used only for products that the consumer is able to purchase at that time…allows for the calibration of a daily budget to fill a capacity as well as allowing for the allocation of budgets between channels…calibrate advertising budgets more effectively to fill pipelines when low and eliminate advertisements budget waste when the capacity of the salesperson is full, or when inefficiencies may be present. The platform uses defined parameters (provided either by human input or machine learning) by which budget calibration and allocation and/or subsequent bidding and/or subsequent statuses can adjust within. This allows the system to self-optimize toward an advertiser's goals of Return On Advertising Spend (ROAS) or opportunities, and/or save money when not advertising is not needed or is inefficient or certain goods and/or services or based on personnel bandwidth. The system may self-optimize toward an advertiser's goals, and to provide an efficient means for allocating budget towards the salesperson or products based on availability of each. The system may aid an individual or business in saving money spent on advertising when not needed, as in, if the salesperson has no bandwidth (i.e., are fully scheduled or have other responsibilities over the next x-number of days, or if a product is out of stock)….aiding in the spending advertising budgets more effectively while remaining in-tune with client's real-time business needs. Further, the system allows for multiple adjustable objectives…, as recited in Palosi.
As per Claim 2, Sharma teaches: The method of claim 1, further comprising:
receiving user feedback in response to the presentation of the recommendation; and (in at least [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps.)
performing an action based on the user feedback. (in at least [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps.)
As per Claim 3, Sharma teaches: The method of claim 2,
wherein the action is to adjust the allocation of resources to the first media channel and the second media channel based on the recommendation. (in at least [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 4, Sharma teaches: The method of claim 1,
wherein the second channel data is uploaded by the user device to the computing device. (in at least [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible.)
As per Claim 5, Sharma teaches: The method of claim 1,
wherein the first media channel is a website of the entity, and the first channel data is transmitted by the website to the computing device. (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 6, Sharma teaches: The method of claim 5,
wherein the website is generated using … include analytics code that is developed by the first platform. (in at least [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible. [0050] Marketing APIs: Open source APIs available used within the available thresholds to fetch campaign data from all underlying channels. These include but are not limited to Impressions, CPM (Cost per mile), CTR %(Click Through rate), Budget spent (Cost), Leads/Conversions/Results, CPA/CPR/CPL (Cost per acquisition, Cost per results, Cost per lead), Views, True Views, Thru plays, 3 sec views, 5 sec views, VTR%( View Through Rate), VCR% (Video Completion Rate), Offsite conversions, onsite conversions, Unique user, reach etc. User of the platform are able to drill down to Campaign level, ad group level, creative level, targeting (age gender, geo) level and affinity audience level.)
Although implied, Sharma does not expressly disclose the following limitations, which however, are taught by Palosi,
…HyperText Markup Language (HTML) code, and wherein the HTML code include analytics code that is developed by the first platform (in at least [0048] the system (i.e. first platform) is world-wide-web (www) based, and the network server is a web server delivering HTML, XML, etc., web pages to the computing devices. In other embodiments, a client-server architecture may be implemented, in which a network server executes enterprise and custom software, exchanging data with custom client applications running on the computing device. [0067] FIG. 6 illustrates a screenshot of the accounts interface 600 including a CRM portion 610 wherein the user is communicating with a CRM interface (e.g., a third party). For example, the user may interact with sales and marketing platforms, social media platforms, and other platforms which may aid in budget calibration and allocation.)
The reason and rationale to combine Sharma and Palosi is the same as recited above.
As per Claim 7, Sharma teaches: The method of claim 6,
wherein the analytics code is a …. (in at least [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible. [0050] Marketing APIs: Open source APIs available used within the available thresholds to fetch campaign data from all underlying channels. These include but are not limited to Impressions, CPM (Cost per mile), CTR %(Click Through rate), Budget spent (Cost), Leads/Conversions/Results, CPA/CPR/CPL (Cost per acquisition, Cost per results, Cost per lead), Views, True Views, Thru plays, 3 sec views, 5 sec views, VTR%( View Through Rate), VCR% (Video Completion Rate), Offsite conversions, onsite conversions, Unique user, reach etc. User of the platform are able to drill down to Campaign level, ad group level, creative level, targeting (age gender, geo) level and affinity audience level.)
Although implied, Sharma does not expressly disclose the following limitations, which however, are taught by Palosi,
…a snippet of JavaScript code… (in at least [0039] the application instructions 140 include software elements corresponding to one or more of the various embodiments described herein. For example, application instructions 140 may be implemented in various embodiments using any desired programming language, scripting language, or combination of programming and/or scripting languages (e.g., C, C++, C#, JAVA, JAVASCRIPT, PERL, etc.). [0067] FIG. 6 illustrates a screenshot of the accounts interface 600 including a CRM portion 610 wherein the user is communicating with a CRM interface (e.g., a third party). For example, the user may interact with sales and marketing platforms, social media platforms, and other platforms which may aid in budget calibration and allocation.)
The reason and rationale to combine Sharma and Palosi is the same as recited above.
As per Claim 8, Sharma teaches: The method of claim 1,
wherein the first media channel is a mobile app of the entity, and the first channel data is transmitted by the mobile app to the computing device. (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 10, Sharma teaches: The method of claim 1, further comprising:
obtaining analytics data for the first platform, wherein the analytics data is associated with …, the analytics data include budget data for the first platform; and (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
processing, using the machine-learning model, the analytics data for the first platform and the second channel data to determine the allocation of resources to the first media channel, the second media channel, and the third media channel of the entity. (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
Although implied, Sharma does not expressly disclose the following limitations, which however, are taught by Palosi,
…a plurality of media channels of the first platform, the plurality of media channels including the first media channel and a third media channel of the entity… (in at least [0025] the advertiser is able to choose their goals, and the system will allocate budgets between advertising channels automatically in order to meet them. For example, if the goal is Leads and Google Ads is generating a lower cost per lead than Bing Ads, the system will shift budget toward Google Ads in a controlled state, to move towards their goals and maximize their budget. [0056] The budget module may be in communication with the machine learning engine 218 to automatically establish a budget and/or goals. The budget module 210 may receive marketing data, product data, salesperson metrics, and other information related to an enterprise's marketing campaigns, strategies, personnel availability, etc. The budget module 210 may be in operable communication with the machine learning engine 128 to autonomously or semi-autonomously determine and recommend more allocation and calibration of a budget such that the ROAS and opportunities are increased. [0057] the budget module 210 may calibrate based on capacity of the salesperson via a pre-established set of rules or artificial intelligence. The budget module allows the system to spend less advertising budget when the salesperson's capacity is full and spend more when the salesperson's capacity is not full. The budget module 210 may enact the rule set automatically, in such autonomously or semi-autonomously managing the advertising budget allocation and calibration. [0058] the budget module 210 may allocate by channel or campaigns based on goals established by the user or group of users. In one example, goal options can be more opportunity volume, or more revenue such as to allocate advertising budget away from low performing salespersons and provide higher performing salespersons with additional budget while ensuring the additional budget is allocate to salespeople who have adequate bandwidth. [0067] FIG. 6 illustrates a screenshot of the accounts interface 600 including a CRM portion 610 wherein the user is communicating with a CRM interface (e.g., a third party). For example, the user may interact with sales and marketing platforms, social media platforms, and other platforms which may aid in budget calibration and allocation. [0069] the term “segment” and/or “segments” refers to the separation of departments (i.e., sales, service, plumbing, HVAC, etc.) within a business and their goals. Further, businesses may be segmented by region (i.e., east, west, etc.). The segment may be any type of label which differentiates individuals and groups of individuals within a business and their unique goals. [0070] FIG. 8 illustrates a screenshot of the objectives interface 800 wherein objectives and opportunities are provided to the user. The opportunities portion 810 and revenue portion 820 includes various segments, campaigns, and other parameters which are provided to the user to aid in budget calibration and allocation. As used herein, the term “objectives” is used to define goals for each segment. [0071] FIG. 9 illustrates a screenshot of the campaigns interface 900, wherein campaign information is displayed to the user. The campaign interface 900 includes account information, campaign information, segment information, and the like. Campaigns are assigned to each advertising platform for different regions or departments (e.g., segment). The campaign interface 900 allows users to define which campaign belongs to a particular segment or group of segments. [0072] FIG. 10 illustrates a screenshot of the budgets interface 1000 wherein budget data is disclosed. The budgets interface 1000 allows for the selection of a budget for each of a plurality of segments. The budget may adjust automatically over time between advertising channels and between days. [0074] FIG. 12 illustrates a screenshot of the rules interface 1200, wherein the user establishes rules utilized by the system to efficiently schedule, adjust the budget, and perform the various other features of the system.)
… determine the allocation of resources to the first media channel, the second media channel, and the third media channel of the entity… (in at least [0025] the advertiser is able to choose their goals, and the system will allocate budgets between advertising channels automatically in order to meet them. For example, if the goal is Leads and Google Ads is generating a lower cost per lead than Bing Ads, the system will shift budget toward Google Ads in a controlled state, to move towards their goals and maximize their budget. [0056] The budget module may be in communication with the machine learning engine 218 to automatically establish a budget and/or goals. The budget module 210 may receive marketing data, product data, salesperson metrics, and other information related to an enterprise's marketing campaigns, strategies, personnel availability, etc. The budget module 210 may be in operable communication with the machine learning engine 128 to autonomously or semi-autonomously determine and recommend more allocation and calibration of a budget such that the ROAS and opportunities are increased. [0057] the budget module 210 may calibrate based on capacity of the salesperson via a pre-established set of rules or artificial intelligence. The budget module allows the system to spend less advertising budget when the salesperson's capacity is full and spend more when the salesperson's capacity is not full. The budget module 210 may enact the rule set automatically, in such autonomously or semi-autonomously managing the advertising budget allocation and calibration. [0058] the budget module 210 may allocate by channel or campaigns based on goals established by the user or group of users. In one example, goal options can be more opportunity volume, or more revenue such as to allocate advertising budget away from low performing salespersons and provide higher performing salespersons with additional budget while ensuring the additional budget is allocate to salespeople who have adequate bandwidth. [0067] FIG. 6 illustrates a screenshot of the accounts interface 600 including a CRM portion 610 wherein the user is communicating with a CRM interface (e.g., a third party). For example, the user may interact with sales and marketing platforms, social media platforms, and other platforms which may aid in budget calibration and allocation. [0069] the term “segment” and/or “segments” refers to the separation of departments (i.e., sales, service, plumbing, HVAC, etc.) within a business and their goals. Further, businesses may be segmented by region (i.e., east, west, etc.). The segment may be any type of label which differentiates individuals and groups of individuals within a business and their unique goals. [0070] FIG. 8 illustrates a screenshot of the objectives interface 800 wherein objectives and opportunities are provided to the user. The opportunities portion 810 and revenue portion 820 includes various segments, campaigns, and other parameters which are provided to the user to aid in budget calibration and allocation. As used herein, the term “objectives” is used to define goals for each segment. [0071] FIG. 9 illustrates a screenshot of the campaigns interface 900, wherein campaign information is displayed to the user. The campaign interface 900 includes account information, campaign information, segment information, and the like. Campaigns are assigned to each advertising platform for different regions or departments (e.g., segment). The campaign interface 900 allows users to define which campaign belongs to a particular segment or group of segments. [0072] FIG. 10 illustrates a screenshot of the budgets interface 1000 wherein budget data is disclosed. The budgets interface 1000 allows for the selection of a budget for each of a plurality of segments. The budget may adjust automatically over time between advertising channels and between days. [0074] FIG. 12 illustrates a screenshot of the rules interface 1200, wherein the user establishes rules utilized by the system to efficiently schedule, adjust the budget, and perform the various other features of the system.)
The reason and rationale to combine Sharma and Palosi is the same as recited above.
As per Claim 11, Sharma teaches: The method of claim 10, wherein determining the allocation of resources to the first media channel, the second media channel, and the third media channel comprises:
determining an optimal budget allocation for the first media channel, the second media channel, and the third media channel; (in at least [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
for the first platform, wherein the first media channel and the third media channel are on the first platform; and (in at least [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
determining an optimal budget allocation for the second platform, wherein the second media channel is on the second platform. (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
As per Claim 12, Sharma teaches: The method of claim 1,
wherein the first media channel is a website, and the second media channel is a mobile app of the entity. (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 13, Sharma teaches: The method of claim 1, wherein the second channel data is raw data obtained from the second platform, the method further comprising: (in at least [002] A significant and robust market exists for marketing digital advertisements on various types of personal computing devices, like computers (desktop and laptop), mobile phones and tablets, and traditional browser-based devices operated by a consumer who is the user of the device. Conventional advertisement tracking systems and methods which were built for personal devices rely on device identification systems and methods to create a record in buyer advertising systems representing showing a digital advertisement (“ad”) to a consumer (the “impression”), and on which personal device a consumer, who was exposed to the impression, took some action (the “event”) in response to the impression (e.g., visiting a website, making an online purchase, calling a telephone number in response to the advertisement, to name a few.). These impressions and events (i.e. raw data) are monitored to derive data like a efficiency of platform and quality of advertisement as well as a various other analytical points. [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API).)
processing the second channel data to generate analytics data for the second platform; and (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
processing, using the machine-learning model, the first channel data and the analytics data for the second platform to determine the allocation of resources to the first media channel and the second media channel. (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible. [0047] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning)
As per Claim 14, Sharma teaches: The method of claim 1, wherein determining the allocation of resources to the first media channel and the second media channel comprises:
determining an optimal budget allocation for the first media channel and the second media channel. (in at least [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 15, Sharma teaches: The method of claim 1, wherein determining the allocation of resources to the first media channel and the second media channel comprises:
receiving a user input associated with a target parameter, and wherein the determination of the allocation of resources to the first media channel and the second media channel is based on the target parameter. (in at least [0042] Campaign trafficking, KPI setting and management: a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms. b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc. d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day. [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 16, Sharma teaches: The method of claim 15,
wherein the target parameter is a cost per conversion. (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting)
As per Claim 17, Sharma teaches: The method of claim 1, further comprising:
generating a graphical representation … determined allocation of resources to the first media channel and the second media channel; and (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps.)
causing a presentation of the graphical representation … determined allocation of resources on the user interface of the user device. (in at least [0036] As shown in FIG. 1 and la, the system for an intelligent cross platform marketing resource optimization comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is provided in a central application server 104 connected to a plurality of dedicated servers 105. The MTAL 101 comprises an analytical module 106, a trafficking module 107, a trigger module 108, a recommendation engine 109, an optimizer 110 and a predictor 111 as shown in FIG. lb. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters which includes but not limited to cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA), cost per click (CPC), click through rate (CTR%), conversion rate (CVR%) and a view through rate (VTR%). The trigger module 108 is connected to the analytical module 106 and comprises a threshold database. The threshold database comprises an optimal value of each performance data and is listed in the mapping the engine as a reference value. The recommendation engine 109 comprises a processing unit connected to the analytical module 106. The optimizer 110 is connected to the recommendation engine 109 and the plurality of third party platforms 102. The predictor 111 is connected to the analytical module, the recommendation engine 109 and the optimizer 110. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform is situated in the dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL and the plurality of third party platforms. The interface module is primarily but not limited to a marketing application programming interface (API). [0037] As shown in FIG. 2, the monitoring and optimization of the campaign running over TPPs is achieved through a computer implemented method. The method implemented a trafficking module provided through a meta technology abstraction layer (MTAL) for trafficking a marketing campaign from a client server to a plurality of third party platform (TPP) (201). The MTAL sets the campaign’s bid value and expenditure for each TPP (202) and tracks a plurality of key performance indicators (KPI) at a channel level as well as a system level (203). The key performance indicators are dynamic in nature and are determined for a campaign on the basis of a TPP compatibility with KPIs. The MTAL traces a dynamic mapping table for the KPI values coming through user interaction with the campaign from one or TPP (204) and comparing each KPI value with a reference value pre-saved in the dynamic mapping table (205). A trigger module in the MTAL triggers a performance degradation for at least one TPP (206) and sending the data of KPIs to the recommendation engine (207). The MTAL activates a recommendation engine to generate a plurality of rule based suggestions list (208) and presents the suggestions list to a user over a user interface of a client computing device (209). An optimizer in the MTAL records an action of the user on the suggestion list (210) and performing an optimization across the TPPs on the basis of the action by the user (211). The optimizer updates the campaign parameters in the analytical module as per user’s response (212) and the optimizer restarts tracking a performance of the campaign after the recorded action and reperforming steps.)
Although implied, Sharma does not expressly disclose the following limitations, which however, are taught by Palosi,
…graphical representation of the determined allocation of resources to the first media channel and the second media channel… (in at least [0066] FIG. 5 illustrates a screenshot of the actions interface 500 provided by the display module wherein the user's dashboard of action tabs is provided including the functionalities provided in FIGS. 5-12 herein. The actions interface 500 includes an overview portion 510 wherein the overview of budget, budget change, and budget capacity is graphically illustrated over a time period. The actions interface 500 further includes a statistics portion 520 including statistics such as number of runs, actions, skips, alerts, etc. The actions interface 500 further includes alerts, budgets paused, increases, recalibrated, decreases, updates, and other information displayed to the user. Budget automation will be established by analyzing capacity and objectives for each client. in at least [0070] FIG. 8 illustrates a screenshot of the objectives interface 800 wherein objectives and opportunities are provided to the user. The opportunities portion 810 and revenue portion 820 includes various segments, campaigns, and other parameters which are provided to the user to aid in budget calibration and allocation. As used herein, the term “objectives” is used to define goals for each segment. [0072] FIG. 10 illustrates a screenshot of the budgets interface 1000 wherein budget data is disclosed. The budgets interface 1000 allows for the selection of a budget for each of a plurality of segments. The budget may adjust automatically over time between advertising channels and between days.)
The reason and rationale to combine Sharma and Palosi is the same as recited above.
As per Claim 18-19 for A computing system (see at least Sharma [0012][0019][0036]), substantially recite the subject matter of Claim 1-2 and are rejected based on the same reasoning and rationale.
As per Claim 20 for One or more computer readable media (see at least Sharma [0012][0019][0036][0049]), substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
Claims 9 is/are rejected under 35 U.S.C. 103 as being unpatentable by WO Patent Publication to WO2022269336A1 to Sharma et al., (hereinafter referred to as “Sharma”) in view of US Patent Publication to US20240070716A1 to Palosi et al., (hereinafter referred to as “Palosi”) in view of US Patent Publication to US20140304069A1 to Lacey et al., (hereinafter referred to as “Lacey”)
As per Claim 9, Sharma teaches: The method of claim 8,
wherein the mobile app is … includes analytics code that is developed by the first platform. (in at least [0044] Campaign optimization (by constantly taking feedback from the underlying channels): a) Classification of campaigns in to - High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending. c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7- lOdays and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks) d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc) f) Targeting adjustment: Geo targeting, audience demography targeting [0046] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party 7 2nd party audience to cross-pollinate with existing 1st party data is also possible. [0050] Marketing APIs: Open source APIs available used within the available thresholds to fetch campaign data from all underlying channels. These include but are not limited to Impressions, CPM (Cost per mile), CTR %(Click Through rate), Budget spent (Cost), Leads/Conversions/Results, CPA/CPR/CPL (Cost per acquisition, Cost per results, Cost per lead), Views, True Views, Thru plays, 3 sec views, 5 sec views, VTR%( View Through Rate), VCR% (Video Completion Rate), Offsite conversions, onsite conversions, Unique user, reach etc. User of the platform are able to drill down to Campaign level, ad group level, creative level, targeting (age gender, geo) level and affinity audience level.)
Although implied, Sharma in view of Palosi does not expressly disclose the following limitations, which however, are taught by Lacey,
… generated using software development kit (SDK) code, and wherein the SDK code … (in at least [0049] In FIG. 2, Web sites 204 can include one or more resources 205 associated with a domain name and hosted by one or more servers. An example Web site 204 a is a collection of Web pages formatted in hypertext markup language (HTML) that can contain text, images, multimedia content, and programming elements, such as scripts. Each Web site 204 can be maintained by a publisher 209, which is an entity that controls, manages and/or owns the Web site 204. [0053] User device 206 a typically stores one or more user applications, such as a Web browser, to facilitate the sending and receiving of data over the network 202. A user device 206 a that is mobile (or simply, “mobile device”), such as a smartphone or a table computer, can include an application (“app”) 207 that allows the user to conduct a network (e.g., Web) search. User devices 206 can also be equipped with software to communicate with a GPS system, thereby enabling the GPS system to locate the mobile device. [0098] The web server, advertisement server, and impression allocation module can be realized by instructions that upon execution cause one or more processing devices to carry out the processes and functions described above. Such instructions can comprise, for example, interpreted instructions, such as script instructions, e.g., JavaScript or ECMAScript instructions, or executable code, or other instructions stored in a computer readable medium. The web server and advertisement server can be distributively implemented over a network, such as a server farm, or can be implemented in a single computer device. [0101] various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Sharma in view of Palosi, as taught by Lacey above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Sharma in view of Palosi with the motivation of, …a Web page can include slots in which ads can be presented. The slots can be allocated to content providers (e.g., advertisers). An auction can be performed for the right to present advertising in a slot. In the auction, content providers submit bids specifying amounts that the content providers are willing to pay for presentation of their content.…Content providers, such as advertisers, may distribute content through an auction, or outside of the context of an auction, based on various types of information. Examples of such information include, but are not limited to, keywords, geography, and demographics. Content providers, however, have limited resources (e.g., money). Content providers attempt to allocate those resources to methods of content distribution that provide an overall benefit, such as an increased number of conversions...it benefits content providers to have a way to allocate that budget in order to achieve an increase on their investment…The relative conversion rates of various distribution methods (e.g., keywords, Web-site, demographic, etc. distribution) may be compared to identify which distribution method(s) provides a desired increase(s) in conversion rate. The method(s) that provide the desired increase(s) may be suggested for use, and corresponding budget allocation, in future campaigns., as recited in Lacey.
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 extension fee 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 date of this final action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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