DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed March 5, 2026. Claims 1, 11, and 16 have been amended. Claims 1-20 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed March 5, 2026 have been fully considered but they are not persuasive.
Regarding the rejection under 35 U.S.C. § 101, Applicant submits that the claimed invention “implements a neural network to identify data patterns from the real-time purchasing data. Similar to the Holding in Ex parte Desjardin, the disclosure learns via the neural network the d” (page 10 of Applicant’s response) Applicant’s argument regarding the rejection under 35 U.S.C. § 101 ends at this point. Using a neural network to identify data patterns is an operation that is generic to all neural networks. Applicant presents no evidence that the claimed use of a neural network is any more than a generic use of neural networks or a general link to the technology of neural networks. The only recitation of the neural network in exemplary claim 1 is “wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.”
Regarding the art rejections, Applicant submits that the cited prior art references do not address the newly amended-in claim limitations (pages 11-12 of Applicant’s response). The rejections have been revised to incorporate the Svirsky reference, responsive to Applicant’s claim 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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “generating targeted promotions and advertisements to consumer segments selected to have as an example, a high return on advertising spend (ROAD) impact, or other key performance indicators (KPI)” (Spec: ¶ 2) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-10), Apparatus (claims 11-15), Article of Manufacture (claims 16-20)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 1] A method, comprising:
receiving a raw data from multiple consumers, wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers;
refining the raw data to capture a data pattern, wherein refining comprises identifying data patterns from the real-time purchasing data;
predicting a consumer behavior based on the data pattern, the consumer behavior defining one or more attributes;
identifying a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment, wherein identifying comprises determining a penetration depth of an advertised product, and
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget;
selecting at least one of an advertising message or a promotional offer to one or more consumers in the consumer segment to include in a payload content;
identifying a media channel to deliver the payload content to one or more consumer devices; and
providing the consumer segment to a display, upon request.
[Claim 11] test, standardize, partition and format a raw data, wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers;
refine the raw data by transformation, feature computation, and using an auxiliary model to capture a data pattern in the raw data, wherein refining comprises identifying data patterns from the real-time purchasing data;
impute one or more consumer attributes to define a target audience and a consumer segment, wherein the consumer segment comprises is defined by a penetration depth of an advertised product;
store multiple consumer preferences for multiple products or brands and multiple consumer sensitivities for marketing impulses;
predict a consumer behavior based on the consumer preferences for products and the marketing impulses;
identify a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment, wherein identifying comprises determining a penetration depth of an advertised product, and
wherein identifying the consumer segment includes:
select a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget; and
provide a payload content, the payload content including a personalized advertisement or coupon for a selected product or brand based on the consumer behavior.
[Claim 16] a method, the method comprising:
receiving a raw data from multiple consumers, wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers;
refining the raw data to capture a data pattern, wherein refining comprises identifying data patterns from the real-time purchasing data;
predicting a consumer behavior based on the data pattern, the consumer behavior defining one or more attributes;
identifying a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment, wherein identifying comprises determining a penetration depth of an advertised product;
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget;
selecting at least one of an advertising message or a promotional offer to one or more consumers in the consumer segment to include in a payload content;
identifying a media channel to deliver the payload content to one or more consumer devices; and
providing the consumer segment to a display, upon request.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. For example, a human user can gather the recited information, perform the recited analyses, make the types of decisions recited in the claims, train information in the sense of applying mathematical analysis to correct models and other assumptions, and present results in a displayed format (e.g., using pen and paper). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “generating targeted promotions and advertisements to consumer segments selected to have as an example, a high return on advertising spend (ROAD) impact, or other key performance indicators (KPI)” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of marketing and managing personal behavior and relationships as well as business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Adjusting a budget is also a fundamental economic practice, which is another example of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 includes a computer-implemented method, comprising: receiving, in a server, a raw data from multiple consumer devices and providing the consumer segment to a display in a client device. Claim 1 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
Claim 11 includes a system comprising various layers and a memory comprising instructions stored thereon, which when executed by the one or more processors, causes the one or more processors to generally implement the operations of the claims. The fact that raw data is received by a server is a general link to technology since the server is not necessarily part of the system; it simply describes the source of the raw data. Claim 11 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
Claim 16 includes a non-transitory, computer readable medium storing instructions which, when executed by a processor, cause a computer to execute a method, the method comprising the recited operations.
Claim 16 recites receiving, in a server, a raw data from multiple consumer devices and providing the consumer segment to a display in a client device.
Claim 16 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 83-92).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
At present, training information is recited very broadly in the claims and may simply incorporate applying mathematical analysis to correct models and other assumptions. Claims 1, 11, and 16 recite wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data. Considering that the implementation of the machine learning model and the training of the model are performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based and neural network-related processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning (including neural network) operations are generic machine learning operations (Spec: ¶¶ 25, 31-32). The Specification presents no assertion that there is any improvement in the automated machine learning (including neural network) process itself. Such a generic recitation of machine learning (including a neural network), as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] wherein identifying the media channel comprises selecting one of an in-store printer, a mobile video, a desktop display, or a third party advertisement, based on a type of the one or more consumer devices and a current location of the consumers.
[Claim 3] receiving a pre-selected universe of consumers and an impact goal for the payload content, wherein the pre-selected universe of consumers includes the consumer segment and is based on a product or brand identified in the payload content, and the impact goal comprises a desired metric associating the consumer segment with the product or brand identified in the payload content.
[Claim 4] determining a time duration of the promotional offer, promotion or recommendation in the payload content based on the one or more attributes of the consumers in the consumer segment.
[Claim 5] selecting a list of products or brands to be included in the payload content based on the one or more attributes of the consumers in the consumer segment.
[Claim 6] selecting a metric for the payload content, the metric associating a product or brand in the payload content to a consumer behavior;
selecting a group of consumers to form a control group based on the one or more attributes, wherein the control group does not receive the payload content;
determining an impact of the payload content on the consumer segment based on a comparison of a value of the metric for the control group with a value of the metric for the consumer segment; and
ranking the consumer segment based on the impact of the payload content on the consumer segment.
[Claim 7] generating a segment profile with a list of attributes and consumer behavior associated with a percentage of consumers in the consumer segment, and providing a graphical view of the segment profile to the display, the graphical view including an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior.
[Claim 8] determining an audience extension beyond the consumer segment for the payload content when a budget and a goal of a campaign for the payload content is not reachable within the consumer segment.
[Claim 9] predicting a campaign performance for the consumer segment based on a number of reachable users and a contact frequency of the payload content; and
accounting for a deterioration of the campaign performance based on an audience extension.
[Claim 10] receiving a request from a user to split the consumer segment into a maximum number of sub-segments to increase an impact of the payload content, wherein a sub-segment includes one or more consumers from the consumer segment.
[Claim 12] select a group of consumers to form a control group based on the one or more consumer attributes, wherein the control group does not receive the payload content.
[Claim 13] evaluate a metric for the payload content associating the selected product or brand in the payload content to a measured consumer behavior.
[Claim 14] evaluate an impact of the payload content on the consumer segment based on a comparison of a metric value for a control group with a metric value for the consumer segment, and to rank the consumer segment based on the impact of the payload content on the consumer segment.
[Claim 15] generate a segment profile with a list of attributes and consumer behavior associated with a percentage of consumers in the consumer segment, and to provide a graphical view of the segment profile to a display, wherein the graphical view includes an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior.
[Claim 17] wherein, in the method, identifying the media channel comprises selecting one of an in-store printer, a mobile video, a desktop display, or a third party advertisement, based on a type of the one or more consumer devices and a current location of the consumers.
[Claim 18] wherein the method further comprises receiving a pre-selected universe of consumers and an impact goal for the payload content, wherein the pre-selected universe of consumers includes the consumer segment and is based on a product or brand identified in the payload content, and the impact goal comprises a desired metric associating the consumer segment with the product or brand identified in the payload content.
[Claim 19] determining a time duration of the promotional offer, promotion or recommendation in the payload content based on the one or more attributes of the consumers in the consumer segment.
[Claim 20] selecting a list of products or brands to be included in the payload content based on the one or more attributes of the consumers in the consumer segment.
The dependent claims present additional details related to the abstract ideas identified in regard to the independent claims above.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. For example, a human user can gather the recited information, perform the recited analyses, make the types of decisions recited in the claims, train information in the sense of applying mathematical analysis to correct models and other assumptions, and present results in a displayed format (e.g., using pen and paper). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “generating targeted promotions and advertisements to consumer segments selected to have as an example, a high return on advertising spend (ROAD) impact, or other key performance indicators (KPI)” (Spec: ¶ 2), which (under its broadest reasonable interpretation) is an example of marketing and managing personal behavior and relationships as well as business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Adjusting a budget is also a fundamental economic practice, which is another example of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of the independent claim from which each depends.
Claim 1 includes a computer-implemented method, comprising: receiving, in a server, a raw data from multiple consumer devices and providing the consumer segment to a display in a client device. Claim 1 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
Claim 3 includes receiving, in the server, from a client device, a pre-selected universe of consumers and an impact goal for the payload content.
Claim 7 includes providing a graphical view of the segment profile to the display in the client device.
Claim 10 includes receiving, in the server, a request.
Claims 11-15 include a system comprising various layers and a memory comprising instructions stored thereon, which when executed by the one or more processors, causes the one or more processors to generally implement the operations of the claims. The fact that raw data is received by a server is a general link to technology since the server is not necessarily part of the system; it simply describes the source of the raw data. Claim 11 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
Claims 16-20 include a non-transitory, computer readable medium storing instructions which, when executed by a processor, cause a computer to execute a method, the method comprising the recited operations.
Claim 16 recites receiving, in a server, a raw data from multiple consumer devices and providing the consumer segment to a display in a client device.
Claim 16 also recites wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data.
Claim 18 recites receiving, in the server, from a client device, a pre-selected universe of consumers and an impact goal for the payload content.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 83-92).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
At present, training information is recited very broadly in the claims and may simply incorporate applying mathematical analysis to correct models and other assumptions. Claims 1, 11, and 16 recite wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data. Considering that the implementation of the machine learning model and the training of the model are performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based and neural network-related processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning (including neural network) operations are generic machine learning operations (Spec: ¶¶ 25, 31-32). The Specification presents no assertion that there is any improvement in the automated machine learning (including neural network) process itself. Such a generic recitation of machine learning (including a neural network), as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-9, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hamedi et al. (US 2019/0034976) in view of Svirsky et al. (US 2014/0058827).
[Claim 1] Hamedi discloses a computer-implemented method (¶ 779), comprising:
receiving, in a server, a raw data from multiple consumer devices (¶ 119 – “The server 125 facilitates and hosts the system and methods that are disclosed herein. It may store for the custom author groupings, calculate and monitor those groupings, and provides the computing device 100 access to the features that are disclosed throughout the present application.”; ¶ 396 – “As disclosed herein, the system 1000 may store a huge corpus of content items from sources such as the Internet and a variety of both online and off-line content sources from which ‘audiences’ are defined for the purpose of analysis and transformation. In the below example, content items can be harvested from any of the audience computing devices 1010, the content sources 1015, and the user computing device 1020.”; ¶¶ 44, 149, 151, 161-162, 217 – Behavior and activities of the authors are tracked, including via electronic devices, and may be obtained through an application and stored in a database.), wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers (¶ 289 – “The system 1000 can source a content item from an external website or content repository (e.g., the content sources 1015) or directly from users (e.g., the audience computing devices 1010 or the user computing device 1020), and can then perform content optimizations on these content items dynamically, depending on the goal of the content publisher.”; ¶ 47 – “In other examples, the system and methods disclosed herein may operate across multiple mediums and platforms such as websites, social media networks, and/or mobile apps. For example, an advertiser may want to define a custom author crowd by performing searches of Facebook™ authors. The advertiser may also wish to find the same authors they already found on Facebook™ on another medium. Examples of other mediums may include a Dictionary.com™ mobile app, a user of ESPN™ Fantasy Football services, or individuals with an account on an online shopping website such as Amazon™. The advertiser may have a particular rationale for discovering or finding users on other mediums as well. For example, the advertiser may operate the mobile app Uber™, which offers taxi-like services. Uber™ may wish to identify authors that use a mobile app that allows tracking of city buses or other transportation related apps. In a further example, Uber™ may wish to identify authors that use any sort of road navigation app such as Google™ Maps. One possible implementation may be to market to those who use such navigation or transportation apps whenever there are a surplus of Uber™ drivers in a certain town or area.”; ¶ 162 – Purchase activity may be tracked.; ¶ 211 – “For example, the system can use data obtained from web analytics platforms, such as Google™ Analytics, through product integration. Such an integration can allow an interrelation between the system and the activity and performance of the user's website (traffic, conversions, sales, etc.) to allow the system to use this data to inform the system's recommended aspects, tactics, and best practices for social media marketing, or other marketing and business purposes.” Analytics are performed using data received from various sources and the collections of data may be acquired via multiple consumer devices and they may also be related to, i.e., “associated with”, purchasing data obtained through multiple channels of different consumers and retailers.);
refining the raw data to capture a data pattern (¶ 45 – “Once a crowd has been defined by a user, that crowd can be stored, analyzed, and/or tracked for various fluctuations within the crowd based on the authors in the crowd's behavior after the crowd has been defined.”; ¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales.”), wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data (¶ 397 – “In some implementations, the system 1000 can implement advanced artificial intelligence and machine learning techniques, such as convolutional neural networks and generative adversarial networks, to acquire and analyze content items.”; ¶ 400 – “The system 1000 can process the harvest content items to determine which transformations, or categories of transformations, the visual features of each harvest content items represent or are reminiscent of. In some implementations, the system 1000 can implement a neural network to process the harvest content items. The matching criterion manager 1040 can then process the harvest content items to produce a matching criterion for each harvest content item that can be used for comparison with other content items at a later time. In some implementations, the matching criterion manager 1040 may implement a neural network to process or classify the harvest content items in this manner. A candidate content item can be provided by a user of the computing device 1020 for transformation by the system 1000. The system 1000 can process the candidate content item, for example using the second neural network referred to above, to identify one or more harvest content items, visual attributes, or categories, that match most closely with the candidate content item.” Identifying similar/matching content items would require an identification of patterns.);
predicting a consumer behavior based on the data pattern, the consumer behavior defining one or more attributes (¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales. In an illustrative embodiment, the user may want to know how many authors have been activated about root beer in Milwaukee, and the rate at which this fluctuation criteria was met over the last year. The user may then leverage this data and other measurements to predict how many authors may be activated at a later time to plan his or her advertisement or engagement campaigns accordingly.”; ¶ 183 – “In another example, the system may determine a different metric in advance of an actual post of future content, action taken, or behavior engaged in. For example, the system may predict how a draft post should be best optimized to achieve a different user goal, such as sales from a website or new registrations to a mailing list, subscription service, clicks and engagement, impressions/reach, etc…In another embodiment, the system may calculate in real time a prediction of how a particular action, behavior, or content will impact other business goals. For example, if a business goal, either predetermined by the system or input by the user, is to increase a followers for a Twitter™ account, the system can also predict/calculate how a post will increase followers to the account. In another embodiment, the system may calculate an impact and/or agility score change while actions and/or behaviors are being scheduled. For example, in a social media management system, such as HootSuite™, a user can schedule posts or other actions/behaviors (e.g., selecting day/time content should be posted). A user may also specify a location to tag a post (e.g., Boston Mass.), or select different targeting options for a post, action, or behavior (e.g., country, mobile device, specific crowd).“);
identifying a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment (¶¶ 51, 56 – Various custom crowds may be created, including crowds with authors who fall into multiple crowds.; ¶ 51 -- “The resulting fluctuation magnitude difference in the crowds may indicate to a user the relative effectiveness of the advertisement or coupon on a particular custom author crowd.”; ¶ 54 – “Multiple custom author crowds may be selected on the basis of demographics, behavioral tendencies, lifestyle indicators, or other specific market segmentation criteria, thus allowing a user to monitor and compare how fluctuations regarding Brand A and Brand B root beers are changing in particular demographic groups or target market segments.”; ¶¶ 81-83, 88 – The user can search for crowds of specified attributes and make adjustments.), wherein identifying comprises determining a penetration depth of an advertised product (Paragraph 45 of Applicant’s Specification explains that an example of “penetration depth” may be “market share of the advertised product.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”);
selecting at least one of an advertising message or a promotional offer to one or more consumers in the consumer segment to include in a payload content (¶ 141 – The user sets up and executes a specific marketing program.; ¶ 83 – “For example, a digital marketer for a department store may want to find all authors on Twitter™ who have mentioned Beyonce Knowles and that department store in the past year, like music, and used shopping-related keywords after December 1st. The advertiser may call this segment, “Beyonce Holiday Shoppers.” The logic in performing this search is that this population might be interested in an offer for Beyonce's new gift set that month.”; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.);
identifying a media channel to deliver the payload content to one or more consumer devices (¶ 81 – “A user may make a custom search query—through either free-form text in a search bar or by selecting from available check boxes—and look for unique objects and characteristics contained within author records on any participating advertising medium, e.g. a social networking platform.”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns.; ¶ 194 – “The system may also include a campaign tactic or a group of campaign tactics that the user could select to move forward with. Tactics can be directed towards one marketing channel or involve conducting activities on multiple marketing channels. Such a system could provide an integrated or multi-channel programmatic ad spending mechanism or some other structure where the system may recommend a suite of marketing channels, platforms, or devices for the user to try out that day and then allow the user to execute certain promotional tactics and new marketing activities. Such an embodiment could convert answers, trends, and other data-driven insights into an array of pre-populated tactics, methods and campaigns that the user may choose to trigger, test, modify, or ignore.”; ¶¶ 203, 206 – The user may set up a multi-channel campaign; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.); and
providing the consumer segment to a display in a client device, upon request (¶ 81 – “For example, a user may search for authors that have authored posts about baseball in the last two months. The system may return groupings of similar authors. In another example, the system may display and return groupings of authors based on a particular baseball team mentioned by the authors. The system may display that 300 authors mentioned Team A, 400 authors mentioned Team B, 200 authors mentioned Team C, etc. In other words, the user may specify a certain market or industry, and have the search results be grouped according to different brands within that industry. How the authors are grouped may be specified by the user. That is, the groupings may be custom defined.”; ¶ 96 – “That is, there are a total quantifiable number of target authors, which can be compared against benchmarks and current activity levels…the system may include a user interface or dashboard-like visualization to display the various crowds, calculations, comparisons, current user performance, benchmarks, comparison to benchmarks, past user performance, competitor performance, user activity levels, user investment or advertisement spending, and/or comparison to other investment levels by competitors or industry averages.”; ¶¶ 81-83, 88 – The user can search for crowds of specified attributes and make adjustments.).
Hamedi does not explicitly disclose:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget.
In paragraphs 14-15, Svirsky explains the following:
[0014] For each ad impression, the probability of a click or action is estimated separately. Thus, for each impression there is an estimate of how likely an ad impression is to result in a click or action. In an embodiment, threshold filters are set up for every ad to decide whether to accept the impression or not based upon the predicted click-through rate (CTR) or action rate (AR) for the impression. For example, some small budget advertiser may have $100 per day to spend and wants 100,000 impressions. In the real world, an agency might have 1,000,000 daily impressions available that match the advertiser bid and targeting. The advertiser may be qualified for any 100,000 of 1,000,000 impressions. Accordingly, the advertiser may have the luxury of cherry-picking, i.e. only accepting those impressions where the predicted click rate is higher. One aspect of the invention provides a controlled feedback mechanism that re-estimates the budgeted number of impressions over fixed time periods to determine if the rate of impressions is on target, if the budget is going to be filled, if there are too many impressions, and the like. If there are too many impressions, the filter threshold is raised. If only a very few impressions are placed, the filter threshold is lowered.
[0015] There is a ratio between impressions and clicks. Clicks are desirable results of an impression. As discussed above, the first goal for purposes of the invention is to deliver impressions, and the second goal is to maximize the number of clicks. The filtering and threshold adjustments effect a form of a throttling based upon sampling. Sampling allows an advertiser to spread their budget. If there are a certain number of impressions available, but it is only necessary to deliver a portion of those that are available, then only some of the impressions are delivered. The sampling ratio and the throttling that is based on a prediction are closely related. Thus, random sampling is used to make sure that the budget is spread throughout an advertising cycle, while filtering operates in addition to sampling, and is based on an estimate or click-through rate for each impression.
The payload budget may be reflected by a number of impressions placed. The impressions (which reflect a payload content budget) may be adjusted over time in accordance with sampling adjustments if a click-through ratio (CTR) is too low or too high and/or if a budget is being spent too quickly or too slowly (Svirsky: ¶ 29 – “The click-through rate filter is initialized with a seed value, which can be any desired non-zero constant. Picking too high value might hurt delivery in the first few iterations because predicted CTR is lower than seed threshold. Picking too small value prolongs searching for ideal threshold, and allows too many impressions with poor CTR. One way to pick seed value is take average expected CTR and divide it by 10.”; ¶ 31 -- “Thus, during the third iteration 24, the sampling is 50 percent and the CTR filter is 0.02. The sampling (pacing filter) and CTR filtering steps are both implemented. In this example, one of the slots that passes the sampling has a click probability of 0.02, which is too low for the CTR filter, and this slot is thus skipped. As a result, the only slots that are left are those with a relatively high click-through rate. e.g. 0.04, 0.03, 0.03. Less impressions are served than are in in the target, i.e. three impression vs. a target of four, but those slots that are selected are the highest value slots.”; ¶ 32 – “For the fourth iteration 26, the sampling is modified accordingly from 50 percent, to a ratio of 2/3, i.e. about 66 percent. This allows more slots, but the value of the CTR filter is also increased to 0.04. As a result, a balance is achieved where more slots are passed to the CTR filter by the pacing and fewer, but higher value, slots are passed through by the CTR filter. At the end of the fourth iteration, the impression vs. target are again considered and the sample ratio and CTR filter are adjusted appropriately. This process repeats for each interval until the impression budget is exhausted. If the budget is expended at a less than desired rate, the CTR filter may be decreased to allow the budget spend to increase and/or the pacing filter may be increased; likewise, if the budget is being expended too quickly, the CTR filter may be increased and the pacing filter may be decreased. Where the budget is spent too quickly, it may be desirable to increase the CTR filter threshold first and thereby select higher value impressions and, if this is not sufficient, the pacing filter can be slowed; likewise, where the budget is being spent too slowly, the pacing filter may first be increased so that the highest value impressions are still being selected and, if this is insufficient, then the CTR filter threshold may be lowered.”)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget
in order to optimize the spending of a budget on advertising impressions by controlling the impression slots selected, pacing, sampling, and budget spend more efficiently over time, as suggested in ¶ 27 of Svirsky (“As discussed above, embodiments can cherry-pick impression bids having a highest CTR. For some bids, it is possible to predict a click-through rate using a dynamic filter (pacing filter) that is calibrated, for example, every 15 minutes. This serves to increase or decrease the budget spend to meet the goal of budget fulfillment, or keep the budget spend the same if spend rate is about right. If the budget is large and it may not possible to fulfill it, then there is not any cherry-picking. That is, the filter is automatically turned off so it has no negative side effect, i.e. to keep it from damping the slot selection process.”).
[Claim 2] Hamedi discloses wherein identifying the media channel comprises selecting one of an in-store printer, a mobile video, a desktop display, or a third party advertisement, based on a type of the one or more consumer devices and a current location of the consumers (¶ 47 – “…the system and methods disclosed herein may operate across multiple mediums and platforms such as websites, social media networks, and/or mobile apps.” An advertiser may target authors using a mobile app for tracking buses and other transportation apps, including road navigation apps, when there is a surplus of Uber™ drivers in a certain town or area to target ads for the Uber™ drivers; ¶ 163 – Also, recommended marketing actions can be performed in response to the question “Which locations should I geotarget?”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns; ¶ 67 – “For example, if the user is a marketing agency, the agency's client may approve the post or advertisement. In another example, the post or advertisement may be approved by the social networking website where the post or advertisement will be published.” The social networking website is an example of a third party.; ¶ 195 – “For example, the system may partner with multiple advertising platforms, such as Google Adwords™, Twitter™ Ads, and Facebook™ Advertising. The various advertising products offered by these platforms can be displayed in the user's campaign menu.”; ¶ 82 – Targeting criteria may include a mobile device.; ¶ 66 – “The automatically published advertisements may come in many various forms. The advertisements may be through sponsored content on a news or pseudo-news website, may be native ads or editorial content on a social networking site or other web property, may be a standard banner advertisement, may be recommended and sponsored content on a shopping website, may be an e-mail, may be a paper mail advertisement, may be a sponsored video, may be a video featuring a product (product placement or subliminal advertising), or any other type of advertising.”).
[Claim 3] Hamedi discloses receiving, in the server, from a client device (¶¶ 44, 119, 149, 151, 161-162, 217, 396, as discussed in the rejection of the independent claim above), a pre-selected universe of consumers and an impact goal for the payload content, wherein the pre-selected universe of consumers includes the consumer segment and is based on a product or brand identified in the payload content, and the impact goal comprises a desired metric associating the consumer segment with the product or brand identified in the payload content (¶¶ 53-54, 57, 65, 94, 168 -- Various metrics can be assessed for various product brands, custom crowds, etc.; ¶ 105 – “By performing measurements on the activities of a specifically-defined crowd, the user is able to determine, for example, the size of a certain crowd of users or discussion group, as well as determine applicable audience activation and user acquisition metrics for that crowd. These measurements will help the user learn if his or her recent initiatives on any one or set of social media channels are positively impacting these metrics.”; ¶ 67 -- The use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.; ¶ 107 – “The system allows for comparison and tracking of two or more customized crowds against each other for the purpose of determining relative achievement and performance. This comparative measure may be a quantification of a desired action taken by certain authors in a crowd against the total crowd or comparisons along any of the metrics aforementioned. For example, the user may decide to compare two custom defined crowds versus viewing one in isolation to determine relative activation (or author acquisition) levels with respect to the total number of social media authors in a crowd or against any other control group. The user may also wish to determine whether he or she was more effective in activating crowd A or crowd B after executing campaigns during a certain time period. In another embodiment, the user may not even define these comparison crowds himself with the system; there may be potential to compare a user-defined crowd to a crowd defined by other users of the system or to a sample crowd or any collection of sample crowds already provided by the system.”; ¶ 200 -- “In another embodiment, the user's goals may be different for different mediums. For example, a user's top goal on Facebook™ may be to use advertising to increase online traffic and conversions on the user's website, while the user's goal on Twitter™ is to increase the number of followers the user has.”).
[Claim 4] Hamedi discloses determining a time duration of the promotional offer, promotion or recommendation in the payload content based on the one or more attributes of the consumers in the consumer segment (¶ 198 – “The user may also specify a total budget, daily budget, a custom bid, or max duration or time frame for a selected campaign.”; ¶ 199 – “The display of all of these suggested items may also reflect a ranking or sorting of the recommended tactics by order of importance or in terms of suggested priority to the user, shelf life (how long until the opportunity expires, or will need to be recalculated), or by the relevance and predicted impact of each tactic the goals of the user, or by the time and effort it will take for the user to execute the recommended tactic.”; ¶ 61 – “In another illustrative embodiment, alerts may be sent out based on temporal factors. For example, an alert on the progress of fluctuation criteria for a custom author crowd may be sent out every two weeks, regardless of whether any predetermined threshold is met. In another embodiment, an alert may be sent out if a predetermined threshold for fluctuation is met within a certain time period. For example, if the fluctuation of a custom author crowd based on a particular fluctuation criteria reaches 3% in one month, an alert may be sent out.”; ¶ 83 – “For example, a digital marketer for a department store may want to find all authors on Twitter™ who have mentioned Beyonce Knowles and that department store in the past year, like music, and used shopping-related keywords after December 1st. The advertiser may call this segment, “Beyonce Holiday Shoppers.” The logic in performing this search is that this population might be interested in an offer for Beyonce's new gift set that month.”; ¶ 67 – payload content).
[Claim 5] Hamedi discloses selecting a list of products or brands to be included in the payload content based on the one or more attributes of the consumers in the consumer segment (¶¶ 53-54, 57, 65, 94, 107, 168 -- Various metrics can be assessed for various product brands, custom crowds, etc.; ¶ 63 – “For example, a user may want to know if a famous celebrity authors a post about a user's product. In one specific example, an under the weather President of the United States may tweet positively about the efficacy of a particular brand of facial tissue. The brand manager of that particular brand of facial tissue may wish to be alerted that such a high profile individual is evacuating his or her nasal cavities upon their particular brand of paper handkerchiefs. The system can alert the brand manager thusly. The brand manager may then choose to promote such a post using the system or take other action based on the alert stemming from the President's now famous nasal mucus.”; ¶ 67 – payload content).
[Claim 7] Hamedi discloses generating a segment profile with a list of attributes and consumer behavior associated with a percentage of consumers in the consumer segment, and providing a graphical view of the segment profile to the display in the client device, the graphical view including an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior (¶ 66 – “In just one specific example, when at least 15% percent of authors in a custom author crowd have authored a post on social media about football, the system may automatically publish an online advertisement to that custom author crowd for a paid television subscription service that offers football programming. In another embodiment, the 15% threshold being met in the custom author crowd may also trigger advertisements for other custom author crowds about football programming, or may trigger advertisements for all authors about football programming. In another illustrative embodiment, the automatically published advertisement may only be published for the authors who have authored a post about football. The automatically published advertisements may come in many various forms. The advertisements may be through sponsored content on a news or pseudo-news website, may be native ads or editorial content on a social networking site or other web property, may be a standard banner advertisement, may be recommended and sponsored content on a shopping website, may be an e-mail, may be a paper mail advertisement, may be a sponsored video, may be a video featuring a product (product placement or subliminal advertising), or any other type of advertising. In another embodiment, the promoted content may be a post of one of the authors.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”; ¶ 95 – “He may also be alerted to and may be capable of viewing the total number of such instances that occurred with respect to his crowd over a certain time period. The user of the system could specify these specific events of interest when setting up a custom search monitor.”; ¶ 96 – “the system may include a user interface or dashboard-like visualization to display the various crowds, calculations, comparisons, current user performance, benchmarks, comparison to benchmarks, past user performance, competitor performance, user activity levels, user investment or advertisement spending, and/or comparison to other investment levels by competitors or industry averages.”; ¶ 100 – “The system may also display performance metrics on a custom-curated crowd.”; ¶ 101 – “As a result, crowd penetration has now grown to 15% (15/100) in that segment. The user can view this percentage change in performance in that crowd and even compare it to other crowds he may have defined with the system. This figure may also be compared to the total number of that account's followers to determine, for example, the proportion of crowd members to general followers at a given time.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; It is further noted that the nature of the displayed content (i.e., “an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior”) is non-functional descriptive material and does not serve to patentably distinguish the claimed invention over the prior art.).
[Claim 8] Hamedi discloses determining an audience extension beyond the consumer segment for the payload content when a budget and a goal of a campaign for the payload content is not reachable within the consumer segment (¶ 103 – “Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria. One or more of the user's crowds may also be included in these achievement calculations. As described, the system can also quantify overall success rates in each custom crowd. In this way, the user can view success measures at a current activity level in relation to the total possible pie at a given time. By having visibility into total possible achievement or relative achievement to other crowds or other advertisers, the advertiser may be incentivized to increase spend levels until reaching 100% or whatever his or her goal may be.” In other words, crowds may be adjusted to reach desired goals, which may including increasing spend levels to 100%, Each custom crowd is understood to include a relative addition/subtraction of authors when compared to another crowd.; ¶ 67 – payload content).
[Claim 9] Hamedi discloses predicting a campaign performance for the consumer segment based on a number of reachable users and a contact frequency of the payload content (¶¶ 170, 183 – Reach and a number of interactions are evaluated as part of the prediction; ¶ 67 – payload content); and
accounting for a deterioration of the campaign performance based on an audience extension (¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria. One or more of the user's crowds may also be included in these achievement calculations. As described, the system can also quantify overall success rates in each custom crowd.” Each custom crowd is understood to include a relative addition/subtraction of authors when compared to another crowd.; ¶ 51 – “the baseline determined using the fluctuation criteria, and the fluctuations determined for the custom author crowds, can be compared to each other. In this way, a difference in fluctuations, called a fluctuation magnitude difference, may be determined as between the multiple custom author crowds.”).
[Claim 11] Hamedi discloses a system, comprising:
a data acquisition layer configured to test, standardize, partition and format a raw data received by a server (¶ 194 – “Such an embodiment could convert answers, trends, and other data-driven insights into an array of pre-populated tactics, methods and campaigns that the user may choose to trigger, test, modify, or ignore.”; ¶ 119 – “The server 125 facilitates and hosts the system and methods that are disclosed herein. It may store for the custom author groupings, calculate and monitor those groupings, and provides the computing device 100 access to the features that are disclosed throughout the present application.”; ¶ 396 – “As disclosed herein, the system 1000 may store a huge corpus of content items from sources such as the Internet and a variety of both online and off-line content sources from which ‘audiences’ are defined for the purpose of analysis and transformation. In the below example, content items can be harvested from any of the audience computing devices 1010, the content sources 1015, and the user computing device 1020.”; ¶¶ 44, 149, 151, 161-162, 217 – Behavior and activities of the authors are tracked, including via electronic devices, and may be obtained through an application and stored in a database; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content. The APIs are understood to generally test, standardize, and partition data in order to process data. Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.), wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers (¶ 289 – “The system 1000 can source a content item from an external website or content repository (e.g., the content sources 1015) or directly from users (e.g., the audience computing devices 1010 or the user computing device 1020), and can then perform content optimizations on these content items dynamically, depending on the goal of the content publisher.”; ¶ 47 – “In other examples, the system and methods disclosed herein may operate across multiple mediums and platforms such as websites, social media networks, and/or mobile apps. For example, an advertiser may want to define a custom author crowd by performing searches of Facebook™ authors. The advertiser may also wish to find the same authors they already found on Facebook™ on another medium. Examples of other mediums may include a Dictionary.com™ mobile app, a user of ESPN™ Fantasy Football services, or individuals with an account on an online shopping website such as Amazon™. The advertiser may have a particular rationale for discovering or finding users on other mediums as well. For example, the advertiser may operate the mobile app Uber™, which offers taxi-like services. Uber™ may wish to identify authors that use a mobile app that allows tracking of city buses or other transportation related apps. In a further example, Uber™ may wish to identify authors that use any sort of road navigation app such as Google™ Maps. One possible implementation may be to market to those who use such navigation or transportation apps whenever there are a surplus of Uber™ drivers in a certain town or area.”; ¶ 162 – Purchase activity may be tracked.; ¶ 211 – “For example, the system can use data obtained from web analytics platforms, such as Google™ Analytics, through product integration. Such an integration can allow an interrelation between the system and the activity and performance of the user's website (traffic, conversions, sales, etc.) to allow the system to use this data to inform the system's recommended aspects, tactics, and best practices for social media marketing, or other marketing and business purposes.” Analytics are performed using data received from various sources and the collections of data may be acquired via multiple consumer devices and they may also be related to, i.e., “associated with”, purchasing data obtained through multiple channels of different consumers and retailers.);
a data enrichment layer, configured to refine the raw data by transformation, feature computation, and training of an auxiliary model to capture a data pattern in the raw data (¶ 45 – “Once a crowd has been defined by a user, that crowd can be stored, analyzed, and/or tracked for various fluctuations within the crowd based on the authors in the crowd's behavior after the crowd has been defined.”; ¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales.” ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.; ¶ 208 – “Various algorithms may be used for text, behavior, and other analysis to analyze data, content, authors, etc. and generate recommended aspects. For example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc. may all be used in various embodiments disclosed herein.”; ¶¶ 239-243 – Transformation of content based on features), wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data (¶ 397 – “In some implementations, the system 1000 can implement advanced artificial intelligence and machine learning techniques, such as convolutional neural networks and generative adversarial networks, to acquire and analyze content items.”; ¶ 400 – “The system 1000 can process the harvest content items to determine which transformations, or categories of transformations, the visual features of each harvest content items represent or are reminiscent of. In some implementations, the system 1000 can implement a neural network to process the harvest content items. The matching criterion manager 1040 can then process the harvest content items to produce a matching criterion for each harvest content item that can be used for comparison with other content items at a later time. In some implementations, the matching criterion manager 1040 may implement a neural network to process or classify the harvest content items in this manner. A candidate content item can be provided by a user of the computing device 1020 for transformation by the system 1000. The system 1000 can process the candidate content item, for example using the second neural network referred to above, to identify one or more harvest content items, visual attributes, or categories, that match most closely with the candidate content item.”);
a memory comprising instructions stored thereon, which when executed by the one or more processors, causes the one or more processors (¶¶ 23, 26, 29, 779) to:
impute one or more consumer attributes to define a target audience and a consumer segment (¶ 52 – “Another pre-defined or curated social community may be determined similar to a custom crowd (by searching based on demographics, posts, etc.) but may be saved in the system perpetually and thus is characterized as a baseline pre-defined social community.”; ¶ 157 – “For example a custom author crowd may be determined by a search criteria such as a demographic trait or user profile trait of the plurality of authors; a subject matter of a social media post authored by the plurality of authors; a related subject matter of a predetermined number of social media posts authored by the plurality of authors; a group association of the plurality of authors such as the following of the unique author or the following of another author; an interaction by the plurality of authors with the unique author, an engagement with a content by the plurality of authors; an amount of time spent viewing a webpage or screen by the plurality of authors; accessing a webpage or screen by the plurality of authors; the selection of a universal resource identifier (URI) by the plurality of authors; an affirmative or negative activity executed through the at least one social network, website, application software, or mobile application software (app); or any other type of search criteria. The custom author crowd may also be the author's immediate following on a social networking site such as Twitter™. In another embodiment, the custom author crowd may be a user's customers. For example, the user can import or upload a list of Twitter™ handles from a customer database maintained by the user. In another example, the user may import an e-mail or mailing list, or any other information that identifies a user's customers. Such identifying information can be used to define a customer author crowd. In such an embodiment, the system may utilize non-social networking site specific data (e.g., name, address, e-mail address) to search social networking sites and identify authors on the social networking sites that likely correspond to the user's customers.”; ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.), wherein the consumer segment comprises is defined by a penetration depth of an advertised product (Paragraph 45 of Applicant’s Specification explains that an example of “penetration depth” may be “market share of the advertised product.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”);
store multiple consumer preferences for multiple products or brands and multiple consumer sensitivities for marketing impulses (¶¶ 53-54, 57, 65, 94, 107, 168 -- Various metrics can be assessed for various product brands, custom crowds, etc.; ¶ 63 – “For example, a user may want to know if a famous celebrity authors a post about a user's product. In one specific example, an under the weather President of the United States may tweet positively about the efficacy of a particular brand of facial tissue. The brand manager of that particular brand of facial tissue may wish to be alerted that such a high profile individual is evacuating his or her nasal cavities upon their particular brand of paper handkerchiefs. The system can alert the brand manager thusly. The brand manager may then choose to promote such a post using the system or take other action based on the alert stemming from the President's now famous nasal mucus.”; ¶ 68 – “The activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium.”; ¶ 154 -- “Such systems and methods allow sensitivity to current market conditions that are needed to provide meaningful information, and timely and consistent business advice.”; ¶ 214 – A likelihood also conveys a sensitivity; ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.);
predict a consumer behavior based on the consumer preferences for products and the marketing impulses (¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales. In an illustrative embodiment, the user may want to know how many authors have been activated about root beer in Milwaukee, and the rate at which this fluctuation criteria was met over the last year. The user may then leverage this data and other measurements to predict how many authors may be activated at a later time to plan his or her advertisement or engagement campaigns accordingly.”; ¶ 183 – “In another example, the system may determine a different metric in advance of an actual post of future content, action taken, or behavior engaged in. For example, the system may predict how a draft post should be best optimized to achieve a different user goal, such as sales from a website or new registrations to a mailing list, subscription service, clicks and engagement, impressions/reach, etc…In another embodiment, the system may calculate in real time a prediction of how a particular action, behavior, or content will impact other business goals. For example, if a business goal, either predetermined by the system or input by the user, is to increase a followers for a Twitter™ account, the system can also predict/calculate how a post will increase followers to the account. In another embodiment, the system may calculate an impact and/or agility score change while actions and/or behaviors are being scheduled. For example, in a social media management system, such as HootSuite™, a user can schedule posts or other actions/behaviors (e.g., selecting day/time content should be posted). A user may also specify a location to tag a post (e.g., Boston Mass.), or select different targeting options for a post, action, or behavior (e.g., country, mobile device, specific crowd).“; ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.);
identifying a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment (¶ 81 – “A user may make a custom search query—through either free-form text in a search bar or by selecting from available check boxes—and look for unique objects and characteristics contained within author records on any participating advertising medium, e.g. a social networking platform.”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns.; ¶ 194 – “The system may also include a campaign tactic or a group of campaign tactics that the user could select to move forward with. Tactics can be directed towards one marketing channel or involve conducting activities on multiple marketing channels. Such a system could provide an integrated or multi-channel programmatic ad spending mechanism or some other structure where the system may recommend a suite of marketing channels, platforms, or devices for the user to try out that day and then allow the user to execute certain promotional tactics and new marketing activities. Such an embodiment could convert answers, trends, and other data-driven insights into an array of pre-populated tactics, methods and campaigns that the user may choose to trigger, test, modify, or ignore.”; ¶¶ 203, 206 – The user may set up a multi-channel campaign; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.), wherein identifying comprises determining a penetration depth of an advertised product (Paragraph 45 of Applicant’s Specification explains that an example of “penetration depth” may be “market share of the advertised product.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”), and
provide a payload content to a consumer device, the payload content including a personalized advertisement or coupon for a selected product or brand based on the consumer behavior (¶ 51 -- “…the multiple custom author crowds may all be the target of an advertisement for root beer or may receive a promotional coupon for root beer….The resulting fluctuation magnitude difference in the crowds may indicate to a user the relative effectiveness of the advertisement or coupon on a particular custom author crowd.”; ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.; ¶ 217 – “a fan in close proximity to a merchandise booth may be texted a special coupon for use at the merchandise booth”; ¶ 67 – payload content).
Hamedi does not explicitly disclose:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget.
In paragraphs 14-15, Svirsky explains the following:
[0014] For each ad impression, the probability of a click or action is estimated separately. Thus, for each impression there is an estimate of how likely an ad impression is to result in a click or action. In an embodiment, threshold filters are set up for every ad to decide whether to accept the impression or not based upon the predicted click-through rate (CTR) or action rate (AR) for the impression. For example, some small budget advertiser may have $100 per day to spend and wants 100,000 impressions. In the real world, an agency might have 1,000,000 daily impressions available that match the advertiser bid and targeting. The advertiser may be qualified for any 100,000 of 1,000,000 impressions. Accordingly, the advertiser may have the luxury of cherry-picking, i.e. only accepting those impressions where the predicted click rate is higher. One aspect of the invention provides a controlled feedback mechanism that re-estimates the budgeted number of impressions over fixed time periods to determine if the rate of impressions is on target, if the budget is going to be filled, if there are too many impressions, and the like. If there are too many impressions, the filter threshold is raised. If only a very few impressions are placed, the filter threshold is lowered.
[0015] There is a ratio between impressions and clicks. Clicks are desirable results of an impression. As discussed above, the first goal for purposes of the invention is to deliver impressions, and the second goal is to maximize the number of clicks. The filtering and threshold adjustments effect a form of a throttling based upon sampling. Sampling allows an advertiser to spread their budget. If there are a certain number of impressions available, but it is only necessary to deliver a portion of those that are available, then only some of the impressions are delivered. The sampling ratio and the throttling that is based on a prediction are closely related. Thus, random sampling is used to make sure that the budget is spread throughout an advertising cycle, while filtering operates in addition to sampling, and is based on an estimate or click-through rate for each impression.
The payload budget may be reflected by a number of impressions placed. The impressions (which reflect a payload content budget) may be adjusted over time in accordance with sampling adjustments if a click-through ratio (CTR) is too low or too high and/or if a budget is being spent too quickly or too slowly (Svirsky: ¶ 29 – “The click-through rate filter is initialized with a seed value, which can be any desired non-zero constant. Picking too high value might hurt delivery in the first few iterations because predicted CTR is lower than seed threshold. Picking too small value prolongs searching for ideal threshold, and allows too many impressions with poor CTR. One way to pick seed value is take average expected CTR and divide it by 10.”; ¶ 31 -- “Thus, during the third iteration 24, the sampling is 50 percent and the CTR filter is 0.02. The sampling (pacing filter) and CTR filtering steps are both implemented. In this example, one of the slots that passes the sampling has a click probability of 0.02, which is too low for the CTR filter, and this slot is thus skipped. As a result, the only slots that are left are those with a relatively high click-through rate. e.g. 0.04, 0.03, 0.03. Less impressions are served than are in in the target, i.e. three impression vs. a target of four, but those slots that are selected are the highest value slots.”; ¶ 32 – “For the fourth iteration 26, the sampling is modified accordingly from 50 percent, to a ratio of 2/3, i.e. about 66 percent. This allows more slots, but the value of the CTR filter is also increased to 0.04. As a result, a balance is achieved where more slots are passed to the CTR filter by the pacing and fewer, but higher value, slots are passed through by the CTR filter. At the end of the fourth iteration, the impression vs. target are again considered and the sample ratio and CTR filter are adjusted appropriately. This process repeats for each interval until the impression budget is exhausted. If the budget is expended at a less than desired rate, the CTR filter may be decreased to allow the budget spend to increase and/or the pacing filter may be increased; likewise, if the budget is being expended too quickly, the CTR filter may be increased and the pacing filter may be decreased. Where the budget is spent too quickly, it may be desirable to increase the CTR filter threshold first and thereby select higher value impressions and, if this is not sufficient, the pacing filter can be slowed; likewise, where the budget is being spent too slowly, the pacing filter may first be increased so that the highest value impressions are still being selected and, if this is insufficient, then the CTR filter threshold may be lowered.”)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget
in order to optimize the spending of a budget on advertising impressions by controlling the impression slots selected, pacing, sampling, and budget spend more efficiently over time, as suggested in ¶ 27 of Svirsky (“As discussed above, embodiments can cherry-pick impression bids having a highest CTR. For some bids, it is possible to predict a click-through rate using a dynamic filter (pacing filter) that is calibrated, for example, every 15 minutes. This serves to increase or decrease the budget spend to meet the goal of budget fulfillment, or keep the budget spend the same if spend rate is about right. If the budget is large and it may not possible to fulfill it, then there is not any cherry-picking. That is, the filter is automatically turned off so it has no negative side effect, i.e. to keep it from damping the slot selection process.”).
[Claim 13] Hamedi discloses wherein the instructions are configured to evaluate a metric for the payload content associating the selected product or brand in the payload content to a measured consumer behavior (¶¶ 103, 107; ¶ 51 -- “…the multiple custom author crowds may all be the target of an advertisement for root beer or may receive a promotional coupon for root beer….The resulting fluctuation magnitude difference in the crowds may indicate to a user the relative effectiveness of the advertisement or coupon on a particular custom author crowd.”; ¶ 67 -- Various applications, APIs, systems, platforms, the Internet, etc. enable the respective functions, thereby conveying that each function or set of functions is performed by a layer and/or module.; ¶ 217 – “a fan in close proximity to a merchandise booth may be texted a special coupon for use at the merchandise booth”; ¶ 67 – payload content); .
[Claim 14] Hamedi discloses wherein the instructions are configured to evaluate an impact of the payload content on the consumer segment based on a comparison of a metric value for a control group with a metric value for the consumer segment, and to rank the consumer segment based on the impact of the payload content on the consumer segment (¶ 107; ¶ 103 – “The system may also include capabilities for benchmarking and ongoing monitoring. The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria. One or more of the user's crowds may also be included in these achievement calculations. As described, the system can also quantify overall success rates in each custom crowd. In this way, the user can view success measures at a current activity level in relation to the total possible pie at a given time. By having visibility into total possible achievement or relative achievement to other crowds or other advertisers, the advertiser may be incentivized to increase spend levels until reaching 100% or whatever his or her goal may be.”; ¶ 67 – payload content).
[Claim 15] Hamedi discloses wherein the instructions are configured to generate a segment profile with a list of attributes and consumer behavior associated with a percentage of consumers in the consumer segment, and to provide a graphical view of the segment profile to a display in a client device, wherein the graphical view includes an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior (¶ 66 – “In just one specific example, when at least 15% percent of authors in a custom author crowd have authored a post on social media about football, the system may automatically publish an online advertisement to that custom author crowd for a paid television subscription service that offers football programming. In another embodiment, the 15% threshold being met in the custom author crowd may also trigger advertisements for other custom author crowds about football programming, or may trigger advertisements for all authors about football programming. In another illustrative embodiment, the automatically published advertisement may only be published for the authors who have authored a post about football. The automatically published advertisements may come in many various forms. The advertisements may be through sponsored content on a news or pseudo-news website, may be native ads or editorial content on a social networking site or other web property, may be a standard banner advertisement, may be recommended and sponsored content on a shopping website, may be an e-mail, may be a paper mail advertisement, may be a sponsored video, may be a video featuring a product (product placement or subliminal advertising), or any other type of advertising. In another embodiment, the promoted content may be a post of one of the authors.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”; ¶ 95 – “He may also be alerted to and may be capable of viewing the total number of such instances that occurred with respect to his crowd over a certain time period. The user of the system could specify these specific events of interest when setting up a custom search monitor.”; ¶ 96 – “the system may include a user interface or dashboard-like visualization to display the various crowds, calculations, comparisons, current user performance, benchmarks, comparison to benchmarks, past user performance, competitor performance, user activity levels, user investment or advertisement spending, and/or comparison to other investment levels by competitors or industry averages.”; ¶ 100 – “The system may also display performance metrics on a custom-curated crowd.”; ¶ 101 – “As a result, crowd penetration has now grown to 15% (15/100) in that segment. The user can view this percentage change in performance in that crowd and even compare it to other crowds he may have defined with the system. This figure may also be compared to the total number of that account's followers to determine, for example, the proportion of crowd members to general followers at a given time.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; It is further noted that the nature of the displayed content (i.e., “an indicator of the percentage of consumers in a consumer universe associated with the list of attributes and consumer behavior”) is non-functional descriptive material and does not serve to patentably distinguish the claimed invention over the prior art.).
[Claim 16] Hamedi discloses a non-transitory, computer readable medium storing instructions which, when executed by a processor, cause a computer to execute a method (¶¶ 23, 26, 29, 779), the method comprising:
receiving, in a server, a raw data from multiple consumer devices (¶ 119 – “The server 125 facilitates and hosts the system and methods that are disclosed herein. It may store for the custom author groupings, calculate and monitor those groupings, and provides the computing device 100 access to the features that are disclosed throughout the present application.”; ¶ 396 – “As disclosed herein, the system 1000 may store a huge corpus of content items from sources such as the Internet and a variety of both online and off-line content sources from which ‘audiences’ are defined for the purpose of analysis and transformation. In the below example, content items can be harvested from any of the audience computing devices 1010, the content sources 1015, and the user computing device 1020.”; ¶¶ 44, 149, 151, 161-162, 217 – Behavior and activities of the authors are tracked, including via electronic devices, and may be obtained through an application and stored in a database.), wherein the raw data is associated with real-time purchasing data obtained from multiple channels including consumers and retailers (¶ 289 – “The system 1000 can source a content item from an external website or content repository (e.g., the content sources 1015) or directly from users (e.g., the audience computing devices 1010 or the user computing device 1020), and can then perform content optimizations on these content items dynamically, depending on the goal of the content publisher.”; ¶ 47 – “In other examples, the system and methods disclosed herein may operate across multiple mediums and platforms such as websites, social media networks, and/or mobile apps. For example, an advertiser may want to define a custom author crowd by performing searches of Facebook™ authors. The advertiser may also wish to find the same authors they already found on Facebook™ on another medium. Examples of other mediums may include a Dictionary.com™ mobile app, a user of ESPN™ Fantasy Football services, or individuals with an account on an online shopping website such as Amazon™. The advertiser may have a particular rationale for discovering or finding users on other mediums as well. For example, the advertiser may operate the mobile app Uber™, which offers taxi-like services. Uber™ may wish to identify authors that use a mobile app that allows tracking of city buses or other transportation related apps. In a further example, Uber™ may wish to identify authors that use any sort of road navigation app such as Google™ Maps. One possible implementation may be to market to those who use such navigation or transportation apps whenever there are a surplus of Uber™ drivers in a certain town or area.”; ¶ 162 – Purchase activity may be tracked.; ¶ 211 – “For example, the system can use data obtained from web analytics platforms, such as Google™ Analytics, through product integration. Such an integration can allow an interrelation between the system and the activity and performance of the user's website (traffic, conversions, sales, etc.) to allow the system to use this data to inform the system's recommended aspects, tactics, and best practices for social media marketing, or other marketing and business purposes.” Analytics are performed using data received from various sources and the collections of data may be acquired via multiple consumer devices and they may also be related to, i.e., “associated with”, purchasing data obtained through multiple channels of different consumers and retailers.);
refining the raw data to capture a data pattern (¶ 45 – “Once a crowd has been defined by a user, that crowd can be stored, analyzed, and/or tracked for various fluctuations within the crowd based on the authors in the crowd's behavior after the crowd has been defined.”; ¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales.”), wherein refining comprises implementing a neural network to identify data patterns from the real-time purchasing data (¶ 397 – “In some implementations, the system 1000 can implement advanced artificial intelligence and machine learning techniques, such as convolutional neural networks and generative adversarial networks, to acquire and analyze content items.”; ¶ 400 – “The system 1000 can process the harvest content items to determine which transformations, or categories of transformations, the visual features of each harvest content items represent or are reminiscent of. In some implementations, the system 1000 can implement a neural network to process the harvest content items. The matching criterion manager 1040 can then process the harvest content items to produce a matching criterion for each harvest content item that can be used for comparison with other content items at a later time. In some implementations, the matching criterion manager 1040 may implement a neural network to process or classify the harvest content items in this manner. A candidate content item can be provided by a user of the computing device 1020 for transformation by the system 1000. The system 1000 can process the candidate content item, for example using the second neural network referred to above, to identify one or more harvest content items, visual attributes, or categories, that match most closely with the candidate content item.”);
predicting a consumer behavior based on the data pattern, the consumer behavior defining one or more attributes (¶ 68 – “In another embodiment, the system may be utilized by users to support forecasting activities. That is, the activation history of one or more crowds with the system may be leveraged in conjunction with planning exercises of the user and/or to help predict when certain crowds or crowd members will engage in certain behaviors or post certain types of content on a particular medium. Such trend data and other variability measures may be helpful when planning campaigns that may span multiple online platforms, or even promote offline sales. In an illustrative embodiment, the user may want to know how many authors have been activated about root beer in Milwaukee, and the rate at which this fluctuation criteria was met over the last year. The user may then leverage this data and other measurements to predict how many authors may be activated at a later time to plan his or her advertisement or engagement campaigns accordingly.”; ¶ 183 – “In another example, the system may determine a different metric in advance of an actual post of future content, action taken, or behavior engaged in. For example, the system may predict how a draft post should be best optimized to achieve a different user goal, such as sales from a website or new registrations to a mailing list, subscription service, clicks and engagement, impressions/reach, etc…In another embodiment, the system may calculate in real time a prediction of how a particular action, behavior, or content will impact other business goals. For example, if a business goal, either predetermined by the system or input by the user, is to increase a followers for a Twitter™ account, the system can also predict/calculate how a post will increase followers to the account. In another embodiment, the system may calculate an impact and/or agility score change while actions and/or behaviors are being scheduled. For example, in a social media management system, such as HootSuite™, a user can schedule posts or other actions/behaviors (e.g., selecting day/time content should be posted). A user may also specify a location to tag a post (e.g., Boston Mass.), or select different targeting options for a post, action, or behavior (e.g., country, mobile device, specific crowd).“);
identifying a consumer segment based on the consumer behavior and a sharing of a one or more attributes among multiple consumers in the consumer segment (¶ 81 – “A user may make a custom search query—through either free-form text in a search bar or by selecting from available check boxes—and look for unique objects and characteristics contained within author records on any participating advertising medium, e.g. a social networking platform.”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns.; ¶ 194 – “The system may also include a campaign tactic or a group of campaign tactics that the user could select to move forward with. Tactics can be directed towards one marketing channel or involve conducting activities on multiple marketing channels. Such a system could provide an integrated or multi-channel programmatic ad spending mechanism or some other structure where the system may recommend a suite of marketing channels, platforms, or devices for the user to try out that day and then allow the user to execute certain promotional tactics and new marketing activities. Such an embodiment could convert answers, trends, and other data-driven insights into an array of pre-populated tactics, methods and campaigns that the user may choose to trigger, test, modify, or ignore.”; ¶¶ 203, 206 – The user may set up a multi-channel campaign; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.), wherein identifying comprises determining a penetration depth of an advertised product (Paragraph 45 of Applicant’s Specification explains that an example of “penetration depth” may be “market share of the advertised product.”; ¶ 103 – “The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria.”; ¶ 83 – “In this way, the system allows the advertiser to group authors in similar contexts and view them in custom categories or crowds that are meaningful to any given marketing or advertising program.”; ¶ 88 – “This conceptualization of a crowd of target authors can be viewed and edited in its original form at any time.”; ¶ 90 – “With this system, the advertiser can view performance measures on specific crowds, specific campaigns, and the channels they are on.”);
selecting at least one of an advertising message or a promotional offer to one or more consumers in the consumer segment to include in a payload content (¶ 141 – The user sets up and executes a specific marketing program.; ¶ 83 – “For example, a digital marketer for a department store may want to find all authors on Twitter™ who have mentioned Beyonce Knowles and that department store in the past year, like music, and used shopping-related keywords after December 1st. The advertiser may call this segment, “Beyonce Holiday Shoppers.” The logic in performing this search is that this population might be interested in an offer for Beyonce's new gift set that month.”; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.);
identifying a media channel to deliver the payload content to one or more consumer devices (¶ 81 – “A user may make a custom search query—through either free-form text in a search bar or by selecting from available check boxes—and look for unique objects and characteristics contained within author records on any participating advertising medium, e.g. a social networking platform.”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns.; ¶ 194 – “The system may also include a campaign tactic or a group of campaign tactics that the user could select to move forward with. Tactics can be directed towards one marketing channel or involve conducting activities on multiple marketing channels. Such a system could provide an integrated or multi-channel programmatic ad spending mechanism or some other structure where the system may recommend a suite of marketing channels, platforms, or devices for the user to try out that day and then allow the user to execute certain promotional tactics and new marketing activities. Such an embodiment could convert answers, trends, and other data-driven insights into an array of pre-populated tactics, methods and campaigns that the user may choose to trigger, test, modify, or ignore.”; ¶¶ 203, 206 – The user may set up a multi-channel campaign; ¶ 67 – “…when the fluctuation criteria are met the system may signal an opportunity or recommend that the user engage in a certain behavior or publish content to capitalize on the favorable conditions within the custom author crowd. In such an example, execution of these actions may be facilitated by sending the fluctuation criteria and other data from the disclosed system into another software application or set of software applications via a customizable application program interface (API). Examples of integrated software applications may include but are not intended to be limited to a social media management system, a social media publishing or engagement platform, a programmatic advertising platform, a real-time bidding (RTB) platform, a demand side platform (DSP), a supply side platform (SSP), an advertising exchange, a content management system, a community platform, a marketing automation system, or any other data management, analysis and optimization, web, Internet, or marketing technology platform. In other words, the system disclosed herein may be an enabler of other functions. For example, the execution of advertising and marketing campaigns may not be done directly via the present system. That is, it may be the case that this system leverages an API that plugs into well-established social media management systems like HootSuite™ that offer post scheduling and publishing functionality. The system may also send data into programmatic ad platforms.” In other words, the use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.); and
providing the consumer segment to a display in a client device, upon request (¶ 81 – “For example, a user may search for authors that have authored posts about baseball in the last two months. The system may return groupings of similar authors. In another example, the system may display and return groupings of authors based on a particular baseball team mentioned by the authors. The system may display that 300 authors mentioned Team A, 400 authors mentioned Team B, 200 authors mentioned Team C, etc. In other words, the user may specify a certain market or industry, and have the search results be grouped according to different brands within that industry. How the authors are grouped may be specified by the user. That is, the groupings may be custom defined.”; ¶ 96 – “That is, there are a total quantifiable number of target authors, which can be compared against benchmarks and current activity levels…the system may include a user interface or dashboard-like visualization to display the various crowds, calculations, comparisons, current user performance, benchmarks, comparison to benchmarks, past user performance, competitor performance, user activity levels, user investment or advertisement spending, and/or comparison to other investment levels by competitors or industry averages.”; ¶¶ 81-83, 88 – The user can search for crowds of specified attributes and make adjustments.).
Hamedi does not explicitly disclose:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget.
In paragraphs 14-15, Svirsky explains the following:
[0014] For each ad impression, the probability of a click or action is estimated separately. Thus, for each impression there is an estimate of how likely an ad impression is to result in a click or action. In an embodiment, threshold filters are set up for every ad to decide whether to accept the impression or not based upon the predicted click-through rate (CTR) or action rate (AR) for the impression. For example, some small budget advertiser may have $100 per day to spend and wants 100,000 impressions. In the real world, an agency might have 1,000,000 daily impressions available that match the advertiser bid and targeting. The advertiser may be qualified for any 100,000 of 1,000,000 impressions. Accordingly, the advertiser may have the luxury of cherry-picking, i.e. only accepting those impressions where the predicted click rate is higher. One aspect of the invention provides a controlled feedback mechanism that re-estimates the budgeted number of impressions over fixed time periods to determine if the rate of impressions is on target, if the budget is going to be filled, if there are too many impressions, and the like. If there are too many impressions, the filter threshold is raised. If only a very few impressions are placed, the filter threshold is lowered.
[0015] There is a ratio between impressions and clicks. Clicks are desirable results of an impression. As discussed above, the first goal for purposes of the invention is to deliver impressions, and the second goal is to maximize the number of clicks. The filtering and threshold adjustments effect a form of a throttling based upon sampling. Sampling allows an advertiser to spread their budget. If there are a certain number of impressions available, but it is only necessary to deliver a portion of those that are available, then only some of the impressions are delivered. The sampling ratio and the throttling that is based on a prediction are closely related. Thus, random sampling is used to make sure that the budget is spread throughout an advertising cycle, while filtering operates in addition to sampling, and is based on an estimate or click-through rate for each impression.
The payload budget may be reflected by a number of impressions placed. The impressions (which reflect a payload content budget) may be adjusted over time in accordance with sampling adjustments if a click-through ratio (CTR) is too low or too high and/or if a budget is being spent too quickly or too slowly (Svirsky: ¶ 29 – “The click-through rate filter is initialized with a seed value, which can be any desired non-zero constant. Picking too high value might hurt delivery in the first few iterations because predicted CTR is lower than seed threshold. Picking too small value prolongs searching for ideal threshold, and allows too many impressions with poor CTR. One way to pick seed value is take average expected CTR and divide it by 10.”; ¶ 31 -- “Thus, during the third iteration 24, the sampling is 50 percent and the CTR filter is 0.02. The sampling (pacing filter) and CTR filtering steps are both implemented. In this example, one of the slots that passes the sampling has a click probability of 0.02, which is too low for the CTR filter, and this slot is thus skipped. As a result, the only slots that are left are those with a relatively high click-through rate. e.g. 0.04, 0.03, 0.03. Less impressions are served than are in in the target, i.e. three impression vs. a target of four, but those slots that are selected are the highest value slots.”; ¶ 32 – “For the fourth iteration 26, the sampling is modified accordingly from 50 percent, to a ratio of 2/3, i.e. about 66 percent. This allows more slots, but the value of the CTR filter is also increased to 0.04. As a result, a balance is achieved where more slots are passed to the CTR filter by the pacing and fewer, but higher value, slots are passed through by the CTR filter. At the end of the fourth iteration, the impression vs. target are again considered and the sample ratio and CTR filter are adjusted appropriately. This process repeats for each interval until the impression budget is exhausted. If the budget is expended at a less than desired rate, the CTR filter may be decreased to allow the budget spend to increase and/or the pacing filter may be increased; likewise, if the budget is being expended too quickly, the CTR filter may be increased and the pacing filter may be decreased. Where the budget is spent too quickly, it may be desirable to increase the CTR filter threshold first and thereby select higher value impressions and, if this is not sufficient, the pacing filter can be slowed; likewise, where the budget is being spent too slowly, the pacing filter may first be increased so that the highest value impressions are still being selected and, if this is insufficient, then the CTR filter threshold may be lowered.”)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi:
wherein identifying the consumer segment includes:
selecting a randomized group of the multiple consumers, and
in response to the randomized group being less than a predetermined threshold, adjusting a payload content budget
in order to optimize the spending of a budget on advertising impressions by controlling the impression slots selected, pacing, sampling, and budget spend more efficiently over time, as suggested in ¶ 27 of Svirsky (“As discussed above, embodiments can cherry-pick impression bids having a highest CTR. For some bids, it is possible to predict a click-through rate using a dynamic filter (pacing filter) that is calibrated, for example, every 15 minutes. This serves to increase or decrease the budget spend to meet the goal of budget fulfillment, or keep the budget spend the same if spend rate is about right. If the budget is large and it may not possible to fulfill it, then there is not any cherry-picking. That is, the filter is automatically turned off so it has no negative side effect, i.e. to keep it from damping the slot selection process.”).
[Claim 17] Hamedi discloses wherein, in the method, identifying the media channel comprises selecting one of an in-store printer, a mobile video, a desktop display, or a third party advertisement, based on a type of the one or more consumer devices and a current location of the consumers (¶ 47 – “…the system and methods disclosed herein may operate across multiple mediums and platforms such as websites, social media networks, and/or mobile apps.” An advertiser may target authors using a mobile app for tracking buses and other transportation apps, including road navigation apps, when there is a surplus of Uber™ drivers in a certain town or area to target ads for the Uber™ drivers; ¶ 163 – Also, recommended marketing actions can be performed in response to the question “Which locations should I geotarget?”; ¶ 133 – Various types of media are available for execution of engagement or advertising campaigns; ¶ 67 – “For example, if the user is a marketing agency, the agency's client may approve the post or advertisement. In another example, the post or advertisement may be approved by the social networking website where the post or advertisement will be published.” The social networking website is an example of a third party.; ¶ 195 – “For example, the system may partner with multiple advertising platforms, such as Google Adwords™, Twitter™ Ads, and Facebook™ Advertising. The various advertising products offered by these platforms can be displayed in the user's campaign menu.”; ¶ 82 – Targeting criteria may include a mobile device.; ¶ 66 – “The automatically published advertisements may come in many various forms. The advertisements may be through sponsored content on a news or pseudo-news website, may be native ads or editorial content on a social networking site or other web property, may be a standard banner advertisement, may be recommended and sponsored content on a shopping website, may be an e-mail, may be a paper mail advertisement, may be a sponsored video, may be a video featuring a product (product placement or subliminal advertising), or any other type of advertising.”).
[Claim 18] Hamedi discloses wherein the method further comprises receiving, in the server, from a client device, a pre-selected universe of consumers and an impact goal for the payload content, wherein the pre- selected universe of consumers includes the consumer segment and is based on a product or brand identified in the payload content, and the impact goal comprises a desired metric associating the consumer segment with the product or brand identified in the payload content (¶¶ 53-54, 57, 65, 94, 168 -- Various metrics can be assessed for various product brands, custom crowds, etc.; ¶ 105 – “By performing measurements on the activities of a specifically-defined crowd, the user is able to determine, for example, the size of a certain crowd of users or discussion group, as well as determine applicable audience activation and user acquisition metrics for that crowd. These measurements will help the user learn if his or her recent initiatives on any one or set of social media channels are positively impacting these metrics.”; ¶ 67 -- The use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.; ¶ 107 – “The system allows for comparison and tracking of two or more customized crowds against each other for the purpose of determining relative achievement and performance. This comparative measure may be a quantification of a desired action taken by certain authors in a crowd against the total crowd or comparisons along any of the metrics aforementioned. For example, the user may decide to compare two custom defined crowds versus viewing one in isolation to determine relative activation (or author acquisition) levels with respect to the total number of social media authors in a crowd or against any other control group. The user may also wish to determine whether he or she was more effective in activating crowd A or crowd B after executing campaigns during a certain time period. In another embodiment, the user may not even define these comparison crowds himself with the system; there may be potential to compare a user-defined crowd to a crowd defined by other users of the system or to a sample crowd or any collection of sample crowds already provided by the system.”; ¶ 200 -- “In another embodiment, the user's goals may be different for different mediums. For example, a user's top goal on Facebook™ may be to use advertising to increase online traffic and conversions on the user's website, while the user's goal on Twitter™ is to increase the number of followers the user has.”).
[Claim 19] Hamedi discloses determining a time duration of the promotional offer, promotion or recommendation in the payload content based on the one or more attributes of the consumers in the consumer segment (¶ 198 – “The user may also specify a total budget, daily budget, a custom bid, or max duration or time frame for a selected campaign.”; ¶ 199 – “The display of all of these suggested items may also reflect a ranking or sorting of the recommended tactics by order of importance or in terms of suggested priority to the user, shelf life (how long until the opportunity expires, or will need to be recalculated), or by the relevance and predicted impact of each tactic the goals of the user, or by the time and effort it will take for the user to execute the recommended tactic.”; ¶ 61 – “In another illustrative embodiment, alerts may be sent out based on temporal factors. For example, an alert on the progress of fluctuation criteria for a custom author crowd may be sent out every two weeks, regardless of whether any predetermined threshold is met. In another embodiment, an alert may be sent out if a predetermined threshold for fluctuation is met within a certain time period. For example, if the fluctuation of a custom author crowd based on a particular fluctuation criteria reaches 3% in one month, an alert may be sent out.”; ¶ 83 – “For example, a digital marketer for a department store may want to find all authors on Twitter™ who have mentioned Beyonce Knowles and that department store in the past year, like music, and used shopping-related keywords after December 1st. The advertiser may call this segment, “Beyonce Holiday Shoppers.” The logic in performing this search is that this population might be interested in an offer for Beyonce's new gift set that month.”; ¶ 67 – payload content).
[Claim 20] Hamedi discloses selecting a list of products or brands to be included in the payload content based on the one or more attributes of the consumers in the consumer segment (¶¶ 53-54, 57, 65, 94, 107, 168 -- Various metrics can be assessed for various product brands, custom crowds, etc.; ¶ 63 – “For example, a user may want to know if a famous celebrity authors a post about a user's product. In one specific example, an under the weather President of the United States may tweet positively about the efficacy of a particular brand of facial tissue. The brand manager of that particular brand of facial tissue may wish to be alerted that such a high profile individual is evacuating his or her nasal cavities upon their particular brand of paper handkerchiefs. The system can alert the brand manager thusly. The brand manager may then choose to promote such a post using the system or take other action based on the alert stemming from the President's now famous nasal mucus.”; ¶ 67 – payload content).
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hamedi et al. (US 2019/0034976) in view of Svirsky et al. (US 2014/0058827), as applied to claims 1 and 11 above, in view of Banerjee et al. (US 2017/0345049).
[Claim 6] Hamedi discloses selecting a metric for the payload content, the metric associating a product or brand in the payload content to a consumer behavior (¶¶ 103, 107; ¶ 67 – payload content);
selecting a group of consumers to form a control group based on the one or more attributes (¶ 67 -- The use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.; ¶ 107 – “The system allows for comparison and tracking of two or more customized crowds against each other for the purpose of determining relative achievement and performance. This comparative measure may be a quantification of a desired action taken by certain authors in a crowd against the total crowd or comparisons along any of the metrics aforementioned. For example, the user may decide to compare two custom defined crowds versus viewing one in isolation to determine relative activation (or author acquisition) levels with respect to the total number of social media authors in a crowd or against any other control group. The user may also wish to determine whether he or she was more effective in activating crowd A or crowd B after executing campaigns during a certain time period. In another embodiment, the user may not even define these comparison crowds himself with the system; there may be potential to compare a user-defined crowd to a crowd defined by other users of the system or to a sample crowd or any collection of sample crowds already provided by the system.”);
determining an impact of the payload content on the consumer segment based on a comparison of a value of the metric for the control group with a value of the metric for the consumer segment (¶¶ 103, 107); and
ranking the consumer segment based on the impact of the payload content on the consumer segment (¶ 103 – “The system may also include capabilities for benchmarking and ongoing monitoring. The system can provide a display that acts as a dashboard monitoring the activity of the advertiser's crowds on each advertising medium. This display may show any type of market share-like key performance indicators (KPIs), such as percentages of awareness, purchase intent, content relevance, crowd membership growth and crowd penetration, advertising fatigue, priming indicators, degree of topic or brand affinity, loyalty rates, crowd acquisition rates, etc. With each of these metrics there may also be a display of an average score and an anonymous industry leader to help instill a sense of competition and encourage continued activity. The conceptualization of a leaderboard may also use identifiable information of top achievers. Relative rankings in achievement may be determined with respect to performance in the same custom author crowd, a specific category of interest, within some competitor set, or along any other dimension that is capable of being tracked via fluctuation criteria. One or more of the user's crowds may also be included in these achievement calculations. As described, the system can also quantify overall success rates in each custom crowd. In this way, the user can view success measures at a current activity level in relation to the total possible pie at a given time. By having visibility into total possible achievement or relative achievement to other crowds or other advertisers, the advertiser may be incentivized to increase spend levels until reaching 100% or whatever his or her goal may be.”; ¶ 67 – payload content).
Hamedi discloses multiple groups, including a control group, to test campaigns (as discussed above); however, Hamedi does not explicitly disclose wherein the control group does not receive the payload content. Banerjee discloses that a treatment group and a control group may be used for A/B testing to assess the effectiveness of an advertising campaign. The treatment group receives an ad from the ad campaign while the control group receives an unrelated ad or no ad at all (Banerjee: ¶ 5). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi wherein the control group does not receive the payload content in order to provide a user with greater insight into the effectiveness of a particular marketing campaign, including its effect on incremental sales (as suggested in ¶ 5 of Banerjee).
[Claim 12] Hamedi discloses wherein the targeting imputation module is further configured to select a group of consumers to form a control group based on the one or more consumer attributes (¶ 67 -- The use of application programming interfaces (APIs) and other programmatic ad platforms to send advertising content is an example of sending the advertising content as payload content.; ¶ 107 – “The system allows for comparison and tracking of two or more customized crowds against each other for the purpose of determining relative achievement and performance. This comparative measure may be a quantification of a desired action taken by certain authors in a crowd against the total crowd or comparisons along any of the metrics aforementioned. For example, the user may decide to compare two custom defined crowds versus viewing one in isolation to determine relative activation (or author acquisition) levels with respect to the total number of social media authors in a crowd or against any other control group. The user may also wish to determine whether he or she was more effective in activating crowd A or crowd B after executing campaigns during a certain time period. In another embodiment, the user may not even define these comparison crowds himself with the system; there may be potential to compare a user-defined crowd to a crowd defined by other users of the system or to a sample crowd or any collection of sample crowds already provided by the system.”).
However. Hamedi does not explicitly disclose wherein the control group does not receive the payload content. Banerjee discloses that a treatment group and a control group may be used for A/B testing to assess the effectiveness of an advertising campaign. The treatment group receives an ad from the ad campaign while the control group receives an unrelated ad or no ad at all (Banerjee: ¶ 5). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi wherein the control group does not receive the payload content in order to provide a user with greater insight into the effectiveness of a particular marketing campaign, including its effect on incremental sales (as suggested in ¶ 5 of Banerjee).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hamedi et al. (US 2019/0034976) in view of Svirsky et al. (US 2014/0058827), as applied to claim 1 above, in view of Silberman et al. (US 2019/0019213).
[Claim 10] Hamedi creates custom author crowds and subsets of customer author crowds and monitors fluctuations for the various crowds (Hamedi: ¶¶ 51, 62, 65) and Hamedi’s invention operates through a server (Hamedi: ¶ 119). Hamedi does not explicitly disclose receiving, in the server, a request from a user to split the consumer segment into a maximum number of sub-segments to increase an impact of the payload content, wherein a sub-segment includes one or more consumers from the consumer segment. Silberman discloses that multiple campaigns may be selected, each campaign targeting a particular subset of the population (Silberman: ¶ 113). Silberman also performs tradeoff analysis to present various campaign scenarios from which a vendor may choose in light of the vendor’s main goal(s). For example, Silberman explains:
[0097] At the end of the process 700, the customers may be grouped into multiple groups (corresponding to the multiple marketing campaigns), with each group including a set of customers that were predicted to respond similarly to the corresponding marketing campaign along with the costs to execute the corresponding marketing campaign, the predicted ending vendor value, the predicted revenue which the corresponding marketing campaign will generate, and the like. As an example, use campaigns 302, 304, and 306 of FIG. 3. Assume the event timelines of 60,000 individuals were analyzed by augmenting each customer's event timeline with each of the campaigns 302, 304, and 306 to create 180,000 augmented event timelines. The augmented event timelines may be analyzed using the VVP model 312 to determine a predicted ending vendor value. A group corresponding to each marketing campaign may be created. For each individual of the 60,000 individuals, the associated three augmented timelines may be analyzed to identify the particular augmented event timeline with the highest ending vendor value. The particular marketing campaign with which the particular event timeline was augmented may be identified. The individual (along with associated data, such as cost, ending vendor value, etc.) may be added to the group corresponding to the particular marketing campaign. Assume a first group corresponding to the first marketing campaign includes 5,000 individuals, a second group corresponding to the second marketing campaign includes 35,000 individuals, and a third group corresponding to the third marketing campaign includes 20,000 individuals. The vendor may analyze the total vendor values and costs for each group and weigh various tradeoffs to determine which marketing campaign(s) to deploy. For example, deploying the second and third marketing campaigns may achieve a high total vendor value while going slightly over a marketing budget. The vendor may choose these tradeoffs and decide to allocate additional funds to the marketing budget to achieve the high total vendor value. Deploying the third marketing campaigns may reach fewer people but may achieve a slightly lower total vendor value while also satisfying the marketing budget. The vendor may choose these tradeoffs and decide to stay within the marketing budget. Deploying the second marketing campaigns may reach a larger number of people than the third marketing campaign alone but may achieve a slightly lower total vendor value than the third marketing campaign while also satisfying the marketing budget. The vendor may choose these tradeoffs and decide to reach more people.
Silberman acknowledges that a number of customers contributes to a per customer cost of each campaign and this is important for the tradeoff analysis (Silberman: ¶¶ 43, 45, 92, 95). In other word, when a maximum budget comes into play, there may be a desire to optimize reach in light of customer costs, thereby suggesting that it would be ideal to identify an optimal number of customers who have a relatively great reach. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Hamedi to perform the step of receiving, in the server, a request from a user to split the consumer segment into a maximum number of sub-segments to increase an impact of the payload content, wherein a sub-segment includes one or more consumers from the consumer segment in order to allow a user to more realistically set parameters, including those regarding a budget, to try to achieve the most reach and overall campaign success, given constraints on the campaign, like a maximum budget.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A