DETAILED ACTION
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 .
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
There is no antecedent basis in the specification for the subject matter of claim 17 (i.e., “wherein data from the marketing ecosystem is provided to generative pre-trained transformers for content generation to drive fan growth and community activation”).
There is no antecedent basis in the specification for the subject matter of claim 19 (i.e., “wherein fandom metrics are used as performance indicators including as a global standard of measurement of fans in order to compare fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index.”).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The “Detailed Description of the Invention” is presented on pages 13-16 of the Specification and is reproduced herein:
DETAILED DESCRIPTION OF THE INVENTION
A preferred embodiment of the present invention is illustrated in FIG. 1.
A client 100 interested in marketing the right content in the right manner to the right fan or fan community, chooses to compose appropriate content through a process of either internal content ideation 102 through an internal department, such as marketing or external content ideation 104 through an external provider. A combination of either or both of these ideation processes can be used for internal content generation 106 or external content generation 108. Content ingest and distribution tools 110 are then used by a distribution provider 112 to provide customized content by distribution channel 114 in a way that maximizes the perceived relevance of the content to the fans receiving the content in each distribution channel. These distribution processes are recorded through a blockchain and user interface 116 that tracks the content as it is delivered to which distribution channel and to which person receiving the content in a manner that is secure and/or verifiable. In a preferred embodiment, the customized content specific to the distribution channel is provided to known fans 118, including those designated by the client or associated with particular fandoms, goods, services, or activities, as well as a range of fans based on their level of engagement and their fanness, which is deemed significant for observation in a controlled setting. While it is possible to determine the set of known fans through social network or other information, the system and method provided here helps to ensure that the pool of known fans is properly sized for each distribution channel and tracks dynamically with the available content, and as fans change their behavior and interest. For a given initial set of known fans, a process of fan clustering 120 using statistical analysis, machine learning, or other clustering approaches helps to subdivide fans by their similarities, interests, characteristics, likes, dislikes, previous behavior, or other data that can help to assess fandom. These clusters help to inform on the likelihood that someone from the much larger universe of all possible fans 122 may also be identified as a known fan of the client or content being distributed. The fans or fan cluster of greatest relevance to the evaluation of content being distributed in a particular distribution channel of interest are invited to enter a marketing ecosystem 124 as a cohort. Within the ecosystem 126, unique content is provided to this cohort in an environment, such as through a secured embargo hub, where the cohort of fans are not allowed to discuss the content publicly but may comment on the content privately within the ecosystem to the provider of the content being distributed or between other members of the ecosystem. A process of machine learning 128 is used to review all of the content and fan interaction within the ecosystem, to help inform about characteristics that are perceived to be of value when labeling possible fans as known fans. This machine learning can be static or dynamic, in real-time or not, to help invite fans to the ecosystem or provide valuable information about what it means to be a fan for the specific distribution channel at that time relative to the content being provided. Within the ecosystem, each cohort interacts with static or dynamic content 130 with the information associated with the ecosystem under the blockchain. Processes of data capture 132 are used along with a cohort analysis 134 to provide information about fan behavior, in terms of the like or dislike of the content being provided, and even in terms of the quality of the fans themselves within the cohort and their dedication to the client and content itself. Information from the cohort analysis can be provided back to the client to help inform their decision making about their product, activity, or service, and the content being generated, helping to complete a cycle of learning about the mapping of content and fan behavior. Such analysis results in new ways to generate novel fandom metrics 136. This analysis about the fans within the ecosystem can be used as input to machine learning for content-fan relations 138 to generate new models of the interaction of fans and content resulting in a different content-fan model 140 and a content model 142. A bi-directional data gateway 144 is used to integrate and transfer the ecosystem data to third parties 148, resulting in efficient fan and community metrics data extraction and data delivery to third parties, or the inclusion of third-party data in the machine learning and content model calculations. The content model can then be used to help generate new content requests 146 for internal content generation in real time which may improve fan interest and interaction in a dynamic manner. The data resulting from this system and method can be visualized 150 and provided to a client in an interactive manner to allow for improved understanding of the dynamics of fan behavior relative to marketing material as well as metrics such as the environmental impact of the marketing.
It is to be expected that the description of the preferred embodiment is not a limitation on variations or extensions of the invention. For example, there may be many ways to measure fan quality within an ecosystem, such as an embargo hub, and in fact the embargo hub itself can become a resource for the analysis of fan behavior and novel fandom metrics. The embargo hub can also be used to help launch and develop content, products, services, advertising or their combination, by communicating content in a manner that is targeted to a cohort of fans securely in a closed marketing ecosystem. This approach also has the benefit of building fan advocacy and fan community for a brand and/or product, giving a special opportunity to view content before other consumers or possible fans. In addition, aspects of the blockchain could include tokenization with or without reward for specific content behaviors.
While our preferred embodiment focuses on a broad view of fan-content interaction and fan-community interaction, those trained in the art of content delivery recognize that our approach could also be either fan-content interaction only or fan-community interaction only. However, our preferred embodiment utilizes both for improved marketing content and delivery over time.
This invention described here is useful for optimizing marketing content to recipients of that content based on the content, distribution channel and engagement and information about the fan through an interactivity. The invention combines information about brands, fans, the fan communities and information about content through a private, secured, ecosystem in the form of an embargo hub that is either digital, analog, or a combination of digital and analog. The system and method can be used to drive new content generation, and media valuation to the right fans at the right time in a dynamic manner and allow for the understanding and identification of new likely fans from the far larger pool of possible fans. While in the preferred embodiment such content is digital, the same approach could also be used to provide analog content or combination of digital and analog content to consumers. While the preferred embodiment addresses content to fans associated with marketing of digital content, such fans or fandoms are of broad scope including already established brands or fandoms such as fans of creative material such as brands such as Apple, Tesla, and Netflix, but also Walmart and Amazon, universities, sports teams, musicians and other entertainment, gaming communities, but also can include any large group of people who feel similar engagement with a brand as in politics or religion. For instance, in the pharmaceutical industry, such engagement could take the form of users who have choices for specific medicines where the embargo hub represents a way to optimize marketing content to such user communities, develop them as fans of the medicine or company producing such medicines, and then market content to help improve market share of a larger community of individuals who would benefit from use of the medicines. Data from the embargo hub helps inform the pharmaceutical company about remote users, their demographics, trends, or other medical issues, assisting with future clinical development or clinical trial design or identification of additional medical indications. Data from the embargo hub can also help inform the pharmaceutical company about which fans are most likely to no longer be fans and hop to a competitor medicine if marketed incorrectly.
It will be appreciated that details of the foregoing embodiments, given for purposes of illustration, are not to be construed as limiting the scope of this invention. Although several embodiments of this invention have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention, which is defined in the following claims and all equivalents thereto. Further, it is recognized that many embodiments may be conceived that do not achieve all of the advantages of some embodiments, particularly of the preferred embodiments, yet the absence of a particular advantage shall not be construed to necessarily mean that such an embodiment is outside the scope of the present invention.
Claims 1-20 largely present concepts related to marketing (both in the “Detailed Description of the Invention” on pages 13-16 of the Specification and in the claims); however, Applicant’s original disclosure does not present sufficient detail to convey that Applicant, at the time the application was filed, had possession of the claimed invention. Examples are identified below.
[Claim 1] Applicant’s original disclosure does not explain how fans are clustered based on their fandom relative to the associated content and brand and specifically how modeling of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness is improved. These concepts require more than generic operations to complete (such as an algorithm), yet they are only mentioned at a high level in Applicant’s Specification and in the claimed invention. For example, Applicant’s original disclosure seems to rely on the use of known clustering approaches (Spec: pp. 13-14 – “For a given initial set of known fans, a process of fan clustering 120 using statistical analysis, machine learning, or other clustering approaches helps to subdivide fans by their similarities, interests, characteristics, likes, dislikes, previous behavior, or other data that can help to assess fandom. These clusters help to inform on the likelihood that someone from the much larger universe of all possible fans 122 may also be identified as a known fan of the client or content being distributed. The fans or fan cluster of greatest relevance to the evaluation of content being distributed in a particular distribution channel of interest are invited to enter a marketing ecosystem 124 as a cohort.”). While there is support for using known clustering approaches to subdivide fans based on certain characteristics, there is a gap between the use of known clustering approaches and how the steps of claim 1 as a whole result in improved modeling of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness.
Regarding fan community size, page 13 of the Specification states, “While it is possible to determine the set of known fans through social network or other information, the system and method provided here helps to ensure that the pool of known fans is properly sized for each distribution channel and tracks dynamically with the available content, and as fans change their behavior and interest.” (Emphasis added)
Regarding content timing, page 15 of the Specification states, “The system
and method can be used to drive new content generation, and media valuation to the right fans at the right time in a dynamic manner and allow for the understanding and identification of new likely fans from the far larger pool of possible fans.” (Emphasis added)
The problem remains that the Specification does not fill in the gaps as to how one takes known clustering algorithms and adapts them to the modeled factors (including all of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness) and yields improvements in the modeling. Applicant’s original disclosure presents some inputs and desired types of outputs, but the ability to get from the inputs to the outputs is presented as a black box of sorts. Applicant’s original disclosure does not fill in these gaps that are crucial to operation of the invention.
There is insufficient detail in Applicant’s original disclosure to explain how these concepts might actively be performed, thereby raising questions as to if Applicant had full possession of the claimed invention at the time the application was filed.
[Claim 20] Claim 20 recites wherein the data resulting from the embargo hub are used to dynamically calculate an environmental impact metric for marketing to a fan, fan community or fans and fan communities within each marketing channel. Regarding an environment impact metric, Applicant’s Specification states, “The data resulting from this system and method can be visualized 150 and provided to a client in an interactive manner to allow for improved understanding of the dynamics of fan behavior relative
to marketing material as well as metrics such as the environmental impact of the marketing.” (Spec: pp. 14-15) There is no explanation in the Specification as to what an environmental impact metric specifically represents or how it is calculated.
It is not clear that Applicant had full possession of the claimed invention at the time the application was filed.
[Claim 2] Applicant’s original disclosure does not explain how clustering fans and possible fans on a spectrum of fandom from least to most relative to brand and content, wherein the information from the embargo hub is used to improve understanding of a fan community, and/or fandom and fan clustering to identify new likely fans, invite fans to the ecosystem, and provide the right content to the right fan at the right time; and wherein the improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels are performed.
These concepts require more than generic operations to complete (such as an algorithm), yet they are only mentioned at a high level in Applicant’s Specification and in the claimed invention. For example, Applicant’s original disclosure seems to rely on the use of known clustering approaches (Spec: pp. 13-14 – “For a given initial set of known fans, a process of fan clustering 120 using statistical analysis, machine learning, or other clustering approaches helps to subdivide fans by their similarities, interests, characteristics, likes, dislikes, previous behavior, or other data that can help to assess fandom. These clusters help to inform on the likelihood that someone from the much larger universe of all possible fans 122 may also be identified as a known fan of the client or content being distributed. The fans or fan cluster of greatest relevance to the evaluation of content being distributed in a particular distribution channel of interest are invited to enter a marketing ecosystem 124 as a cohort.”). While there is support for using known clustering approaches to subdivide fans based on certain characteristics, there is a gap between the use of known clustering approaches and how the steps of claim 2 as a whole result in improved understanding to provide the right content to the right fan at the right time and in improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels.
Regarding content timing, page 15 of the Specification states, “The system
and method can be used to drive new content generation, and media valuation to the right fans at the right time in a dynamic manner and allow for the understanding and identification of new likely fans from the far larger pool of possible fans.” (Emphasis added)
The problem remains that the Specification does not fill in the gaps as to how one takes known clustering algorithms and adapts them to all of the evaluated factors (including providing the right content at the right time). Additionally, Applicant’s original disclosure does not provide any explanation as to how enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels is carried out. Applicant’s original disclosure presents some inputs and desired types of outputs, but the ability to get from the inputs to the outputs is presented as a black box of sorts. Applicant’s original disclosure does not fill in these gaps that are crucial to operation of the invention.
There is insufficient detail in Applicant’s original disclosure to explain how these concepts might actively be performed, thereby raising questions as to if Applicant had full possession of the claimed invention at the time the application was filed.
[Claim 11] Claim 11 recites wherein the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content provided through an embargo hub.
While there are references to “value” and “valuation” in the “Detailed Description of the Invention” (Spec: pp. 13-16) and Applicant’s original disclosure seems to rely on the use of known clustering approaches (Spec: pp. 13-14 – “For a given initial set of known fans, a process of fan clustering 120 using statistical analysis, machine learning, or other clustering approaches helps to subdivide fans by their similarities, interests, characteristics, likes, dislikes, previous behavior, or other data that can help to assess fandom. These clusters help to inform on the likelihood that someone from the much larger universe of all possible fans 122 may also be identified as a known fan of the client or content being distributed. The fans or fan cluster of greatest relevance to the evaluation of content being distributed in a particular distribution channel of interest are invited to enter a marketing ecosystem 124 as a cohort.”), there are no specific details regarding how the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content.
It is not clear that Applicant had full possession of the claimed invention at the time the application was filed.
[Claim 15] Claim 15 recites wherein the improved understanding of fandom takes the form of quantitative measures, qualitative measures, or a combination of quantitative and qualitative measures.
While there are references to “value” and “valuation” in the “Detailed Description of the Invention” (Spec: pp. 13-16) and Applicant’s original disclosure seems to rely on the use of known clustering approaches (Spec: pp. 13-14 – “For a given initial set of known fans, a process of fan clustering 120 using statistical analysis, machine learning, or other clustering approaches helps to subdivide fans by their similarities, interests, characteristics, likes, dislikes, previous behavior, or other data that can help to assess fandom. These clusters help to inform on the likelihood that someone from the much larger universe of all possible fans 122 may also be identified as a known fan of the client or content being distributed. The fans or fan cluster of greatest relevance to the evaluation of content being distributed in a particular distribution channel of interest are invited to enter a marketing ecosystem 124 as a cohort.”), there are no specific details regarding how the improved understanding of fandom takes the form of quantitative measures, qualitative measures, or a combination of quantitative and qualitative measures.
It is not clear that Applicant had full possession of the claimed invention at the time the application was filed.
[Claim 19] Claim 19 recites wherein fandom metrics are used as performance indicators including as a global standard of measurement of fans in order to compare fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index. Applicant’s original disclosure does not provide guidance as to how fandoms are compared across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index.
It is not clear that Applicant had full possession of the claimed invention at the time the application was filed.
Each dependent claim inherits the rejection(s) of the claim(s) from which each respectively depends.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
[Claim 1] Claim 1 recites:
A system for the improved understanding and use of fans, fandom, and fanness, the system comprising:
a secured marketing ecosystem and data storage system for storing information about content, fans, and their behavior, wherein internally or externally derived content is distributed to fans of any chosen degree of fanness;
where fans are clustered based on their fandom relative to the associated content and brand;
wherein specific fans of a known fan community and/or fandom are invited to the ecosystem to engage with content;
with data captured about the fan cohorts, content, and their interaction;
for the purpose of improved modeling of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness.
Claim 1 and dependent claims 3-10 and 20 present a system comprising a secured marketing ecosystem and data storage system for storing information about content, fans, and their behavior, wherein internally or externally derived content is distributed to fans of any chosen degree of fanness (particularly referencing the language of independent claim 1). A secured marketing ecosystem may simply be an arrangement of entities in a marketing environment, which presents a general concept. The data storage system may also simply be an arrangement, e.g., of data. While “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” implies some link to technology, such a link is not presented as a limiting structural element of system claim 5; therefore, the phrase “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” is currently just presented as an unembodied concept. The concepts alone are defined in terms of data. There are no structural elements presented in claims 1, 3-10, and 20. It is unclear if claims 1, 3-10, and 20 are meant to be an apparatus claim or not. If so, the claim lacks structural limitations that define the scope of an apparatus. As explained in MPEP § 2114(II), “[A]pparatus claims cover what a device is, not what a device does." Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1469, 15 USPQ2d 1525, 1528 (Fed. Cir. 1990) (emphasis in original). A claim containing a "recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus" if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987) (The preamble of claim 1 recited that the apparatus was "for mixing flowing developer material" and the body of the claim recited "means for mixing ..., said mixing means being stationary and completely submerged in the developer material." The claim was rejected over a reference which taught all the structural limitations of the claim for the intended use of mixing flowing developer. However, the mixer was only partially submerged in the developer material. The Board held that the amount of submersion is immaterial to the structure of the mixer and thus the claim was properly rejected.).” These claims appear to attempt to present an apparatus. Since there are no structural elements presented in claims 1, 3-10, and 20, there are no limitations defining claims 1, 3-10, and 20, thereby raising confusion regarding Applicant’s intended metes and bounds of these claims.
[Claim 3] Claim 3 recites:
The system of claim 1, wherein the marketing ecosystem can be analog or digital or a combination of analog and digital.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 3 and there are no limitations defining claim 3, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 4] Claim 4 recites:
The system of claim 1, wherein content ideation by a client is either internal or external and informed by the results of the marketing ecosystem.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 4, there are no limitations defining claim 4, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 5] Claim 5 recites:
The system of claim 1, wherein distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 5 and there are no limitations defining claim 5, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 6] Claim 6 recites:
The system of claim 1, wherein the set of fans of the known fan community or fandom or combination of known fan community and fandom are used to initialize an embargo hub within the marketing ecosystem to engage with content and wherein approaches of fan clustering or other fan metrics are used to identify possible fans that can also be invited to the ecosystem as a cohort if so desired.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 6 and there are no limitations defining claim 6, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 7] Claim 7 recites:
The system of claim 1, wherein processes of machine learning, artificial intelligence, statistical inference or their combination are used to learn about fans, their fan community, their interaction, their clustering, and behavior within the marketing ecosystem resulting in a content-fan community model that can be used to predict the success or failure of content to fans of different types over time and be used to identify possible new fans for specific content and be used to adjust new content ideation or generation in the form of a content model.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 7 and there are no limitations defining claim 7, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 8] Claim 8 recites:
The system of claim 7, wherein the content model is used to help inform content ideation, content generation or both content ideation and generation in light of a request for content that could be used for marketing.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 8 and there are no limitations defining claim 8, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 9] Claim 9 recites:
The system of claim 1, wherein the data captured about the fan cohorts, content, and their interaction are used for cohort analysis to understand and improve metrics about fans, their quality, their dynamics, and relation to content.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 9 and there are no limitations defining claim 9, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 10] Claim 10 recites:
The system of claim 1, wherein data captured about the fan cohorts can be used directly by clients to improve their own understanding of fans and their fan communities, of their products or services.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 10 and there are no limitations defining claim 10, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 20] Claim 20 recites:
The system of claim 1, wherein the data resulting from the embargo hub are used to dynamically calculate an environmental impact metric for marketing to a fan, fan community or fans and fan communities within each marketing channel.
As explained above (interpreting this claim as an apparatus), there are no structural elements presented in claim 20 and there are no limitations defining claim 20, thereby raising confusion regarding Applicant’s intended metes and bounds of this claim.
[Claim 2] Claim 2 recites:
A method for the improved understanding and use of fans, fandom, and fanness, the method comprising:
providing internal or external content through distribution tools, providers, and channels via a blockchain secured platform consisting of a marketing ecosystem wherein:
content is provided interactively to fans of any chosen degree of fanness, wherein data is captured about the interaction of fans and content, wherein data analysis can be used to improve understandings of content, fans, and their combination;
identifying and clustering fans and possible fans on a spectrum of fandom from least to most relative to brand and content;
wherein the information from the embargo hub is used to improve understanding of a fan community, and/or fandom and fan clustering to identify new likely fans, invite fans to the ecosystem, and provide the right content to the right fan at the right time;
wherein the improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels.
Claim 2 and dependent claims 11-19 are directed to a method (process). MPEP § 2106.03(I) states, “A process defines ‘actions’, i.e., an invention that is claimed as an act or step, or a series of acts or steps. As explained by the Supreme Court, a ‘process’ is ‘a mode of treatment of certain materials to produce a given result. It is an act, or a series of acts, performed upon the subject-matter to be transformed and reduced to a different state or thing.’ Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972) (italics added) (quoting Cochrane v. Deener, 94 U.S. 780, 788, 24 L. Ed. 139, 141 (1876)). See also Nuijten, 500 F.3d at 1355, 84 USPQ2d at 1501 ("The Supreme Court and this court have consistently interpreted the statutory term ‘process’ to require action"); NTP, Inc. v. Research in Motion, Ltd., 418 F.3d 1282, 1316, 75 USPQ2d 1763, 1791 (Fed. Cir. 2005) (‘[A] process is a series of acts.’) (quoting Minton v. Natl. Ass’n. of Securities Dealers, 336 F.3d 1373, 1378, 67 USPQ2d 1614, 1681 (Fed. Cir. 2003)). As defined in 35 U.S.C. 100(b), the term ‘process’ is synonymous with ‘method.’”
Claim 2 recites “wherein data analysis can be used to improve understandings of content, fans, and their combination.” “Can be” is an optional phrase, thereby raising questions as to the intended limitations imparted by this phrase in claim 2 (since optional actions are not positively recited steps). Additionally, “to improve understandings of content, fans, and their combination” is intended use and does not convey active steps to define how the improvement is achieved, which also raises questions regarding the intended metes and bounds of the claim.
The limitations “wherein the information from the embargo hub is used to improve understanding of a fan community, and/or fandom and fan clustering to identify new likely fans, invite fans to the ecosystem, and provide the right content to the right fan at the right time” and “wherein the improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels” (claim 2) are very narrative in nature. These limitations present intended goals, but it is not clear which positively recited steps (if any) are implied by these limitations. Even though “data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products” (claim 2), this just seems to describe the data as being inherently useful without necessarily explaining how the data is actively used as part of the method of the claim. Additionally, the limitation “enabling the quantification of the monetary value of marketing content relative to fan engagement” does not actively recite quantification as a limiting step of the method, thereby raising questions as to the intended metes and bounds of “enabling the quantification of the monetary value of marketing content relative to fan engagement” in terms of defining limiting steps of the method.
Additionally, some of the claim language presents general concepts of goals to be achieved, such as the limitation “thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels” (claim 2). It is unclear what the scope of “facilitating informed content investment decisions and predicting content elements” is or what is meant by content elements that are “likely to yield the highest returns based on fan behavior and engagement levels.” It is not clear how yield likelihood is assessed, for example.
[Claim 11] Claim 11 recites:
The method of claim 2, wherein the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content provided through an embargo hub.
“Can be” language is optional. Method claims are defined by positively recited steps/actions. It is not clear if Applicant intended for the spectrums of fandom or fanness to be quantified and understood over time relative to static or dynamic content provided through an embargo hub within the scope of claim 11 or not. Additionally, it is unclear what is meant by the spectrums of fandom or fanness being understood over time. This phrase is subjective and it is not clear what it meant by “being understood over time” or how such understanding would be consistently assessed.
[Claim 14] Claim 14 recites:
The method of claim 2, wherein fans may communicate and share insight together within the ecosystem in a secured manner.
“May” language is optional. Method claims are defined by positively recited steps/actions. It is not clear if Applicant intended for the fans to necessarily communicate and share insight together within the ecosystem in a secured manner or not.
[Claim 19] Claim 19 recites:
The method of claim 2, wherein fandom metrics are used as performance indicators including as a global standard of measurement of fans in order to compare fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index.
It is not clear what is meant by “compar[ing] fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index.” The scope of “in an equivalent manner” is vague and indefinite since it is unclear what would constitute an “equivalent” versus and “inequivalent” manner.
Each dependent claim inherits the rejection(s) of the claim(s) from which each respectively depends.
Appropriate correction is required.
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 “systems and methods for optimizing the delivery of marketing and non- marketing content to fans making use of an ecosystem, such as an embargo hub, for the analysis and understanding of fan behavior in terms of fan-content interaction, fan-community interaction, and their combination” (Spec: p. 10) without significantly more.
Step
Analysis
1: Statutory Category?
No – Claims 1, 3-10, and 20 present a system comprising a secured marketing ecosystem and data storage system for storing information about content, fans, and their behavior, wherein internally or externally derived content is distributed to fans of any chosen degree of fanness (particularly referencing the language of independent claim 1). A secured marketing ecosystem may simply be an arrangement of entities in a marketing environment, which presents a general concept. The data storage system may also simply be an arrangement, e.g., of data. While “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” implies some link to technology, such a link is not presented as a limited structural element of system claim 5; therefore, the phrase “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” is currently just presented as an unembodied concept. The concepts alone are defined in terms of data. There are no structural elements presented in claims 1, 3-10, and 20. These claims are directed to data per se, which does not fall into any of the four categories of patent eligible subject matter.
** In the interest of compact prosecution, claims 1, 3-10, and 20 will continue to be examined below as if they were proper system (apparatus) claims; however, appropriate correction is required.
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Apparatus (** claims 1, 3-10, 20), Process (claims 2, 11-19)
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 system for the improved understanding and use of fans, fandom, and fanness, the system comprising:
a secured marketing ecosystem and data storage system for storing information about content, fans, and their behavior, wherein internally or externally derived content is distributed to fans of any chosen degree of fanness;
where fans are clustered based on their fandom relative to the associated content and brand;
wherein specific fans of a known fan community and/or fandom are invited to the ecosystem to engage with content;
with data captured about the fan cohorts, content, and their interaction;
for the purpose of improved modeling of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness.
[Claim 2] A method for the improved understanding and use of fans, fandom, and fanness, the method comprising:
providing internal or external content through distribution tools, providers, and channels consisting of a marketing ecosystem wherein:
content is provided interactively to fans of any chosen degree of fanness, wherein data is captured about the interaction of fans and content, wherein data analysis can be used to improve understandings of content, fans, and their combination;
identifying and clustering fans and possible fans on a spectrum of fandom from least to most relative to brand and content;
wherein the information from the embargo hub is used to improve understanding of a fan community, and/or fandom and fan clustering to identify new likely fans, invite fans to the ecosystem, and provide the right content to the right fan at the right time;
wherein the improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels.
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. 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 “systems and methods for optimizing the delivery of marketing and non- marketing content to fans making use of an ecosystem, such as an embargo hub, for the analysis and understanding of fan behavior in terms of fan-content interaction, fan-community interaction, and their combination” (Spec: p. 10), which (under its broadest reasonable interpretation) is an example of marketing and managing social interactions (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.
Overall, the claims facilitate the selection of fans to whom certain content may be targeted, which is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
[Claim 1] Claim 1 does not incorporate any additional elements that serve to limit the scope of the system; this claim merely recites abstract ideas per se.
[Claim 2] Claim 2 recites providing internal or external content through distribution tools, providers, and channels via a blockchain secured platform.
The method 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.
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)).
It is noted that machine learning is recited as an option in claims 7, 8, 12, and 13 and machine learning, thus, is not necessarily used within the scope of claims 7, 8, 12, and 13. Nevertheless, even if the claims were recited to generally require the implementation of machine learning, 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 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 operations are generic machine learning operations. The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, 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 3] The system of claim 1, wherein the marketing ecosystem can be analog or digital or a combination of analog and digital.
[Claim 4] The system of claim 1, wherein content ideation by a client is either internal or external and informed by the results of the marketing ecosystem.
[Claim 5] The system of claim 1, wherein distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel. [While “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” implies some link to technology, such a link is not presented as a limited structural element of system claim 5; therefore, the phrase “distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel” is currently just presented as an unembodied concept.]
[Claim 6] The system of claim 1, wherein the set of fans of the known fan community or fandom or combination of known fan community and fandom are used to initialize an embargo hub within the marketing ecosystem to engage with content and wherein approaches of fan clustering or other fan metrics are used to identify possible fans that can also be invited to the ecosystem as a cohort if so desired.
[Claim 7] The system of claim 1, wherein processes of machine learning, artificial intelligence, statistical inference or their combination are used to learn about fans, their fan community, their interaction, their clustering, and behavior within the marketing ecosystem resulting in a content-fan community model that can be used to predict the success or failure of content to fans of different types over time and be used to identify possible new fans for specific content and be used to adjust new content ideation or generation in the form of a content model.
[Claim 8] The system of claim 7, wherein the content model is used to help inform content ideation, content generation or both content ideation and generation in light of a request for content that could be used for marketing.
[Claim 9] The system of claim 1, wherein the data captured about the fan cohorts, content, and their interaction are used for cohort analysis to understand and improve metrics about fans, their quality, their dynamics, and relation to content.
[Claim 10] The system of claim 1, wherein data captured about the fan cohorts can be used directly by clients to improve their own understanding of fans and their fan communities, of their products or services.
[Claim 20] The system of claim 1, wherein the data resulting from the embargo hub are used to dynamically calculate an environmental impact metric for marketing to a fan, fan community or fans and fan communities within each marketing channel.
[Claim 11] The method of claim 2, wherein the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content provided through an embargo hub.
[Claim 12] The method of claim 2, wherein the identifying and clustering of fans makes use of statistical analysis, machine learning, or combination of statistics and machine learning.
[Claim 13] The method of claim 12, wherein machine learning includes a combination of neural networks, deep learning, generative models, language models, evolutionary algorithms, reinforcement learning, support vector machines, random forest methods, swarm optimization and fuzzy logic. [Claim 13 further defines details of the machine learning, which is optional in claim 12 and, thus, not necessarily used within the scope of claims 12 and 13.]
[Claim 14] The method of claim 2, wherein fans may communicate and share insight together within the ecosystem in a secured manner.
[Claim 15] The method of claim 2, wherein the improved understanding of fandom takes the form of quantitative measures, qualitative measures, or a combination of quantitative and qualitative measures.
[Claim 16] The method of claim 2, wherein the fan user and marketing data from the ecosystem, and provided to the ecosystem, is provided via third-party systems and methods to inform decision making.
[Claim 18] The method of claim 2, wherein fan users may be human, other biological entities, representations of humans or biological entities, or completely autonomous, intelligent, non-biological forms.
[Claim 19] The method of claim 2, wherein fandom metrics are used as performance indicators including as a global standard of measurement of fans in order to compare fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
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. 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 “systems and methods for optimizing the delivery of marketing and non- marketing content to fans making use of an ecosystem, such as an embargo hub, for the analysis and understanding of fan behavior in terms of fan-content interaction, fan-community interaction, and their combination” (Spec: p. 10), which (under its broadest reasonable interpretation) is an example of marketing and managing social interactions (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.
Overall, the claims facilitate the selection of fans to whom certain content may be targeted, which is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
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 their independent claims.
[Claims 1, 3-10, 20] Claims 1, 3-10, and 20 do not incorporate any additional elements that serve to limit the scope of the system; these claims merely recites abstract ideas per se.
[Claim 2] Claim 2 recites providing internal or external content through distribution tools, providers, and channels via a blockchain secured platform.
[Claim 16] Claim 16 recites wherein the fan user and marketing data from the ecosystem, and provided to the ecosystem, is provided via third-party systems and methods using a bi-directional data gateway to inform decision making.
[Claim 17] Claim 17 recites wherein data from the marketing ecosystem is provided to generative pre-trained transformers for content generation to drive fan growth and community activation.
The method 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.
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)).
It is noted that machine learning is recited as an option in claims 7, 8, 12, and 13 and machine learning, thus, is not necessarily used within the scope of claims 7, 8, 12, and 13. Additionally, claim 17 recites wherein data from the marketing ecosystem is provided to generative pre-trained transformers for content generation to drive fan growth and community activation. Even if claims 7, 8, 12, and 13 were recited to generally require the implementation of machine learning, and regarding claim 17 (which references pre-trained transformers, i.e., types of large language models), 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 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 operations are generic machine learning operations. The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, 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.
** Considering the rejections under 35 U.S.C. § 112(b) above, the Examiner has applied prior art to the claims in light of her best understanding of the claimed invention.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-10, and 20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Pavic et al. (US 2021/0241307).
[Claim 1] Pavic discloses a system for the improved understanding and use of fans, fandom, and fanness (¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”), the system comprising:
a secured marketing ecosystem and data storage system for storing information about content, fans, and their behavior, wherein internally or externally derived content is distributed to fans of any chosen degree of fanness (fig. 1 --
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; ¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.” Sending a particular advertisement to a group of individuals matching a particular audience cluster is an example of distributing content to fans of a chosen degree or fanness.);
where fans are clustered based on their fandom relative to the associated content and brand (¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.”; ¶ 23 – “The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“);
wherein specific fans of a known fan community and/or fandom are invited to the ecosystem to engage with content (¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.”; ¶ 27 – “FIG. 2 is a process flow chart depicting an example processor-implemented method of targeting an appropriate promotion to an appropriate audience.”; fig. 2 –
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with data captured about the fan cohorts, content, and their interaction (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.”; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”);
for the purpose of improved modeling of content, content generation, content timing, fan community size, fan behavior, fan clustering, fandom, and fanness (¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“; It is additionally noted that “for the purpose of…” presents intended use and a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.).
NOTE: Claim 1 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 3] Pavic discloses wherein the marketing ecosystem can be analog or digital or a combination of analog and digital (¶ 24 – “The promotion engine 108 is implemented by the controller and is configured to generate one or more personalized promotions and cause a personalized promotion identified by the matching algorithm 106 to be sent using a digital display channel 112 (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of an audience cluster identified by the matching algorithm 106.”).
NOTE: Claim 3 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 4] Pavic discloses wherein content ideation by a client is either internal or external and informed by the results of the marketing ecosystem (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”).
NOTE: Claim 4 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 5] Pavic discloses wherein distribution of content to fans is through modern internet standards, blockchain, and user experience services, and customized by distribution channel (¶ 24 – “The promotion engine 108 is implemented by the controller and is configured to generate one or more personalized promotions and cause a personalized promotion identified by the matching algorithm 106 to be sent using a digital display channel 112 (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of an audience cluster identified by the matching algorithm 106.”; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity. Additionally, use of the blockchain allows distinct organization to share information to be used with the DAMPE system such as to allow A/B testing to be executed with multiple parties controlled by smart contracts. In that regard, a DAMPE system may be configured to: retrieve, from a blockchain data structure, success and failure data used to train a first matching algorithm, used by a first entity, to match a first personalized promotion to a first audience cluster; and train a second matching algorithm, used by a second entity using the retrieved success and failure data, to match a second personalized promotion to a second audience cluster; apply the second matching algorithm to match the second personalized promotion to the second audience cluster; and send the second personalized promotion using a digital display channel to a group of individuals matching characteristics of the second audience cluster.”).
NOTE: Claim 5 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 6] Pavic discloses wherein the set of fans of the known fan community or fandom or combination of known fan community and fandom are used to initialize an embargo hub within the marketing ecosystem to engage with content and wherein approaches of fan clustering or other fan metrics are used to identify possible fans that can also be invited to the ecosystem as a cohort if so desired (¶ 35 – “The iteratively adjusting may include refining the matching algorithm using machine learning techniques based on A/B testing. A/B testing may include: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement.” A/B testing may be performed on a particular audience cluster and some people in the cluster would receive one advertisement with a promotion while the other people in the cluster would serve as a control group and they would receive an advertisement without the promotion. All individuals with characteristic of the identified audience cluster maybe seen as possible fans of the cohort. It is also noted that the operation of inviting possible fans to the ecosystem as a cohort “can be performed…if so desired,” thereby rendering this operation optional within the scope of the claim.).
NOTE: Claim 6 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 7] Pavic discloses wherein processes of machine learning, artificial intelligence, statistical inference or their combination are used to learn about fans, their fan community, their interaction, their clustering, and behavior within the marketing ecosystem resulting in a content-fan community model that can be used to predict the success or failure of content to fans of different types over time and be used to identify possible new fans for specific content and be used to adjust new content ideation or generation in the form of a content model (¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity. Additionally, use of the blockchain allows distinct organization to share information to be used with the DAMPE system such as to allow A/B testing to be executed with multiple parties controlled by smart contracts. In that regard, a DAMPE system may be configured to: retrieve, from a blockchain data structure, success and failure data used to train a first matching algorithm, used by a first entity, to match a first personalized promotion to a first audience cluster; and train a second matching algorithm, used by a second entity using the retrieved success and failure data, to match a second personalized promotion to a second audience cluster; apply the second matching algorithm to match the second personalized promotion to the second audience cluster; and send the second personalized promotion using a digital display channel to a group of individuals matching characteristics of the second audience cluster.”; ¶¶ 31-35 – Iteratively adjust the algorithm, promotion, etc. based on results. This implies that adjustments are made over time.).
NOTE: Claim 7 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 8] Pavic discloses wherein the content model is used to help inform content ideation, content generation or both content ideation and generation in light of a request for content that could be used for marketing (¶ 32 – “The example process 200 includes iteratively adjusting the personalized promotion based on the results from the personalized promotion (operation 212). The iteratively adjusting may include: adjusting terms of the personalized promotion, sending the adjusted personalized promotion using a digital display channel to a plurality of individuals matching characteristics of the first audience cluster, recording the success and failure of the adjusted personalized promotion in driving a desired business result (e.g., margin, revenue, market share, etc.), measuring the success of the adjusted personalized promotion, and repeating the adjusting, sending, recording, and measuring until the desired business result is obtained or a predetermined promotion adjustment ending point has been reached (e.g., predetermined number or maximum promotion level reached).”).
NOTE: Claim 8 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 9] Pavic discloses wherein the data captured about the fan cohorts, content, and their interaction are used for cohort analysis to understand and improve metrics about fans, their quality, their dynamics, and relation to content (¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity. Additionally, use of the blockchain allows distinct organization to share information to be used with the DAMPE system such as to allow A/B testing to be executed with multiple parties controlled by smart contracts. In that regard, a DAMPE system may be configured to: retrieve, from a blockchain data structure, success and failure data used to train a first matching algorithm, used by a first entity, to match a first personalized promotion to a first audience cluster; and train a second matching algorithm, used by a second entity using the retrieved success and failure data, to match a second personalized promotion to a second audience cluster; apply the second matching algorithm to match the second personalized promotion to the second audience cluster; and send the second personalized promotion using a digital display channel to a group of individuals matching characteristics of the second audience cluster.”; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; It is additionally noted that “are used for …” presents intended use and a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.).
NOTE: Claim 9 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 10] Pavic discloses wherein data captured about the fan cohorts can be used directly by clients to improve their own understanding of fans and their fan communities, of their products or services (¶ 23 – “The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.”; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity. Additionally, use of the blockchain allows distinct organization to share information to be used with the DAMPE system such as to allow A/B testing to be executed with multiple parties controlled by smart contracts. In that regard, a DAMPE system may be configured to: retrieve, from a blockchain data structure, success and failure data used to train a first matching algorithm, used by a first entity, to match a first personalized promotion to a first audience cluster; and train a second matching algorithm, used by a second entity using the retrieved success and failure data, to match a second personalized promotion to a second audience cluster; apply the second matching algorithm to match the second personalized promotion to the second audience cluster; and send the second personalized promotion using a digital display channel to a group of individuals matching characteristics of the second audience cluster.”; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; It is additionally noted that “can be used…to…” presents intended use and a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.).
NOTE: Claim 10 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
[Claim 20] Pavic discloses wherein the data resulting from the embargo hub are used to dynamically calculate an environmental impact metric for marketing to a fan, fan community or fans and fan communities within each marketing channel (¶ 24 – “The promotion engine 108 is implemented by the controller and is configured to generate one or more personalized promotions and cause a personalized promotion identified by the matching algorithm 106 to be sent using a digital display channel 112 (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of an audience cluster identified by the matching algorithm 106. The promotion engine 108 is further configured to receive and record results from a personalized promotion it caused to be sent. The results may be received from a ROI (return on investment) engine implemented by a controller that collect sales results. The results may be included in a optimization report 114. The results may include data regarding the success and failure (e.g., conversion rate, did the promotion increase sales, did promotion cannibalize sales that would have taken place without the promotion, did promotion cause sales that would have taken place to take place earlier, etc.) of a personalized promotion.”; ¶ 30 – “The example process 200 includes applying a matching algorithm to match a personalized promotion to a particular audience cluster (operation 206) and sending the personalized promotion using a digital display channel (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of the particular audience cluster (operation 208). The applying and sending may include applying the matching algorithm to match a second personalized promotion to a second audience cluster; and sending the second personalized promotion using a digital display channel to a plurality of individuals matching characteristics of the second audience cluster.” A digital display channel is one example of a marketing channel that is monitored.).
NOTE: Claim 20 is an apparatus claim, but there are no structurally limiting elements recited in the claim; therefore, none of the claim limitations serve to patentably distinguish the claim over the prior art.
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 2, 11-12, 14-15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Pavic et al. (US 2021/0241307) in view of Dutta (US 2002/0072980).
[Claim 2] Pavic discloses a method for the improved understanding and use of fans, fandom, and fanness (¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”), the method comprising:
providing internal or external content through distribution tools, providers, and channels via a blockchain secured platform consisting of a marketing ecosystem wherein:
content is provided interactively to fans of any chosen degree of fanness, wherein data is captured about the interaction of fans and content, wherein data analysis can be used to improve understandings of content, fans, and their combination (fig. 1 --
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; ¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.” Sending a particular advertisement to a group of individuals matching a particular audience cluster is an example of distributing content to fans of a chosen degree or fanness.; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“; It is additionally noted that “wherein data analysis can be used to improve understandings of content, fans, and their combination” presents an optional limitation (i.e., due to “can be used”) and an intended use (i.e., due to “to improve understanding…”), neither of which are positively recited steps; therefore, this limitation does not serve to distinguish the claim over the prior art.);
identifying and clustering fans and possible fans on a spectrum of fandom including one most relative to brand and content (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.” Sending a particular advertisement to a group of individuals matching a particular audience cluster is an example of distributing content to fans of a chosen degree or fanness.; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“);
wherein the information from the embargo hub is used to improve understanding of a fan community, and/or fandom and fan clustering to identify new likely fans, invite fans to the ecosystem, and provide the right content to the right fan at the right time (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.” Sending a particular advertisement to a group of individuals matching a particular audience cluster is an example of distributing content to fans of a chosen degree or fanness. Targeting individuals in an particular audience cluster with certain content is an example of inviting them to like (i.e., be a fan of) the content and join others who also likely like the content.; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“ In other words, customer preferences are time-dependent, thereby implying that targeting particular offers is also time-dependent, i.e., should be targeted “at the right time.”);
wherein the improved understanding of fan and content interaction leads to improved content generation, improved understanding of fandom, and improved brand and marketing value, and where data driven fandom metrics are configured to analyze fan engagement with specific brands and/or products, enabling the quantification of the monetary value of marketing content relative to fan engagement, thereby facilitating informed content investment decisions and predicting content elements that are likely to yield the highest returns based on fan behavior and engagement levels (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”; ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.; ¶ 26 – “The information regarding the success and failure of the adjusted personalized promotion may be recorded in a blockchain data structure for future use by a DAMPE system adapted for use by a different entity.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.” Sending a particular advertisement to a group of individuals matching a particular audience cluster is an example of distributing content to fans of a chosen degree or fanness. Targeting individuals in an particular audience cluster with certain content is an example of inviting them to like (i.e., be a fan of) the content and join others who also likely like the content.; ¶ 16 – “The DAMPE system 100 uses a dynamic allocation engine (comprising a matching algorithm 106) that will assign relevant promotions to relevant audiences. The DAMPE system 100 is configured to consider data regarding the success and failure of promotions and iterate to improve the ability of the dynamic allocation engine to match specific audience groups with the right promotion.”; ¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“ In other words, customer preferences are time-dependent, thereby implying that targeting particular offers is also time-dependent, i.e., should be targeted “at the right time.”; ¶ 37 – “The subject matter described herein discloses apparatus, systems, techniques and articles for targeting an appropriate promotion to an appropriate audience. The apparatus, systems, techniques and articles described herein may automatically suggest target audiences for sales promotions, automatically suggest promotions that should be utilized based on inherent profitability, and ultimately deliver specific promotions to specific audiences in an effort to achieve maximum sales lift optimized against profitability. In one embodiment, a method of targeting a promotion to an appropriate audience includes: performing clustering analysis on anonymized customer data using artificial intelligence to segment the anonymized data into a plurality of audience clusters for optimized evaluation of the full populations such that the results can be applied across the entire population with a high degree of correlation; applying a matching algorithm to match a personalized promotion to a first audience cluster; sending the personalized promotion using a digital display channel to a plurality of individuals matching characteristics of the first audience cluster; receiving and recording results from the personalized promotion; and iteratively adjusting the personalized promotion based on the results from the personalized promotion. The iteratively adjusting includes: adjusting terms of the personalized promotion, sending the adjusted personalized promotion to a plurality of individuals, recording the success and failure of the adjusted personalized promotion, measuring the success of the adjusted personalized promotion, and repeating the adjusting, sending, recording, and measuring until a desired business result is obtained or a predetermined promotion adjustment ending point has been reached.”; ¶ 39 – “The method may further comprise: refining the matching algorithm using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a first advertisement having a fourth promotion to a group of individuals matching a fourth audience cluster and sending a second advertisement without the fourth promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) from the first advertisement and the second advertisement, and determining the success of the fourth promotion based on the comparison of results from the first advertisement and the second advertisement. The recording the success and failure of the adjusted personalized promotion may comprise recording the success and failure data in a blockchain data structure. The method may further comprise: retrieving, from a blockchain data structure, success and failure data used to train a first matching algorithm, used by a first entity, to match a first personalized promotion to a first audience cluster; training a second matching algorithm, used by a second entity using the retrieved success and failure data, to match a second personalized promotion to a second audience cluster; applying the second matching algorithm to match the second personalized promotion to the second audience cluster; and sending the second personalized promotion using a digital display channel to a group of individuals matching characteristics of the second audience cluster.”; ¶ 23 – “The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.”);
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As discussed above, Pavic performs the steps of identifying and clustering fans and possible fans on a spectrum of fandom including one most relative to brand and content (Pavic: ¶ 22 – “The matching algorithm 106 may be further configured to determine a SPP (susceptibility to purchase with a promotion) and degree of SPP for each of a plurality of audience clusters for a plurality of promotions based on sales results reported from past promotions. The matching algorithm 106 may then use the SPP and degree of SPP of an audience cluster to match the audience cluster to a particular promotion. The matching algorithm 106 may also use the SPP and degree of SPP of an audience cluster to identify the audience cluster that is the best match to a particular promotion.” Identifying an audience cluster that is the best match is an example of a chosen degree of fanness.).
However, Pavic does not explicitly disclose that the spectrum of fandom is from least to most relative to brand and content. However, Dutta provides a quantitative system for ranking customers from groups that are least relevant (e.g., lower-ranked) to most relevant (e.g., higher-ranked) (Dutta: fig. 9, ¶ 55). 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 Pavic such that the spectrum of fandom is from least to most relative to brand and content so that a marketer can categorize which customers should be targeted with content in light of a given situation, such as ones that are more likely to positively respond to targeted content and/or yield greater profitability or new customers or another preferred group, thereby more effectively meeting the varying needs of the marketer.
[Claim 11] Pavic does not explicitly disclose wherein the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content provided through an embargo hub. However, Pavic does not explicitly disclose that the spectrum of fandom is from least to most relative to brand and content. However, Dutta provides a quantitative system for ranking customers from groups that are least relevant (e.g., lower-ranked) to most relevant (e.g., higher-ranked) (Dutta: fig. 9, ¶ 55).
In ¶ 14, Pavic states, “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.“ In other words, customer preferences are time-dependent, thereby implying that targeting particular offers is also time-dependent, i.e., should be targeted “at the right time.” This suggests that content may be relevant in the moment, but its relevance may change over time.
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 Pavic wherein the spectrums of fandom or fanness can be quantified and understood over time relative to static or dynamic content provided through an embargo hub so that a marketer can categorize which customers should be targeted with content in light of a given situation, such as ones that are more likely to positively respond to targeted content and/or yield greater profitability or new customers or another preferred group, thereby more effectively meeting the varying needs of the marketer.
[Claim 12] Pavic discloses wherein the identifying and clustering of fans makes use of statistical analysis, machine learning, or combination of statistics and machine learning (¶ 33 – “The iteratively adjusting may allow for refining a clustering algorithm using machine learning techniques based on iteratively adjusting a plurality of promotions to determine the type of audience cluster to define for a particular type of promotion.”).
[Claim 14] Pavic discloses wherein fans may communicate and share insight together within the ecosystem in a secured manner (¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”).
[Claim 15] Pavic discloses wherein the improved understanding of fandom takes the form of quantitative measures, qualitative measures, or a combination of quantitative and qualitative measures ((¶ 24 – “The promotion engine 108 is implemented by the controller and is configured to generate one or more personalized promotions and cause a personalized promotion identified by the matching algorithm 106 to be sent using a digital display channel 112 (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of an audience cluster identified by the matching algorithm 106. The promotion engine 108 is further configured to receive and record results from a personalized promotion it caused to be sent. The results may be received from a ROI (return on investment) engine implemented by a controller that collect sales results. The results may be included in a optimization report 114. The results may include data regarding the success and failure (e.g., conversion rate, did the promotion increase sales, did promotion cannibalize sales that would have taken place without the promotion, did promotion cause sales that would have taken place to take place earlier, etc.) of a personalized promotion.”; ¶ 30 – “The example process 200 includes applying a matching algorithm to match a personalized promotion to a particular audience cluster (operation 206) and sending the personalized promotion using a digital display channel (e.g., social media, content management system, website) to a plurality of individuals matching characteristics of the particular audience cluster (operation 208). The applying and sending may include applying the matching algorithm to match a second personalized promotion to a second audience cluster; and sending the second personalized promotion using a digital display channel to a plurality of individuals matching characteristics of the second audience cluster.”).
[Claim 18] Pavic discloses wherein fan users may be human, other biological entities, representations of humans or biological entities, or completely autonomous, intelligent, non-biological forms (¶ 14 – “The example system 100 uses AI to create segmented customer audiences. Audiences are segmented based on their susceptibility to different promotional categories since different audience groups have different buying preferences. As an example, one audience group may respond to discounts these people desire the lowest price; another audience group may respond to exclusive content these people desire special or early access to products such as fancy sneakers, clothing, electronics, etc.; another audience group may respond to earning more loyalty points or a status such as those who choose flights or hotels based on earning points or those with an emotional loyalty to a company (e.g., always buy Ford); and another audience group may respond to ethical or environmental messaging a growing segment of people exist who want to make purchases that align with their personal beliefs. The example DAMPE system 100 is configured to create intelligent audience segments based on data. This can be challenging because a potential customer may be in a different audience group depending on the product type, extent of the offer, and a potential customer's preferences may change over time.”).
[Claim 19] Pavic discloses wherein fandom metrics are used as performance indicators including as a global standard of measurement of fans in order to compare fandoms across markets, companies, brands, or communities in an equivalent manner for the evaluation of relative brand performance or as a new consumer sentiment index (¶ 12 – “The subject matter described herein discloses apparatus, systems, techniques and articles for targeting an appropriate promotion to an appropriate audience. The apparatus, systems, techniques and articles described herein may automatically suggest target audiences for sales promotions, automatically suggest promotions that should be utilized based on inherent profitability, and ultimately deliver specific promotions to specific audiences in an effort to achieve maximum sales lift optimized against profitability.”; ¶ 23 – “The matching algorithm 106 may be refined using machine learning techniques based on A/B testing, wherein the A/B testing includes: sending a particular advertisement having a particular promotion to a group of individuals matching a particular audience cluster and sending a different advertisement without the particular promotion to a control group, measuring the results (e.g., conversion rate and/or profitability) for each advertisement, and determining the success of the promotion based on the comparison of difference in results from each advertisement. The matching algorithm 106 may also be adapted to identify an audience cluster that was defined for one product or service that may be matched to a personalized promotion for a different product or service.”; ¶ 14 – A product may be brand-specific.).
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Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pavic et al. (US 2021/0241307) in view of Dutta (US 2002/0072980), as applied to claims 2 and 12 above, in view of Walzthony et al. (US 2025/0218431).
[Claims 13, 17] Pavic does not explicitly disclose:
[Claim 13] wherein machine learning includes a combination of neural networks, deep learning, generative models, language models, evolutionary algorithms, reinforcement learning, support vector machines, random forest methods, swarm optimization and fuzzy logic;
[Claim 17] wherein data from the marketing ecosystem is provided to generative pre-trained transformers for content generation to drive fan growth and community activation.
In the area of audience sentiment reporting and regarding claim 13, Walzthony discloses wherein machine learning includes a combination of neural networks (¶ 72), deep learning (¶ 72), generative models (¶ 72), language models (¶ 72), evolutionary algorithms (¶ 82), reinforcement learning (¶ 69), support vector machines (¶ 68), random forest methods (¶ 68), swarm optimization (¶ 82) and fuzzy logic (¶ 82). Regarding claim 17, Walzthony discloses wherein data is provided to generative pre-trained transformers (¶¶ 68, 72).
Pavic uses machine learning to evaluate and refine its algorithms (Pavic: ¶¶ 20-21, 23) and Pavic evaluates data from the marketing ecosystem for content generation to drive fan growth and community activation (Pavic: ¶¶ 14, 16). 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 Pavic:
[Claim 13] wherein machine learning includes a combination of neural networks, deep learning, generative models, language models, evolutionary algorithms, reinforcement learning, support vector machines, random forest methods, swarm optimization and fuzzy logic;
[Claim 17] wherein data from the marketing ecosystem is provided to generative pre-trained transformers for content generation to drive fan growth and community activation
in order to provide Pavic with greater flexibility in identifying the best machine learning approaches that are most appropriate to analyze certain data sets. Additionally, the substitution of the various machine learning approaches disclosed in Walzthony for Pavic’s machine learning approaches would have been well within the technical capability of those skilled in the art prior to Applicant’s invention and such a substitution would have yielded predictable and expected results.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Pavic et al. (US 2021/0241307) in view of Dutta (US 2002/0072980), as applied to claim 2 above, in view of Zura et al. (US 2010/0282839).
[Claim 16] Pavic discloses wherein the fan user and marketing data from the ecosystem, and provided to the ecosystem, is provided via third-party systems (Pavic: ¶ 19 – “The example segmentation algorithm 104 applies AI, is executed by the controller, and is configured to cause anonymized audience data to be stored in the audience repository 102 along with any pre-existing customer data. The segmentation algorithm 104 is configured to ingest information regarding customer responses to promotions from multiple sources including resulting sales lift data, third party data sources 105, and any data that can be used to enrich the algorithm (including user provided data 107). The segmentation algorithm 104 is configured to ingest anonymized data regarding thousands of individuals who purchase goods and/or services in the marketplace, wherein the anonymized data may include demographic information and sales data, and wherein the sales data may include data regarding purchases made in response to some form of advertising and/or promotion and data regarding an increase in sales due to the advertising and/or promotion. The anonymized data may come from one company's data set or the anonymized data may come from the data sets of a plurality of unrelated companies. The segmentation algorithm 104 may also be configured to directly incorporate user provided information 107 which can be provided in the clear or via blockchain encoded data. When incorporating user provided information, the segmentation algorithm 104 is configured to incorporate the information in a way that allows the identity of the user to be unknown. This can allow audience groups to include user provided information, and the user provided information to influence promotion selection without the identity of the user being shared.”).).
Pavic does not explicitly disclose that the fan user and marketing data is provided via methods using a bi-directional data gateway to inform decision making. In the area of tracking the events of individuals (Zura: abstract), Zura “provides a bidirectional gateway to ensure data synchronization between the database 120 and database 122 operation under the control of an SQL server.” (Zura: ¶ 28) 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 Pavic such that the fan user and marketing data is provided via methods using a bi-directional data gateway to inform decision making in order to facilitate the synchronization of data between databases. Additionally, the substitution of Zura’s bidirectional data gateway for Pavic’s mode of communication would have been well within the technical capability of those skilled in the art prior to Applicant’s invention and such a substitution would have yielded predictable and expected results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Taubeneck et al. (US 11,100,533v) – Uses a blockchain to link an advertiser identifier to a target audience member identifier.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights