Distribution and Feedback Administrator

§101 §103

Detailed Action Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Acknowledgments The submission filed on 06/04/2026 is acknowledged. Status of Claims Claims 1-20 are pending. In the Amendment filed on 06/04/2026, claims 1, 5, 6, 8, 12-15, 19 and 20 were amended, and no claims were cancelled or added. Claims 1-20 are rejected. Information Disclosure Statement As noted in the previous Office Action, the information disclosure statement filed on 10/10/2025 omitted English-language translations of certain cited foreign documents. Applicant states (Amendment, p. 18) that the omitted English-language translations were submitted with the instant Amendment. However, these translations were not received, and the electronic filing receipt shows they were not included, with the instant submission. Applicant is advised that information material to the application should be submitted by way of an Information Disclosure Statement, pursuant to 37 CFR 1.56, 1.97 and 1.98. MPEP 609.04. Response to Arguments Regarding the drawing objections In view of the instant amendments to the specification, the drawing objections are overcome. Regarding the claim objections In view of the instant amendments to the claims, the claim objections are overcome. Regarding the rejection under 35 U.S.C. 101 Applicant's arguments have been fully considered but they are not persuasive. The Office responds to Applicant's arguments below. Headings and page numbers in the discussion below refer to Applicant's Response. Note the remarks at pp. 8-9 constitute merely a discussion of the law and guidance, except for the last sentence before section A (discussed below), which states: When analyzed correctly, and avoiding the improperly high level of generality applied in the Office Action, the character of amended claim 1 as a whole is directed to a technological improvement of generating an industry profile including a second type of content based at least partly on an output of the machine learning model based on receiving as training data, consumer actions based on a distributed industry profile including a first type of content. (p. 9; emphasis added) With the above sentence, Applicant squarely confirms the ineligibility of the claims. The bolded portion above, which is conceded to be that to which “the character of amended claim 1 as a whole is directed,” and which is alleged to be a technological improvement, is merely abstract idea. The machine learning model and the training thereof, assuming for the sake of argument that they were positively recited and not merely descriptors of data as now recited, would constitute merely generic computer elements recited at a high level of generality and not described, used off-the-shelf as a tool in their ordinary capacities to apply the abstract idea, or alternatively would constitute generally linking the use of a judicial exception to a particular technological environment or field of use.1 Step 2A, Prong One: The Claims Do Not Recite a Judicial Exception (pp. 9-11) Applicant argues that the claims “do not recite an abstract idea” (p. 10). Applicant argues that its claims do not “attempt to claim the underlying human activity or automation of the underlying human activity,” but rather “represent a specific technological improvement in generating an industry profile, not merely the concept of generating an industry profile generally” (p. 10; emphasis in original). The Office respectfully disagrees. As explained above and further below, there is no technological improvement in the claims. Accordingly, the claims do indeed claim the underlying method of organizing human activity and its automation. For the sake of completeness, even assuming arguendo that a claim encompassed ‘a technological improvement of generating an industry profile’, the claim still encompasses the concept of generating an industry profile, which is an abstract idea, so the claim still recites an abstract idea. Moreover, since generating an industry profile is an abstract idea, it is not clear what is meant by ‘a technological improvement of generating an industry profile’, since an improvement of an abstract idea is by definition not a technological improvement. Applicant further argues that claim 1 is “a technological improvement in generating an industry profile that includes a machine learning model that is specialized to select a particular type of content for the industry profile” (p. 10). The Office respectfully disagrees. Claim 1 merely baldly recites a machine learning model, not any specialization of a machine learning model. Claim 1 specifies, to a limited extent, the type of data that the machine learning model outputs, by reciting that a type of content is based on that output. This specifying reflects the abstract idea and does not represent any specialization (or improvement) of the machine learning model; thus, the claim does not represent any improvement in computer functioning/ other technology. The remainder of section A. (paragraph bridging pp. 10-11) merely rehearses the claim language alleged to constitute a technological improvement. This has already been addressed in that it has already been explained that the claim does not reflect a technological improvement. Applicant persists throughout asserting that because claim 1 represents a technological improvement in generating an industry profile, it does not “recite” an abstract idea. To reiterate, first, generating an industry profile is itself an abstract idea, and the claim recites this explicitly (namely, “generate an industry profile”), so that, even assuming arguendo that the claim had some technological improvement, it would still recite an abstract idea. Second, again, generating an industry profile is itself an abstract idea, so it does not seem to make sense to speak of a technological improvement in generating an industry profile, as this appears to posit a technological improvement in an abstract idea. (Note: an improvement in an abstract idea does not render a claim eligible under 35 U.S.C. 101.) Step 2A, Prong Two: The Claims Integrate Any Alleged Judicial Exception into a Practical Application (pp. 11-12) This section is merely an introduction to the following subsections 1-3. The Claims Provide a Technological Solution to a Technological Problem (pp. 12-13) Applicant argues: … an "important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome." … Here amended claim 1 provides a particular technological solution to the problem of static content generation devices that produce outputs that do not account for consumer actions based on previously distributed industry profiles. (p. 12; emphasis added) The Office responds: Insofar as the claims recite a particular way to achieve an outcome, this is merely a specifying/narrowing of the abstract idea. The problem allegedly solved by claim 1 as stated by Applicant (“static content generation devices that produce outputs that do not account for consumer actions based on previously distributed industry profiles”) is a matter of the abstract idea, not of technology or computer functioning. Applicant further argues: The claim recites a device tailored to solve this problem by integrating a uniquely structured closed-loop machine learning model. The device receives, as training data for the machine learning model, consumer actions based on a distributed industry profile including a first type of content from at least one presentation resource. The device generates, based on receiving the training data, a second industry profile including a second type of content based at least partly on a second output of the machine learning model. Thus, the device is structured in a particular way to improve industry profile generation by selecting a type of content based on receiving the consumer actions as training data. (p. 12; emphasis added) The Office responds: The content Applicant presents has already been addressed above: it amounts to merely an abstract idea applied using generic computer elements or generally linked to a particular technological environment or field of use. The claims do not recite any particular “structure” of the machine learning model, the device, or other additional elements, and even when we turn to the specification we find the additional elements are not particularly structured. What Applicant apparently means by “structured” is that the device/model uses or outputs particular data. The specifying of data does not constitute an improvement in technology or computer functioning. Applicant further argues that the claims are analogous to Desjardins (paragraph bridging pp. 12-13). The Office respectfully disagrees. As Applicant notes, in Desjardins the claims were found eligible as an improvement in machine learning. In stark contrast, Applicant’s claims include merely a bald recitation of machine learning, operating on particular data. The paragraph on p. 13 cites support in the specification for elements of claim 1. Applicant sums up this paragraph by saying that the claims “recite receiving specific training data and refining specific outputs of the machine learning model based on receiving the training data.” In response, as indicated, even when we turn to the specification we find the additional elements are presented at a high level of generality and are not described and thus do not provide any improvement in the additional elements. As Applicant has here summed up, the putative innovation/technological improvement is merely the specific training data and outputs of the machine learning: this putative improvement in data does not constitute a putative improvement in technology/computer functioning. The Claimed Limitations Are Not Mere “Apply It” Instructions (pp. 13-14) Applicant argues: “[A]dditional elements are more than 'apply it' or are not 'mere instructions' when the claim recites a technological solution to a technological problem. [sic, end quotation marks missing] MPEP § 2106.05(f)(1). As discussed above, the limitations of amended claim 1 are highly particular. The claim does not merely provide instructions for generating an industry profile. Instead, it details a distinct and specialized device tailored to generate an industry profile including a first type of content based at least partly on a first output of the machine learning model, distribute the industry profile, and then generate a second industry profile including a second type of content based on receiving as training data consumer actions based on the distributed industry profile. These interrelated technical operations far exceed merely applying the idea of generating an industry profile, and instead recite specific and concrete limitations that represent a technological solution, going beyond the alleged judicial exception to dynamically change a type of content of a generated industry profile based on training data from consumer actions. (pp. 13-14; emphasis added) The Office responds: This argument merely repeats previous arguments. As explained above, the particularity/specificity/concreteness of the claim constitutes a specificity/narrowing of the abstract idea. The device, including any additional elements, is not specialized but merely generic, recited at a high level of generality and not described. The Claims Do Not Monopolize an Abstract Idea (pp. 14-15) Applicant argues: A claim integrates a judicial exception into a practical application by applying the judicial exception in a manner that imposes a meaningful limit on the exception, ensuring that the claim does not monopolize or preempt the abstract idea. See MPEP § 2106.04(d). The device of claim 1 does not preempt or monopolize general concepts related to the generation of an industry profile, the generation of an industry profile of a first type of content or a second type of content, or even the general use of machine learning to generate an industry profile. Instead, amended claim 1 integrates a machine learning model into a device that generates an industry profile including a second type of content based at least partly on an output of the machine learning model based on receiving as training data, consumer actions based on a distributed industry profile including a first type of content. That is, the device is intrinsically tied to a specific implementation of an iterative feedback loop configured to improve generation of industry profiles via a specialized machine learning model. Thus, the claimed device is limited to a particular and non-preemptive solution for generating the industry profile using machine learning. In conclusion, the claims integrate any alleged judicial exception into a practical application by reciting a particular technological improvement to a device that generates an industry profile including a type of content based at least partly on an output of the machine learning model. The claims detail a distinct and specialized device tailored to generate an industry profile including a first type of content based at least partly on a first output of the machine learning model, distribute the industry profile, and then generate a second industry profile including a second type of content based on receiving as training data consumer actions based on the distributed industry profile. These interrelated technical operations far exceed merely applying the idea of generating an industry profile, and instead recite specific and concrete limitations that represent a technological solution, going beyond any alleged judicial exception to dynamically change a type of content of a generated industry profile based on training data from consumer actions. Therefore, the claims are not directed to a judicial exception under Step 2A, Prong Two, and the rejection under 35 U.S.C. § 101 should be withdrawn. (pp. 14-15; emphasis added) The Office responds: The alleged non-preemption merely reflects a specificity/narrowing of the abstract idea. What is “integrated” into the claim/device is a recitation of generic machine learning (as a descriptor of data, not positively recited). The “iterative feedback loop” is abstract idea. There is no “specialized” machine learning claimed; rather, there is a bald recitation of “machine learning” (not positively recited), which operates with data, where Applicant apparently deems the data innovative. The conclusion (2nd paragraph above) merely repeats previous arguments, which have been addressed above. Regarding the rejection under 35 U.S.C. 103 Applicant's arguments have been fully considered but are moot in view of the new combinations of references being used in the instant rejections. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a device, non-transitory computer-readable medium, or method, which are/is one of the statutory categories of invention. (Step 1: YES) Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a device, non-transitory computer-readable medium, and method for generating and distributing marketing outreach content to (potential) customers (see specification at 0002-0003, 0017-0019). For claims 1, 8 and 15 (claim 1 being deemed representative), the limitations (indicated below in bold) of: one or more processors configured to: provide a dashboard to a client; receive, via the dashboard, industry information related to the client; identify a set of search terms based at least partly on the industry information; and generate an industry profile including a first type of content based at least partly on (i) the set of search terms and (ii) a first output of a machine learning model;2 distribute the industry profile to at least one presentation resource; receive, as training data for the machine learning model,3 consumer actions based on the distributed industry profile from the at least one presentation resource; and generate, based on receiving the training data, a second industry profile including a second type of content based at least partly on a second output of the machine learning model.4 as drafted, constitute a process that, under the broadest reasonable interpretation, covers "certain methods of organizing human activity," specifically, "fundamental economic practices or principles" and/or "commercial or legal interactions." The Examiner notes that "fundamental economic practices" or "fundamental economic principles" describe concepts relating to the economy and commerce, including hedging, insurance, and mitigating risks, and "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. MPEP 2106.04(a)(2)II.A.,B. If a claim limitation, under its broadest reasonable interpretation, covers "fundamental economic practices or principles" and/or "commercial or legal interactions," then it falls within the "certain methods of organizing human activity" grouping of abstract ideas. Accordingly, claims 1, 8 and 15 recite an abstract idea. (Step 2A - Prong 1: YES. The claims recite an abstract idea.) This judicial exception is not integrated into a practical application. Claims 1, 8 and 15 recite the additional elements of "provide a dashboard"; "via the dashboard"; and “at least one presentation resource” (the foregoing recited in claims 1, 8 and 15), "one or more processors configured to:" (the foregoing recited in claim 1), and "A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:" (the foregoing recited in claim 8 ), that implement the abstract idea. These additional elements are not described by the applicant and they are recited at a high level of generality (i.e., one or more generic computer elements performing generic computer functions), such that they amount to no more than mere instructions to apply the exception using generic computer elements. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A - prong 2: NO. The additional elements do not integrate the abstract idea into a practical application.) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of "provide a dashboard"; "via the dashboard"; and “at least one presentation resource” (the foregoing recited in claims 1, 8 and 15), "one or more processors configured to:" (the foregoing recited in claim 1), and "A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:" (the foregoing recited in claim 8 ), to perform the noted steps amount to no more than mere instructions to apply the exception using generic computer elements. Mere instructions to apply an exception using generic computer elements cannot provide an inventive concept ("significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more. As such, claims 1, 8 and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.) Dependent claims 2-7, 9-14 and 16-20 are similarly rejected because they further define/narrow the abstract idea of independent claims 1, 8 and 15 as discussed above, and/or do not integrate the abstract idea into a practical application or provide an inventive concept such as would render the claims eligible, whether each is considered individually or as an ordered combination. As for further defining/narrowing the abstract idea: Dependent claims 2, 9 and 16 merely further describe wherein the industry profile comprises a listing of frequently asked questions (FAQs) associated with the set of search terms. Dependent claims 3, 10 and 17 merely further describe generate an interview script based at least partly on the listing of FAQs; provide … a prompt for each FAQ in the listing of FAQs; and capture a response to the prompt. Dependent claims 4, 11 and 18 merely further describe wherein each of the responses comprises … content of the client. Dependent claims 5, 12 and 19 merely further describe generate … content based at least partly on the responses; and distribute the … content …. Dependent claims 6 and 13 merely further describe wherein generating the interview script comprises … to the listing of FAQs. Dependent claims 7 and 14 merely further describe collect feedback and based at least partly on the collected feedback. Dependent claim 20 merely further describes at least partly generating the interview script by … to the listing of FAQs, collecting feedback, and based at least partly on the collected feedback. As for additional elements: Dependent claim 3 recites "the one or more processors further configured to:" and "via the dashboard." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claims 4, 11 and 18 recite "audiovisual." This recitation is at a high level of generality such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 5 recites "the one or more processors further configured to:"; "multimedia"; and "at least one presentation resource." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "the one or more processors further configured to:" and "at least one presentation resource.") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "multimedia"). Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claims 6 and 13 recite "applying the machine learning model." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or alternatively such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 7 recites "the one or more processors further configured to:"; "the at least one presentation resource"; and "train the machine learning model." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "the one or more processors further configured to:"; "at least one presentation resource"; and "train the machine learning model") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "train the machine learning model"). (The limitation "train the machine learning model" may be grouped under "mere instructions to apply the exception using a generic computer element" or alternatively under "generally linking the use of a judicial exception to a particular technological environment or field of use.") Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 10 recites "the plurality of processor-executable instructions further to:" and "via the dashboard." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 12 recites "the plurality of processor-executable instructions further to:"; "multimedia"; and "at least one presentation resource." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "the plurality of processor-executable instructions further to:" and "at least one presentation resource") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "multimedia"). Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 14 recites "the plurality of processor-executable instructions further to:"; "the at least one presentation resource"; and "train the machine learning model." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "the plurality of processor-executable instructions further to:"; "at least one presentation resource"; and "train the machine learning model") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "train the machine learning model"). (The limitation "train the machine learning model" may be grouped under "mere instructions to apply the exception using a generic computer element" or alternatively under "generally linking the use of a judicial exception to a particular technological environment or field of use.") Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 17 recites "via the dashboard." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 19 recites "multimedia"; and "at least one presentation resource." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, "at least one presentation resource.") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "multimedia"). Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claim 20 recites "applying the machine learning model"; "the at least one presentation resource"; and "training the machine learning model." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element (namely, “applying the machine learning model"; "the at least one presentation resource"; and "training the machine learning model") or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, "applying the machine learning model" and "training the machine learning model"). (The limitations "applying the machine learning model" and "training the machine learning model" may be grouped under "mere instructions to apply the exception using a generic computer element" or alternatively under "generally linking the use of a judicial exception to a particular technological environment or field of use.") Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Dependent claims 2, 9, and 16 do not recite any additional elements, and accordingly, for the reasons provided above with respect to the independent claims, are not patent eligible. Therefore, dependent claims 2-7, 9-14 and 16-20 are not patent eligible. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries set forth in Gearhart v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gearhart et al. (U.S. Patent Application Publication No. 2023/0086901 A1), hereafter Gearhart, in view of Hjelming et al. (U.S. Patent Application Publication No. 2014/0149379 A1), hereafter Hjelming, and further in view of Cella et al. (U.S. Patent Application Publication No. 2026/0154435 A1), hereafter Cella. Regarding Claims 1, 8 and 15 Gearhart teaches: (claim 1) one or more processors configured to: (0116 "FIG. 6 shows the directory method full video script generation process. In this figure, the vendor has already accessed the presentation module [of the listing site, shown in Fig. 1, not numbered, see 0062] and has chosen an option to record a full video. In a preferred embodiment, the option may be accessed using resources provided on the presentation module, but it may be accessed separately in one or more places, either in addition to or in place of the presentation module"; 0118 "The functions above [i.e., Fig. 6, 6020, 0118] can be done manually or by a software program." -- software running on the listing site to perform the disclosed operations, e.g., 6020, teaches "one or more processors configured to:"; note, more comprehensively, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010, teach that the vendor starts the entire processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the entire creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "one or more processors configured to:" with respect to the entire video creation process.) (claim 8) A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to: (0116 "FIG. 6 shows the directory method full video script generation process. In this figure, the vendor has already accessed the presentation module [of the listing site, shown in Fig. 1, not numbered, see 0062] and has chosen an option to record a full video. In a preferred embodiment, the option may be accessed using resources provided on the presentation module, but it may be accessed separately in one or more places, either in addition to or in place of the presentation module"; 0118 "The functions above [i.e., Fig. 6, 6020, 0118] can be done manually or by a software program." -- software running on the listing site to perform the disclosed operations, e.g., 6020, teaches "A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:"; note, more comprehensively, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010, teach that the vendor starts the entire processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the entire creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:" with respect to the entire video creation process.) provide a dashboard (0102, Fig. 4, presentation module) to a client; (0062 "The listing site contains a home page 1000 that introduces a vendor, interviewer, and end user to the presentation module and contains a menu that shows the listing site navigation." 0087, 0097, 0109, 0116 vendor accesses presentation module) receive, via the dashboard, industry information related to the client; (Fig. 6, 6010, 0117-0118, e.g., "The vendor starts the process 6000 by providing the media content sites where their content resides 6010. … The vendor may also provide the parameters on their target demographic or other information. [0018] … Alternately, the vendor can submit just the name of the entity, the given vendor type can be determined, and the media sites to be analyzed can be determined using that."; regarding via the dashboard: as per 0116, operation 6010 is performed by the vendor via the presentation module, which the vendor has accessed) identify a set of search terms based at least partly on the industry information; and (Fig. 6, 6020, 0118-0119, "The vendor is classified into a given vendor type, and media sites 6020 are analyzed for key words and key phrases that are present in the content sites the vendor has provided. … [0019] The key words and key phrases identified in 6020 are used to search media and search engines for questions using the key words and key phrases.") generate an … profile including at least a first type of content based at least partly on (i) the set of search terms and …; (0117-0121, Fig. 6, 6030, 6050 an interview script is generated based on FAQs derived from or obtained based on search terms, where the search terms pertain to a specific industry (e.g., surfboarding), the interview script includes text (a first type of content); 0090-0092, Fig. 2, 2040, 2070, 0098-0099, Fig. 3, 3040, 3060, 0113-0115, Fig. 5, 5060, 5090, 0121, Fig. 6, 6050 a vendor profile (e.g., video created from interview script / vendor page including video and other information) is generated; the vendor profile includes, e.g., video, transcript, business information, and links (0099) (a first type of content); note also 0070 “vendor profile page”) distribute the … profile to at least one presentation resource; (0090-0092, Fig. 2, 2040, 2070, 2080, 0098-0099, Fig. 3, 3050, 3060, 3070, 0114, 0121 the presentation resource is taught by, e.g., the listing site and/or other platforms (e.g., social media)) … As per above, Gearhart teaches a vendor profile, based on an industry (interview script of video is based on FAQs derived from or obtained based on search terms pertaining to a specific industry). However, Gearhart does not explicitly disclose an "industry profile." Nonetheless, Hjelming teaches an "industry profile" (as well as the recited context of this claim term) and accordingly remedies this deficiency of Gearhart. Hjelming teaches: generate an industry profile including at least a first type of content based at least partly on (i) the set of search terms and …; distribute the industry profile to at least one presentation resource; (Abstract, 0001-0004, claims 1, 12, 18, Fig. 3: regarding an industry profile including at least a first type of content: Hjelming teaches "business micro sites, also called business profile web pages" (claim 1, 0002), which include a questions and answers (Q&A) section (0001, 0004). The micro site "lists local and related businesses ranked by SIC (Standard Industry Code)" (0003), and includes a “local and related businesses content section … comprising: [a section listing other micro sites related by SIC (Standard Industry Code)]" (claim 18). "The Q&A section [embedded in the micro site] contains content about the business, product(s) and location (city, state) and also contains embedded contextual web links that links to similar content within the same business vertical determined by SIC (Standard Industry Code)." (Abstract). Thus, Hjelming's micro site and/or the micro site's Q&A section teaches the recited "industry profile." The Q & A section and other information/links on the micro site constitute a first type of content.; regarding a first type of content based at least partly on the set of search terms: "Micro sites may also contain a questions and answers (Q&A) text content section that further enhances the localization relevance and keyword matching which improves page rank on search engines. The Q&A is built primarily to flag localized relevance for search engines index spiders, but this section also provides human readable and formatted content relevant for web consumption. … Its purpose is to provide relevant local keywords in the content. The technique auto-generates pages for local search relevance, thus achieving top search engine ranking for business relevant keywords within each SIC (Standard Industry Code) category. Since the technique utilizes … highly localized keyword selection within the actual web page content …." (0004), "A special questions and answers content section [in the micro site] … comprising: … links embedded in each question and answer, comprising: … each html link contains business selected keywords within its text field;" (claim 12, see Fig. 3) -- inasmuch as the micro site/Q&A section is generated for the purpose of providing relevant keywords and thus keywords are intentionally included in the content of the micro site/Q&A section, the micro site/Q&A section is a first type of content (generated) based at least partly on the set of search terms (note also the term "keyword" refers to "keywords that customers actually use to find local businesses" (0001), i.e., a keyword is a search term); regarding generated: "This system provides a web site [micro site] …. It does this by providing a web page [micro site] containing the proprietary Question & Answer content section (Abstract); "The purpose with this invention is to automatically build relevant local area pages called micro sites …. YouInWeb has generated millions business profile web pages called micro sites" (0002); "The technique auto-generates pages" (0004); regarding distribute the industry profile to at least one presentation resource: Abstract, 0002, 0004, claim 1, the business micro site / business profile web page, including its Q & A section, is “distributed” (claim 1) or “deployed”/”hosted” (Abstract), that is to say, on a platform that hosts websites/webpages) It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified Gearhart's video business directory (directory of videos profiling businesses for presenting the businesses to consumers), by incorporating therein these teachings of Hjelming regarding generating and distributing business profile micro sites including Q&A sections based on search terms, because providing such micro sites to businesses would improve their page rank on search engines (improve their page rank position in search results), which is a particular problem for small to medium sized local businesses, see Hjelming, 0001-0008. Gearhart in view of Hjelming does not explicitly disclose the limitations involving machine learning but Cella remedies this deficiency (as well as additional recited context of these limitations in the first “generating” step below). Cella teaches: generate …* including a first type of content based at least partly on … (ii) a first output of a machine learning model; … receive, as training data for the machine learning model, consumer actions based on the distributed …* from the at least one presentation resource (e.g., 1056, Fig. 34, client device 3414); and generate, based on receiving the training data, a second …* including a second type of content based at least partly on a second output of the machine learning model. (1050-1056, see also Figs. 33, 34, e.g., “[1051] In some cases, an embedded marketplace 3304 may include a personalized content generation service that generates content based on personalized content of the user 3416. … The content generation trainers can review the user's past content, select and clean the data, and train a machine learning model … to generate content that is consistent with the user's style. In particular, the content generated by the trained machine learning model is based on a style of content that the user 3416 previously generated and/or approved (e.g., content that the user 3416 previously created, edited, selected, and/or accepted, optionally using one or more content creation tools). [1052] In embodiments, content selected by the user 3416 to train the machine learning model may be associated with metadata that informs the training of the machine learning model. For example, the user 3416 may highlight portions of the content that the user 3416 particularly likes and/or that the user 3416 believes to be associated with the user's personal and/or preferred style of content, which may cause the trained machine learning model to produce more content that includes, reflects, refers to, and/or is consistent with such highlighted content. ... The user 3416 may receive some content generated by a trained machine learning model and may provide feedback related to the generated [sic, content] (e.g., editing, one or more ratings, commentary, or the like), which may cause the trained machine learning model to produce more content based on the provided feedback. … [1056] … The marketplace embedding module 3410 may present the generated content to the user 3416 on the client device 3414, and/or may take further actions based on the generated content (e.g., transmitting the generated content to another user or device; posting the generated content on the Internet; revising the generated content based on feedback from the user 3416; and/or generating new content based on feedback from the user 3416).”) * Note the (generated and distributed) “industry profile” (replaced by the asterisked ellipses in the above limitations) is taught by Hjelming, as set forth above. It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Gearhart's video business directory (directory of videos profiling businesses for presenting the businesses to consumers), as modified by Hjelming's teachings regarding generating and distributing business profile micro sites including Q&A sections based on search terms, by incorporating therein these teachings of Cella regarding, when distributing content to users, generating content whose type, style, etc. is consistent with the preferences of a particular user, based on the user’s actions regarding distributed content and responses to distributed content, including training a machine learning model to generate the appropriate type, style, etc. of content based on the user’s actions/feedback in respect of distributed content, for both initially distributed content and subsequently distributed content distributed subsequently to training the machine learning model based on user actions/feedback, because customizing/curating content tailored to a user’s preferences increases user engagement/satisfaction/interest with/in the content and therefore increases satisfaction on the part of the user and on the part of the marketer reaching out to the user as potential new customer or the like. See Cella, 1050-1056, e.g., 1050 (content customized/curated/personalized to align with user preferences/tastes, etc. overcomes the prior art problem of generating stock content for all users). Regarding Claims 2, 9 and 16 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1, 8 and 15 as set forth above. Gearhart teaches generating an interview script based on FAQs derived from or obtained based on search terms, where the search terms pertain to a specific industry (e.g., surfboarding), see 0117-0121, Fig. 6, 6030, 6050, and thus Gearhart teaches "… a listing of frequently asked questions (FAQs) associated with the set of search terms", but Gearhart does not explicitly disclose an "industry profile." However, Hjelming further teaches: wherein the industry profile comprises a listing of frequently asked questions (FAQs) associated with the set of search terms. (Abstract, 0001-0004, claims 1, 12, 22: "This system provides a web site [micro site] … providing a web page [micro site] containing the proprietary Question & Answer content section ... The Q&A section contains content about the business, product(s) and location (city, state) and also contains embedded contextual web links that links to similar content within the same business vertical determined by SIC (Standard Industry Code)." (Abstract); "Micro sites may also contain a questions and answers (Q&A) text content section that further enhances the localization relevance and keyword matching which improves page rank on search engines. The Q&A is built primarily to flag localized relevance for search engines index spiders, but this section also provides human readable and formatted content relevant for web consumption. The Q&A section lists ten or more questions about each business. Its purpose is to provide relevant local keywords in the content. The technique auto-generates pages for local search relevance, thus achieving top search engine ranking for business relevant keywords within each SIC (Standard Industry Code) category. Since the technique utilizes … highly localized keyword selection within the actual web page content …." (0004); "A special questions and answers content section … comprising: … links embedded in each question and answer, comprising: … each html link contains business selected keywords within its text field;" (claim 12, see Fig. 3). As per Abstract and 0004, Hjelming's micro site and/or its Q&A section teaches "the industry profile comprises a listing of frequently asked questions (FAQs)." As per 0004 and claim 12, Hjelming teaches "wherein the industry profile comprises a listing of frequently asked questions (FAQs) associated with the set of search terms.") It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Gearhart's video business directory (directory of videos profiling businesses for presenting the businesses to consumers), as modified by Hjelming's teachings regarding generating and distributing business profile micro sites including Q&A sections based on search terms, and as further modified by Cella regarding, when distributing content to users, generating content whose type, style, etc. is consistent with the preferences of a particular user, based on the user’s actions regarding distributed content and responses to distributed content, including training a machine learning model to generate the appropriate type, style, etc. of content based on the user’s actions/feedback in respect of distributed content, for both initially distributed content and subsequently distributed content distributed subsequently to training the machine learning model based on user actions/feedback, by incorporating therein these further teachings of Hjelming regarding the business profile micro sites including Q&A sections based on search terms, because providing such micro sites to businesses would improve their page rank on search engines (improve their page rank position in search results), which is a particular problem for small to medium sized local businesses, see Hjelming, 0001-0008. Regarding Claims 3, 10 and 17 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1, 8 and 15 and intervening claims 2, 9 and 16 as set forth above. Gearhart further teaches: (claim 3) the one or more processors further configured to: (0116 "FIG. 6 shows the directory method full video script generation process. In this figure, the vendor has already accessed the presentation module [of the listing site, shown in Fig. 1, not numbered, see 0062] and has chosen an option to record a full video. In a preferred embodiment, the option may be accessed using resources provided on the presentation module, but it may be accessed separately in one or more places, either in addition to or in place of the presentation module"; 0118 "The functions above [i.e., Fig. 6, 6020, 0118] can be done manually or by a software program." -- software running on the listing site to perform the disclosed operations, e.g., script generation, e.g., 6020, teaches "one or more processors configured to:" with respect to "generate an interview script based at least partly on the listing of FAQs;"; further, 0108-0110 ("[0109] The process starts 5000 when the vendor accesses the presentation module 5010. The option is chosen to record a full video 5020. In the preferred embodiment, the option is a feature on the presentation module and preferably on the listing site." [0110] … a mobile or web-based application 5080 as the interviewer."; 0114-0115 ("If the vendor chooses to use a mobile or web-based application 5080 to generate their full video, they access the software and record an interview as a full video 5090. [0115] The mobile or web-based application may employ an avatar to ask questions, may have questions appear on a screen, or may employ another method for presenting questions to the vendor.") -- the mobile or web-based application (0110, 0114-0115) provided on the presentation module/listing site to perform the interview process, i.e., to conduct and record the interview by presenting questions and capturing answers (Fig. 5, 0108-0115) teaches "one or more processors configured to:" with respect to "provide, via the dashboard, a prompt for each FAQ in the listing of FAQs; and capture a response to the prompt"; note, more comprehensively, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010, teach that the vendor starts the entire processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the entire creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "one or more processors configured to:" with respect to the entire video creation process.) (claim 10) the plurality of processor-executable instructions further to: (0116 "FIG. 6 shows the directory method full video script generation process. In this figure, the vendor has already accessed the presentation module [of the listing site, shown in Fig. 1, not numbered, see 0062] and has chosen an option to record a full video. In a preferred embodiment, the option may be accessed using resources provided on the presentation module, but it may be accessed separately in one or more places, either in addition to or in place of the presentation module"; 0118 "The functions above [i.e., Fig. 6, 6020, 0118] can be done manually or by a software program." -- software running on the listing site to perform the disclosed operations, e.g., script generation, e.g., 6020, teaches "the plurality of processor-executable instructions further to:" with respect to "generate an interview script based at least partly on the listing of FAQs;"; further, 0108-0110 ("[0109] The process starts 5000 when the vendor accesses the presentation module 5010. The option is chosen to record a full video 5020. In the preferred embodiment, the option is a feature on the presentation module and preferably on the listing site." [0110] … a mobile or web-based application 5080 as the interviewer."; 0114-0115 ("If the vendor chooses to use a mobile or web-based application 5080 to generate their full video, they access the software and record an interview as a full video 5090. [0115] The mobile or web-based application may employ an avatar to ask questions, may have questions appear on a screen, or may employ another method for presenting questions to the vendor.") -- the mobile or web-based application (0110, 0114-0115) provided on the presentation module/listing site to perform the interview process, i.e., to conduct and record the interview by presenting questions and capturing answers (Fig. 5, 0108-0115) teaches "the plurality of processor-executable instructions further to:" with respect to "provide, via the dashboard, a prompt for each FAQ in the listing of FAQs; and capture a response to the prompt"; note, more comprehensively, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010, teach that the vendor starts the entire processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the entire creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "the plurality of processor-executable instructions further to:" with respect to the entire video creation process.) generate an interview script based at least partly on the listing of FAQs; (Fig. 6, 6050, 0121, e.g., "A full video script 6050 is generated using the identification of the highest ranked questions and/or questions based on the key words and phrases."; regarding the listing of FAQs: under broadest reasonable interpretation, the selection of a limited number of highest ranked questions (to create the script), as per 0120-0121, comprises a set (list) of questions; relatedly, this same set of questions is referred to (with respect to the completed video) as a list, see 0013 ("The vendor may also be provided a list of questions that they answer while being videotaped"), 0114 ("The vendor may also be given the option to choose a number of questions from a list"); 0113 ("either using the video script generated in FIG. 6 , [or] using a different set of questions" -- indicating that the video script generated in FIG. 6 (i.e., the selection of a limited number of highest ranked questions, as per 0120-0121) comprises a set (list) of questions)) provide, via the dashboard (0102, Fig. 4, presentation module), a prompt for each FAQ in the listing of FAQs; and (regarding provide a prompt for each FAQ: Fig. 5, 5090, 0114-0115, e.g., "The mobile or web-based application may employ an avatar to ask questions, may have questions appear on a screen, or may employ another method for presenting questions to the vendor." (0115) (alternatively, Fig. 5, 5060, 0113); 0012 "posing those questions to the vendors"; 0013 "The vendor may also be provided a list of questions that they answer while being videotaped"; 0014 e.g., "by giving prompts"; regarding via the dashboard: the interview is conducted on the presentation module, see 0109 ("The process starts 5000 when the vendor accesses the presentation module 5010. The option is chosen to record a full video 5020. In the preferred embodiment, the option is a feature on the presentation module and preferably on the listing site."); regarding in the listing of FAQs: 0013 "The vendor may also be provided a list of questions that they answer while being videotaped"; 0114 "The vendor may also be given the option to choose a number of questions from a list"; 0113 "either using the video script generated in FIG. 6 , [or] using a different set of questions" -- indicating that the video script generated in FIG. 6 (i.e., the selection of a limited number of highest ranked questions, as per 0120-0121) comprises a set (list) of questions) capture a response to the prompt. (Fig. 5, 5080, 0114-0115 (alternatively, Fig. 5, 5060, 0113), 0012-0014 (note 0012 "their answers"), 0007 ("answering"), 0008 ("answer"), the conducting of the interview / recording of the video, in which the vendor answers the questions, includes the capturing of the vendor's answers (responses) to the questions (prompts)) Regarding Claims 4, 11 and 18 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1, 8 and 15 and intervening claims 2, 3, 9, 10, 16 and 17 as set forth above. Gearhart further teaches: wherein each of the responses comprises audiovisual content of the client. (0012-0014, Fig. 5, 5080, 0114-0115 (alternatively, Fig. 5, 5060, 0113), inasmuch as the interview is conducted by having the vendor (client) answer questions, and the interview as a whole, including the vendor's answers, are recorded as a video, the answers that are recorded comprise audiovisual content of the vendor; note here one of ordinary skill in the art understands that a video, and a video recording device/software (0004, 0113-0114, 0127, 0129), as taught by Gearhart records both video (visual) and audio, hence audiovisual, content; 0115 "an avatar to ask questions" is taught as an alternative to "have questions appear on a screen," suggesting (rendering obvious) that the avatar asks questions (and receives answers) using audio) Regarding Claims 5, 12 and 19 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1, 8 and 15 and intervening claims 2, 3, 9, 10, 16 and 17 as set forth above. Gearhart further teaches: (claim 5) the one or more processors further configured to: (0108-0110 ("[0109] The process starts 5000 when the vendor accesses the presentation module 5010. The option is chosen to record a full video 5020. In the preferred embodiment, the option is a feature on the presentation module and preferably on the listing site." [0110] … a mobile or web-based application 5080 as the interviewer."; 0114-0115 ("If the vendor chooses to use a mobile or web-based application 5080 to generate their full video, they access the software and record an interview as a full video 5090. [0115] The mobile or web-based application may employ an avatar to ask questions, may have questions appear on a screen, or may employ another method for presenting questions to the vendor.") (or alternatively to 0114-0115, 5080, 5090: 0112-0113, 5040-5060) -- the mobile or web-based application (0110, 0114-0115) provided on the presentation module/listing site (or alternatively, in the case of the human interviewer, the website and remote recording software, as per 0112-0113, 5040-5060) for performing the interview process, i.e., for conducting and recording the interview by presenting questions and capturing answers (Fig. 5, 0108-0115), teaches "one or more processors configured to:" with respect to "generate multimedia content based at least partly on the responses"; further, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010: similarly to the above, the vendor starts the processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "one or more processors configured to:" with respect to "generate multimedia content based at least partly on the responses" and with respect to "distribute the multimedia content to at least one presentation resource") (claim 12) the plurality of processor-executable instructions further to: (0108-0110 ("[0109] The process starts 5000 when the vendor accesses the presentation module 5010. The option is chosen to record a full video 5020. In the preferred embodiment, the option is a feature on the presentation module and preferably on the listing site." [0110] … a mobile or web-based application 5080 as the interviewer."; 0114-0115 ("If the vendor chooses to use a mobile or web-based application 5080 to generate their full video, they access the software and record an interview as a full video 5090. [0115] The mobile or web-based application may employ an avatar to ask questions, may have questions appear on a screen, or may employ another method for presenting questions to the vendor.") (or alternatively to 0114-0115, 5080, 5090: 0112-0113, 5040-5060) -- the mobile or web-based application (0110, 0114-0115) provided on the presentation module/listing site (or alternatively, in the case of the human interviewer, the website and remote recording software, as per 0112-0113, 5040-5060) for performing the interview process, i.e., for conducting and recording the interview by presenting questions and capturing answers (Fig. 5, 0108-0115), teaches "the plurality of processor-executable instructions further to:" with respect to "generate multimedia content based at least partly on the responses"; further, 0087, 0090, 0097-0098, Fig. 2, 2010, Fig. 3, 3010: similarly to the above, the vendor starts the processes of Figs. 2 and 3, wherein the video is created and distributed, by accessing 2010, 3010 the presentation module (of the listing site); as such, the creation and distribution processes are carried out using the presentation module (of the listing site) -- this teaches "the plurality of processor-executable instructions further to:" with respect to "generate multimedia content based at least partly on the responses" and with respect to "distribute the multimedia content to at least one presentation resource") generate multimedia content based at least partly on the responses; and (Fig. 5, 5090, 0114-0115 (alternatively, Fig. 5, 5060, 0113) generating the (completed, recorded) video teaches generate multimedia content based at least partly on the responses) distribute the multimedia content to the at least one presentation resource. (Fig. 3, 3050, 0098, Fig. 2, 2040, 2070, 0090-0092, 0113-0114, completed video (multimedia content) is uploaded/downloaded (distribute) to platform/listing site (presentation resource)) Regarding Claims 6 and 13 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1 and 8 and intervening claims 2, 3, 5, 9, 10 and 12 as set forth above. Gearhart further teaches: wherein generating the interview script comprises applying … to the listing of FAQs. (As per the same prior art that is set forth in the rejection of claims 3 and 10 above, namely: Fig. 6, 6050, 0121, e.g., "A full video script 6050 is generated using the identification of the highest ranked questions and/or questions based on the key words and phrases." (generating the interview script). Further, generating the interview script is performed by using (applying) a computer (albeit not a machine learning model), again as taught by the same prior art that is set forth in the rejection of claims 3 and 10 above, namely: 0116 "FIG. 6 shows the directory method full video script generation process. In this figure, the vendor has already accessed the presentation module [of the listing site, shown in Fig. 1, not numbered, see 0062] and has chosen an option to record a full video. In a preferred embodiment, the option may be accessed using resources provided on the presentation module, but it may be accessed separately in one or more places, either in addition to or in place of the presentation module"; 0118 "The functions above [i.e., Fig. 6, 6020, 0118] can be done manually or by a software program." -- software running on the listing site to perform the disclosed operations, e.g., script generation, e.g., 6020, teaches "one or more processors configured to:" (claim 3) and likewise applying a computer with respect to "generate an interview script based at least partly on the listing of FAQs"; regarding the listing of FAQs: again this limitation is taught by the same prior art that is set forth in the rejection of claims 3 and 10 above, namely: under broadest reasonable interpretation, the selection of a limited number of highest ranked questions (to create the script), as per 0120-0121, comprises a set (list) of questions; relatedly, this same set of questions is referred to (with respect to the completed video) as a list, see 0013 ("The vendor may also be provided a list of questions that they answer while being videotaped"), 0114 ("The vendor may also be given the option to choose a number of questions from a list"); 0113 ("either using the video script generated in FIG. 6 , [or] using a different set of questions" -- indicating that the video script generated in FIG. 6 (i.e., the selection of a limited number of highest ranked questions, as per 0120-0121) comprises a set (list) of questions)) Cella further teaches: … applying the machine learning model …. (Cella teaches uses of a machine learning model for tasks comparable to / in a context comparable to Gearhart's generation of scripts, where for Gearhart a script comprises questions generated from the same questions. Specifically, Cella teaches applying a machine learning model for: natural language processing of various sorts/scenarios including language recognition, synthesis, conversion (e.g., among text, speech), formulating and answering questions, etc., see 0207-0208; generating prompts to tell a story, generating related story content from other story content, generating content based on other related content, generating one version or perspective of a story from a different version or perspective of the story, generating a story based on a prompt containing events of the story, updating the story based on user feedback, generating and refining media/media content based on a prompt, other media, or user interaction, generating a story board from a theme, creating a story from tropes, see 0263-0267, 0280-0283; generating text/conversation, comparing dialogue with a script, for navigating a customer journey, see 0287; generating patent claims from a patent disclosure, generating a patent disclosure from figures, generating prompt-response sets for IP-related (e.g., litigation) actions, generating a patent application from an outline, etc., see 0303-0306; generating scripts from audio for voice over of data and/or presentation material, so as to generate lifelike AI-generated voices for podcasts, presentations, etc., mitigating the need for vocal artists or complex recording equipment, see 0308; generating a response in response to a query, generating a translation, etc., see 0752, 0755-0756; marketplace description module generates descriptions of a marketplace or of marketplace entities such as products, participants, etc., including comparisons of goods, prices, etc., descriptions based on a particular context based on a prompt, descriptions relative to other descriptions, such as of an update, trend, or comparison of different marketplaces or of a single marketplace at different times, etc., see 1008) It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Gearhart's video business directory (directory of videos profiling businesses for presenting the businesses to consumers), as modified by Hjelming's teachings (applied in the rejections of claims 1, 2, 8, 9, 15 and 16 above) regarding generating and distributing business profile micro sites including Q&A sections based on search terms, and as further modified by Cella regarding, when distributing content to users, generating content whose type, style, etc. is consistent with the preferences of a particular user, based on the user’s actions regarding distributed content and responses to distributed content, including training a machine learning model to generate the appropriate type, style, etc. of content based on the user’s actions/feedback in respect of distributed content, for both initially distributed content and subsequently distributed content distributed subsequently to training the machine learning model based on user actions/feedback, by incorporating therein these further teachings of Cella regarding applying machine learning to tasks such as generating a script, because applying machine learning to Gearhart's script generation task would improve task performance (e.g., accuracy, efficiency). Regarding Claims 7 and 14 Gearhart in view of Hjelming and Cella teaches the limitations of base claims 1 and 8 and intervening claims 2, 3, 5, 6, 9, 10, 12 and 13 as set forth above. Cella further teaches: (claim 7) the one or more processors further configured to: (1051, 1056, Fig. 34, processor(s) of the computer(s)/server(s) on which the embedded marketplace platform 1950, marketplace embedding module 3410, and/or content generation trainers run; see 0351-0352, 0799, 0993, 0997-0998, 1087-1089, 1109-1113) (claim 14) the plurality of processor-executable instructions further configured to: (0351-0352, 1087-1089, 1108; see 1051, 1056, Fig. 34, instructions for operations carried out by embedded marketplace platform 1950, marketplace embedding module 3410, and/or content generation trainers; see 0799, 0993, 0997-0998, 1109-1113) collect feedback from the at least one presentation resource; and (1050-0156) train the machine learning model based at least partly on the collected feedback. (1050-0156) It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Gearhart's video business directory (directory of videos profiling businesses for presenting the businesses to consumers), as modified by Hjelming's teachings (applied in the rejections of claims 1, 2, 8, 9, 15 and 16 above) regarding generating and distributing business profile micro sites including Q&A sections based on search terms, as further modified by Cella regarding (1), when distributing content to users, generating content whose type, style, etc. is consistent with the preferences of a particular user, based on the user’s actions regarding distributed content and responses to distributed content, including training a machine learning model to generate the appropriate type, style, etc. of content based on the user’s actions/feedback in respect of distributed content, for both initially distributed content and subsequently distributed content distributed subsequently to training the machine learning model based on user actions/feedback, and (2) applying machine learning to tasks such as generating a script, by incorporating therein these further teachings of Cella regarding training a machine learning model based on feedback (e.g., feedback regarding customer preferences as to type of content for distributed content), because customizing/curating content tailored to a user’s preferences increases user engagement/satisfaction/interest with/in the content and therefore increases satisfaction on the part of the user and on the part of the marketer reaching out to the user as potential new customer, or the like, see Cella, 1050-1056, e.g., 1050 (content customized/curated/personalized to align with user preferences/tastes, etc. overcomes the prior art problem of generating stock content for all users), and more generally because training a machine learning model based on feedback is a basic aspect of machine learning and serves to improve the machine learning model’s performance. Regarding Claim 20 The content of claim 20 is identical or almost identical to the content of claims 6, 7, 13 and 14, collectively. As such, the prior art cited as teaching claims 6, 7, 13 and 14, collectively, also teaches claim 20. As such, claim 20 is rejected on the same grounds as are claims 6, 7, 13 and 14, collectively. Conclusion The prior art made of record and not relied upon, as set forth in the accompanying Notice of References Cited (PTO-892), is considered pertinent to applicant's disclosure. Francis (US-20190302993-A1) teaches creating a multimedia presentation from a template, including determining prompts (questions) based on the template, presenting the prompts to the user so as to elicit from the user one or more answers to the questions, recording the answers, and compiling the multimedia presentation including the answers. Byron (US-20180211287-A1) teaches generating a script based on FAQs, and inserting the script (FAQs) into a template to generate digital content, such as an advertisement, where the generated script may be a video clip. Cohen-Martin (US-20100023863-A1) teaches generating from a template a customized video advertisement including data objects from websites. Cohee (US-20120004982-A1) teaches generating from a template a customized video advertisement, publishing it to online distribution venues, and performing video search engine optimization (SEO) for the video advertisement. Grouf (US-20070156524-A1) teaches providing a customized advertisement, comprising receiving information related to an advertiser; based on the information related to the advertiser, presenting to the advertiser a subset of a set of templates of advertisement components; receiving from the advertiser a selection of a template; playing the template that the advertiser selected, without custom information from the advertiser; prompting the advertiser to provide input to create a customized advertisement based on the selected template; and creating a customized advertisement from the selected template and the input provided by the advertiser, where playing the template comprises playing video and/or audio. Bierner (US-8463648-B1) teaches systems and methods for automatically identifying and extracting topics from questions posted by users to a consultation system, for creating a new consultation category or creating new keywords for promotion of a new category serving an underserved or unserved topic, comprising breaking down posted questions into semantic components to determine topics related to a desired subject matter segment, and using the best topics in pay-per-click advertising related to the subject matter segment. Sarid (US-20070136248-A1) teaches a system and method that searches for news story articles including at least one question, where the at least one question is associated with at least one search keyword received from a user, retrieves at least one news story article including at least one question associated with the at least one keyword, and provides to the user a heading, including the at least one question, associated with the retrieved at least one news story article. Williams (US-20240281410-A1) teaches crawling a primary online content object to create a set of results from the crawling, parsing the set of results to generate key phrases and a content corpus from the primary online content object, processing the key phrases and the content corpus to create topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity, generating a suggested topic that is similar to a topic of the topic clusters, generating a strategy for development of online presence content, and providing a list of the suggested topics that are of highest semantic relevance for an enterprise based on the parsing of the set of results from the crawling. Whalin (US-20250054045-A1) teaches receiving, e.g., from a search engine, a user query corresponding to a business, receiving data related to the business and data related to a user associated with the user query, and generating a curated profile corresponding to the business based on the data related to the business and the data related to the user associated with the query, wherein generating the curated profile includes generating, by a machine learning model, a business description, the business description providing an overview description of the business. Figueroa (US-20090222328-A1) teaches receiving keywords from business profile pages, associating the keywords with business profiles, creating a profile page database including the keywords, and, when a search request is received from a search, identifying profile pages containing keywords matching keywords in the search, and sending the pages to the searcher. Blemaster (US-20150081440-A1) teaches, inter alia, determining keywords related to a given industry type or to given products and services, posting a brand name to a keyword directory website associated with its industry or products/services, and suggesting domain names that may be beneficial for the brand owner to register. Chitrapura (US-11295375-B1) teaches, inter alia, keywords or terminology that define a business context (e.g., industry vertical). Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS W PINSKY whose telephone number is (571)272-4131. The examiner can normally be reached on 8:30 am - 5:30 pm ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DOUGLAS W. PINSKY/ Examiner, Art Unit 3626 /JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626 1 Note the independent claims do not positively recite training a machine learning model, generating output by a machine learning model, or any other use of a machine learning model. The limitations that recite “training” or “machine learning” merely describe/characterize data, not actual training or use of machine learning. (Contrast dependent claims 6, 7, 13, 14 and 20.) However, in order to promote compact prosecution, the Office’s remarks assume arguendo that the independent claims could be amended to positively recite training and use of machine learning, and accordingly the Office’s remarks address that hypothetical scenario of amending. 2 Note “first output of a machine learning model” is merely data that is described as a certain kind of data, namely, as output of a machine learning model. The independent claims do not positively recite training a machine learning model, generating output by a machine learning model, or any other use of a machine learning model. 3 Note “training data for the machine learning model” is merely data that is described as a certain kind of data, namely, as training data for the machine learning model. The independent claims do not positively recite training a machine learning model, generating output by a machine learning model, or any other use of a machine learning model. 4 Note “second output of a machine learning model” is merely data that is described as a certain kind of data, namely, as output of a machine learning model. The independent claims do not positively recite training a machine learning model, generating output by a machine learning model, or any other use of a machine learning model.