DETAILED ACTION
Status of Claims
This Action is in response to App. 19303,187 filed 08/18/2025. The present Application is a continuation of 18/064,197 filed 12/09/2022. The preliminary amendment filed 10/06/2025 has been acknowledged. Claims 1-20 are cancelled. Claims 21-40 are added. Claims 21-40 are currently pending and have been examined.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12417470 (hereafter Pat. 470). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are anticipated by reference claims as shown:
As per claim 21:
A computer system for optimizing delivery of digital secondary content, comprising: one or more processors and corresponding memory of a digital content optimization service to:
determine, using one or more trained machine learning models trained to learn engagement patterns for respective types of calls to action (CTA) in different ones of one or more digital delivery contexts, performance scores of a plurality of digital secondary content versions indicating version performance in each of one or more digital delivery contexts; (See Pat. 470 claim 3, “The system of claim 1, wherein the audio ad delivery system is configured to: store the conversion patterns as structured records, wherein a structured record of a conversion pattern indicates a type of conversion result, a combination of ad features, user features, or context attributes that is correlated with the type of conversion result, and a pattern score associated with the conversion pattern; and output the structured records via a programmatic interface of the audio ad delivery system, wherein the structured records are used by an audio ad production system to programmatically generate new audio ads or modify one or more audio ads in the group.”)
determine, using the engagement patterns for respective types of CTAs in the different ones of the one or more digital delivery contexts, particular digital secondary content versions for programmatic delivery to target user devices in particular ones of the one or more digital delivery contexts; and (See Pat. 470 claim 1, “automatically optimize delivery of subsequent audio ads, comprising deliver, via the one or more audio ad servers, additional plays of the audio ad files, wherein the one or more audio ad servers use the conversion patterns for respective types of CTAs in the different listening contexts to select particular versions of the audio ads for the additional plays in particular listening contexts of the listening contexts.”)
control automated delivery of subsequent digital transmissions of the particular digital secondary content versions to the target user devices, wherein the controlled automated delivery of the particular digital secondary content versions, determined using the engagement patterns for respective types of CTAs, to the target user devices increases an engagement metric for a group of the delivered digital secondary content versions. (See Pat. 470 claim 1, “automatically optimize delivery of subsequent audio ads, comprising deliver, via the one or more audio ad servers, additional plays of the audio ad files, wherein the one or more audio ad servers use the conversion patterns for respective types of CTAs in the different listening contexts to select particular versions of the audio ads for the additional plays in particular listening contexts of the listening contexts.”)
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.
The factual inquiries 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.
Claim(s) 21, 25-26, 28, 32-33, 35, 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duke et al. (US 20190392487 A1) (hereafter Duke), in view of Ratiu et al. (US 20170118303 A1) (hereafter Ratiu).
As per claim 21:
A computer system for optimizing delivery of digital secondary content, comprising: one or more processors and corresponding memory of a digital content optimization service to:
determine, using one or more trained machine learning models trained to learn engagement patterns for respective types of calls to action (CTA) in different ones of one or more digital delivery contexts, performance scores of a plurality of digital secondary content versions indicating version performance in each of one or more digital delivery contexts; (See Duke ¶0056, “In the fourth step, a Performance Data Collector 226 causes performance data of prior ads to be obtained or imported into the system. This data enables the system of the present invention to generate insights for automatically or semi-automatically generating elements and then using those elements in constructing and creating better-performing ads based on the performance of previous ads, as the system knows what is working well or what is working poorly. The performance data supplied is, for example, where the ads appeared (e.g., website); Intended target audience (e.g., who we were trying to reach and details of that audience); Results (e.g., what was the click-through rate); and/or other performance parameters (e.g., previous cost-per-click; previous cost-per-mille; previous conversion rate; or the like).” See also Duke ¶0066, “Additionally or alternatively, the ML process of the present invention may determine that the particular combination of a Call to Action in the right side of the ad, with a Logo on the left side of the text, had yielded poor performance of such ads; and therefore this combination or word use, should be avoided, for example, by choosing different placement of these components and different headlines, subheads or Call to Action (CTA elements) within the generated permutations. Other suitable criteria may be used, and other modifications of location, placement, size, colors, inclusion of ad components, discarding of ad components, or other determinations may be performed based on ML processes that take into account the historic performance of ads having such ad components therein.” Duke discloses the concept of collecting performance information of different advertisement versions with different CTA with respect to user context.)
determine, using the engagement patterns for respective types of CTAs in the different ones of the one or more digital delivery contexts, particular digital secondary content versions for programmatic delivery to target user devices in particular ones of the one or more digital delivery contexts; and (See Duke ¶0026, “For example, the method of the present invention may analyze all the extracted data, as well as performance data of each previous ad; and may generate insights that indicate that previous ads that included a first particular combination of components had performed well, or that ads that included a second particular combination of components had performed poorly. For example, the system may determine automatically, based on data analysis, that: (i) previous ads in which the Logo of the client appeared, and in which the advertised produce was shown occupying at least 25 percent of the ad canvas, have performed well (e.g., have achieved a click-through rate of at least K percent, wherein K is a pre-defined threshold value); and/or, (ii) previous ads in which the Logo of the client did not appear, and in which a call-to-action of “Click Here Now” had appeared in the left half of the canvas, and in which an animation component was included, have performed poorly (e.g., have achieved a click-through rate of not more than M percent, wherein M is a pre-defined threshold value). Other suitable insights may be deduced by the system and method of the present invention, based on ML analysis of the performance data of previous ads vis-à-vis the combinations of components of such previous ads.” Duke discloses the concept of determining optimal version of the advertisement including optimal call to action.)
control automated delivery of subsequent digital transmissions of the particular digital secondary content versions to the target user devices, wherein the controlled automated delivery of the particular digital secondary content versions, determined using the engagement patterns for respective types of CTAs, to the target user devices increases an engagement metric for a group of the delivered digital secondary content versions. (See Duke ¶0086, “The system of the present invention may further utilize Artificial Intelligence (AI) in order to automatically and instantaneously create brand-compliant, data-driven, static (single frame) or multi-frame (e.g., animated), advertising concepts and actual placement-ready ads or ad units or digital ad units, that are optimized and personalized and tailored to a specific customer or prospect, at any digital point of interaction with the customer or prospect. To ensure brand compliance, the system analyzes and learns from existing (past) work and past ads of that brand, detecting and identifying a house style or look-and-feel and a preferred way of laying out an ad, in view of previous ads of that customer and optionally by taking into account their past performance; optionally augmented with brand guidelines and/or business rules which may be inputted into the system. The generated ads may be a single-frame static ad, or may be a multiple-frame animated ad or video-based ad, having length and complexity that are only limited by the available data and processing power. The system uniquely generates advertising concepts using a combination of (i) analysis and learning from existing (historical) ads and their past performance, and (ii) the automatically ingested client creative briefing document; in order to automatically generate completely new creative concepts and digital ad units, via a computerized platform that is able to analyze the data and generate proposed ad units in a matter of milliseconds or in a few seconds; thereby replacing dozens of hours of manual labor that a team of human marketing experts would need to invest in order to come up with a similar proposed ad, which (if performed by humans) would not even be able to correctly identify the ad elements and the ad characteristics that have led in the past to superb or increased or improved performance of certain previous ads, and/or which (if performed by humans) would not even be able to correctly identify other ad elements and the characteristics that have led in the past to poor or inadequate or reduced performance of some previous ads.” Duke discloses providing optimized advertisement versions including determined optimized CTA for a particular user/context.)
Although Duke discloses the above-enclosed invention, Duke fails to explicitly disclose the concept of determining a performance score.
However Ratiu as shown, which talks about notification modification, teaches the concept of performance scoring.
(See Ratiu ¶0082, “In particular embodiments, policy engine 322 may also retrieve historical notification information about a user's responses to past notifications (e.g., conversion rates or CTR for different notification/context/type/content/delivery patterns) and about prior context/delivery patterns (if any) for the current notification 312 or components (e.g., CTA), as well as interaction levels, rankings, or other suitable scoring, if any, based on those prior context/type/content/delivery patterns from history service 324.” Ratiu teaches the concept of determining a performance score for different creatives for individual target audiences based on response from the audience.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu with the invention of Duke. As shown, Duke discloses the concept of optimizing advertisements including determining response to different versions of the advertisement with different call to actions. Ratiu further teaches the concept of determining and utilizing performance scores as scores are a known alternative for reflecting performance (See Ratiu ¶0082). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu to have substituted utilizing a performance score as these are known and suitable ways of representing performance of advertisements.
As per claim 25:
The computer system of claim 21, the one or more processors and corresponding memory to: output, via a graphical user interface (GUI):one or more of the engagement patterns, or at least one indication of strength of correlation between attributes of the digital secondary content and corresponding engagement results for the digital secondary content. (See Duke ¶0064, “A Creative Requirements Definition Unit 235 enables Chris to review or select elements that will define how the creative will look. For example, it allows Chris to select a presentation style, from a list of available options such as: text only; design elements+text; image+text; image+text+design elements; other options such as the number of frames required. One of the other options that may be generated by the system may be, for example, “use the profile that had performed the best in the most-recent N months (or, in the most-recent M campaigns; or, in the year 2018; or generate 3 versions for testing, or the like). Upon selection, and particularly if the “best performing profile” option is selected, the system may obtain new images and/or new text for the new ad; for example, enabling the user to type them manually, to upload them, to point to a linked library of images or text, or the like.” Duke discloses an interface including presenting information regarding attributes of the different versions and performance.)
As per claim 26:
The computer system of claim 21, the one or more processors and corresponding memory to: store the engagement patterns as structured records, wherein a structured record of an engagement pattern indicates a type of engagement result, a combination of digital secondary content features, user features, or context attributes that is correlated with the type of engagement result, and a pattern score associated with the engagement pattern; and output the structured records via a programmatic interface, wherein the structured records are used to programmatically generate new digital secondary content or modify one or more digital secondary content. (See Duke ¶0091, “In a demonstrative example, an advertising platform or advertising system or a search engine or a social network is being utilized, via an electronic device (e.g., laptop computer, desktop computer, smartphone, tablet, smart-watch, smart TV, gaming device, or other device), by a particular end-user. The system may have access to data that indicates that this particular end-user is a male of 25 years old, or is a male in the age-range of 20 to 30 years old, or is a male that in the past have browsed online stores that sell high-tech gadgets; and may even have access to other data of that user (e.g., full name, exact age or date-of-birth, email address, current location) as obtained from a logged-in social network session (e.g., the end-user is currently logged-in to Facebook or Twitter or YouTube or Instagram or Pinterest or LinkedIn, or to his Gmail or Google or Yahoo account). The advertising system determines that this user, that requested information (e.g., an article, a product, a search query) about a particular topic (e.g., “Virtual Reality head-gear”), should be served with an advertisement from an Electronics Gadget manufacturer; for example, based on a real-time bidding or auction in which that particular manufacturer has bid 70 cents to show an ad to males in the age-range of 20 to 30 years old that are located in Florida and that have entered a search query that include the string “Virtual Reality”. However, instead of merely fetching a pre-defined ad from a pool of suitable ads/ad elements of that particular Manufacturer, the system of the present invention may operate in real time to generate and to construct on-the-fly a particular digital ad unit that is specifically tailored to this specific end-user; by performing rapid analysis of the past performance of historical ads, of that same advertiser, that had been served in the past to males in the age range of 20 to 30 days, or even, only of historical ads that were served in the past by that particular advertiser to this particular end-user, and by determining which ad layouts and ad content elements have performed the best in such historical ad servings; and based on such analysis, which may be performed within milliseconds or very few seconds given suitable processing resources and memory resources, the system generates and constructs in real time a digital ad unit that comprises ad elements that were either extracted or newly generated based on learnings from historical ads as best-performing or as optimally-performing for this specific advertiser when served to this specific end-user (or, to this specific type of end-users that the current end-user belongs to); and the digital ad unit is then immediately served to that specific end-user, and its performance is tracked (e.g., via a unique Ad ID or Ad Tag) and is fed-back to the system's database to allow for even more accurate user-specific tailored ad construction on-the-fly.” Duke discloses storing results of advertisements including context and properties of the advertisement.)
As per claim 28:
A method for optimizing delivery of digital secondary content, the method comprising: performing, by one or more processors with associated memory that implement a digital secondary content delivery system:
determining, using one or more trained machine learning models trained to learn engagement patterns for respective types of calls to action (CTA) in different ones of one or more digital delivery contexts, performance scores of a plurality of digital secondary content versions indicating version performance in each of one or more digital delivery contexts; (See Duke ¶0056, “In the fourth step, a Performance Data Collector 226 causes performance data of prior ads to be obtained or imported into the system. This data enables the system of the present invention to generate insights for automatically or semi-automatically generating elements and then using those elements in constructing and creating better-performing ads based on the performance of previous ads, as the system knows what is working well or what is working poorly. The performance data supplied is, for example, where the ads appeared (e.g., website); Intended target audience (e.g., who we were trying to reach and details of that audience); Results (e.g., what was the click-through rate); and/or other performance parameters (e.g., previous cost-per-click; previous cost-per-mille; previous conversion rate; or the like).” See also Duke ¶0066, “Additionally or alternatively, the ML process of the present invention may determine that the particular combination of a Call to Action in the right side of the ad, with a Logo on the left side of the text, had yielded poor performance of such ads; and therefore this combination or word use, should be avoided, for example, by choosing different placement of these components and different headlines, subheads or Call to Action (CTA elements) within the generated permutations. Other suitable criteria may be used, and other modifications of location, placement, size, colors, inclusion of ad components, discarding of ad components, or other determinations may be performed based on ML processes that take into account the historic performance of ads having such ad components therein.” Duke discloses the concept of collecting performance information of different advertisement versions with different CTA with respect to user context.)
determining, using the engagement patterns for respective types of CTAs in the different ones of the one or more digital delivery contexts, particular digital secondary content versions for programmatic delivery to target user devices in particular ones of the one or more digital delivery contexts; and (See Duke ¶0026, “For example, the method of the present invention may analyze all the extracted data, as well as performance data of each previous ad; and may generate insights that indicate that previous ads that included a first particular combination of components had performed well, or that ads that included a second particular combination of components had performed poorly. For example, the system may determine automatically, based on data analysis, that: (i) previous ads in which the Logo of the client appeared, and in which the advertised produce was shown occupying at least 25 percent of the ad canvas, have performed well (e.g., have achieved a click-through rate of at least K percent, wherein K is a pre-defined threshold value); and/or, (ii) previous ads in which the Logo of the client did not appear, and in which a call-to-action of “Click Here Now” had appeared in the left half of the canvas, and in which an animation component was included, have performed poorly (e.g., have achieved a click-through rate of not more than M percent, wherein M is a pre-defined threshold value). Other suitable insights may be deduced by the system and method of the present invention, based on ML analysis of the performance data of previous ads vis-à-vis the combinations of components of such previous ads.” Duke discloses the concept of determining optimal version of the advertisement including optimal call to action.)
controlling automated delivery of subsequent digital transmissions of the particular digital secondary content versions to the target user devices, wherein the controlled automated delivery of the particular digital secondary content versions, determined using the engagement patterns for respective types of CTAs, to the target user devices increases an engagement metric for a group of the delivered digital secondary content versions. (See Duke ¶0086, “The system of the present invention may further utilize Artificial Intelligence (AI) in order to automatically and instantaneously create brand-compliant, data-driven, static (single frame) or multi-frame (e.g., animated), advertising concepts and actual placement-ready ads or ad units or digital ad units, that are optimized and personalized and tailored to a specific customer or prospect, at any digital point of interaction with the customer or prospect. To ensure brand compliance, the system analyzes and learns from existing (past) work and past ads of that brand, detecting and identifying a house style or look-and-feel and a preferred way of laying out an ad, in view of previous ads of that customer and optionally by taking into account their past performance; optionally augmented with brand guidelines and/or business rules which may be inputted into the system. The generated ads may be a single-frame static ad, or may be a multiple-frame animated ad or video-based ad, having length and complexity that are only limited by the available data and processing power. The system uniquely generates advertising concepts using a combination of (i) analysis and learning from existing (historical) ads and their past performance, and (ii) the automatically ingested client creative briefing document; in order to automatically generate completely new creative concepts and digital ad units, via a computerized platform that is able to analyze the data and generate proposed ad units in a matter of milliseconds or in a few seconds; thereby replacing dozens of hours of manual labor that a team of human marketing experts would need to invest in order to come up with a similar proposed ad, which (if performed by humans) would not even be able to correctly identify the ad elements and the ad characteristics that have led in the past to superb or increased or improved performance of certain previous ads, and/or which (if performed by humans) would not even be able to correctly identify other ad elements and the characteristics that have led in the past to poor or inadequate or reduced performance of some previous ads.” Duke discloses providing optimized advertisement versions including determined optimized CTA for a particular user/context.)
Although Duke discloses the above-enclosed invention, Duke fails to explicitly disclose the concept of determining a performance score.
However Ratiu as shown, which talks about notification modification, teaches the concept of performance scoring.
(See Ratiu ¶0082, “In particular embodiments, policy engine 322 may also retrieve historical notification information about a user's responses to past notifications (e.g., conversion rates or CTR for different notification/context/type/content/delivery patterns) and about prior context/delivery patterns (if any) for the current notification 312 or components (e.g., CTA), as well as interaction levels, rankings, or other suitable scoring, if any, based on those prior context/type/content/delivery patterns from history service 324.” Ratiu teaches the concept of determining a performance score for different creatives for individual target audiences based on response from the audience.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu with the invention of Duke. As shown, Duke discloses the concept of optimizing advertisements including determining response to different versions of the advertisement with different call to actions. Ratiu further teaches the concept of determining and utilizing performance scores as scores are a known alternative for reflecting performance (See Ratiu ¶0082). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu to have substituted utilizing a performance score as these are known and suitable ways of representing performance of advertisements.
As per claim 32:
The method of claim 28, further comprising: outputting, via a graphical user interface (GUI):one or more of the engagement patterns, or at least one indication of strength of correlation between attributes of the digital secondary content and corresponding engagement results for the digital secondary content. (See Duke ¶0064, “A Creative Requirements Definition Unit 235 enables Chris to review or select elements that will define how the creative will look. For example, it allows Chris to select a presentation style, from a list of available options such as: text only; design elements+text; image+text; image+text+design elements; other options such as the number of frames required. One of the other options that may be generated by the system may be, for example, “use the profile that had performed the best in the most-recent N months (or, in the most-recent M campaigns; or, in the year 2018; or generate 3 versions for testing, or the like). Upon selection, and particularly if the “best performing profile” option is selected, the system may obtain new images and/or new text for the new ad; for example, enabling the user to type them manually, to upload them, to point to a linked library of images or text, or the like.” Duke discloses an interface including presenting information regarding attributes of the different versions and performance.)
As per claim 33:
The method of claim 28, further comprising: storing the engagement patterns as structured records, wherein a structured record of an engagement pattern indicates a type of engagement result, a combination of digital secondary content features, user features, or context attributes that is correlated with the type of engagement result, and a pattern score associated with the engagement pattern; and outputting the structured records via a programmatic interface, wherein the structured records are used to programmatically generate new digital secondary content or modify one or more digital secondary content. (See Duke ¶0091, “In a demonstrative example, an advertising platform or advertising system or a search engine or a social network is being utilized, via an electronic device (e.g., laptop computer, desktop computer, smartphone, tablet, smart-watch, smart TV, gaming device, or other device), by a particular end-user. The system may have access to data that indicates that this particular end-user is a male of 25 years old, or is a male in the age-range of 20 to 30 years old, or is a male that in the past have browsed online stores that sell high-tech gadgets; and may even have access to other data of that user (e.g., full name, exact age or date-of-birth, email address, current location) as obtained from a logged-in social network session (e.g., the end-user is currently logged-in to Facebook or Twitter or YouTube or Instagram or Pinterest or LinkedIn, or to his Gmail or Google or Yahoo account). The advertising system determines that this user, that requested information (e.g., an article, a product, a search query) about a particular topic (e.g., “Virtual Reality head-gear”), should be served with an advertisement from an Electronics Gadget manufacturer; for example, based on a real-time bidding or auction in which that particular manufacturer has bid 70 cents to show an ad to males in the age-range of 20 to 30 years old that are located in Florida and that have entered a search query that include the string “Virtual Reality”. However, instead of merely fetching a pre-defined ad from a pool of suitable ads/ad elements of that particular Manufacturer, the system of the present invention may operate in real time to generate and to construct on-the-fly a particular digital ad unit that is specifically tailored to this specific end-user; by performing rapid analysis of the past performance of historical ads, of that same advertiser, that had been served in the past to males in the age range of 20 to 30 days, or even, only of historical ads that were served in the past by that particular advertiser to this particular end-user, and by determining which ad layouts and ad content elements have performed the best in such historical ad servings; and based on such analysis, which may be performed within milliseconds or very few seconds given suitable processing resources and memory resources, the system generates and constructs in real time a digital ad unit that comprises ad elements that were either extracted or newly generated based on learnings from historical ads as best-performing or as optimally-performing for this specific advertiser when served to this specific end-user (or, to this specific type of end-users that the current end-user belongs to); and the digital ad unit is then immediately served to that specific end-user, and its performance is tracked (e.g., via a unique Ad ID or Ad Tag) and is fed-back to the system's database to allow for even more accurate user-specific tailored ad construction on-the-fly.” Duke discloses storing results of advertisements including context and properties of the advertisement.)
As per claim 35:
One or more non-transitory computer-accessible storage media storing program instructions that when executed on one or more processors of a digital secondary content delivery system, cause the digital secondary content delivery system to perform:
determining, using one or more trained machine learning models trained to learn engagement patterns for respective types of calls to action (CTA) in different ones of one or more digital delivery contexts, performance scores of a plurality of digital secondary content versions indicating version performance in each of one or more digital delivery contexts; (See Duke ¶0056, “In the fourth step, a Performance Data Collector 226 causes performance data of prior ads to be obtained or imported into the system. This data enables the system of the present invention to generate insights for automatically or semi-automatically generating elements and then using those elements in constructing and creating better-performing ads based on the performance of previous ads, as the system knows what is working well or what is working poorly. The performance data supplied is, for example, where the ads appeared (e.g., website); Intended target audience (e.g., who we were trying to reach and details of that audience); Results (e.g., what was the click-through rate); and/or other performance parameters (e.g., previous cost-per-click; previous cost-per-mille; previous conversion rate; or the like).” See also Duke ¶0066, “Additionally or alternatively, the ML process of the present invention may determine that the particular combination of a Call to Action in the right side of the ad, with a Logo on the left side of the text, had yielded poor performance of such ads; and therefore this combination or word use, should be avoided, for example, by choosing different placement of these components and different headlines, subheads or Call to Action (CTA elements) within the generated permutations. Other suitable criteria may be used, and other modifications of location, placement, size, colors, inclusion of ad components, discarding of ad components, or other determinations may be performed based on ML processes that take into account the historic performance of ads having such ad components therein.” Duke discloses the concept of collecting performance information of different advertisement versions with different CTA with respect to user context.)
determining, using the engagement patterns for respective types of CTAs in the different ones of the one or more digital delivery contexts, particular digital secondary content versions for programmatic delivery to target user devices in particular ones of the one or more digital delivery contexts; and (See Duke ¶0026, “For example, the method of the present invention may analyze all the extracted data, as well as performance data of each previous ad; and may generate insights that indicate that previous ads that included a first particular combination of components had performed well, or that ads that included a second particular combination of components had performed poorly. For example, the system may determine automatically, based on data analysis, that: (i) previous ads in which the Logo of the client appeared, and in which the advertised produce was shown occupying at least 25 percent of the ad canvas, have performed well (e.g., have achieved a click-through rate of at least K percent, wherein K is a pre-defined threshold value); and/or, (ii) previous ads in which the Logo of the client did not appear, and in which a call-to-action of “Click Here Now” had appeared in the left half of the canvas, and in which an animation component was included, have performed poorly (e.g., have achieved a click-through rate of not more than M percent, wherein M is a pre-defined threshold value). Other suitable insights may be deduced by the system and method of the present invention, based on ML analysis of the performance data of previous ads vis-à-vis the combinations of components of such previous ads.” Duke discloses the concept of determining optimal version of the advertisement including optimal call to action.)
controlling automated delivery of subsequent digital transmissions of the particular digital secondary content versions to the target user devices, wherein the controlled automated delivery of the particular digital secondary content versions, determined using the engagement patterns for respective types of CTAs, to the target user devices increases an engagement metric for a group of the delivered digital secondary content versions. (See Duke ¶0086, “The system of the present invention may further utilize Artificial Intelligence (AI) in order to automatically and instantaneously create brand-compliant, data-driven, static (single frame) or multi-frame (e.g., animated), advertising concepts and actual placement-ready ads or ad units or digital ad units, that are optimized and personalized and tailored to a specific customer or prospect, at any digital point of interaction with the customer or prospect. To ensure brand compliance, the system analyzes and learns from existing (past) work and past ads of that brand, detecting and identifying a house style or look-and-feel and a preferred way of laying out an ad, in view of previous ads of that customer and optionally by taking into account their past performance; optionally augmented with brand guidelines and/or business rules which may be inputted into the system. The generated ads may be a single-frame static ad, or may be a multiple-frame animated ad or video-based ad, having length and complexity that are only limited by the available data and processing power. The system uniquely generates advertising concepts using a combination of (i) analysis and learning from existing (historical) ads and their past performance, and (ii) the automatically ingested client creative briefing document; in order to automatically generate completely new creative concepts and digital ad units, via a computerized platform that is able to analyze the data and generate proposed ad units in a matter of milliseconds or in a few seconds; thereby replacing dozens of hours of manual labor that a team of human marketing experts would need to invest in order to come up with a similar proposed ad, which (if performed by humans) would not even be able to correctly identify the ad elements and the ad characteristics that have led in the past to superb or increased or improved performance of certain previous ads, and/or which (if performed by humans) would not even be able to correctly identify other ad elements and the characteristics that have led in the past to poor or inadequate or reduced performance of some previous ads.” Duke discloses providing optimized advertisement versions including determined optimized CTA for a particular user/context.)
Although Duke discloses the above-enclosed invention, Duke fails to explicitly disclose the concept of determining a performance score.
However Ratiu as shown, which talks about notification modification, teaches the concept of performance scoring.
(See Ratiu ¶0082, “In particular embodiments, policy engine 322 may also retrieve historical notification information about a user's responses to past notifications (e.g., conversion rates or CTR for different notification/context/type/content/delivery patterns) and about prior context/delivery patterns (if any) for the current notification 312 or components (e.g., CTA), as well as interaction levels, rankings, or other suitable scoring, if any, based on those prior context/type/content/delivery patterns from history service 324.” Ratiu teaches the concept of determining a performance score for different creatives for individual target audiences based on response from the audience.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu with the invention of Duke. As shown, Duke discloses the concept of optimizing advertisements including determining response to different versions of the advertisement with different call to actions. Ratiu further teaches the concept of determining and utilizing performance scores as scores are a known alternative for reflecting performance (See Ratiu ¶0082). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Ratiu to have substituted utilizing a performance score as these are known and suitable ways of representing performance of advertisements.
As per claim 38:
The non-transitory computer-accessible storage media of claim 35, wherein the program instructions cause the one or more processors to perform: outputting, via a graphical user interface (GUI):one or more of the engagement patterns, or at least one indication of strength of correlation between attributes of the digital secondary content and corresponding engagement results for the digital secondary content. (See Duke ¶0064, “A Creative Requirements Definition Unit 235 enables Chris to review or select elements that will define how the creative will look. For example, it allows Chris to select a presentation style, from a list of available options such as: text only; design elements+text; image+text; image+text+design elements; other options such as the number of frames required. One of the other options that may be generated by the system may be, for example, “use the profile that had performed the best in the most-recent N months (or, in the most-recent M campaigns; or, in the year 2018; or generate 3 versions for testing, or the like). Upon selection, and particularly if the “best performing profile” option is selected, the system may obtain new images and/or new text for the new ad; for example, enabling the user to type them manually, to upload them, to point to a linked library of images or text, or the like.” Duke discloses an interface including presenting information regarding attributes of the different versions and performance.)
As per claim 39:
The non-transitory computer-accessible storage media of claim 35, wherein the program instructions cause the one or more processors to perform: storing the engagement patterns as structured records, wherein a structured record of an engagement pattern indicates a type of engagement result, a combination of digital secondary content features, user features, or context attributes that is correlated with the type of engagement result, and a pattern score associated with the engagement pattern; and outputting the structured records via a programmatic interface, wherein the structured records are used to programmatically generate new digital secondary content or modify one or more digital secondary content. (See Duke ¶0091, “In a demonstrative example, an advertising platform or advertising system or a search engine or a social network is being utilized, via an electronic device (e.g., laptop computer, desktop computer, smartphone, tablet, smart-watch, smart TV, gaming device, or other device), by a particular end-user. The system may have access to data that indicates that this particular end-user is a male of 25 years old, or is a male in the age-range of 20 to 30 years old, or is a male that in the past have browsed online stores that sell high-tech gadgets; and may even have access to other data of that user (e.g., full name, exact age or date-of-birth, email address, current location) as obtained from a logged-in social network session (e.g., the end-user is currently logged-in to Facebook or Twitter or YouTube or Instagram or Pinterest or LinkedIn, or to his Gmail or Google or Yahoo account). The advertising system determines that this user, that requested information (e.g., an article, a product, a search query) about a particular topic (e.g., “Virtual Reality head-gear”), should be served with an advertisement from an Electronics Gadget manufacturer; for example, based on a real-time bidding or auction in which that particular manufacturer has bid 70 cents to show an ad to males in the age-range of 20 to 30 years old that are located in Florida and that have entered a search query that include the string “Virtual Reality”. However, instead of merely fetching a pre-defined ad from a pool of suitable ads/ad elements of that particular Manufacturer, the system of the present invention may operate in real time to generate and to construct on-the-fly a particular digital ad unit that is specifically tailored to this specific end-user; by performing rapid analysis of the past performance of historical ads, of that same advertiser, that had been served in the past to males in the age range of 20 to 30 days, or even, only of historical ads that were served in the past by that particular advertiser to this particular end-user, and by determining which ad layouts and ad content elements have performed the best in such historical ad servings; and based on such analysis, which may be performed within milliseconds or very few seconds given suitable processing resources and memory resources, the system generates and constructs in real time a digital ad unit that comprises ad elements that were either extracted or newly generated based on learnings from historical ads as best-performing or as optimally-performing for this specific advertiser when served to this specific end-user (or, to this specific type of end-users that the current end-user belongs to); and the digital ad unit is then immediately served to that specific end-user, and its performance is tracked (e.g., via a unique Ad ID or Ad Tag) and is fed-back to the system's database to allow for even more accurate user-specific tailored ad construction on-the-fly.” Duke discloses storing results of advertisements including context and properties of the advertisement.)
Claim(s) 27, 34, 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Duke et al. (US 20190392487 A1) (hereafter Duke), in view of Ratiu et al. (US 20170118303 A1) (hereafter Ratiu), in view of Barnett (US 20180124438 A1) (hereafter Barnett).
As per claim 27:
Although the combination of Duke and Ratiu discloses the above-enclosed invention including tracking the features of the call to action, the combination fails to explicitly disclose the CTA to be audio and associated with time indicators.
However Barnett as shown, which talks about targeting advertisements during media downtime, teaches the concept of CTA to be associated with time indicators.
The computer system of claim 21, wherein the features from the digital secondary content include one or more of: a speaker voice or a music property of a CTA in the digital secondary content; a number of times that the CTA is played in the digital secondary content; or an indication of when the CTA is played during the digital secondary content. (See Barnett ¶0209, “Call-to-action 2110, or any other content displayed on a second screen of user 101 such as mobile device 840, may be displayed in response to instruction from social networking system 160 or social TV dongle 810. For example, social networking system 160 may send instructions to mobile device 840 either directly or via social TV dongle 810 to display call-to-action 2110. In certain embodiments, the instructions are sent to mobile device 840 based on metadata embedded in video stream 850. For example, metadata embedded within video stream 850 may indicate to display a certain call-to-action 2110 at a certain time in a program. Social TV dongle 810 or social networking system 160 may analyze the metadata and then send the instructions to mobile device 840 to display call-to-action 2110 at the appropriate time.” Barnett teaches the concept of maintaining records regarding when the call to action is presented.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett with the combination of Duke and Ratiu. As shown, the combination discloses the concept of generating customized advertisement creatives including tracking different call to actions and how call to action is presented. Barnett further teaches the concept of call to action to be in a multimedia format and further tracking presentation time. Barnett teaches this concept to further manage the presentation of multi-media advertisements including the call to action including proper timing of presentation (See Barnett ¶0209), as well as tracking user response to the advertisement and call to action (See Barnett ¶0054). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett to further adapt dynamic call to actions as disclosed by the combination to multimedia and further enabling the tracking of additional performance metrics.
As per claim 34:
Although the combination of Duke and Ratiu discloses the above-enclosed invention including tracking the features of the call to action, the combination fails to explicitly disclose the CTA to be audio and associated with time indicators.
However Barnett as shown, which talks about targeting advertisements during media downtime, teaches the concept of CTA to be associated with time indicators.
The method of claim 28, wherein the features from the digital secondary content include one or more of: a speaker voice or a music property of a CTA in the digital secondary content; a number of times that the CTA is played in the digital secondary content; or an indication of when the CTA is played during the digital secondary content. (See Barnett ¶0209, “Call-to-action 2110, or any other content displayed on a second screen of user 101 such as mobile device 840, may be displayed in response to instruction from social networking system 160 or social TV dongle 810. For example, social networking system 160 may send instructions to mobile device 840 either directly or via social TV dongle 810 to display call-to-action 2110. In certain embodiments, the instructions are sent to mobile device 840 based on metadata embedded in video stream 850. For example, metadata embedded within video stream 850 may indicate to display a certain call-to-action 2110 at a certain time in a program. Social TV dongle 810 or social networking system 160 may analyze the metadata and then send the instructions to mobile device 840 to display call-to-action 2110 at the appropriate time.” Barnett teaches the concept of maintaining records regarding when the call to action is presented.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett with the combination of Duke and Ratiu. As shown, the combination discloses the concept of generating customized advertisement creatives including tracking different call to actions and how call to action is presented. Barnett further teaches the concept of call to action to be in a multimedia format and further tracking presentation time. Barnett teaches this concept to further manage the presentation of multi-media advertisements including the call to action including proper timing of presentation (See Barnett ¶0209), as well as tracking user response to the advertisement and call to action (See Barnett ¶0054). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett to further adapt dynamic call to actions as disclosed by the combination to multimedia and further enabling the tracking of additional performance metrics.
As per claim 40:
Although the combination of Duke and Ratiu discloses the above-enclosed invention including tracking the features of the call to action, the combination fails to explicitly disclose the CTA to be audio and associated with time indicators.
However Barnett as shown, which talks about targeting advertisements during media downtime, teaches the concept of CTA to be associated with time indicators.
The non-transitory computer-accessible storage media of claim 35, wherein the features from the digital secondary content include one or more of: a speaker voice or a music property of a CTA in the digital secondary content; a number of times that the CTA is played in the digital secondary content; or an indication of when the CTA is played during the digital secondary content. (See Barnett ¶0209, “Call-to-action 2110, or any other content displayed on a second screen of user 101 such as mobile device 840, may be displayed in response to instruction from social networking system 160 or social TV dongle 810. For example, social networking system 160 may send instructions to mobile device 840 either directly or via social TV dongle 810 to display call-to-action 2110. In certain embodiments, the instructions are sent to mobile device 840 based on metadata embedded in video stream 850. For example, metadata embedded within video stream 850 may indicate to display a certain call-to-action 2110 at a certain time in a program. Social TV dongle 810 or social networking system 160 may analyze the metadata and then send the instructions to mobile device 840 to display call-to-action 2110 at the appropriate time.” Barnett teaches the concept of maintaining records regarding when the call to action is presented.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett with the combination of Duke and Ratiu. As shown, the combination discloses the concept of generating customized advertisement creatives including tracking different call to actions and how call to action is presented. Barnett further teaches the concept of call to action to be in a multimedia format and further tracking presentation time. Barnett teaches this concept to further manage the presentation of multi-media advertisements including the call to action including proper timing of presentation (See Barnett ¶0209), as well as tracking user response to the advertisement and call to action (See Barnett ¶0054). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Barnett to further adapt dynamic call to actions as disclosed by the combination to multimedia and further enabling the tracking of additional performance metrics.
Allowable Subject Matter
Claims 22-24, 29-31, 36-37 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
As currently claimed, claims 22, 23, 29, 30, and 36 contain limitations similar to the independent claims of parent application 18/064,197. As currently claimed, these claims further recite the concept of performing analysis on the different calls to action including identifying audio attributes/characteristics. The Examiner notes, this concept, in combination with the implementation of dynamic call to actions were previously determined to be non-obvious over the prior art as discussed in the parent application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Donamukkala et al. (US 11062360 B1), which talks about optimization of conversion rates including performing analysis on impression and conversions.
Gao et al. (US 20200336450 A1), which talks about customized and personalized messages including specialized call to actions.
Marchenko (US 20200090212 A1), which talks about evaluating campaign performance including optimizing future campaigns
Swaminathan et al. (US 20200021873 A1), which talks about utilizing AI for optimized generation of advertisements including analysis of individual advertisement elements.
Hamedi et al. (US 20190034976 A1), which talks about automated content transformation including testing advertisements.
Peles et al. (US 20160007065 A1), which talks about dynamic personalization of multimedia content.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT M CAO whose telephone number is (571)270-5598. The examiner can normally be reached Monday - Friday 11-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ILANA SPAR can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT M CAO/Primary Examiner, Art Unit 3622
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