DETAILED ACTION
Status of the Claims
This office action is submitted in response to the amendment filed on 7/1/25.
Examiner notes that this application claims priority from provisional application 63455677.
Examiner further notes Applicant’s priority date of 3/30/23, which stems from the aforementioned provisional application.
Claims 12-13 have been amended.
Claims 15-19 are new.
Therefore, claims 1-19 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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 15 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, claim 15 describes various features in which different data and/or executable instructions are stored in one of six different possible memories, and executed by one of 4 different processors. Examiner notes that there is no support for multiple processors and/or memories in Applicant’s disclosures.
Claims 16-19 are likewise rejected due to their dependency on claim 15.
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 1-19 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.
Independent claims 1, 6, and 15, in part, describe an invention comprising: collecting a group of banner ads; collecting a group of banner ad performance records; analyzing the ads and performance records to produce a graph model of the ads and performance records; analyzing the graph to generate a group of banner ads using a genetic algorithm; assigning a design score to each of the generated ads; and ranking the generated ads based on a weighted ranking of the performance score and the design score. As such, the invention is directed to the abstract idea of collecting ad information, analyzing ad information, and ranking the ads based on the results of the data collection and analysis, which, pursuant to MPEP 2106.04(a), is aptly categorized as a method of organizing human activity (i.e., advertising and marketing). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional elements that are associated with the judicial exception, including: storing the ads and performance records in electronic files. Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
Next, independent claims 1 and 6 do not positively recite any elements that necessarily constitute a system or apparatus, such as computer hardware. As a result, it is not clear what structure is included or excluded by the claim language, and the functions listed in the claim could merely be that of software. Examiner notes that paragraph 11 of Applicant’s specification defines an “engine” as an “AI-based learning model and the processes it uses to collect data, evaluate data, and construct relationships between the data”. Examiner notes that this is nothing more than a feature of software. Software per se is not patentable under §101; therefore, the claimed invention does not fall within a statutory class of patentable subject matter. See MPEP 2106.01.
Independent claim 15 further recites additional elements, including: “memory” for storing banner ads; “memory” for storing executable instructions; and “processors” for executing the steps of the process. These limitations are recited at a high level of generality, and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment (e.g., a generic computer utilizing AI software).
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Claims 2-5, 7-14, and 16-19 are dependent on the aforementioned independent claims, and include all the limitations contained therein. These claims do not recite any technical elements, and simply disclose additional limitations that further limit the abstract idea with details regarding data organization, modifying the generated ads if they don’t meet a minimum ranking, determining if the ads are anomalous, and the content of the performance records. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1-19 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, and 6 are rejected under 35 USC 103 as being unpatentable over Thomas (20180276718) in view of Peris (20220076299), and in further view of Goswami (20200160403).
Claim 1: Thomas discloses a system comprising:
a group of banner ads comprising one or more banner ads, each of the banner ads having one or more banner ad elements (Paragraph 40. The system identifies a collection of ads comprising ad copy elements);
a group of banner ad performance records comprising one or more banner ad performance records, each of the banner ad performance records corresponding to at least one of the one or more banner ads (Paragraphs 40 and 43. The ads are tested and then used to aggregate performance data, such as KPI data);
a data acquisition engine which collects the group of banner ads and stores the group of banner ads in a first electronic file and collects the group of banner ad performance records and stores the banner ad performance records in a second electronic file (Paragraph 42. The ads and their associated performance data are stored within ad units of the digital advertising account); and
a banner ad creation engine which analyzes [the MMKG] and produces a group of AI generated banner ads using a genetic algorithm, comprising one or more AI generated banner ads comprised of conglomerations of the banner ad elements (Paragraphs 46 and Fig. 1 #118. The system analyzes the ad data to generate new ads using AI. The AI engine utilizes a genetic algorithm to generate the ads, which are comprised of various ad elements), and stores the AI generated banner ads in a fourth electronic file (Paragraph 30 and 42. The generated ads are stored in an electronic file).
Thomas does not explicitly describe a system comprising a multi-modal knowledge graph (MMKG) engine which analyzes the first electronic file and the second electronic file to produce an MMKG, the MMKG comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records, and stores the MMKG in a third electronic file.
Peris, however, discloses a system comprising a multi-modal knowledge graph (MMKG) engine which analyzes the first electronic file and the second electronic file to produce an MMKG, the MMKG comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records (Paragraphs 199 and 222. The system utilizes an “inference engine” that uses the ad data and associated performance data to build a knowledge graph. A series of relationships may then be determined by analyzing various points of connection between the nodes), and stores the MMKG in a third electronic file (Paragraph 215. The knowledge graph is stored as an electronic file in a database).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine these features of Peris with those of Thomas. One would have been motivated to do this in order to establish relationships between various points of connection with respect to the ads (Peris, Paragraph 199).
Finally, neither Thomas nor Peris explicitly describe a system comprising a banner ad ranking engine which analyzes the group of AI generated banner ads and assigns each of the AI generated banner ads a design score and a performance score and then ranks the AI generated banner ads based on a weighted ranking of the performance score and the design score of each of the AI generated banner ads.
Goswami, however, discloses a system comprising a banner ad ranking engine which analyzes the group of [AI generated] banner ads (Paragraph 4. The ads include banner ads, among others) and assigns each of the [AI generated] banner ads a design score and a performance score and then ranks the [AI generated] banner ads based on a weighted ranking of the performance score and the design score of each of the [AI generated] banner ads (Paragraphs 115-116. The system ranks ads based on a performance score (in the form of a click-through rate) and the visual characteristics of the images associated with the ads).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Goswami with those of Thomas and Peris. One would have been motivated to do this in order to quantify the overall quality of a particular ad.
Claim 2: The Thomas/Peris/Goswami combination discloses those limitations cited above. Thomas, however, further discloses a system wherein the first electronic file and the second electronic file are the same electronic file (Paragraphs 40 and 43. The ads and their associated performance data are stored together in the same file).
Claim 6: Thomas discloses a method comprising the steps of:
collecting a group of banner ads comprising one or more banner ads, each of the banner ads having one or more banner ad elements (Paragraph 40. The system identifies a collection of ads comprising ad copy elements);
collecting a group of banner ad performance records comprising one or more banner ad performance records, each of the banner ad performance records corresponding to at least one of the one or more banner ads (Paragraphs 40 and 43. The ads are tested and then used to aggregate performance data, such as KPI data);
analyzing the [MMKG] to produce a group of AI generated banner ads using a genetic algorithm, comprising one or more AI generated banner ads comprised of conglomerations of the banner ad elements (Paragraphs 46 and Fig. 1 #118. The system analyzes the ad data to generate new ads using AI. The AI engine utilizes a genetic algorithm to generate the ads, which are comprised of various ad elements); and
storing the AI generated banner ads in an electronic file (Paragraph 30 and 42. The generated ads are stored in an electronic file).
Thomas does not explicitly describe a method for analyzing the group of banner ads and the group of banner ad performance records to produce a multi-modal knowledge graph (MMKG) comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records.
Peris, however, discloses a method for analyzing the group of banner ads and the group of banner ad performance records to produce a multi-modal knowledge graph (MMKG) comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records (Paragraphs 199 and 222. The system utilizes an “inference engine” that uses the ad data and associated performance data to build a knowledge graph. A series of relationships may then be determined by analyzing various points of connection between the nodes).
The rationale for combining Peris with Thomas is articulated above and reincorporated herein.
Finally, neither Thomas nor Peris describe a method for analyzing the group of AI generated banner ads to assign each of the AI generated banner ads a design score and a performance score; and ranking the AI generated banner ads based on a weighted ranking of the performance score and the design score of each of the AI generated banner ads.
Goswami, however, discloses a method for analyzing the group of [AI generated] banner ads (Paragraph 4. The ads include banner ads, among others) to assign each of the [AI generated] banner ads a design score and a performance score; and ranking the [AI generated] banner ads based on a weighted ranking of the performance score and the design score of each of the [AI generated] banner ads. (Paragraphs 115-116. The system ranks ads based on a performance score (in the form of a click-through rate) and the visual characteristics of the images associated with the ads).
The rationale for combining Goswami with Thomas and Peris is articulated above and reincorporated herein.
Claims 3 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami in view of Narayan (20200349606).
The Thomas/Peris/Goswami combination discloses those limitations cited above. In particular, Thomas discloses a method that alters the AI generated banner ads in the subgroup with the genetic algorithm to produce a group of altered AI generated banner ads, and uses the subgroup in generating a second generation of AI generated banner ads (Paragraphs 46 and Fig. 1 #118. The system analyzes the ad data to generate new ads using AI. The AI engine utilizes a genetic algorithm to generate the ads, which are comprised of various ad elements). However, none of these references explicitly describe a method wherein the banner ad ranking engine determines if a minimum number of AI generated banner ads meets and/or exceeds a minimum weighted ranking and if not, selects a subgroup of the AI generated banner ads, alters the AI generated banner ads in the subgroup with the genetic algorithm to produce a group of altered AI generated banner ads, and uses the subgroup in generating a second generation of AI generated banner ads.
Narayan, however, discloses a method wherein the banner ad ranking engine determines if a minimum number of [AI generated] banner ads meets and/or exceeds a minimum weighted ranking and if not, selects a subgroup of the [AI generated] banner ads (Claims 1 and 4. The ads are given an outcome score. When the score falls below a particular threshold, the system replaces parts of the ads to generate a revised composite ad comprising the new features.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Narayan with those of Thomas/Peris/Goswami. One would have been motivated to do this in order to dynamically generate ads that are more likely to result in engagement.
Claims 4-5, 8-10, and 12-14 are rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami in view of Zlotin (20070233566).
Claims 4, 8, 10, 12, and 14: The Thomas/Peris/Goswami combination discloses those limitations cited above, but does not explicitly describe a method wherein the banner ad creation engine determines if one or more of the AI generated banner ads is more anomalous from the group of banner ads in the MMKG than a predetermined anomalousness threshold and if so, not offer such anomalous AI generated banner ads for presentation to customers.
Zlotin, however, discloses a method wherein the banner ad creation engine determines if one or more of the [AI generated] banner ads is more anomalous from the group of banner ads [in the MMKG] than a predetermined anomalousness threshold and if so not offer such anomalous [AI generated] banner ads for presentation to customers. (Paragraph 77. The system detects the presence of any errors within an ad campaign, and deactivates or deletes the erroneous ads so they aren’t presented to users.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Zlotin with those of Thomas/Peris/Goswami. One would have been motivated in order to ensure that the ad campaign correctly conveys ads to users.
Claims 5, 9, and 13: As noted above, Thomas discloses a method in which ads are generated via an AI process based on a combination of ad elements. However, the Thomas/Peris/Goswami combination does not explicitly describe a method wherein the banner ad creation engine determines if one or more of the altered AI generated banner ads is more anomalous from the group of banner ads in the MMKG than a predetermined anomalousness threshold and if so not use such anomalous altered AI generated banner ads in generating the next generation of AI generated banner ads.
Zlotin, however, discloses a method wherein the banner ad creation engine determines if one or more of the [altered AI generated] banner ads is more anomalous from the group of banner ads [in the MMKG] than a predetermined anomalousness threshold and if so not use such anomalous [altered AI generated] banner ads [in generating the next generation of AI generated banner ads]. (Paragraph 77. (Paragraph 77. The system detects the presence of any errors within an ad campaign, and deactivates or deletes the erroneous ads so they aren’t presented to users.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine the error removal feature of Zlotin with the AI generated ads of Thomas in order to ensure that the system only generates ads that are accurate and effective.
Claim 7 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami in view of Collins (20070027753).
As noted above, Thomas discloses a method comprising selecting a subgroup of the AI generated banner ads; altering the AI generated banner ads in the subgroup with the genetic algorithm to produce a group of altered AI generated banner ads; and generating a second generation of AI generated banner ads using the group of altered AI generated banner ads as an input to the genetic algorithm (Paragraph 46), but does not explicitly describe a method for determining if a minimum number of AI generated banner ads meets and/or exceeds a minimum weighted ranking, if so stopping the method otherwise proceeding to the next step.
Collins, however, discloses a method for determining if a minimum number of [AI generated] banner ads meets and/or exceeds a minimum weighted ranking, if so stopping the method otherwise proceeding to the next step. (Paragraph 45. The system selects ads that have an associated weight above a predetermined threshold. If the ad does not meet or exceed the threshold, the ad is not selected and/or presented.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Collins with those of Thomas/Peris/Goswami. One would have been motivated to do this in order to determine which ads will be generated and utilized based on various quantified, algorithmic, and mathematical determinations.
Claim 11 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami in view of Zheng (12086840).
The Thomas/Peris/Goswami combination discloses those limitations cited above. Peris further discloses a method for deploying ads that are determined to do well within a particular demographic (Paragraph 83). The Thomas/Peris/Goswami combination does not explicitly describe a method wherein the group of banner ad performance records covers performance of the banner ads with regard to at least two distinct demographic groups.
Zheng, however, discloses a method wherein the group of banner ad performance records covers performance of the banner ads with regard to at least two distinct demographic groups. (Col. 3, Lines 28-36. The system determines ad performances for multiple groups of people grouped demographically, geographically, by professions, by neighborhood culture, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Zheng with those of Thomas/Peris/Goswami. One would have been motivated to do this in order to learn what features appeal to various segments of a user population (Zheng, Col. 3, Lines 33-36).
Claims 15-16 are rejected under 35 USC 103 as being unpatentable over Thomas (20180276718) in view of Peris (20220076299), and in further view of Goswami (20200160403), Gupta (8817315) and Singh (20250191026).
Claim 15: Thomas discloses a system comprising:
a group of banner ads comprising one or more banner ads, each of the banner ads having one or more banner ad elements (Paragraph 40. The system identifies a collection of ads comprising ad copy elements);
a group of banner ad performance records comprising one or more banner ad performance records, each of the banner ad performance records corresponding to at least one of the one or more banner ads (Paragraphs 40 and 43. The ads are tested and then used to aggregate performance data, such as KPI data);
a data acquisition engine which collects the group of banner ads and stores the group of banner ads in a first electronic file and collects the group of banner ad performance records and stores the banner ad performance records in a second electronic file (Paragraph 42. The ads and their associated performance data are stored within ad units of the digital advertising account); and
a banner ad creation engine which analyzes [the MMKG] and produces a group of AI generated banner ads using a genetic algorithm, comprising one or more AI generated banner ads comprised of conglomerations of the banner ad elements (Paragraphs 46 and Fig. 1 #118. The system analyzes the ad data to generate new ads using AI. The AI engine utilizes a genetic algorithm to generate the ads, which are comprised of various ad elements), and stores the AI generated banner ads in a fourth electronic file (Paragraph 30 and 42. The generated ads are stored in an electronic file).
Thomas does not explicitly describe a system comprising a multi-modal knowledge graph (MMKG) engine which analyzes the first electronic file and the second electronic file to produce an MMKG, the MMKG comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records, and stores the MMKG in a third electronic file.
Peris, however, discloses a system comprising a multi-modal knowledge graph (MMKG) engine which analyzes the first electronic file and the second electronic file to produce an MMKG, the MMKG comprising a graph model of the correlated banner ad elements and corresponding banner ad performance records (Paragraphs 199 and 222. The system utilizes an “inference engine” that uses the ad data and associated performance data to build a knowledge graph. A series of relationships may then be determined by analyzing various points of connection between the nodes), and stores the MMKG in a third electronic file (Paragraph 215. The knowledge graph is stored as an electronic file in a database).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine these features of Peris with those of Thomas. One would have been motivated to do this in order to establish relationships between various points of connection with respect to the ads (Peris, Paragraph 199).
Finally, neither Thomas nor Peris explicitly describe a system comprising a banner ad ranking engine which analyzes the group of AI generated banner ads and assigns each of the AI generated banner ads a design score and a performance score and then ranks the AI generated banner ads based on a weighted ranking of the performance score and the design score of each of the AI generated banner ads.
Goswami, however, discloses a system comprising a banner ad ranking engine which analyzes the group of [AI generated] banner ads (Paragraph 4. The ads include banner ads, among others) and assigns each of the [AI generated] banner ads a design score and a performance score and then ranks the [AI generated] banner ads based on a weighted ranking of the performance score and the design score of each of the [AI generated] banner ads (Paragraphs 115-116. The system ranks ads based on a performance score (in the form of a click-through rate) and the visual characteristics of the images associated with the ads).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Goswami with those of Thomas and Peris. One would have been motivated to do this in order to quantify the overall quality of a particular ad.
Next, the Thomas/Peris/Goswami combination does not appear to explicitly describe a method in which the ads and the ad performance data are stored in separate memories.
Gupta, however, describes a system in which the ads are stored in one memory and data associated with the ads are stored in a separate memory (Fig. 3 #310 and #314; Col. 5, Lines 1-15).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine this feature of Gupta with those of Thomas/Peris/Goswami. One would have been motivated to do this in order to not overload any one storage unit. Alternatively, pursuant to MPEP 2144.04V, it would have been obvious as a matter of law to make these features integral or separable. See also In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961).
Finally, none of the aforementioned references describe a system in which the executable instructions for the different features are stored on different memories and/or executed by different processors.
Singh, however, discloses a method in which the application instructions are stored in separate storage devices (Fig. 2A #222 and #238; Paragraph 64), and the processes are executed by multiple separate processors (Paragraph 64).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine this feature of Gupta with those of Thomas/Peris/Goswami. One would have been motivated to do this in order to not overload any one storage unit. Alternatively, pursuant to MPEP 2144.04V, it would have been obvious as a matter of law to make these features integral or separable. See also In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961).
Claim 17 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami/Gupta/Singh in view of Narayan (20200349606).
The Thomas/Peris/Goswami/Gupta/Singh combination discloses those limitations cited above. In particular, Thomas discloses a method that alters the AI generated banner ads in the subgroup with the genetic algorithm to produce a group of altered AI generated banner ads, and uses the subgroup in generating a second generation of AI generated banner ads (Paragraphs 46 and Fig. 1 #118. The system analyzes the ad data to generate new ads using AI. The AI engine utilizes a genetic algorithm to generate the ads, which are comprised of various ad elements). However, none of these references explicitly describe a method wherein the banner ad ranking engine determines if a minimum number of AI generated banner ads meets and/or exceeds a minimum weighted ranking and if not, selects a subgroup of the AI generated banner ads, alters the AI generated banner ads in the subgroup with the genetic algorithm to produce a group of altered AI generated banner ads, and uses the subgroup in generating a second generation of AI generated banner ads.
Narayan, however, discloses a method wherein the banner ad ranking engine determines if a minimum number of [AI generated] banner ads meets and/or exceeds a minimum weighted ranking and if not, selects a subgroup of the [AI generated] banner ads (Claims 1 and 4. The ads are given an outcome score. When the score falls below a particular threshold, the system replaces parts of the ads to generate a revised composite ad comprising the new features.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Narayan with those of Thomas/Peris/Goswami/Gupta/Singh. One would have been motivated to do this in order to dynamically generate ads that are more likely to result in engagement.
Claim 18 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami/Gupta/Singh in view of Zlotin (20070233566).
The Thomas/Peris/Goswami/Gupta/Singh combination discloses those limitations cited above, but does not explicitly describe a method wherein the banner ad creation engine determines if one or more of the AI generated banner ads is more anomalous from the group of banner ads in the MMKG than a predetermined anomalousness threshold and if so, not offer such anomalous AI generated banner ads for presentation to customers.
Zlotin, however, discloses a method wherein the banner ad creation engine determines if one or more of the [AI generated] banner ads is more anomalous from the group of banner ads [in the MMKG] than a predetermined anomalousness threshold and if so not offer such anomalous [AI generated] banner ads for presentation to customers. (Paragraph 77. The system detects the presence of any errors within an ad campaign, and deactivates or deletes the erroneous ads so they aren’t presented to users.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine this feature of Zlotin with those of Thomas/Peris/Goswami/Gupta/Singh. One would have been motivated in order to ensure that the ad campaign correctly conveys ads to users.
Claim 19 is rejected under 35 USC 103 as being unpatentable over Thomas/Peris/Goswami/Gupta/Singh/Narayan in view of Zlotin (20070233566).
The Thomas/Peris/Goswami/Gupta/Singh/Narayan combination discloses those limitations cited above. Thomas further discloses a method in which ads are generated via an AI process based on a combination of ad elements. However, the Thomas/Peris/Goswami combination does not explicitly describe a method wherein the banner ad creation engine determines if one or more of the altered AI generated banner ads is more anomalous from the group of banner ads in the MMKG than a predetermined anomalousness threshold and if so not use such anomalous altered AI generated banner ads in generating the next generation of AI generated banner ads.
Zlotin, however, discloses a method wherein the banner ad creation engine determines if one or more of the [altered AI generated] banner ads is more anomalous from the group of banner ads [in the MMKG] than a predetermined anomalousness threshold and if so not use such anomalous [altered AI generated] banner ads [in generating the next generation of AI generated banner ads]. (Paragraph 77. (Paragraph 77. The system detects the presence of any errors within an ad campaign, and deactivates or deletes the erroneous ads so they aren’t presented to users.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine the error removal feature of Zlotin with the AI generated ads of Thomas/Peris/Goswami/Gupta/Singh/Narayan in order to ensure that the system only generates ads that are accurate and effective.
Other Relevant Prior Art
Though not cited in the above rejections, the following references are nevertheless deemed to be relevant to Applicant’s disclosures:
Bian et al. (20220084077), directed to a method for automatically generating advertisements.
Kidder et al. (20080270164), directed to a method for managing a plurality of advertising networks.
Bhatia et al. (20120004981), directed to a method for ad and campaign evaluation with bucket testing in guaranteed delivery of online ads.
Kim et al. (11676180), directed to an AI-based campaign and creative target segment recommendation on shared and personal device.
Goswami et al. (20160154822), directed to a method for image quality assessment to merchandise an item.
Response to Arguments
Applicant’s arguments regarding the sufficiency of the claims under 35 USC 101 are unpersuasive.
First, Applicant argues that Examiner described the features of the claims as mental processes. Examiner never made such an assertion. Rather, Examiner noted that the claims are directed to an abstract idea (organizing human activity) and claims 1 and 6 do not contain any hardware components. As such, claims 1 and 6 were rejected as being software per se. Because Applicant has not amended the aforementioned independent claims, those rejections are sustained.
Next, Applicant argues that the claims provide a technical solution to a problem of generating banner ads with a certain probability of appealing to one or more demographics; and reducing the number of banner ads predicted and/or presented to consumers which are unlikely to be effective. Simply put, these are not technical features. Rather, Examiner finds that the “solutions” to which the Applicant refers are business improvements rather than improvements to a technological or technical field. Furthermore, Applicant has not provided any evidence that the programming related to their “solutions” would entail anything atypical from conventional programming. And, as the Federal Circuit clearly stated, “after Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” DDR Holdings, LLC v. Hotels.com, 773 F.3d 1245, 1256 (Fed. Cir. 2014).
Furthermore, there is no indication in the specification that any technologically novel or inventive hardware is required to perform the method. See Affinity Labs of Texas, LLC v. DIRECTV, LLC, 838 F.3d 1253, 1263 (Fed. Cir. 2016); see also Enfish, 822 F.3d. at 1336 (focusing on whether the claim is “an improvement to [the] computer functionality itself, not on economic or other tasks for which a computer is used in its ordinary capacity”). Simply put, “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (citing Alice, 134 S. Ct. at 2359); see also Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d at 1367 (“claiming the improved speed or efficiency inherent with applying the abstract idea on a computer [does not] provide a sufficient inventive concept”). Thus, in sum, “the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.” Elec. Power Grp., 830 F.3d at 1354.
As noted above, independent claims 1 and 6 do not disclose any hardware elements (servers, processors, memory, computer networks, etc.). Newly added claim 15 discloses processors and memory, but these are nothing more than generic computer components that do not render the claims eligible.
Next, Applicant argues that the machine generated data and processing techniques cannot be practically performed in the human mind. Examiner notes that the Federal Circuit was clear in FairWarning that “First of all, we do not rely on the pen and paper test to reach our holding of patent eligibility in this case. At the same time, we note that, in viewing the facts in FairWarning’s favor, the inability of the human mind to perform each claim step does not alone confer patentability. As we have explained, “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.” Bancorp Servs., 687 F.3d. at 1278.”
As noted above, the invention merely discloses a method for collecting banner ads; collecting banner ad performance records; analyzing the banner ads and performance data; generating a graph of the correlated banner ad elements and corresponding banner ad performance records (i.e., matching data points on a graph); using the graph to product AI-generated banner ads; analyzing the AI-generated banner ads; and ranking the ads. This amounts to nothing more than collecting ad data, analyzing the ad data, producing results of the data collection and analysis, and ranking the results. The Federal Circuit has repeatedly held that claims that “are directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner” are claims to abstract ideas. In re Killian, No. 21-2113, 2022 WL 3589496 at *4 (Fed. Cir. Aug. 23, 2022); see also Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353–54 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017); In re TLI Commc’ns LLC Patent Litig., 823 F.3d 607, 613 (Fed. Cir. 2016).
Furthermore, it is clear that the claims do not recite any technological features
associated with the claimed process (aside from simply inputting and received data from an AI chatbot); rather, they merely describe a generic process of
collecting, observing, and matching data. At best, these features are disclosed in such
a way that generally links them to computer technology, and the claims are silent with
respect to how these processes are technically performed. Therefore, the claims do not
necessarily arise in the realm of computer networks. See Bridge and Post, Inc. v.
Verizon, 778 F. App’x 882, 887, 891 (finding that “targeted marketing
and market segmentation were developed to increase the effectiveness of
advertisements placed in traditional media such as radio, television, and printed
newspapers and magazines’ are not “problems necessarily rooted in computer
technology,” but “recite the performance of a business practice known from the pre-
internet world.”).
Finally, Applicant incorrectly asserts that Examiner failed to comply with the requirement under Berkheimer. This represents a fundamental misunderstanding of 101. As noted above (and in the previous office action), Examiner provided evidence that the additional element of storing ads is extrasolution activity, as well as well-understood, routine, and conventional under Berkheimer.
Therefore, for at least these reasons, the rejection under 35 USC 101 is sustained.
With respect to the rejections under 35 USC 103, Applicant’s principal argument rests on the notion that the MMKG in Peris does not effectively read on the claim elements. Examiner disagrees. A MMKG is nothing more than a graphic that shows relationships between multiple data points (nodes). These can be presented as images, text, or other data. The following is an example:
PNG
media_image1.png
260
386
media_image1.png
Greyscale
As is readily apparent, a MMKG merely takes 2 or more data points, displays them graphically, and matches the data points with each other based on their relationship to each other. In the instant application, the system analyzes two data points – banner ads and banner ad performance records. While the Peris reference may not explicitly refer to its graph as a “MMKG,” it accomplishes the exact task that is claimed in Applicant’s invention. Ad data and associated performance data are collected and used to construct a “knowledge graph,” which is used to determine relationships between them. (See paragraphs 198-199). When combined with the AI-generated ads of Thomas, Examiner remains steadfast in asserting that the prior art effectively anticipates the claim limitations.
Finally, Applicant argues that Goswami is insufficient and teaches away from the invention. Examiner disagrees entirely. Like the instant invention, Goswami describes a method of online advertising. Also similar to the instant application, Goswami describes a process for evaluating image quality of ads and assigns scores to them (in terms of click through rates). The visual characteristics of images may be analyzed to raise or lower the rankings of the images. Not only does this reference effectively anticipate the aforementioned limitations, but it does not, in any way, shape, or form, teach away from the disclosures of the other references and/or the claims of the instant application.
Therefore, for at least these reasons, the rejection under 35 USC 103 is sustained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BUSCH whose telephone number is (571)270-7953. The examiner can normally be reached M-F 10-7.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621