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 .
DETAILED ACTION
Claims 1-19, 21 have been examined.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are not found persuasive. On 12/4/25, Applicant amended the independent claims by adding “campaign parameters”. Also, Applicant’s Remarks address these amended features. Examiner notes that “campaign parameters” is not in Applicant Spec or original claims. Campaign and parameters occurs in the same claim but Examiner could not find an example of these campaign parameters in the Applicant Spec. Hence, these campaign parameter features are open to a broad interpretation. See the detailed citations for campaign parameters in the rejection below.
Also, the 2/10/25 amendments and arguments are considered substantive to pass 101.
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 1-19, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Muriqi (20240046318).
Claims 1, 10, 19. Examiner notes that based on Applicant Spec at [2] that a sponsored content item can be a paid for ad; and that [80] gives some examples of campaign modifications. Also, in regards to Applicant Spec, Examiner notes that “campaign parameters” is not in Applicant Spec or original claims. Campaign and parameters occurs in the same claim but Examiner could not find an example of these campaign parameters in the Applicant Spec. Hence, these campaign parameter features are open to a broad interpretation. Muriqi discloses a method, performed at a computer system comprising a processor and a non-transitory computer readable medium (Fig. 4), comprising:
obtaining, at an online system and from a device of a publishing user, a campaign that includes one or more sponsored content items and a set of campaign parameters defining how the one or more sponsored content items are to be published (see ad campaign at [481, 482]; also see ads and optimize at [155, 467]; also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]);
publishing, according to the set of campaign parameters, the one or more sponsored content items to a plurality of devices associated with viewing users of the online system, wherein the publishing causes the plurality of devices to display the one or more sponsored content items (see ad campaign at [481, 482]; also see ads and optimize at [155, 467]; also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]);
logging data describing interactions by the plurality of devices with the one or more sponsored content items (see ads and optimize and large language model at [155, 467]; see optimize ads at [146]).
Muriqi further discloses tuning a large language model using a dataset, the dataset comprising information about presentation of content, information about modifications made to the content, and data indicating a change in a performance metric after the modifications were made, wherein the performance metric includes a rate at which the viewing users performed a specific action after the modifications were presented to the viewing users (“[0155] AI, and in particular, large language model based neural network systems, hold promise for application to targeting advertising. The purpose of the targeted advertising is to increase the efficiency of advertising, ultimately reflected in increased profits of the seller for a typical commercial advertiser. This may be achieved through … increased conversion of advertising impressions to sales, .. The LLM, or multimodal-enhanced LLM, in a larger system that implements the function, can model … the characteristics of various users (targets), … to optimize the particular ads delivered to a target, and the valuation of the ad placement. The optimization is an economic optimization employing a value function, based on the desires, needs and value function of the user. In general, the neural networks are pretrained, … The user profile and characteristics are adaptive, and are generic for all advertising and possibly the content targeting as well. Ads fed to the system are processed to extract their salient characteristics, and a metadata file with the characteristics is associated with the ad.”, also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes; “[0467] …. The LLM, such as a GPT, can target ads and content to a user, synthesize entertainment content,“; Train the LLM on data, see train or pretrain and LLM at [69, 155, 87, 90, 449, 451]; also see adaptively at [69]).
Muriqi does not explicitly disclose tuning the LLM using prior campaigns, the dataset of the prior campaigns…, information about modifications made to the previous campaigns. However, Muriqi discloses training to optimize advertising and CTR by using historical data [166]. And, Muriqi further discloses using AI and models to use past history to better predict future preferences (“[162]… One such technique is the RNN model, which is employed in GRU4Rec to determine future preferences by using the past click behaviors of users.”) and using history to improve CTR [166, 167] and [143] and ad campaigns and behavior history (“[154]… Comparing with existing advertisements, the two kinds of Internet advertisements rely on the behavior history of users, such as consumers' or netizens' clicks and purchases, and valuable information can be obtained from several promotions. ..Internet advertisements can show great marketing ability by processing data from multiple channels to convey information, understanding what users want, and approaching them easily.”). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Muriqi’s history data and optimizing ad campaigns and click rates to Muriqi’s training LLMs to optimizing ad campaigns and click rates. One would have been motivated to do this in order to better use LLMs to optimize ad campaigns and click rates.
In further regards to these features preceding, Muriqi discloses AI and LLM to determine and present media content [69] and optimizing various metrics and revenues [484] and discloses messages and content and targeting data or metadata and a valuation function for parameters [34]. Muriqi discloses tracking ads and their characteristics in the system (“[155]… Ads fed to the system are processed to extract their salient characteristics, and a metadata file with the characteristics is associated with the ad.”) and optimizing a value function related to ads presented [155]. And, critically, Muriqi further discloses using AI/machine learning to take vectors and adjust parameters and optimize the parameters in order to better label or characterize the data or patterns [0386]. And, Muriqi discloses optimizing and economic optimizations to better present ads and that these labels are used in that optimizing [447]. Also, Muriqi discloses predicting CTR and tracking high dimensional feature interactions and learning nonlinearity by converting specific historical feature into vectors [159]. So, Muriqi uses AI and LLM to optimize and this optimize includes adjusting parameters for better vectors and better labeling. Also, Muriqi discloses predicting CTR by learning nonlinearity using historical features and vectors. Hence, Muriqi discloses a data record that pairs a specific modification that was made to a campaign parameter with the resulting change in a performance metric of the campaign. And, Muriqi discloses tuning a model to generate suggestions to edit a campaign based on such a dataset. So, these citations demonstrate that Muriqi modifies a content publishing campaign, correlates modifications made to campaigns with change to a performance metric.
Muriqi further discloses generating a prompt for the large language model, the prompt including: information about the campaign, the information including the set of campaign parameters defining how the one or more sponsored content items are to be published, at least a portion of the logged data describing interactions by the plurality of devices with the one or more sponsored content items, and a request that the large language model identify one or more potential modifications to the set of campaign parameters (see ads and optimize and large language model at [155, 467]; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]);
providing the prompt to the large language model (see ads and optimize and large language model at [155, 467]);
obtaining, from the large language model, one or more potential modifications to the set of campaign parameters (see ads and optimize and large language model at [155-157, 467]; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]);
generating display instructions for an interface displaying one or more of the potential modifications to the set of campaign parameters and a selectable interface element corresponding with each of the one or more potential modifications (see AI and recommend ads, optimize prizing at [448]; see manage ad campaign and user interface at [131, 151]; see interface and advertising at [27]; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]);
transmitting the display instructions from the online system to the device of the publishing user, wherein the transmitting causes the device of the publishing user to display the interface ([131, 151]) and also including the one or more of the potential modifications to the set of campaign parameters and a selectable interface element corresponding with each of the one or more potential modifications ([155-157, 467, 448]; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]). Muriqi does not explicitly disclose display the interface including the one or more of the potential modifications. That is, Muriqi does not explicitly disclose the interface displays the potential modifications for selection. However, Muriqi discloses old and well known and also interface for optimizing campaign with modifications [131, 151] and also extensive features for optimizing a campaign and LLM for optimizing an ad campaign [155-157, 467, 448]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add an Muriqis interface for managing a campaign to Muriqis LLM and AI for optimizing an ad campaign via modifications . One would have been motivated to do this in order to better manage the campaign and better actually implement the optimizing modifications.
Muriqi further discloses receiving, from the device of the publishing user, a selection of one of the selectable interface elements [131, 151];
responsive to receiving the selection of one of the selectable interface elements, modifying the campaign according to the potential modification to the set of campaign parameters associated with the selected selectable interface element ([131, 151] and see optimizing at [155-157, 467, 448] and note obviousness statement above; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]); and
publishing, according to the modified set of campaign parameters, the one or more sponsored content items to a subsequent plurality of devices associated with viewing users of the online system, wherein the publishing causes the plurality of devices to display the one or more sponsored content items ([154-160] and [467, 448]; note AI and recommend ads, optimize prizing at [448]; note optimize ads actually presented at [155, 156] and selected optimal ad at [157], see distribute ads at [174]; also note display the ad at [83]; also note in [155] that the user profile and characteristics are adaptive so Examiner interprets that campaign parameters like user profile and characteristic can be adapted or tuned as the system is run, also note at [513] that the system tests sensitivity of the campaign targeting parameters to profile changes, also for campaign parameters see: “[27]… The advertising campaign may pay per view, click (interaction), sale, commission on sale, subscription, etc.” and “[0058] The advertisements may be provided in a traditional manner, through an advertisement server and according to campaign parameters. The campaign may target…” , And see ad campaigns by different competing advertisers with different competing campaigns that have different campaign parameters [49] and also campaign parameters with different measures [58] and also defining and managing ad campaigns with different parameters/characteristics [73, 131, 151, 481] and also ad campaigns with “adaptive” parameters and “testing” of ad campaign characteristics including user profile characteristics that adapt and are used as campaign parameters [482]).
Claim 2, 11. Muriqi does not explicitly disclose the method of claim 1, further comprising: including, in the generated prompt, a request to provide a text description describing each potential modification to the set of parameters; receiving, from the large language model, a set of text descriptions describing each potential modification to the set of parameters; and including, in the generated display instructions, the received text descriptions describing each potential modification to the set of parameters. However, Muriqi discloses providing text of the possibilities to an LLM [455] and optimizing by the LLM and the LLM presenting options ([154-160] and [467, 448]; note AI and recommend ads, optimize prizing at [448]) and the AI outputting text [457] and confirming optimization suggestions [158]. And Muriqi also discloses the LLM “thinking out loud” [455]. Examiner interprets this thinking out loud as potential modifications. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add LLM thinking out loud/potential modifications to LLM optimization and AI text output. One would have been motivated to do this in order to better present optimization possibilities.
Claim 3, 12. Muriqi does not explicitly disclose the method of claim 1, wherein generating the prompt for the large language model comprises: generating an embedding for the campaign based on the data describing the sponsored content items of the campaign, at least a subset of the logged data describing interactions by the plurality of devices with the one or more sponsored content items, and the set of parameters defining how the one or more sponsored content items are to be published; determining measures of similarity between the embedding for the campaign and embeddings for supplemental examples corresponding to additional campaigns in an index, each supplemental example including data describing sponsored content items of the additional campaign, data describing presentation of the sponsored content items of the campaign, a set of actions for modifying the additional campaign, and modifications to the additional campaign; selecting a supplemental example with an embedding having a maximum measure of similarity to the embedding for the campaign; and including, in the generated prompt, information about the selected supplemental example. However, Muriqi discloses LLM for ad campaign optimization [155-160] and using embedding and vectors for click thru rates and interests [166, 167, 169] and using embedding and index [166] and also similarity analysis [383, 387, 396]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Muriqis similarity analysis, embeddings and index to Muriqis LLM and optimal ad presenting. One would have been motivated to do this in order to better use standard techniques related to modeling to better present optimal ads.
Claim 4, 13. Muriqi further discloses the method of claim 1, wherein logging data describing interactions by the plurality of devices with the one or more sponsored content items comprises logging contextual data describing presentation of the sponsored content items of the campaign (see measuring effectiveness at [159]; for context see nature of ad slot at [156] or context at [27]).
Claim 5, 14. Muriqi further discloses the method of claim 4, wherein logging the contextual data comprises logging one or more questions about the campaign the computer system received from the publishing user (see LLM and [455, 453]).
Claim 6, 15. Muriqi further discloses the method of claim 4, wherein logging the contextual data comprises logging one or more modifications to the campaign made by the publishing user (see ads and history of users at [154]; also see comparing advertisements [154]).
Claim 7, 16. Muriqi further discloses the method of claim 4, wherein logging the contextual data comprises logging one or more additional sponsored content items associated with the publishing user (see comparing advertisements [154]).
Claim 8, 17. Muriqi further discloses the method of claim 4, wherein logging the contextual data comprises logging an item catalog associated with the publishing user, the item catalog identifying items offered by the publishing user (see competing products at [146]; see nature of product [155], see category and brand of product [167]).
Claim 9, 18. Muriqi does not explicitly disclose the method of claim 1, wherein the large language model is trained based on stored data describing one or more additional campaigns including additional sponsored content items presented to viewing users, the additional campaigns each having an embedding within a threshold distance of an embedding for the campaign based on the data describing the sponsored content items of the campaign, at least the logged data describing interactions by the plurality of devices with the one or more sponsored content items, and the potential modifications to the set of parameters. However, Muriqie discloses LLM and training [453] and LLM and ad optimization [155] and using embedding and vectors for click thru rates and interests [166, 167, 169] and distance functions [381, 383, 384] and logged data (see ads and history of users at [154]; also see comparing advertisements [154]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Muriqis embedding and sitance calculations and LLM training to Muriqis LLM for optimal ad presenting. One would have been motivated to do this in order to better use modeling and LLM to optimally present ads.
Claim 21. Muriqi further discloses the method of claim 1, wherein the selectable interface element is operable by a single user input that, when selected, automatically causes the online system to perform the action corresponding to the potential modification without requiring further user input (see slidebar for adjust score and then the system recalculates at [78]; see optimize revenues and user interface for adjust parameters and then recalculate optimization with adjusted parameters at [484, 485]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
these disclose LLM and optimal ads and embedding: McNulty; Muriqi llm [[155]] and [69, 87, 90] [136, 146, 155]; and West [74, 123]; these disclose LLM and optimal ads: Vakil.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621
12/15/25