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 .
Response to Amendment
This is a Final Office Action in response to amendments filed on 1/2/2026. Claims 3, 6, 15, and 18 were previously cancelled. Claims 3 and 18 are now cancelled. Claims 1, 11, and 16 have been amended. Therefore, claims 1, 2, 4, 5, 7-14, 16, 17, 19, and 20 are pending and addressed below.
Claim Interpretation
Independent claims 1 and 11 recite, “identify a salient attribute” and claim 16 recites, “receiving…a salient attribute”. The disclosure does not provide a definition, an explanation, any kind of details or examples as to what is meant by “a salient attribute”, what criteria are being used to identify that an attribute is salient, or how a salient attribute is being identified. The online Meriam-Webster dictionary provides several definitions for the adjective “salient”. As such, it is unclear which definition or which interpretation the inventors of the instant application intended. See Salient Definition & Meaning - Merriam-Webster. For these reasons, Examiner will interpret “salient attribute” as any kind of attribute.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2, 4, 5, 7-14, 16, 17, 19, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, and 16 recite, “predict a performance of an advertisement payload via data trained on the neural network.” Applicant points to par. 0024 for this amendment. Par. 0024 states, “Algorithm 244 may include a neural network (NN) trained over databases 252, to select digital promotion payload 227 targeted to the specific preferences of a consumer when the consumer grants application 222 to track user transactions.” This paragraph does not explain that the “data trained on the neural network” is being used to “predict a performance of an advertisement payload”. Therefore, it appears that Applicant does not have written description of this claim amendment as the effective filing date of the application.
Claims 2, 4, 5, 7-10, 12-14, 17, 19, and 20 are also rejected because of their dependencies on claims 1, 11, or 16.
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, 2, 4, 5, 7-14, 16, 17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Under step 1, claim 1 recites a method, claim 11 recites a system, and claim 16 recites a method. Thus, claims 1 and 11 are directed to statutory categories of patentable subject matter.
Step 2A, Prong 1: Independent claim 1 recites, “A computer-implemented method, comprising: receiving data including an impression value and an attribution value for a list item in an advertising campaign; correlating the data with multiple advertising attributes of the advertising campaign and historical advertising campaigns to identify a salient attribute for an expected result of the advertising campaign; implementing a neural network trained on the data; in response to the data being correlated with the historical advertising campaign, implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload via data trained on the neural network, and modify the salient attribute in the advertisement payload, wherein modifying the salient attribute in the advertisement payload comprises one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload; and providing the advertisement payload including the salient attribute to a server in a network for distribution among users communicatively coupled to the network.” Independent claim 11 recites, “A system, comprising: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause the system to perform operations, comprising: receive data including an impression value and an attribution value for a list item in an advertising campaign; correlate the data with multiple advertising attributes of the advertising campaign and historical advertising campaigns to identify a salient attribute for an expected result of the advertising campaign; implementing a neural network trained on the data; in response to the data being correlated with the historical advertising campaign, implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload via data trained on the neural network, and modify the salient attribute in the advertisement; and provide the advertisement payload including the salient attribute to a server in a network for distribution among users communicatively coupled to the network, wherein modifying the salient attribute in the advertisement payload comprising changing an advertising channel of the advertisement pay load for one or more users coupled to the network.” Independent claim 16 recites, “A computer-implemented method, comprising: receiving, in a server, an advertisement payload from a campaign server, the advertisement payload including a salient attribute, for distribution among users communicatively coupled to the server; identifying a channel for transmission of the advertisement payload; selecting at least one user based on the salient attribute; retrieving an identification for a client device associated to the at least one user based on the channel for transmission; implementing a neural network trained on the data; implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload via data trained on the neural network, and modify the salient attribute in the advertisement payload; and providing the advertisement payload to the client device via the channel for transmission.” These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity such as advertising and marketing activities and behaviors since the claims receive impression and attribution value, correlate data of the advertising campaign and historical advertising campaigns, predict a performance of an advertisement payload, modify a salient attribute of an advertisement payload, receiving an advertisement payload, identifying a channel, selecting a user, retrieving identification of a user, and providing the advertisement payload, which are all directed to advertising and marketing activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. The independent claims recite the additional elements of “a dynamic optimization engine”, “a server”, “a network”, “one or more processors”, “a memory”, “campaign server”, “client device”, “implementing a neural network trained on the data”; and “data trained on the neural network”. These additional elements are recited at a high-level of generality (i.e., as a generic device performing a generic computer function) such that they amount to no more than mere instructions to apply the exception using a computer. Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a dynamic optimization engine”, “a server”, “a network”, “one or more processors”, “a memory”, “campaign server”, “client device”, “implementing a neural network trained on the data”; and “data trained on the neural network” are generic computing elements. Therefore, the independent claims are not patent eligible.
Dependent claims 2, 4, 5, 7-10, 12-14, 17, 19, and 20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 11, and 16 without significantly more.
Claim 2 recites, “wherein modifying the salient attribute in the advertisement payload comprising changing an advertising channel of the advertisement payload for one or more users coupled to the network.” The limitation changing an advertising channel” is part of the same abstract idea of claim 1. The network does not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 4 recites, “wherein one of the advertising attributes of the advertising campaign comprises an advertising channel, and providing the advertisement payload to a server comprises selecting the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the network.” The steps of providing and selecting are part of the same abstract idea of claim 1. The additional elements, as italicized, do not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 5 recites, “wherein correlating the data with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload. The step of extracting is part of the same abstract idea of claim 1.
Claim 7 recites, “wherein receiving data including an impression value and an attribution value for a list item in an advertising campaign comprises correlating an impression datum provided by a client device with a consumer with an attribution datum provided by a point of sale device with a retailer.” The step of correlating is part of the same abstract idea of claim 1. The additional elements of “a client device” and “a point of sale device” are additional elements that are generically recited. As such, they do not integrate the abstract idea into a practical application and are not significantly more.
Claim 8 recites, “further comprising determining a performance value of the advertising campaign as a ratio of the attribution value to the impression value for a selected advertisement channel.” The step of determining is part of the same abstract idea of claim 1.
Claim 9 recites, “wherein a selected brand is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected brand relative to a total number of consumers of the selected brand.” The step of determining is part of the same abstract idea of claim 1.
Claim 10 recites, “wherein a selected product category is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected product category relative to a total number of consumers of the selected product category.” The step of determining is part of the same abstract idea of claim 1.
Claim 12 recites, “wherein to modify the salient attribute in the advertisement payload the one or more processors execute instructions to modify one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload.” The step of modifying one of…” is part of the same abstract idea of claim 11. The additional element, as italicized, does not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 13 recites, “The system of claim 11, wherein one of the advertising attributes of the advertising campaign comprises an advertising channel, and to provide the advertisement payload to a server the one or more processors execute instructions to select the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the network.” The step of selecting is part of the same abstract idea of claim 11. The additional elements, as italicized, do not integrate the abstract idea into a practical application and are not significantly more for the same reasons as explained above.
Claim 14 recites, “wherein to correlate the data with multiple advertising attributes the one or more processors execute instructions extract a semantic meaning of a textual content in the advertisement payload.” The step of extracting is part of the same abstract idea of claim 11. The additional element, as italicized, does not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 17 recites, “further comprising modifying the salient attribute in the advertisement payload by changing an advertising channel of the advertisement payload for one or more users coupled to the server.” The step of modifying is part of the same abstract idea of claim 16. The additional element, as italicized, does not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 19 recites, “further comprising providing the advertisement payload to a server by selecting the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the server, and wherein an attribute of the advertisement payload comprises an advertising channel,.” The steps of providing and selecting are part of the same abstract idea of claim 16. The additional elements, as italicized, do not integrate the abstract idea into a practical application and is not significantly more for the same reasons as explained above.
Claim 20 recites, “further comprising correlating data collected from multiple client devices for the at least one user coupled to the server and from multiple point of sale devices in retailer stores with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload.” The steps of correlating and extracting are part of the same abstract idea of claim 16. The additional elements, as italicized, do not integrate the abstract idea into a practical application and is not significantly more because they are generic computing elements recited in a generic manner.
As such, when claims 1, 2, 4, 5, 7-14, 16, 17, 19, and 20 are considered individually, as a whole, or in combinations, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459).
Regarding claim 11, Kalb teaches
A system, comprising: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause the system to perform operations, comprising ([0187]):
receive data including an impression value and an attribution value for a list item in an advertising campaign ([0097] "This data is formed by the cumulative history of short code responses to advertisements, which participate in the real-time off-line ad campaign optimization method, by a particular individual customer 2." [0101] "The present disclosure makes it possible to measure conversions [attribution value] in off-line settings for its publishing, advertiser or agency clients by providing a hosted web page where viewers of an ad can post the discount coupon or promotion number they received in their text message. Viewers will then receive a discount or product [list item] as advertised." [0113] "Also as shown in FIG. 4, as the customer 2 responds to the advertisements by making a purchase 280 [attribution value] or sending a short code text message 290, the disclosed system collects the purchase data 25 but also most importantly collects response data in a collect short codes process 450. The RTOO system then performs the operations necessary to, process short code information 460 for storage of this captured information in the RTOO data center 370. When the customer makes a purchase, the purchase information database 25 is updated and the good or service provider, if they want this information available to improve the campaign analysis, communicates this information to the RTOO data center." See also [0107], [0102], and [0127].):
correlate the data with multiple advertising attributes of the advertising campaign and historical advertising campaigns to identify a salient attribute for an expected result of the advertising campaign ([0179] "An example the operation of the promo code embodiment is illustrated in the flow diagram of FIG. 30. At step 3001 a user selects an ad campaign for analysis. At step 3002 the system retrieves the metrics [multiple advertisement attributes] for that ad campaign. The metrics are data points that the user has selected to determine the effectiveness of the campaign. The metrics may include sales, ad penetration, responses, response times, channel of response, channel of off-line advert, and the like. At step 3003 the system retrieves current analytic data acquired by the system. At step 3004 the current analytic data is compared [correlating] to the metrics. At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified [modifying the salient attribute] at step 3006. The modification can include channels of delivery, times of day, change of creative, change of location, and the like. After changing the advert campaign, monitoring continues at step 3002." [0040] "analyze history of response to a multitude of off-line advertisements [historical advertising campaigns]. See also [0041], [0180] and [0126].);
in response to the data being correlated with the historical advertising campaign, implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload ([0041] "The history accumulated for each customer includes various time of response information. Determinations can accordingly be made of the best time and channel place to reach customer groups with any desired demographics criteria. Therefore, once the advertisement design process creates possible ads for a multitude of media types, and the vendor or manufacturer identifies the desired consumer group to be targeted, the RTOO system [dynamic optimization engine] assists in the selection process which determines which ads to run, and to predict the most cost effective schedule for the ads on each media type. Moreover, as the ads are run, feedback begins to be accumulated immediately [dynamically] and adjustments are made based on closed-loop decision making in order to optimize the cost and effectiveness of the overall off-line ad campaign." See also [0137].), and
modify the salient attribute in an advertisement payload ([0179] "At step 3003 the system retrieves current analytic data acquired by the system. At step 3004 the current analytic data is compared to the metrics. At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified at step 3006. The modification can include channels of delivery [modify salient attribute], times of day, change of creative, change of location, and the like."); and
provide the advertisement payload including the salient attribute to a server in a network for distribution among users communicatively coupled to the network ([0179] "After changing the advert campaign, monitoring continues at step 3002." [0171] "At step 2704 the system finalizes the adverts and they are published on schedule at step 2705."[0089] "In all its various embodiments an RTOO system 300 [server] can perform control of advertisement placement and scheduling for maximum efficiency of a campaign while minimizing required efforts from a campaign manager. The RTOO system 300 can be implemented within a single computer or be distributed over a number of devices." See also [0109] and [0182]. Examiner notes that "for distribution among users communicatively coupled to the network" is intended use, not positively recited, and given little patentable weight.),
wherein modifying the salient attribute in the advertisement payload comprising changing an advertising channel of the advertisement pay load for one or more users coupled to the network ([0179] "At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified [modifying the salient attribute] at step 3006. The modification can include channels of delivery [advertising channel], times of day, change of creative, change of location, and the like. After changing the advert campaign, monitoring continues at step 3002." See also Fig. 4.).
Kalb does not specifically teach
implementing a neural network trained on the data;
via data trained on the neural network.
However, Plourde teaches
implementing a neural network trained on the data ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured." See also [0035].);
via data trained on the neural network ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured. [0057] The result of the aforementioned pairing process is the production 25 of a set of customer-specific product affinity data associated with a set of customers, specified previously, to be targeted during a particular campaign. Said data constitute a quantized predictive measurement corresponding to the extent to which one would have calculated, for each client from among said set of clients-given the available behavior histories-possessed a particular interest for a given set of products retained from the advertising content base (M01) for a particular 5 campaign. See also [0035], [0038] and [0040].)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising system of Kalb with the advertising system of Plourde by adding implementing a neural network trained on the data; via data trained on the neural network, as taught by Plourde, since Kalb and Plourde are analogous art, and in order to facilitate the conclusion of certain deductions (Plourde, [0048]).
Regarding claim 13, Kalb teaches
The system of claim 11, wherein one of the advertising attributes of the advertising campaign comprises an advertising channel ([0121] "Once the ad members of the campaign are entered, the user assigns each to a media channel [advertising channel] with the select channel process 740. Once the media outlet channel is determined for a particular ad,"),
and to provide the advertisement payload to a server the one or more processors execute instructions to select the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the network ([0169] "It should be noted that the system provides a new manner of tracking the effectiveness of off-line adverts, the system is not limited to such and may in fact combine off-line and online formats ( email, social networks, affiliates, paid and free search engine [a browser], etc.)." [0127] "The real-time off-line ad optimization system monitors, measures and analyzes the performance of an off-line advertisement by receiving clicks from viewers who opt in to request information or respond via a mobile phone's. SMS text messaging for example. This response data is captured within the RTOO data center. FIG. 4 shows the complete functionality and flow of the RTOO system for all including the capture of viewer response data." Since the viewers are able to send SMS text messaging the client device of the viewers are communicatively coupled to the network.).
Claims 1, 2, 4, 7, 8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459), in view of Touil (P. G. Pub. No. 2019/0114678).
Regarding claim 1, Kalb teaches
A computer-implemented method, comprising: receiving data including an impression value and an attribution value for a list item in an advertising campaign [0097] "This data is formed by the cumulative history of short code responses to advertisements, which participate in the real-time off-line ad campaign optimization method, by a particular individual customer 2." [0101] "The present disclosure makes it possible to measure conversions [attribution value] in off-line settings for its publishing, advertiser or agency clients by providing a hosted web page where viewers of an ad can post the discount coupon or promotion number they received in their text message. Viewers will then receive a discount or product [list item] as advertised." [0113] "Also as shown in FIG. 4, as the customer 2 responds to the advertisements by making a purchase 280 [attribution value] or sending a short code text message 290, the disclosed system collects the purchase data 25 but also most importantly collects response data in a collect short codes process 450. The RTOO system then performs the operations necessary to, process short code information 460 for storage of this captured information in the RTOO data center 370. When the customer makes a purchase, the purchase information database 25 is updated and the good or service provider, if they want this information available to improve the campaign analysis, communicates this information to the RTOO data center." [See also [0107], [0102], and [0127].);
correlating the data with multiple advertising attributes of the advertising campaign and historical advertising campaigns to identify a salient attribute for an expected result of the advertising campaign; modifying the salient attribute in the advertisement payload ([0179] "An example the operation of the promo code embodiment is illustrated in the flow diagram of FIG. 30. At step 3001 a user selects an ad campaign for analysis. At step 3002 the system retrieves the metrics [multiple advertisement attributes] for that ad campaign. The metrics are data points that the user has selected to determine the effectiveness of the campaign. The metrics may include sales, ad penetration, responses, response times, channel of response, channel of off-line advert, and the like. At step 3003 the system retrieves current analytic data acquired by the system. At step 3004 the current analytic data is compared [correlating] to the metrics. At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified [modifying the salient attribute] at step 3006. The modification can include channels of delivery, times of day, change of creative, change of location, and the like. After changing the advert campaign, monitoring continues at step 3002." [0040] "analyze history of response to a multitude of off-line advertisements [historical advertising campaigns]. See also [0041], [0180] and [0126].);
in response to the data being correlated with the historical advertising campaign, implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload ([0041] "The history accumulated for each customer includes various time of response information. Determinations can accordingly be made of the best time and channel place to reach customer groups with any desired demographics criteria. Therefore, once the advertisement design process creates possible ads for a multitude of media types, and the vendor or manufacturer identifies the desired consumer group to be targeted, the RTOO system [dynamic optimization engine] assists in the selection process which determines which ads to run, and to predict the most cost effective schedule for the ads on each media type. Moreover, as the ads are run, feedback begins to be accumulated immediately [dynamically] and adjustments are made based on closed-loop decision making in order to optimize the cost and effectiveness of the overall off-line ad campaign." See also [0137].), and
modify the salient attribute in the advertisement payload ([0179] "At step 3003 the system retrieves current analytic data acquired by the system. At step 3004 the current analytic data is compared to the metrics. At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified at step 3006. The modification can include channels of delivery [modify salient attribute], times of day, change of creative, change of location, and the like."),
providing the advertisement payload including the salient attribute to a server in a network for distribution among users communicatively coupled to the network ([0179] "After changing the advert campaign, monitoring continues at step 3002." [0171] "At step 2704 the system finalizes the adverts and they are published on schedule at step 2705."[0089] "In all its various embodiments an RTOO system 300 [server] can perform control of advertisement placement and scheduling for maximum efficiency of a campaign while minimizing required efforts from a campaign manager. The RTOO system 300 can be implemented within a single computer or be distributed over a number of devices." See also [0109] and [0182]. Examiner notes that "for distribution among users communicatively coupled to the network" is intended use, not positively recited, and given little patentable weight.).
Kalb does not specifically teach
implementing a neural network trained on the data;
via data trained on the neural network.
However, Plourde teaches
implementing a neural network trained on the data ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured." See also [0035].);
via data trained on the neural network ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured. [0057] The result of the aforementioned pairing process is the production 25 of a set of customer-specific product affinity data associated with a set of customers, specified previously, to be targeted during a particular campaign. Said data constitute a quantized predictive measurement corresponding to the extent to which one would have calculated, for each client from among said set of clients-given the available behavior histories-possessed a particular interest for a given set of products retained from the advertising content base (M01) for a particular 5 campaign. See also [0035], [0038] and [0040].)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising system of Kalb with the advertising system of Plourde by adding implementing a neural network trained on the data; via data trained on the neural network, as taught by Plourde, since Kalb and Plourde are analogous art, and in order to facilitate the conclusion of certain deductions (Plourde, [0048]).
Kalb modifies the salient attribute in [0179]. Kalb and Plourde do not specifically teach
wherein modifying the salient attribute in the advertisement payload comprises one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload.
However, Touil teaches
wherein modifying the salient attribute in the advertisement payload comprises one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload ([0024] "Each seed image [advertisement payload] can have certain features and attributes, including but not limited to: image size, image compression, image encoding type, frame count, file weight, image metadata, image colors, image context, image resolution (e.g., DPI), image category, one or more shapes in the image, or one or more objects in the image. The system [dynamic optimization engine] can apply a series of mutations to a seed image in order to generate a plurality of different images ( also called candidate images) that can have different features and attributes from the seed image." [0012], [0035] and [0036] discuss historical and predicted performance.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising systems of Kalb and Plourde with the advertising system of Touil by substituting one kind of modification to the salient attribute of Kalb with another kind of modification to the salient attribute of Touil since it has been held to be within the general skill of a worker in the art to select a known item on the basis of its suitability for the intended use as a matter of obvious design choice. Modification of salient attributes are well known in the art and substituting one modification for another would not change the way the overall apparatus functions. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 2, Kalb teaches
The computer-implemented method of claim 1, wherein modifying the salient attribute in the advertisement payload comprising changing an advertising channel of the advertisement payload for one or more users coupled to the network ([0179] "At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified [modifying the salient attribute] at step 3006. The modification can include channels of delivery [advertising channel], times of day, change of creative, change of location, and the like. After changing the advert campaign, monitoring continues at step 3002." See also Fig. 4.).
Regarding claim 4, Kalb teaches
The computer-implemented method of claim 1, wherein one of the advertising attributes of the advertising campaign comprises an advertising channel ([0121] "Once the ad members of the campaign are entered, the user assigns each to a media channel [advertising channel] with the select channel process 740. Once the media outlet channel is determined for a particular ad,"),
and providing the advertisement payload to a server comprises selecting the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the network ([0169] "It should be noted that the system provides a new manner of tracking the effectiveness of off-line adverts, the system is not limited to such and may in fact combine off-line and online formats ( email, social networks, affiliates, paid and free search engine [a browser], etc.)." [0127] "The real-time off-line ad optimization system monitors, measures and analyzes the performance of an off-line advertisement by receiving clicks from viewers who opt in to request information or respond via a mobile phone's. SMS text messaging for example. This response data is captured within the RTOO data center. FIG. 4 shows the complete functionality and flow of the RTOO system for all including the capture of viewer response data." Since the viewers are able to send SMS text messaging the client device of the viewers are communicatively coupled to the network.).
Regarding claim 7, Kalb teaches
The computer-implemented method of claim 1, wherein receiving data including an impression value and an attribution value for a list item in an advertising campaign comprises ([0097] "This data is formed by the cumulative history of short code responses to advertisements, which participate in the real-time off-line ad campaign optimization method, by a particular individual customer 2." [0101] "The present disclosure makes it possible to measure conversions [attribution value] in off-line settings for its publishing, advertiser or agency clients by providing a hosted web page where viewers of an ad can post the discount coupon or promotion number they received in their text message. Viewers will then receive a discount or product [list item] as advertised." [0113] "Also as shown in FIG. 4, as the customer 2 responds to the advertisements by making a purchase 280 [attribution value] or sending a short code text message 290, the disclosed system collects the purchase data 25 but also most importantly collects response data in a collect short codes process 450. The RTOO system then performs the operations necessary to, process short code information 460 for storage of this captured information in the RTOO data center 370. When the customer makes a purchase, the purchase information database 25 is updated and the good or service provider, if they want this information available to improve the campaign analysis, communicates this information to the RTOO data center." See also [0107], [0102], and [0127].)
correlating an impression datum provided by a client device with a consumer with an attribution datum provided by a point of sale device with a retailer ([0163] "It should also be understood that a purchase of the offered goods may also be considered a response in gauging the effectiveness of an advert. The system contemplates reporting from POS (point-of-sale) locations to supplement the data from the advert as part of the feedback loop.").
Regarding claim 8, Kalb teaches
The computer-implemented method of claim 1, further comprising determining a performance value of the advertising campaign as a ratio of the attribution value to the impression value for a selected advertisement channel ([0122] "The RTOO system allows the user to append a call-to-action message to an advertisement in order to increase brand recognition [performance] by viewers or the number of conversions [impression value] to [ratio] actual purchases [attribute value] for the advertiser by performing a generate real-time short code, schedule, region and other info process 470. Once the ad is fully configured in terms of channel [selected channel],").
Regarding claim 12, Kalb modifies the salient attribute in [0179]. Kalb and Plourde do not specifically teach
wherein to modify the salient attribute in the advertisement payload the one or more processors execute instructions to modify one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload.
However, Touil teaches
wherein to modify the salient attribute in the advertisement payload the one or more processors execute instructions to modify one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload ([0024] "Each seed image [advertisement payload] can have certain features and attributes, including but not limited to: image size, image compression, image encoding type, frame count, file weight, image metadata, image colors, image context, image resolution (e.g., DPI), image category, one or more shapes in the image, or one or more objects in the image. The system [dynamic optimization engine] can apply a series of mutations to a seed image in order to generate a plurality of different images ( also called candidate images) that can have different features and attributes from the seed image." [0012], [0035] and [0036] discuss historical and predicted performance.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising systems of Kalb and Plourde with the advertising system of Touil by substituting one kind of modification to the salient attribute of Kalb with another kind of modification to the salient attribute of Touil since it has been held to be within the general skill of a worker in the art to select a known item on the basis of its suitability for the intended use as a matter of obvious design choice. Modification of salient attributes are well known in the art and substituting one modification for another would not change the way the overall apparatus functions. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459), in view of Touil (P. G. Pub. No. 2019/0114678), and in view of Navin (P. G. Pub. No. 2016/0381435).
Regarding claim 5, Kalb, Plourde, and Touil correlate data with multiple attributes. Kalb, Plourde, and Touil do not specifically teach
The computer-implemented method of claim 1, wherein correlating the data with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload.
However, Navin teaches
The computer-implemented method of claim 1, wherein correlating the data with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload ([0015] "In another aspect, a method of a networked device includes applying an automatic content recognition algorithm to determine a content identifier of an audio-visual data, and associating the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and/or the content identifier. In this other aspect, a capture infrastructure annotates the audio-visual data with a brand name and/or a product name by comparing entries in the master database with a closed captioning data of the audio-visual data and/or through an application of an optical character recognition algorithm in the audio-visual data.").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the data is correlated of Kalb, Plourde, and Touil by adding wherein correlating the data with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload, as taught by Navin, since Kalb, Plourde, Touil, and Navin are analogous art and in order to not miss key marketing opportunities to the user to deliver highly targeted and relevant advertising to the user (Navin, [0009]).
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459), in view of Touil (P. G. Pub. No. 2019/0114678), in further view of D’Imporzano (P. G. Pub. No. 2009/0307054).
Regarding claim 9, Kalb, Plourde, and Touil discuss the performance of advertising products or services and Kalb shows that each advertisement shows the RTOO trade name branding. Kalb, Plourde, and Touil do not specifically teach
The computer-implemented method of claim 1, wherein a selected brand is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected brand relative to a total number of consumers of the selected brand.
However, D’Imporzano teaches
The computer-implemented method of claim 1, wherein a selected brand is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected brand relative to a total number of consumers of the selected brand ([0199] "FIG.17 shows an exemplary screen display 1700 for a parametric filter user interface 1702, a graphical report 1704, and an analytical result data table 1706, respectively, for the brand analysis for customer joiners [new consumers], leavers and repeaters.").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the performance of an advertising campaign are determined of Kalb, Plourde, and Touil by adding wherein a selected brand is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected brand relative to a total number of consumers of the selected brand, as taught by D’Imporzano, since Kalb, Plourde, Touil, and D’Imporzano are analogous art, in order to explore shopper behavior and in order to provide insights into a company’s performance to allow the executives to make effective decisions (D’Imporzano, [0002] and [0198]).
Regarding claim 10, Kalb, Plourde, and Touil discuss the performance of advertising products. Kalb, Plourde, and Touil do not specifically teach
The computer-implemented method of claim 1, wherein a selected product category is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected product category relative to a total number of consumers of the selected product category.
However, D’Imporzano teaches
The computer-implemented method of claim 1, wherein a selected product category is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected product category relative to a total number of consumers of the selected product category ([0133] "FIG. 12 shows an exemplary screen display for a parametric filter user interface 1202, a graphical report 1204, and an analytical result data table 1206, respectively, for the product analysis for new item introduction. As shown in FIG. 12, the analysis requires filling up in the input user interface 1202 the following groups of filters: [0134] Select Subcategory [selected product category]." [0139] Table 4 shows "number of triers" and "% of triers" [percentage of new customers relative total number of consumers]. See also [0141].).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the performance of an advertising campaign are determined of Kalb, Plourde, and Touil by adding wherein a selected product category is an advertising campaign subject, further comprising determining a performance value of the advertising campaign as a percentage of new consumers added to the selected product category relative to a total number of consumers of the selected product category, as taught by D’Imporzano, since Kalb, Plourde, Touil, and D’Imporzano are analogous art, in order to explore shopper behavior and in order to provide insights into a company’s performance to allow the executives to make effective decisions (D’Imporzano, [0002] and [0198]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459), in view of Navin (P. G. Pub. No. 2016/0381435).
Regarding claim 14, Kalb and Plourde correlate data with multiple attributes but do not specifically teach
wherein to correlate the data with multiple advertising attributes the one or more processors execute instructions to extract a semantic meaning of a textual content in the advertisement payload.
However, Navin teaches
wherein to correlate the data with multiple advertising attributes the one or more processors execute instructions to extract a semantic meaning of a textual content in the advertisement payload ([0015] "In another aspect, a method of a networked device includes applying an automatic content recognition algorithm to determine a content identifier of an audio-visual data, and associating the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and/or the content identifier. In this other aspect, a capture infrastructure annotates the audio-visual data with a brand name and/or a product name by comparing entries in the master database with a closed captioning data of the audio-visual data and/or through an application of an optical character recognition algorithm in the audio-visual data.").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the data is correlated of Kalb and Plourde, by adding wherein to correlate the data with multiple advertising attributes the one or more processors execute instructions to extract a semantic meaning of a textual content in the advertisement payload, as taught by Navin, since Kalb, Plourde, and Navin are analogous art and in order to not miss key marketing opportunities to the user to deliver highly targeted and relevant advertising to the user (Navin, [0009]).
Claims 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Illowsky (P. G. Pub. No. 2015/0324865), in view of Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459).
Regarding claim 16, Illowsky teaches
A computer-implemented method, comprising: receiving, in a server, an advertisement payload from a campaign server ([0035] "In addition to keywords, for each ad group, a number of advertisements [advertisement payload] for selection by the ad server when an advertising opportunity becomes available that matches the budget, ad schedule, maximum bids, keywords, and other targeting criteria are specified. Different types of ads can be included in an ad group, such as a text ad, an image ad, a local business ad, a mobile ad, and so on." See also [0027].),
the advertisement payload including a salient attribute ([0036] "Other aspects of the ad group can be defined in terms of various advertising parameters [salient attribute] and specified by advertiser entered values or default values for those various advertising parameters. After all the required advertising parameters for each level and aspect of the ad campaign(s) are specified, the advertising campaign data ( e.g., data including the campaign structure and the advertising parameters on each level within the campaign structure) are uploaded to the management system 116, and the data are stored in the campaign data store 126." See also [0029] and [0030].),
for distribution among users communicatively coupled to the server (Figure 1 shows users # 108-1 through 108-n coupled to #102 Advertising Management system [the server]. Examiner notes that "for distribution among users communicatively coupled to the server" is intended use and given little patentable weight.);
identifying a channel for transmission of the advertisement payload ([0087] " The goal manager 118 displays, in the display device, a channel recommendation interface that includes a set of channel recommendations that are selected, in part, based on the selected goal (1108). For example, as shown in FIG. 4, the goal manager 118 displays the channel recommendation interface 400. The set of channel recommendations includes one or more recommendations for advertising channels, where each advertising channel is a particular media channel type for use in providing advertisements for the advertising campaign. Furthermore, each media channel type that is recommended is different from the other media channel types that are recommended in the set. For example, with respect to FIG. 4, the recommended advertising channel 402 is a desktop search media channel type, the recommended advertising channel 404 is a mobile search media channel type, and the recommended advertising channel 406 is a desktop browsing media channel type.");
selecting at least one user based on the salient attribute ([0043] "The advertising serving system 120 can select advertisements from the advertising content store 124 for each ad request based on a match between an advertiser's campaign criteria [salient attribute] in the campaign data store 126 and the user characteristics and advertising context associated with the ad request.");
retrieving an identification for a client device associated to the at least one user based on the channel for transmission ([0042] "The ad requests are optionally associated with user characteristics (e.g., user's age, gender, income, language preferences, and so on) and advertising context (e.g., keywords associated with webpage content, location, local time of ad request, and so on). In some implementations, the ad requests can be associated explicit user characteristics or inferred characteristics associated with a browser, cookie identifier [identification of the client device], data indexed by a cookie identifier, etc.[0058] "The recommended advertising channels 402, 404, and 406 and the corresponding recommended advertising campaigns 408-418 can be recommended based on historical user activity information [identification for a client device], occurring on available advertising channels, that is related to the selected advertising campaign goal(s). For example, the historical user activity information, which may be accessed from the campaign statistics store 128 described above with respect to FIG. 1, may indicate that there are more searches for the business, the business type, or products and services offered by the business being performed on the recommended advertising channels 402, 404, and 406 than on other available advertising channels." See also [0068].); and
providing the advertisement payload to the client device via the channel for transmission ([0041] "The advertising serving system 120 of the advertising management system 102 responds to the ad requests by sending advertisements to the requesting user client device 108 for insertion into appropriate ad slots in the publisher's webpages or content items as rendered on the requesting user client device 108." See also [0068].).
Illowsky does not explicitly teach
implementing a neural network trained on the data;
via data trained on the neural network;
implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to:
predict a performance of an advertisement payload , and
modify the salient attribute in the advertisement payload.
However, Kalb teaches
implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload ([0041] "The history accumulated for each customer includes various time of response information. Determinations can accordingly be made of the best time and channel place to reach customer groups with any desired demographics criteria. Therefore, once the advertisement design process creates possible ads for a multitude of media types, and the vendor or manufacturer identifies the desired consumer group to be targeted, the RTOO system [dynamic optimization engine] assists in the selection process which determines which ads to run, and to predict the most cost effective schedule for the ads on each media type. Moreover, as the ads are run, feedback begins to be accumulated immediately [dynamically] and adjustments are made based on closed-loop decision making in order to optimize the cost and effectiveness of the overall off-line ad campaign." See also [0137].), and
modify the salient attribute in the advertisement payload ([0179] "At step 3003 the system retrieves current analytic data acquired by the system. At step 3004 the current analytic data is compared to the metrics. At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified at step 3006. The modification can include channels of delivery [modify salient attribute], times of day, change of creative, change of location, and the like.").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising system of Illowsky with the advertising system of Kalb by adding implementing a dynamic optimization engine, wherein the dynamic optimization engine is configured to: predict a performance of an advertisement payload, and modify the salient attribute in the advertisement payload, as taught by Kalb, since Illowsky and Kalb are analogous art and for more efficient performance and to produce an improvement in ad effectiveness (Kalb, [0037] and [0180]).
Illowsky and Kalb does not specifically teach
implementing a neural network trained on the data;
via data trained on the neural network.
However, Plourde teaches
implementing a neural network trained on the data ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured." See also [0035].);
via data trained on the neural network ([009] "These attribute data may be diversely aggregated, whether by the direct intervention of a human administrator of advertising campaigns using an embodiment of the present invention, or else dynamically through the machine learning capabilities [neural network] by which an embodiment of the invention would have been configured. [0057] The result of the aforementioned pairing process is the production 25 of a set of customer-specific product affinity data associated with a set of customers, specified previously, to be targeted during a particular campaign. Said data constitute a quantized predictive measurement corresponding to the extent to which one would have calculated, for each client from among said set of clients-given the available behavior histories-possessed a particular interest for a given set of products retained from the advertising content base (M01) for a particular 5 campaign. See also [0035], [0038] and [0040].)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the advertising systems of Illowsky and Kalb with the advertising system of Plourde by adding implementing a neural network trained on the data; via data trained on the neural network, as taught by Plourde, since Illowsky, Kalb and Plourde are analogous art, and in order to facilitate the conclusion of certain deductions (Plourde, [0048]).
Regarding claim 17, Illowsky identifies channels for transmission and modifies the salient attribute but not specifically
The computer-implemented method of claim 16, further comprising modifying the salient attribute in the advertisement payload by changing an advertising channel of the advertisement payload for one or more users coupled to the server.
However, Kalb teaches
The computer-implemented method of claim 16, further comprising modifying the salient attribute in the advertisement payload by changing an advertising channel of the advertisement payload for one or more users coupled to the server ([0179] "At decision block 3005 it is determined if the data is within bounds of the metrics established for the campaign. If not, the advert campaign is modified [modifying the salient attribute] at step 3006. The modification can include channels of delivery [advertising channel], times of day, change of creative, change of location, and the like. After changing the advert campaign, monitoring continues at step 3002." See also Fig. 4.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the salient attribute is modified of Illowsky by adding modifying the salient attribute in the advertisement payload by changing an advertising channel of the advertisement payload for one or more users coupled to the server, as taught by Kalb, since Illowsky and Kalb are analogous art and for more efficient performance and to produce an improvement in ad effectiveness (Kalb, [0037] and [0180]).
Regarding claim 19, Illowsky teaches
further comprising providing the advertisement payload to a server ([0035] "In addition to keywords, for each ad group, a number of advertisements [advertisement payload] for selection by the ad server when an advertising opportunity becomes available that matches the budget, ad schedule, maximum bids, keywords, and other targeting criteria are specified. Different types of ads can be included in an ad group, such as a text ad, an image ad, a local business ad, a mobile ad, and so on.")
by selecting the advertisement channel from a group consisting of a desktop, a mobile application, or a browser, based on a client device for one or more users communicatively coupled to the server ([0091] "For example, the campaign summary interface 900 includes the selectable advertising campaign groups 936, 938, and 940, which are associated with media channel types of desktop search, mobile search, and desktop browsing, respectively."),
and wherein an attribute of the advertisement payload comprises an advertising channel ([0088] "where each advertising campaign includes default campaign values for a plurality of campaign parameters, and where the plurality of default campaign values are selected, in part, based on the set of channel recommendations (1110). The default campaign values define a default advertising campaign strategy. For example, as shown in FIG. 6, the campaign development interface 600 includes default campaign data defining the advertisement campaigns 601 and 604. The campaign development interface 600 includes, among other default campaign values, default campaign values 620-648 for the campaign parameters 610-618. The default campaign values 620-648 are selected, in part, based on the recommended advertising channels 602 and 605."),
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Illowsky (P. G. Pub. No. 2015/0324865), in view of Kalb (P. G. Pub. No. 2012/0179536), in view of Plourde (FR 3016459), in view of Navin (P. G. Pub. No. 2016/0381435)..
Regarding claim 20, Illowsky teaches
The computer-implemented method of claim 16, further comprising correlating data collected from multiple client devices for the at least one user coupled to the server ([0040] "Referring now to the publishers 106, for some of the publishers 106, a respective publisher 106 can correspond to a particular media channel type. For example, in FIG. 1, there are n media channel types shows, and each channel type has a corresponding set of publishers 106, advertisers 110, and user devices 108 that send and receive data of the particular media channel type.")
Illowsky does not specifically teach
and from multiple point of sale devices in retailer stores with multiple advertising attributes comprises extracting a semantic meaning of a textual content in the advertisement payload.
However, Kalb teaches
and from multiple point of sale devices in retailer stores with multiple advertising attributes ([0163] "It should also be understood that a purchase of the offered goods may also be considered a response in gauging the effectiveness of an advert. The system contemplates reporting from POS (point-of-sale) locations to supplement the data from the advert as part of the feedback loop." See also [0166] for multiple advertising attributes.).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify from where the data is correlated of Illowsky by adding and from multiple point of sale devices in retailer stores with multiple advertising attributes, as taught by Kalb, since Illowsky and Kalb are analogous art and for more efficient performance and to produce an improvement in ad effectiveness (Kalb, [0037] and [0180]).
Illowsky, Kalb, and Plourde do not specifically teach
comprises extracting a semantic meaning of a textual content in the advertisement payload.
However, Navin teaches
comprises extracting a semantic meaning of a textual content in the advertisement payload ([0015] "In another aspect, a method of a networked device includes applying an automatic content recognition algorithm to determine a content identifier of an audio-visual data, and associating the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and/or the content identifier. In this other aspect, a capture infrastructure annotates the audio-visual data with a brand name and/or a product name by comparing entries in the master database with a closed captioning data of the audio-visual data and/or through an application of an optical character recognition algorithm in the audio-visual data.").
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify how the data is correlated of Illowsky, Kalb, and Plourde by adding comprises extracting a semantic meaning of a textual content in the advertisement payload, as taught by Navin, since Illowsky, Kalb, Plourde, and Navin are analogous art and in order to not miss key marketing opportunities to the user to deliver highly targeted and relevant advertising to the user (Navin, [0009]).
Response to Argument
With regards to the 101, on page 8, Applicant states, “Furthermore, assuming, arguendo, the claims contain an abstract idea (which Applicant does not concede), Prong Two of the Revised Step 2A requires the additional claim elements integrate the alleged abstract idea into a practical application, which renders the claims-as a whole-patent eligible. Similar to the Holding in Ex parte Desjardin, there may be sufficient evidence of patentable subject matter when the technical improvement is in the manner the model operates or learns, not merely in what the model predicts. See Ex parte Desjardins Appeal No. 2024-000567 (ARP Sept. 26, 2025) (precedential) For example, "in some embodiments, an algorithm 244 stores commands which, when executed by processor 212-2, causes server 130 to integrate digital promotion payload 227. Algorithm 244 may include a neural network (NN) trained over databases 252, to select digital promotion payload 227 targeted to the specific preferences of a consumer when the consumer grants application 222 to track user transactions. See paragraph [0024]. Here, the Applicant discloses a specific manner in which a neural network can be trained and operates because the specific preferences are targeted when transactions are tracked. Consequently, under Prong Two of the Revised Step 2A procedure, the claims are not directed to an abstract idea. With respect to the claim amendments, reconsideration and withdrawal of these rejections are respectfully requested.” Examiner respectfully disagrees. Par. 0024 of the specification simply states “Algorithm 244 may include a neural network (NN) trained over databases 252, to select digital promotion payload 227 targeted to the specific preferences of a consumer when the consumer grants application 222 to track user transactions.” However, this paragraph and the rest of the specification do not provide “a specific manner in which a neural network can be trained” and operates” Furthermore, the specification does not link the neural network to specific preferences targeted when transaction are tracked. Therefore, Examiner is not persuaded.
On p. 10, Applicant states, “The Office Action contends Kalb teaches "modifying a salient attribute." Applicant respectfully disagree. Kalb discloses "modification can include channels of delivery, times of day change of creative and change of location.", however, does not teach modifying the salient attribute comprises one of a color, a format, a size, a theme, a shade, a gradation in a graphical element of the advertisement payload; As for the other applied references, Applicant respectfully submit that the cited portions of such references do not remedy the above-noted deficiencies of the cited portions of Kalb.” This argument is moot since the Kalb reference was not used to teach this claim limitation. See the 103 rejection above.
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 MARIE P. BRADY whose telephone number is (571)272-4855. The examiner can normally be reached Tues-Thurs 8:00 - 2:00 ET.
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/MARIE P BRADY/Primary Examiner, Art Unit 3622