DETAILED ACTION
This is in reference to communication received 30 October 2025. Addition of claim 61 is acknowledged. Claims 21 – 26, 28 – 37, 39 – 40 and 61 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 21 – 26, 28 – 37, 39 – 40 and 61 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 32, representative of claims 21 and 40, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 31 recites invention directed to launching an advertising campaign on plurality of channels and tracking characteristics of users who have interacted with the launched advertising and made an associated purchase, identifying a new keyword as a targeting criteria for one of the plurality of channels and providing the advertising content to the user whey they have made a query including that included the identified keyword on that channel, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. advertising). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including:
Using trackers associated with user device (e.g., a cookie, IEMI, user-clicks, etc.) for tracking characteristics associated with user and their interaction with the advertising campaign; and determining effectiveness of the launched advertising campaign using the machine-learning models. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 21 and 40, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 21), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 40).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
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 element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 22 – 31, 33 – 39 and 61, these claims recite limitations that further define the same abstract idea and simply disclose additional limitations that further limit the abstract idea with details regarding defining components of user device, which type of interaction data will be considered, using user-clicks as an indicator to determine that there has been an interaction activity by the user, tracking of user characteristics, making a determination whether user activities on the launched campaign exceeds a minimum threshold, providing advertisements to user based upon their search keywords (search-query), identifying which channel is performing better, and removing the campaign from channel that has lower performance success, defining what information will be outputted by the tracker. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 21 – 26, 28 – 37, 39 – 40 and 61 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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 32 and represented claims 21 and 40 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Applicant added limitations “edit code of a webpage associated with one of the at least two channels to remove communication content based on the determined effectiveness.”, however, applicant has not disclosed how webpage associated with one of the plurality of channels will be edited to remove communication content based on the determined effectiveness, without undue experimentation by one skilled in the art.
Dependent claims inherit the deficiency of the parent claim they claim dependency from, therefore dependent claims 22 – 31, 33 – 39 and 61 are also rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph.
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 21 – 40 and 61 are rejected under 35 U.S.C. 103 as being unpatentable Lopez et al. US Patent 10,861,052 in view of Survey-Monkey published article “Marketing demographic: what they are and how to apply them” hereinafter referred to as Survey-Monkey, Vijayaraghavan US Publication 2014/0156383 and Liu et al. US Publication 2015/0006295.
Regarding claim 32 and represented claims 21 and 40, Lopez teaches system and method for multi-channel campaign management for determining marketing effectiveness on a channel level (Lopez. Provide the content using various types of channels in ways that are likely to be most effective; The interaction of a particular user with campaign content can be monitored or analyzed over time to determine which channels or types of content are most effective, and even which channels are most or least effective for specific types of content or campaigns.) [Lopez, col. 7, lines 5 – 7; col. 10, lines 14 – 18], the system and method comprising
a memory storing instructions (Lopez, The device includes at least one processor 702 for executing instructions that can be stored in a memory device or element 704) [Lopez, col. 14, lines 3 – 6]; and
at least one processor configured to execute the instructions (Lopez, The device includes at least one processor 702 for executing instructions that can be stored in a memory device or element 704) [Lopez, col. 14, lines 3 – 6]:
launching, using first data associated with an advertising campaign, an advertising campaign to a plurality of users on at least two channels (Lopez, resources of a resource provider environment can be configured to work with various channel managers 204 to send notifications over various communication channels 202, as may include an email channel, a Web or Internet channel, a messaging (e.g., SMS or IM) channel, or an application specific channel, among others) [Lopez, col. 4, lines 27 – 35];
Lopez does not explicitly teach user characteristics to comprise at least one of a membership status in a company program, an age of the user, or a gender of the user. However, Lopez teaches that campaign criteria can include, for example, users viewing specific types of primary content, having yet to perform a certain task, fitting a specific user profile, or meeting various other types of criteria [Lopez, col. 11, lines 50 – 54]. Survey-Monkey teaches Marketing demographics are used in customer segmentation to find specific groups in your target audience. This knowledge is then used to market to each segment more specifically for more effective marketing strategies. Let’s take a closer look at marketing demographics. [Survey-Monkey, page 1]. Survey-Monkey further recites The most useful demographic characteristics for your marketing strategies are age, gender, income, family status, religion/race/nationality, and education. Let’s look at each one of these demographics and examples of how they can be used in marketing. [Survey-Monkey, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Lopez by adopting teachings of Survey-Monkey and track user demographics to improve marketing efficiency, increase personalization, improve customer loyalty.
Lopez in view of Survey-Monkey teaches system and method further comprising:
tracking, at least one user characteristic of each of the plurality of users (Lopez, A campaign manager 316 can keep track of the actions taken by various users, and can make sure that the campaign content is displayed to the targeted users using approaches that are more likely to be successful) [Lopez, lines 20 – 24; 8 – 12], wherein the at least one user characteristic comprises at least one of: a membership status in a company program, an age of the user, or a gender of the user (Survey-Monkey, The most useful demographic characteristics for your marketing strategies are age, gender, income, family status, religion/race/nationality, and education. Let’s look at each one of these demographics and examples of how they can be used in marketing.) [Survey-Monkey, page 3];
tracking, for each of the plurality of users, using second data from a tracker associated with at least one user device, at least one user interaction with the launched advertising campaign leading up to a purchase by the user (Vijayaraghavan, a determination can be made whether a user who searched for a particular product using certain search terms eventually purchased that product. Mapping chat and/or voice data with the search terms keyed in by the user leads to an enhanced identification of the user's intent.) [Vijayaraghavan, 0082, 0033]
Lopez in view of Survey-Monkey does not explicitly teach tracking user characteristics using a tracker associated with at least one user device. However, Vijayaraghavan teaches Once the system is able to track users across session, a unique identifier can be associated with the user, for example ANis or Web cookies can be identified as belonging to same user [Vijayaraghavan, 0033].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Lopez in view of Survey-Monkey by adopting teachings of Vijayaraghavan to associate tracked user demographic with the user.
Lopez in view of Survey-Monkey and Vijayaraghavan teaches system and method further comprising:
tracking, at least one user characteristic of each of the plurality of users (Lopez, A campaign manager 316 can keep track of the actions taken by various users, and can make sure that the campaign content is displayed to the targeted users using approaches that are more likely to be successful) [Lopez, lines 20 – 24; 8 – 12] using a tracker associated with at least one user device (as responded to above) [Vijayaraghavan, 0033], wherein the at least one user characteristic comprises at least one of: a membership status in a company program, an age of the user, or a gender of the user (Survey-Monkey, The most useful demographic characteristics for your marketing strategies are age, gender, income, family status, religion/race/nationality, and education. Let’s look at each one of these demographics and examples of how they can be used in marketing.) [Survey-Monkey, page 3];
tracking, for each of the plurality of users, using second data from a tracker associated with at least one user device, at least one user interaction with the launched advertising campaign leading up to a purchase by the user (Vijayaraghavan, a determination can be made whether a user who searched for a particular product using certain search terms eventually purchased that product. Mapping chat and/or voice data with the search terms keyed in by the user leads to an enhanced identification of the user's intent.) [Vijayaraghavan, 0082, 0033];
Lopez in view of Survey-Monkey and Vijayaraghavan does not teach feeding user interaction into a model wherein model is created based on past user interaction. However, Liu teaches system and method for targeting user based on previous advertising campaigns. Liu further teaches Server 114 may use the stored information about advertising campaigns, employment opportunities and, more generally, the types of content or recommendations, the sharing of content by the users, user profiles and/or user behaviors to train machine-learning models that can be used to predict user responses to future advertising campaigns (and, thus, to place the users into different target groups to facilitate targeted advertising), user interests (and, thus, to identify interest segments that the users may like and/or categorize the users according to those segments), and/or user responses to future content (and, thus, to identify rules that can be used to identify which users may be interested in particular content or types of content, such as specific employment opportunities or particular types of employment). [Liu, 0034].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Lopez in view of Survey-Monkey and Vijayaraghavan by adopting teaching of Iiu to identify a subset of the users to target with particular recommendations.
Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method further comprising:
determining the effectiveness of the launched advertising campaign, by: feeding the at least one user interaction into a model which weighs the impact that each of the at least two channels has on the user; wherein the model is created based on at least one past tracked user interaction and the at least one user characteristic of each of the plurality of users (Liu further teaches Server 114 may use the stored information about advertising campaigns, employment opportunities and, more generally, the types of content or recommendations, the sharing of content by the users, user profiles and/or user behaviors to train machine-learning models that can be used to predict user responses to future advertising campaigns (and, thus, to place the users into different target groups to facilitate targeted advertising), user interests (and, thus, to identify interest segments that the users may like and/or categorize the users according to those segments), and/or user responses to future content (and, thus, to identify rules that can be used to identify which users may be interested in particular content or types of content, such as specific employment opportunities or particular types of employment).) [Liu, 0034]; and
retrieving a new keyword for one channel of the at least two channels from a database of keywords based on the determined effectiveness (Vijayaraghavan, Ad-words entered when performing a keyword search, along with interactions across multiple communications channels, are analyzed to predict which ads have the highest relevance to the search. By placing highly relevant ads, the customer search is more readily converted into a transaction, thus maximizing return on investment (ROI).) [Vijayaraghavan, 0022];
updating instructions for the one channel to display an advertisement in response to user input including the new keyword (Vijayaraghavan, user performs a keyword search for a product or service. …. Based on the intent prediction, advertisements that are determined to be the most relevant are displayed along with the search results.) [Vijayaraghavan, 0009]; and
rendering the advertisement on the at least one user device upon receiving the user input including the new keyword (Vijayaraghavan, user performs a keyword search for a product or service. …. Based on the intent prediction, advertisements that are determined to be the most relevant are displayed along with the search results.) [Vijayaraghavan, 0009];
editing code of a webpage associated with one of the at least two channels to remove communication content based on the determined effectiveness (as best understood by examiner, Liu, advertisements may be targeted to the users in the subset based on the association with the interest segment and/or the target group. Moreover, the order of the operations may be changed, and/or two or more operations may be combined into a single operation.) [Liu, 0100].
Regarding claim 22 and represented claim 33, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method, wherein the second data associated with at least one user device comprises interaction data including at least one of click data or impression data (Lopez, Some channels may have higher performance values than others, such as where a user is more likely to click on a push notification than an in-app advertisement) [Lopez, col. 8, lines 55 – 57].
Regarding claim 23 and represented claim 34, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method, wherein the second data associated with at least one user device comprises interaction data including at least one of:
interaction data indicating a number of interactions on each of the at least two channels,
interaction data including a subset of the interaction data apportioned between each of the at least two channels based on the subset of interaction data occurring in a time window,
interaction data that categorizes interaction as organic interactions which are not a result of advertising and non-organic interactions which are a result of advertising,
interaction data that is weighted according to its proximity to a transaction,
interaction data wherein each interaction is linked to a channel,
interaction data wherein each interaction is linked to a publisher, or
interaction data wherein each interaction is linked to an advertising campaign identifier
(Lopez, provide the content using various types of channels in ways that are likely to be most effective for the campaign. Such an approach requires management and tracking of content conveyed over the various channels, in order to ensure that the user is not getting bombarded with the content to the point where the user is unlikely to take the desired action) [Lopez, col. 7, lines 6 – 29].
Regarding claim 24 and represented claim 35, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method,
wherein the interaction data is click data (Lopez, Some channels may have higher performance values than others, such as where a user is more likely to click on a push notification than an in-app advertisement) [Lopez, col. 8, lines 55 – 57]; and
wherein the click data includes click data wherein each click is linked to an advertising campaign identifier (Lopez, In many instances, this will include advertisements or sponsored links that upon selection will direct a user to obtain content relevant to the campaign) [Lopez, col. 6, 13 – 15]; and
wherein determining the effectiveness of the launched advertising campaign includes determining whether the advertising campaign identifier matches an advertising campaign identifier of the launched advertising campaign (Lopez, In many instances, this will include advertisements or sponsored links that upon selection will direct a user to obtain content relevant to the campaign; campaign management service can monitor a state of the targeted users to ensure that the campaign content is provided to those users until the users perform a desired action corresponding to the campaign) [Lopez, col. 6, 13 – 15; col. 1, lines 64 – 67].
Regarding claim 25 and represented claim 36, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method, wherein the processor is further configured to:
tracking, for each of the plurality of users, using third data associated with at least one user device, the at least one characteristic of the user, the user being associated with the at least one user device (Lopez, A campaign manager 316 can keep track of the actions taken by various users, and can make sure that the campaign content is displayed to the targeted users using approaches that are more likely to be successful) [Lopez, lines 20 – 24; 8 – 12].
Regarding claim 26 and represented claim 37, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method, wherein determining the effectiveness of the launched advertising campaign further comprises at least one of:
determining, for at least one of the at least two channels, whether the launched advertising campaign is performing above a threshold level,
determining, for at least one of the at least two channels, whether the launched advertising campaign is performing better than a second launched advertising campaign on the at least one channel,
determining, for each of the at least two channels, whether the launched advertising campaign is performing above a threshold level,
determining, for each of the at least two channels, a portion of a price for the purchases made by the plurality of users that is attributable to the launched advertising campaign on that channel, or
determining a portion of a price for the purchases made by the plurality of users that is attributable to the launched advertising campaign
(Lopez, The recording of the displaying can help to ensure that any subsequent opportunities to provide the campaign content are evaluated based on this recent displaying to ensure that no campaign criteria or thresholds are exceeded) [Lopez, col. 13, lines 60 – 64].
Regarding claim 28 and represented claim 39, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method,
wherein determining the effectiveness of the launched advertising campaign comprises determining, for each of the at least two channels, whether the launched advertising campaign is performing above a threshold level (Lopez, The recording of the displaying can help to ensure that any subsequent opportunities to provide the campaign content are evaluated based on this recent displaying to ensure that no campaign criteria or thresholds are exceeded) [Lopez, col. 13, lines 60 – 64]; and
removing the launched advertising campaign from a channel of the at least two channels upon determining it is not performing above the threshold level (Lopez, In some embodiments channels that perform below a specified level, in aggregate or for specific users or types of users, can be removed from the campaign) [Lopez, col. 9, lines 18 – 21].
Regarding claim 29, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method, wherein determining the effectiveness of the launched advertising campaign comprises determining, for at least one of the at least two channels, whether the launched advertising campaign is performing better than a second launched advertising campaign on the at least one channel (Lopez, As known for the advertising promotional content the customer may also specify bid prices for specific keywords or topics that can be compared against other bids relevant to the primary content) [Lopez, col. 6, lines 29 – 32]; and
removing the launched advertising campaign from the at least one channel upon determining that it is not performing better than the second launched advertising campaign (Lopez, In some embodiments channels that perform below a specified level, in aggregate or for specific users or types of users, can be removed from the campaign) [Lopez, col. 9, lines 18 – 21].
Regarding claim 30, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method,
wherein determining the effectiveness of the launched advertising campaign comprises determining, for each of the at least two channels, a portion of a price for the purchases made by the plurality of users that is attributable to the launched advertising campaign on that channel (Lopez, As known for the advertising promotional content the customer may also specify bid prices for specific keywords or topics that can be compared against other bids relevant to the primary content) [Lopez, col. 6, lines 29 – 32]; and
removing the launched advertising campaign from a channel of the at least two channels upon determining the portion of the purchase price attributable to the launched advertising campaign on that channel is below a threshold value (Lopez, As known for selecting advertising or promotional content, various keywords or other criteria can be analyzed using an advertising selection algorithm, and advertising content can be selected based upon factors such as relevance and bid pricing) [Lopez, col. 5, lines 56 – 60].
Regarding claim 31, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system and method,
wherein determining the effectiveness of the launched advertising campaign comprises determining a portion of a price for the purchases made by the plurality of users that is attributable to the launched advertising campaign (Lopez, As known for the advertising promotional content the customer may also specify bid prices for specific keywords or topics that can be compared against other bids relevant to the primary content) [Lopez, col. 6, lines 29 – 32]; and removing the launched advertising campaign upon determining that the portion of the purchase price attributable to the launched advertising campaign is below a threshold (Lopez, In some embodiments channels that perform below a specified level, in aggregate or for specific users or types of users, can be removed from the campaign) [Lopez, col. 9, lines 18 – 21].
Regarding claim 61, as combined and under the same rationale as above, Lopez in view of Survey-Monkey, Vijayaraghavan and Liu teaches system further comprising:
outputting, by the tracker, a channel identifier indicating a channel associated with the at least one user interaction (Vijayaraghavan, Once the system is able to track users across session, a unique identifier can be associated with the user, for example ANis or Web cookies can be identified as belonging to same user) [Vijayaraghavan, 0033],
wherein the at least one user interaction is fed into the model based on the output channel identifier (Liu, Server 114 may use the stored information about advertising campaigns, employment opportunities and, more generally, the types of content or recommendations, the sharing of content by the users, user profiles and/or user behaviors to train machine-learning models that can be used to predict user responses to future advertising campaigns (and, thus, to place the users into different target groups to facilitate targeted advertising), user interests (and, thus, to identify interest segments that the users may like and/or categorize the users according to those segments), and/or user responses to future content (and, thus, to identify rules that can be used to identify which users may be interested in particular content or types of content, such as specific employment opportunities or particular types of employment).) [Liu, 0034].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because claimed invention is not "directed to" an abstract idea, and because "the claim[s] as a whole integrate the recited judicial exception into a practical application, is acknowledged and considered.
However, applicant’s arguments are responded in Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent because combination of cited prior art does not teach the claimed invention, is acknowledged and considered.
However, while performing an undated search additional prior art were found which have been cited in this office action. Therefore, applicant’s arguments are moot under new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
April 16, 2026
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