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 .\
This office action is in response to communication filed on 3/18/2025.
Claims 1-20 are presented for examination.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1-10 recite a series of steps, thus falling within one of the four statutory classes; i.e., a process. Claims 11-17 describe tangible system components, thus falling within one of the four statutory classes; i.e., machine. Claims 18-20 describe a non-transitory computer storage medium, thus falling within one of the four statutory classes; i.e., manufacture.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 1 recites:
receiving a predetermined total resource allocation for connecting entities for a period, a predetermined number of communication channels, and multiple total target numbers of connected entities from multiple target groups, wherein the multiple target groups are based on a plurality of demographical parameters;
constructing a connection model based on the predetermined total resource allocation, the predetermined number of communication channels, the multiple total target numbers of connected entities to be from multiple target groups, and multiple resource distribution parameters associated with the predetermined number of communication channels and the multiple target groups;
receiving current connection data corresponding to the multiple target groups from the predetermined number of communication channels during a current period via network communication;
learning the multiple resource distribution parameters for the current period from multiple explorations of the predetermined number of communication channels based on corresponding allocated exploration resources using reinforcement learning algorithm;
updating the multiple resource distribution parameters for the connection model in a subsequent period based on the current connection data and historical connection data;
determining optimized resource allocations for the predetermined number of communication channels in the subsequent period by maximizing a total number of connected entities from the multiple target groups and minimizing deviation from connection trajectories for the multiple target groups respectively based on multiple updated resource distribution parameters for the connection model and the current connection data using one or more convex optimization algorithms; and
providing the optimized resource allocations to the predetermined number of communication channels in the subsequent period.
That is, other than reciting “online reinforcement learning algorithm” (claims 1, 11 and 18), “one or more processors” (claims 11 and 18) nothing in the claims elements precludes the steps from practically being performed in the mind. For example, but for the “by one or more hardware processors” language, “receiving”, “constructing”, “learning”, “updating”, “determining”, and “providing” in the context of this claim encompasses actions that a human could perform; e.g., a human can optimize resource allocations across multiple communication channels, such as advertising platforms to connect with entities from different target groups.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In addition, the limitations mentioned above (i.e., “receiving”, “constructing”, “learning”, “updating”, “determining”, and “providing”) in the context of this claim as drafted, are processes that, under their broadest reasonable interpretations, exemplify commercial interactions (including advertising, marketing or sales activities or behaviors; business relations); or relationships or interactions between people (including social activities, teaching, and following rules or instructions), but for the recitation of generic computer components.
That is, other than reciting “online” and “one or more processors”, nothing in the claim elements disqualifies the steps from being commercial interactions including advertising. For example, but for “online” and “one or more processors” language, the steps of “receiving”, “constructing”, “learning”, “updating”, “determining”, and “providing”.
If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Online reinforcement learning algorithm (claims 1, 11 and 18);
One or more processors (claims 18 and 21)
one or more non-transitory computer-readable medium (claim 18);
The “online”, “one or more processors”, and “one or more non-transitory computer-readable medium” are recited at a high-level of generality (i.e., as generic processors) such that they amount no more than mere instructions to apply the exception using generic computer components. They are no more than a tool to perform the “receiving”, “constructing”, “learning”, “updating”, “determining”, and “providing” steps.
The additional elements of online”, “one or more processors”, and “one or more non-transitory computer-readable medium” are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
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. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations “online”, “one or more processors”, and “one or more non-transitory computer-readable medium”, as seen above, these limitations have been interpreted as “apply it”. However, these limitations can be additionally interpreted as insignificant extra-solution activity. As such, these limitations alone and in combination, does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05).
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 online”, “one or more processors”, and “one or more non-transitory computer-readable medium”, alone and in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations online”, “one or more processors”, and “one or more non-transitory computer-readable medium” it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, these additional elements do not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 6-11, 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by (Canney 2023/0376994).
With respect to claims 1, 11 and 18, Canney teaches methods and apparatus for receiving a predetermined total resource allocation (resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign)(Figure 1 and paragraph 0022), for connecting entities for a period a predetermined number of communication channels (see campaign parameters 151)(Figure 1), and multiple total target numbers of connected entities from multiple target groups, wherein the multiple target groups are based on a plurality of demographical parameters (parameters can include, for example, product type, target demographic, target media platforms)(figure 1 and paragraph 0017);
constructing a connection model based on the predetermined total resource allocation, the predetermined number of communication channels, the multiple total target numbers of connected entities to be from multiple target groups, and multiple resource distribution parameters associated with the predetermined number of communication channels and the multiple target groups (The resource distribution model 125 receives the parameters from the resource planning tool 120 and determines an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign)(see Figure 1 and paragraph 0018);
receiving current connection data corresponding to the multiple target groups from the predetermined number of communication channels during a current period via network communication; learning the multiple resource distribution parameters for the current period from multiple explorations of the predetermined number of communication channels based on corresponding allocated exploration resources using an online reinforcement learning algorithm (The resource distribution model 125 can receive the parameters from the resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign. One or more embodiments generate the resource distribution model 125 by training a machine learning model trained using a machine learning algorithm.)(paragraph 0022)
updating the multiple resource distribution parameters for the connection model in a subsequent period based on the current connection data and historical connection data (historical parameters 213 of promotional campaigns in the storage system 209. The historical parameters 213 can include information of prior budgets, demographics, weights, allocations, returns on investments, and the like. One or more embodiments also store historical effectiveness 215 of prior promotional campaigns) (Figure 2 and paragraph 0021);
determining optimized resource allocations for the predetermined number of communication channels in the subsequent period by maximizing a total number of connected entities from the multiple target groups and minimizing deviation from connection trajectories for the multiple target groups respectively based on multiple updated resource distribution parameters for the connection model and the current connection data using one or more convex optimization algorithms ( At block 311, the system (e.g., using resource distribution model 125) determines recommended divisions of resource among the plurality of information platforms for the parameters obtained at block 303. One or more embodiments determine the recommendations as described below with reference to FIG. 4. For example, the recommendation can be based on a predicted ROI resulting from budget divisions included in the parameters. The recommendation can include a predicted ranking among a predetermined range of potential ROI rankings. For example, based on the parameters obtained at block 303, the system can indicate whether the ROI is one of a set of predefined categories, such as: unfavorable, favorable, and exceptional. Additionally, the system can score the return a promotional campaign using, for example, a scale 1 to 10, wherein 1 is poor and 10 is exceptional);
providing the optimized resource allocations to the predetermined number of communication channels in the subsequent period (At block 313, the system communicates the recommended divisions, ROI level, and ROI score to the client via the user interface).
With respect to claim 3, Canney further teaches wherein the predetermined number of communication channels comprises one or more digital advertising platforms (The media platforms can include traditional televisions services (e.g., linear television services), connected television services (e.g., streaming television services), and digital streaming services (e.g., on-demand Internet services)(paragraph 0003).
With respect to claims 4, 13 Canney further teaches the current connection data comprises aggregated numbers of connected entities from the multiple target groups via the predetermined number of communication channels based on current resource allocations in the current period (At block 311, the system (e.g., using resource distribution model 125) determines recommended divisions of resource among the plurality of information platforms for the parameters obtained at block 303. One or more embodiments determine the recommendations as described below with reference to FIG. 4. For example, the recommendation can be based on a predicted ROI resulting from budget divisions included in the parameters. The recommendation can include a predicted ranking among a predetermined range of potential ROI rankings. For example, based on the parameters obtained at block 303, the system can indicate whether the ROI is one of a set of predefined categories, such as: unfavorable, favorable, and exceptional. Additionally, the system can score the return a promotional campaign using, for example, a scale 1 to 10, wherein 1 is poor and 10 is exceptional).
With respect to claims 6-7, 14 Canney further teaches :determining the optimized resource allocations for the predetermined number of communication channels during the subsequent period further based on the allocated exploration resources; determining the optimized resource allocations for the predetermined number of communication channels in the subsequent period by minimizing a total resource allocation (The resource distribution model 125 can receive the parameters from the resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign. One or more embodiments generate the resource distribution model 125 by training a machine learning model trained using a machine learning algorithm )(paragraph 0022).
With respect to claim 8, 15, 19 Canney further teaches determining the connection trajectories by proportionating the multiple total target numbers of connected entities from the multiple target groups over a total period; and adjusting the connection trajectories based on the current connection data corresponding to the multiple target groups from the predetermined number of communication channels during the current period (modify a promotional campaign by communicating parameters of the campaign. Additionally, the resource planning tool 120 can interact with the resource distribution model 125 to communicate the parameters of the promotional campaign for processing and receive recommended resource allocations for communication to a user. The resource distribution model 125 can receive the parameters from the resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign)(paragraph 0022).
With respect to claims 9-10, 16-17 Canney teaches wherein the one or more convex optimization algorithms comprises a flexible linear programming model, wherein the method further comprises relaxing one or more constraints associated with the multiple total target numbers of connected entities from multiple target groups based on the flexible linear programming mode and quadratic model ( Blocks 415, 419, 423, and 427 can be performed by a machine learning model that maps the polynomial function to the data points and determines a maximum ROI. For example, as described regarding FIG. 5 below, the historical parameters and historical effectiveness information (e.g., historical parameters 213 and historical effectiveness 215) can be used as training data for the machine learning model, the system can iteratively determine weights of a polynomial function. The system can determine a set of historical data points corresponding to individual sets of demographics (e.g., adult females between 18 and 25). The system can use the data points to build polynomial functions corresponding to the individual demographic groups that computes ROI as a function of 9-dimensions and paragraph 0035 for example, the machine learning model can be a random forest model. Other machine learning models applied can include, but are not limited to linear regression, logistic regression, linear discriminant analysis, classification and regression trees, naïve Bayes, k-nearest neighbors, learning vector quantization, support vector machine, bagging and random forest, boosting, backpropagation, and/or clustering).
With respect to claim 20, Canney further teaches the resource distribution model 125 can receive the parameters from the resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign. One or more embodiments generate the resource distribution model 125 by training a machine learning model trained using a machine learning algorithm (the resource distribution model 125 can receive the parameters from the resource planning tool 120 and determine an allocation of resources among the media platforms predicted to maximize an effectiveness (e.g., ROI) of the promotional campaign. One or more embodiments generate the resource distribution model 125 by training a machine learning model trained using a machine learning algorithm)(paragraph 0022) .
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.
Claim 2, 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Canney.
Claims 2 and 12 further recite the period being current week and the subsequent period being a week following the current week for the advertising platforms. Official Notice is taken that it is old and well known to evaluate subsequent events, for the period beginning during current week and the subsequent period being a week following the current week because such a modification would allow to better schedule goals and events for the next phase. It would have been obvious to a person of ordinary skill in the art at the time of Applicant’s invention to have included in the scheduling of Canney for the period being current week and the subsequent period being a week following the current week because will help better adjust the scheduling goals of the advertising platforms.
Claim 5 further recites multiple testing operations in a communication channel. Official Notice is taken that it is old and well known to test the operations, to ensure that a system is reliable, scalable and fully prepared. It would have been obvious to a person of ordinary skill in the art at the time of Applicant’s invention to have included, multiple testing operations in the communication channel/campaigns of Canney because such a modification would ensure that campaigns of Canney are reliable, scalable and fully prepared.
Other refences of record but not applied to the current rejections:
CN 114444652 A, Jiao teaches accurately extracting user space feature and behaviour characteristics, convenient for operator to rationally optimize the resource allocation and accurately marketing the user.
Collection of pages from www.overture.com, teaches under the paid inclusion
model, advertisers set up an account, submit listings and pay a fee to be viewed
by the algorithm. However, there is no guarantee the listings will always be
included in the search results, or where they might be ranked on a given page.
Still, paid inclusion helps increase relevance of results by allowing search
engines to crawl deeper into businesses' Web sites and onto areas
previously indexed only via spam techniques. As a result, individual Internet
users are given access to better quality search listings. In paid placement,
advertisers pay to receive premium placement within the search results listings.
For example, at Overture, advertisers bid for placement in its search results and
only pay the company when a consumer clicks on the advertiser's listing.
Following a rigorous screening for relevance by Overture's
editorial team, the company then distributes its search results to thousands of
Web sites across the Internet, including popular destinations such as
MSN, Yahoo! and Lycos.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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/RAQUEL ALVAREZ/Primary Examiner, Art Unit 3622
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