DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-20 are currently pending and have been addressed below.
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 .
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 07/23/2025 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
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 a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method for evaluating a marketing campaign, comprising: receiving detection of an attributable event; predicting a predicted amount of web traffic to a website, over a period of time subsequent, had the attributable event not occurred; receiving electronic data indicating an actual amount of web traffic to the website over the period of time subsequent; generating an indication based on comparing the actual amount of web traffic with the predicted amount of web traffic; and storing the indication. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In this case, predicting an amount of web traffic to a website is considered a marketing or sales activity. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, 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 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: an electronic signal occurring at a first computing device; a machine learning model; and a memory of a second computing device.
The first computing device is merely used to receive an electronic signal indicating detection of an attributable event (Paragraphs 0006 & 0021). The machine learning is merely used to predict a predicted amount of web traffic to a website, over a period of time subsequent to receiving the electronic signal (Paragraph 0021). The memory of a second computing device is merely used to store an indication comparing the actual amount of web traffic with the predicted amount of web traffic (Paragraph 0021). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “first computing device,” “machine learning,” and “memory of a second computing device” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the first computing device is considered “field of use” since it’s just used to receive an attributable event (e.g., advertisement data) for a prediction analysis, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, 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. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of predicting an amount of web traffic to a website. The specification shows that the first computing device is merely used to receive an electronic signal indicating detection of an attributable event (Paragraphs 0006 & 0021). The machine learning is merely used to predict a predicted amount of web traffic to a website, over a period of time subsequent to receiving the electronic signal (Paragraph 0021). The memory of a second computing device is merely used to store an indication comparing the actual amount of web traffic with the predicted amount of web traffic (Paragraph 0021). In this case, the claim lacked details as to how about how the machine learning generates the predictions (MPEP 2106.05f). Further, the step of “storing an indication” is considered a well-understood, routine, and conventional function since it's just “receiving or transmitting data over a network” and “storing information in a memory” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 11 is directed to a system at step 1, which is a statutory category. Claim 11 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 11 further recites: a memory; and a processor – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. The claim is ineligible.
Independent claim 18 is directed to a program at step 1, which is a statutory category. Claim 18 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 18 further recites: a computer-readable medium; and a processor – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these limitations are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b. The claim is ineligible.
Dependent claims 2, 7-10, 12-14, 16-17, and 19 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as: receiving a time series of past web traffic to the website prior to receiving the electronic signal; identifying a time window associated with an attributable event in the time series; removing the past web traffic corresponding to the identified time window from the time series to thereby form a training data set; and providing the training data set to the machine learning model; training a second machine learning model based on the comparing of the actual amount of web traffic with the predicted amount of web traffic and/or one or more variables of the marketing campaign being changed; and wherein the machine learning model is a negative binomial distribution. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the step of “training” is recited at a high level of generality. For example, the plain meaning of “training” is merely describing how the machine learning is receiving continuous data to iteratively adjust the values/parameters to minimize a loss function (see MPEP 2106.05d, receiving or transmitting data over a network and performing repetitive calculations). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 3-6, 15, and 20 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of the abstract idea mentioned above - such as: wherein the attributable event is one included in the group consisting of an advertisement being presented in general, a type of advertisement, a manner in which an advertisement is presented, the content discussed within a TV program during which the advertisement is presented, an associated sentiment of the TV program content, a search and social platform paid advertisement bid, a weather event, a political event, and a cultural event; wherein the content is a phrase or keyword; wherein the type of advertisement is a TV commercial; wherein the attributable event is a TV commercial airing, and wherein the indication includes a lift attribution value for the TV commercial; and wherein generating the indication includes determining an attribution value for the attributable event based on comparing the actual amount of web traffic with the predicted amount of web traffic. These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to certain “certain methods of organizing human activity” which include “commercial or legal interactions.” In addition, there are no additional elements to consider at Step 2A Prong 2 and Step 2B. Therefore, the claims still recite an abstract idea that can be grouped into certain methods of organizing human activity.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-13 and 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ortiz et al. (US 2020/0007934 A1).
Regarding claim 1, Ortiz et al. discloses a method for evaluating a marketing campaign, comprising (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole):
receiving an electronic signal indicating detection of an attributable event occurring at a first computing device (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; As stated in Paragraph 0006 of Applicant’s specification, an attributable event may include an advertisement being presented in general);
predicting, using a machine learning model, a predicted amount of web traffic to a website, over a period of time subsequent to receiving the electronic signal, had the attributable event not occurred (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms);
receiving electronic data indicating an actual amount of web traffic to the website over the period of time subsequent to receiving the electronic signal (Paragraph 0112, In various exemplary embodiments of the invention, interlocking methods and systems are provided to allow entities to: (1) select a set of one or more broadcasters, target demographics, or other factors during the Creation stage 101; (2) automatically manage and distribute pieces of multimedia content across multiple social media and other platforms (e.g., TWITCH™, both in-stream and in-profile, TWITTER™, FACEBOOK™, etc.) during Execution stage 102; and (3) provide metrics to evaluate the ongoing reach and success of a campaign during Evaluation stage 103, which can then be used to refine the future creation and/or execution of such campaigns via links 104 and 105; Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions);
generating an indication based on comparing the actual amount of web traffic with the predicted amount of web traffic; and storing the indication in a memory of a second computing device (Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns).
Regarding claim 11, Ortiz et al. discloses a system for evaluating a marketing campaign, comprising: a memory; and a processor in communication with the memory, the processor configured to (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole; Paragraph 0101, FIG. 3 is a diagram depicting the structure of an exemplary system for performing the methods depicted in FIGS. 1-2):
receive an electronic signal indicating detection of an attributable event occurring at a computing device (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; As stated in Paragraph 0006 of Applicant’s specification, an attributable event may include an advertisement being presented in general);
predict, using a machine learning model, a predicted amount of web traffic to a website, over a period of time subsequent to receiving the electronic signal, had the attributable event not occurred (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms);
receive electronic data indicating an actual amount of web traffic to the website over the period of time subsequent to receiving the electronic signal (Paragraph 0112, In various exemplary embodiments of the invention, interlocking methods and systems are provided to allow entities to: (1) select a set of one or more broadcasters, target demographics, or other factors during the Creation stage 101; (2) automatically manage and distribute pieces of multimedia content across multiple social media and other platforms (e.g., TWITCH™, both in-stream and in-profile, TWITTER™, FACEBOOK™, etc.) during Execution stage 102; and (3) provide metrics to evaluate the ongoing reach and success of a campaign during Evaluation stage 103, which can then be used to refine the future creation and/or execution of such campaigns via links 104 and 105; Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions);
generate an indication based on comparing the actual amount of web traffic with the predicted amount of web traffic; and store the indication in the memory (Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns).
Regarding claims 2 and 12, which are dependent of claims 1 and 11, Ortiz et al. discloses all the limitations in claims 1 and 11. Ortiz et al. further discloses training the machine learning model, which includes: receiving a time series of past web traffic to the website prior to receiving the electronic signal; identifying a time window associated with an attributable event in the time series; removing the past web traffic corresponding to the identified time window from the time series to thereby form a training data set; and providing the training data set to the machine learning model (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0134, In certain embodiments, the information relating to the platforms comprises the time period during which the platforms broadcast one or more pieces of multimedia content associated with one or more promotional campaigns, Thus, the system can focus on the time period during which the platforms caused the audience to experience pieces of multimedia content in the past; Paragraph 0141, In certain embodiments, once the broadcasters have been filtered using the campaign parameters 307, the broadcaster feature vector 310 is constructed. This feature vector may, in certain embodiments, use and include information extracted from both the broadcaster database and campaign database to create the training data fed into the machine learning model. Each row of training data can be thought of as a pairing of a broadcaster and a campaign containing information on a broadcaster, the campaign in which the broadcaster participated, and the broadcaster's performance during that campaign. In certain embodiments, inputting the first and second sets of information into a machine-learning model comprises creating a feature vector from the two sets of information and inputting the vector into the model; Examiner notes that the performance of the campaign is evaluated for one or more pieces of multimedia content. Therefore, the machine learning is learning how the performance (e.g. traffic) is improved or degraded when the one or more pieces of multimedia content is provided to the audience during a specific time period/window).
Regarding claim 3, which is dependent of claim 1, Ortiz et al. discloses all the limitations in claim 1. Ortiz et al. further discloses wherein the attributable event is one included in the group consisting of: an advertisement being presented in general, a type of advertisement, a manner in which an advertisement is presented, the content discussed within a TV program during which the advertisement is presented, an associated sentiment of the TV program content, a search and social platform paid advertisement bid, a weather event, a political event, and a cultural event (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0094, In certain embodiments, the invention is directed to a method of creating, executing, and evaluating an advertising campaign. In those embodiments, the method comprises creating the campaign; executing the campaign; and evaluating the campaign; It can be noted that the claim language is written in alternative form. The limitation taught by Ortiz et al. is based on “an advertisement being presented in general").
Regarding claim 4, which is dependent of claim 3, Ortiz et al. discloses all the limitations in claim 3. Ortiz et al. further discloses wherein the content is a phrase or keyword (Paragraph 0016, In certain embodiments, the at least one piece of multimedia content comprises at least one of a static graphic, a dynamic graphic, a webpage capture, a movie, an animation, an audiovisual stream, an audio file, a weblink, a coupon, a game, a virtual reality environment, an augmented reality environment, a mixed reality environment, and textual content).
Regarding claim 5, which is dependent of claim 3, Ortiz et al. discloses all the limitations in claim 3. Ortiz et al. further discloses wherein the type of advertisement is a TV commercial (Paragraph 0004, Many services exist that allow users to consume prerecorded media, ranging from content produced by high-profile companies to content produced by self-funded users. These services range from conventional multimedia distribution formats (such as, for example, traditional televised content) to newer platforms that allow individuals to both distribute and consume content; Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole; Paragraph 0172, In these embodiments of the invention, pieces of multimedia content in the form of media, images, text, or any other form are distributed to any number of content creators or platforms across multiple channels).
Regarding claim 6, which is dependent of claim 3, Ortiz et al. discloses all the limitations in claim 3. Ortiz et al. further discloses wherein generating the indication includes determining an attribution value for the attributable event based on comparing the actual amount of web traffic with the predicted amount of web traffic (Paragraph 0112, In various exemplary embodiments of the invention, interlocking methods and systems are provided to allow entities to: (1) select a set of one or more broadcasters, target demographics, or other factors during the Creation stage 101; (2) automatically manage and distribute pieces of multimedia content across multiple social media and other platforms (e.g., TWITCH™, both in-stream and in-profile, TWITTER™, FACEBOOK™, etc.) during Execution stage 102; and (3) provide metrics to evaluate the ongoing reach and success of a campaign during Evaluation stage 103, which can then be used to refine the future creation and/or execution of such campaigns via links 104 and 105; Paragraph 0201, FIG. 4 depicts examples of Auto-Generated Tracking Links 214. In certain embodiments, all pieces of multimedia content within the system carry a unique, shortened, auto-generated URL. Each link is associated to a specific combination of a campaign channel and campaign version of the piece of multimedia content. In a non-limiting example, a user will set a series of conventional target URLs meant as target destinations for content creators to direct their viewers towards. When a piece of multimedia content is created or uploaded to the system, the user is prompted to select from this list of existing target links. The target links are then used to generate a series of shortened links of the form GO.AVD.GG/XXXXXX where X is a conventional base64 encoded character. In its entirety, the use of unique links allows the system to determine exact attribution for a campaign's pieces of multimedia content to be used for performance-based metrics in the evaluation phase of the platform).
Regarding claims 7 and 17, which are dependent of claims 1 and 16, Ortiz et al. discloses all the limitations in claims 1 and 16. Ortiz et al. further discloses training a second machine learning model based on the comparing of the actual amount of web traffic with the predicted amount of web traffic (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0146, In certain embodiments, once the feature vector has been established, sets of platform/campaign pairing data can be fed into the deep-learning performance prediction system 311. When a machine-learning model is used, in certain embodiments the invention comprises training the machine-learning model. The machine-learning model can be trained by, for example, inputting performance data for a plurality of pieces of previously used multimedia content).
Regarding claims 8 and 16, which are dependent of claims 1 and 11, Ortiz et al. discloses all the limitations in claims 1 and 11. Ortiz et al. further discloses predicting, using a second machine learning model, a second predicted amount of web traffic to the website over the period of time subsequent to receiving the electronic signal with one or more variables of the marketing campaign being changed, wherein the indication is generated based additionally on the second predicted amount of web traffic (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0146, In certain embodiments, once the feature vector has been established, sets of platform/campaign pairing data can be fed into the deep-learning performance prediction system 311. When a machine-learning model is used, in certain embodiments the invention comprises training the machine-learning model. The machine-learning model can be trained by, for example, inputting performance data for a plurality of pieces of previously used multimedia content).
Regarding claim 9, which is dependent of claim 8, Ortiz et al. discloses all the limitations in claim 8. Ortiz et al. further discloses wherein the variables of the marketing campaign include at least one in the group consisting of: a type of advertisement, a TV market within which an advertisement is shown, a particular network affiliate or streaming platform through which an advertisement is ran, a particular channel on which an advertisement is presented, a particular time during which an advertisement is presented, content being presented in an advertisement, the TV program an advertisement runs during, the content being discussed within the TV program, associated sentiment of the TV program content, a percent reach to which an advertisement was shown, the search engine an advertisement is run on, the social media platform an advertisement is run on, characteristics of the audience, household, or individual to which an advertisement is being shown, and the particular frequency of which an advertisement is shown to a certain audience, household or individual (Paragraph 0004, Many services exist that allow users to consume prerecorded media, ranging from content produced by high-profile companies to content produced by self-funded users. These services range from conventional multimedia distribution formats (such as, for example, traditional televised content) to newer platforms that allow individuals to both distribute and consume content; Paragraph 0013, We have invented a system that uses machine-learning models to simulate the performance of multimedia content when distributed on different platforms; Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0094, In certain embodiments, the invention is directed to a method of creating, executing, and evaluating an advertising campaign. In those embodiments, the method comprises creating the campaign; executing the campaign; and evaluating the campaign; Paragraph 0019, In certain embodiments, the one or more desired demographics comprise one or more of the ages, gender, education levels, interests, income levels, occupations and geographic locations of a desired audience for the at least one promotional campaign; It can be noted that the claim language is written in alternative form. The limitation taught by Ortiz et al. is based on “a particular network affiliate or streaming platform through which an advertisement is ran," “content being presented in an advertisement,” and/or “characteristics of the audience”).
Regarding claim 10, which is dependent of claim 8, Ortiz et al. discloses all the limitations in claim 8. Ortiz et al. further discloses wherein the second machine learning model is trained with the generated indication (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0146, In certain embodiments, once the feature vector has been established, sets of platform/campaign pairing data can be fed into the deep-learning performance prediction system 311. When a machine-learning model is used, in certain embodiments the invention comprises training the machine-learning model. The machine-learning model can be trained by, for example, inputting performance data for a plurality of pieces of previously used multimedia content).
Regarding claim 13, which is dependent of claim 11, Ortiz et al. discloses all the limitations in claim 11. Ortiz et al. further discloses wherein the processor is further configured to predict, using a second machine learning model, a second predicted amount of web traffic to the website over a period of time subsequent to generating the indication (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0146, In certain embodiments, once the feature vector has been established, sets of platform/campaign pairing data can be fed into the deep-learning performance prediction system 311. When a machine-learning model is used, in certain embodiments the invention comprises training the machine-learning model. The machine-learning model can be trained by, for example, inputting performance data for a plurality of pieces of previously used multimedia content).
Regarding claim 15, which is dependent of claim 11, Ortiz et al. discloses all the limitations in claim 11. Ortiz et al. further discloses wherein the attributable event is a TV commercial airing, and wherein the indication includes a lift attribution value for the TV commercial (Paragraph 0002, In recent years, the amount of multimedia content that is generated and that is available for consumption has greatly increased. In particular, in addition to content generated by traditional mass media entities and distributed through conventional channels (for example, broadcast television or film), it has become increasingly practical for the average person to generate, distribute, and/or consume multimedia content; Paragraph 0114, in embodiments of the present invention, one of the built-in tools for feedback is A/B testing on particular pieces of multimedia content. Broadly speaking, A/B testing involves testing two versions of a piece of content to see which is more successful. In this particular implementation of A/B testing, two different versions of a piece of multimedia content can be deployed, and the results compared to determine which piece of multimedia content performed better. Thus, a piece of multimedia content such as a live graphic shown during a live stream can be deployed in different versions to different broadcasters on the campaign at different times. Embodiments of the invention can recommend and automatically deploy high performing versions of the pieces of multimedia content while withdrawing lower performing versions of the pieces of multimedia content, ensuring that each piece of multimedia content is delivering its highest return on effective cost per action/acquisition (“eCPA”) (e.g., favoring pieces of multimedia content with high attribution rates) and reaching the widest possible audience (e.g., favor TWITTER™-based pieces of multimedia content with high Retweet numbers); Paragraph 0201, The use of unique links allows the system to determine exact attribution for a campaign's pieces of multimedia content to be used for performance-based metrics in the evaluation phase of the platform).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ortiz et al. (US 2020/0007934 A1), in view of Ramachandran et al. (US 2018/0004497 A1).
Regarding claim 14, which is dependent of claim 11, Ortiz et al. discloses all the limitations in claim 11. Although Ortiz et al. discloses a machine learning, Ortiz et al. does not specifically discloses wherein the machine learning model is a negative binomial distribution.
However, Ramachandran et al. discloses wherein the machine learning model is a negative binomial distribution (Paragraph 0057, The data processing system 120 can predict the performance of a content campaign for an application based on an input value using the merged data. In some cases, the data processing system 120 can predict the performance of the content campaign for the application without using ping data from the application; Paragraph 0058, The data processing system 120 can use the machine learning technique to generate scores for signals in the merged data. In some cases, the data processing system 120 can use a Poisson regression with regularizations. A Poisson regression can refer to a regression analysis that models count data and contingency tables. When using the Poisson regression, the data processing system can determine that a response variable Y (e.g., a desired application event) has a Poisson distribution, and further determine that a logarithm of its expected value can be modeled by a linear combination of unknown parameters (e.g., signals of the merged data). In some cases, the data processing system 120 can use a negative binomial regression, which generalizes the Poisson regression).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system for predicting, using a machine learning model, an amount of web traffic to a website of the invention of Ortiz et al. to further specify wherein the machine learning model is a negative binomial distribution of the invention of Ramachandran et al. because doing so would allow the system to use a negative binomial regression, which generalizes the Poisson regression (see Ramachandran et al., Paragraph 0058). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 18-20 is rejected under 35 U.S.C. 103 as being unpatentable over Ortiz et al. (US 2020/0007934 A1), in view of Kahn (US 2023/0267499 A1).
Regarding claim 18, Ortiz et al. discloses a … storing instructions, which when executed by a processor, cause the processor to (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole; ; Paragraph 0101, FIG. 3 is a diagram depicting the structure of an exemplary system for performing the methods depicted in FIGS. 1-2):
receive an electronic signal indicating detection of an attributable event occurring at a first computing device (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; As stated in Paragraph 0006 of Applicant’s specification, an attributable event may include an advertisement being presented in general);
predict, using a machine learning model, a predicted amount of web traffic to a website, over a period of time subsequent to receiving the electronic signal, had the attributable event not occurred (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0050, In certain embodiments, the predicted performance metrics comprise at least one of a number of predicted selections of one or more of the pieces of previously broadcast multimedia content, a number of predicted visits to web pages linked to one or more of the plurality of pieces of previously broadcast multimedia content, a number of predicted views of one or more of the plurality of pieces of previously broadcast multimedia content, and a number of predicted times that one or more of the plurality of pieces of previously broadcast multimedia content was liked and/or shared on one or more social media platforms);
receive electronic data indicating an actual amount of web traffic to the website over the period of time subsequent to receiving the electronic signal (Paragraph 0112, In various exemplary embodiments of the invention, interlocking methods and systems are provided to allow entities to: (1) select a set of one or more broadcasters, target demographics, or other factors during the Creation stage 101; (2) automatically manage and distribute pieces of multimedia content across multiple social media and other platforms (e.g., TWITCH™, both in-stream and in-profile, TWITTER™, FACEBOOK™, etc.) during Execution stage 102; and (3) provide metrics to evaluate the ongoing reach and success of a campaign during Evaluation stage 103, which can then be used to refine the future creation and/or execution of such campaigns via links 104 and 105; Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions);
generate an indication based on comparing the actual amount of web traffic with the predicted amount of web traffic; and store the indication in a memory of a second computing device (Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns).
Although Ortiz et al. discloses a method/system for evaluating a marketing campaign, Ortiz et al. does not specifically disclose a non-transitory, computer-readable medium.
However, Kahn et al. discloses a non-transitory, computer-readable medium storing instructions, which when executed by a processor, cause the processor to (Paragraph 0015, FIG. 9 includes a flow diagram of a process for training a machine learning algorithm and then implementing the resulting machine learning model as part of an inferencing operation, in order to predict performance of a company in the absence of an advertising campaign; Paragraph 0026, In order to determine what would have happened had the advertising campaign not been run, the analysis platform can train a machine learning algorithm (or simply “algorithm”) to predict performance using data leading up to the introduction of the advertising campaign; Claim 1, A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising; see provisional application # 63/313,609, filed on 02/24/22, Paragraphs 0021 & 0028).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method/system for evaluating a marketing campaign of the invention of Ortiz et al. to further specify a non-transitory computer-readable medium of the invention of Kahn et al. because doing so is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 19, which is dependent of claim 18, the combination of Ortiz et al. and Kahn et al. discloses all the limitations in claim 18. Ortiz et al. further discloses wherein the machine learning model is trained only on web traffic data associated with the website (Paragraph 0014, Further, we have invented a system that uses machine-learning methods to gather, analyze, and classify audience reactions to particular pieces of media content (such as advertisements), and allows for automatic assessment of and feedback on the performance of those pieces of media content and the performance of an advertising campaign as a whole. The machine-learning systems and methods we have invented can use this feedback to dynamically improve the machine-learning systems for simulating the performance of pieces of multimedia content; Paragraph 0125, The information could also include performance data for previously-used pieces of multimedia content. In these exemplary embodiments of the present invention, the invention uses past performance to help simulate how the entity's current piece of multimedia content will perform relative to the entity's desired goals. The information relating to the previously-used content could include a number of selections of the previously-used multimedia content, a number of visits to web pages linked to one or more of the plurality of pieces of previously-used content, a number of views of those pieces of previously-used content, or a number of times that the previously-used multimedia content was reacted to (by, for example, liking, sharing, or retweeting the content). If the information includes the number of selections, it could include any of the total number of selections, visits, views, or reactions, and the average number of selections, visits, views, or reactions; Paragraph 0126, In certain embodiments, campaign database 306 includes both the campaign information and the evaluation metrics 308 from current and previous campaigns; Paragraph 0141, In certain embodiments, once the broadcasters have been filtered using the campaign parameters 307, the broadcaster feature vector 310 is constructed. This feature vector may, in certain embodiments, use and include information extracted from both the broadcaster database and campaign database to create the training data fed into the machine learning model. Each row of training data can be thought of as a pairing of a broadcaster and a campaign containing information on a broadcaster, the campaign in which the broadcaster participated, and the broadcaster's performance during that campaign. In certain embodiments, inputting the first and second sets of information into a machine-learning model comprises creating a feature vector from the two sets of information and inputting the vector into the model; Examiner notes that the performance used to train the machine learning model includes traffic data associated with the website (e.g., a number of visits to the web pages)).
Regarding claim 20, which is dependent of claim 18, the combination of Ortiz et al. and Kahn et al. discloses all the limitations in claim 18. Ortiz et al. further discloses wherein the indication includes at least one metric in the group consisting of: an amount in sales associated with the attributable event, a customer conversion rate associated with the attributable event, and lift attribution of the attributable event (Paragraph 0002, In recent years, the amount of multimedia content that is generated and that is available for consumption has greatly increased. In particular, in addition to content generated by traditional mass media entities and distributed through conventional channels (for example, broadcast television or film), it has become increasingly practical for the average person to generate, distribute, and/or consume multimedia content; Paragraph 0114, in embodiments of the present invention, one of the built-in tools for feedback is A/B testing on particular pieces of multimedia content. Broadly speaking, A/B testing involves testing two versions of a piece of content to see which is more successful. In this particular implementation of A/B testing, two different versions of a piece of multimedia content can be deployed, and the results compared to determine which piece of multimedia content performed better. Thus, a piece of multimedia content such as a live graphic shown during a live stream can be deployed in different versions to different broadcasters on the campaign at different times. Embodiments of the invention can recommend and automatically deploy high performing versions of the pieces of multimedia content while withdrawing lower performing versions of the pieces of multimedia content, ensuring that each piece of multimedia content is delivering its highest return on effective cost per action/acquisition (“eCPA”) (e.g., favoring pieces of multimedia content with high attribution rates) and reaching the widest possible audience (e.g., favor TWITTER™-based pieces of multimedia content with high Retweet numbers); Paragraph 0201, The use of unique links allows the system to determine exact attribution for a campaign's pieces of multimedia content to be used for performance-based metrics in the evaluation phase of the platform; It can be noted that the claim language is written in alternative form. The limitation taught by Ortiz et al. is based on “lift attribution of the attributable event").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Manus et al. (US 2021/0297739 A1) – discloses during the signal processing, the machine learning module 520 estimates a base line level of a KPI and then recognizes an effect of an advertisement to the curve of the KPI response. In some embodiments, the machine learning module 520 estimates the effect using a Gibbs sampler (see at least Paragraph 0036).
Cohen Dror et al. (WO 2020234860 A1) – discloses the use a ML classification model, based on decision trees methodology, such as Random Forest or Extreme Gradient Boosting (Boost) algorithms, or on support vector machines, or neural network methodology such as Deep Neural Network algorithms (DNN’s), will predict the probability for visiting the specific advertised product or service related website during a broadcast linear TV ad campaign (see at least Paragraph 0074).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624
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