Systems and methods for presentation of a consolidated media dashboard

Abstract

Systems and methods are provided for presentation of a consolidated media dashboard. The consolidated media dashboard provides a medium through which users may manage content distribution and view metrics relating to such content distribution (for example, viewership metrics, interaction metrics, etc.). In addition to allowing a user to view metrics, the consolidated media dashboard may also automatically predict, using a machine learning model, for example, future metrics. The system may not only automatically make such predictions, but may also automatically distribute different types of content to end-users via various types of mediums based on the predictions without requiring any manual intervention from a user (but the content distribution may also be performed manually as well).

Description

81396046 25040-6137SYSTEMS AND METHODS FOR PRESENTATION OF A CONSOLIDATED MEDIA DASHBOARDCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority benefit of U.S. Provisional Patent Application No. 63 / 716,864, filed November 6, 2024, which is incorporated herein by reference in its entirety.BACKGROUND

[0002] Content providers may conventionally distribute content to end users in various regions through various types of content mediums. For example, a television series may be distributed to end users for viewing through a streaming service, a cable television broadcast, etc. Advertisers may distribute advertisements to end users through television and / or live streaming commercials, traditional print advertisements, digital displays in retail stores and other locations, etc. However, there may not exist an effective mechanism by which standardized metrics relating to the content may be captured, analyzed, and presented to a user responsible for managing distribution of the content. Additionally, there may also not exist an effective mechanism by which future content distribution plans may be simulated and automatically performed based on such simulations.BRIEF DESCRIPTION OF THE DRAWINGS

[0003] The detailed description is set forth with reference to the accompanying drawings. The drawings are provided for purposes of illustration only and merely depict example embodiments of the disclosure. The drawings are provided to facilitate understanding of the disclosure and shall not be deemed to limit the breadth, scope, or applicability’ of the disclosure. In the drawings, the left-most digit(s) of a reference numeral may identify the drawing in which the reference numeral first appears. The use of the same reference numerals indicates similar, but not necessarily the same or identical components. However, different reference numerals may be used to identify similar components as well. Various embodiments may utilize elements or components other than those illustrated in the drawings, and some elements and / or components may not be present in various embodiments. The use of singular terminology to describe a component or element may81396046 25040-6137 depending on the context, encompass a plural number of such components or elements and vice versa.

[0004] FIGS. 1 depicts an example use case for presentation of a consolidated media dashboard in accordance with one or more example embodiments of the disclosure.

[0005] FIG. 2 depicts an example method for presentation of a consolidated media dashboard in accordance with one or more example embodiments of the disclosure.

[0006] FIGS. 3-12 depict various user interfaces for presentation of a consolidated media dashboard in accordance with one or more example embodiments of the disclosure.

[0007] FIG. 13 depicts an example system for presentation of a consolidated media dashboard in accordance with one or more example embodiments of the disclosure.

[0008] FIG. 14 depicts an example computing device in accordance with one or more example embodiments of the disclosure.DETAILED DESCRIPTION

[0009] This disclosure relates to, among other things, systems and methods for the presentation of a consolidated media dashboard. The consolidated media dashboard (also generally referred to as a “dashboard” herein) provides improved user interfaces in the form of a comprehensive dashboard through which users may manage content distribution and view metrics and other logistics information relating to such content distribution, for example. The underlying system associated with the dashboard generally also represents a technical improvement over existing systems used to manage content distribution in that the system may automatically predict, using a machine learning model, for example, future metrics relating to content viewership, interactions, etc. The system also represents a technical improvement in that the system may not only automatically make such predictions, but may also automatically distribute different types of content to end-users based on the predictions without requiring any manual intervention from a user (however, the system may also be configured to allow a user to manually control the distribution of content via the dashboard as well). For example, the system may automatically determine the types of content to distribute, the specific end users or regions or end users to distribute the content to, the days and times at which the content is distributed, the mechanisms by which the content is presented to the end users (referred to as “touchpoints” herein), etc.

[0010] Subsequent to the content distributions, the system may also obtain further data about the results of such distributions to automatically re-train itself (for example, the81396046 25040-6137 machine learning model may be trained in a supervised manner) to improve future predictions and content distribution strategies. As a simplified example, the machine learning model may determine that a first type of touchpoint resulted in greater viewership numbers than a second type of touchpoint for a certain type of content. Based on this, the machine learning model may re-train itself to provide greater weight to the first type of touchpoint when performing future simulations involving the type of content. However, the training may also occur in any other suitable manner.

[0011] Example user interfaces that may be presented via the dashboard (as well as examples of different types of information that may be presented and the manner in which the information may be presented) are shown in at least FIGS. 3-12. Although FIGS. 3-12 show one particular use case representing one example of types of information that may be presented via the dashboard, this use case is not intended to be limiting and the dashboard may be applicable in other use cases involving other types of information as well.

[0012] Additionally, another beneficial aspect of the system is that a single quantified value may be determined that is standardized and provides a point of comparison between data associated with different types of content. In this manner, if the dashboard is used to view and manipulate data about different types of content (for example, a user may establish different ‘'profiles” to view metrics for different content, as described below), then the quantified value may be used to compare at least some aspects of the data in a standardized manner. Returning to the example use case of advertisement content, the quantified value may provide the system (and / or a user) a quantified point of comparison for different types of advertisement campaigns for different products that are presented to users via different touchpoints (for example, printed advertisements, digital displays in stores, etc.). The quantified value may be referred to herein as a quality of content value and may be determined based on a combination of the number of impacts generated by the content and the quality of the media associated with the content. However, other types of standardized quantified values based on other factors may also be used.

[0013] The number of impacts may refer to a single instance of exposure of that media to a single user. The quality of media value may be a standardized value that is based on one or more quality dimensions. In embodiments, eight different quality dimensions may be scored to determine the quality7of media for each piece of content. In such embodiments, the eight dimensions may include expanse, environment exclusivity7, enquired, exposure, sensorality, engagement, shareability, and transaction capability. Expanse may refer to the81396046 25040-6137 size of the unit both on its own and in the context of the typical field of attention (for example, focused attention of a user using a mobile device versus driving past a billboard on the side of a highway). Environment exclusivity may refer to the degree of “clutter” (for example, other stimuli such as other buildings, cars, advertisements, displays, etc.) present within the environment where the content is provided and to the exclusivity for the brand. Enquired may refer to the manner in which an impression associated with the content originates. Exposure may refer to the amount of time that the content is typically viewed (or heard depending on the type of content), which may depend on factors other than the duration of the content. Sensorahty may refer to the degree of sensory stimulation and immersive of the content (for example, the more senses that are involved and the higher the immersion of the content, the higher the sensorality score may be). Engagement may refer to the degree of interactivity with the content provided by the medium and the directness and nature of the interactivity with the brand (a higher amount of interactivity may be more desirable). Shareability may refer to the ability of the content to be shared based on sharing mechanisms afforded by the medium and a likelihood that the content will be shared (greater ease of sharing and likely virality of the media is more desirable). Transaction capabilitymay refer to the degree of directness of the content to the point of transaction. These dimensions are exemplar}' and not intended to be limiting, any other number of dimensions may also be used.

[0014] These different quality- dimensions may be applied to any number of different types of “touchpoints.” A touchpoint may generally refer to a mechanism or medium by which a type of content may be viewed and / or interacted with by an end user. Continuing the use case where the ty pe of interactive content is marketing content, non-limiting examples of touchpoints may include a linear television advertisement, brand / product placement in existing content, program content sponsorship, a smart television startup screen advertisement, an online video / streaming video advertisement, a cinema pre-screen advertisement, a mobile application startup screen advertisement, a mobile application banner advertisement, an in-game advertisement, an influencer / creator live-stream, a food delivery application advertisement, an advertisement on a third party- e-commerce website, an audio streaming advertisement, a billboard advertisement, an outdoor display advertisement, advertisements on displays inside public transportation and / or displays at terminals associated with public transportation, a residential display advertisement, a printed advertisement, a short form video advertisement, a website banner advertisement, a social81396046 25040-6137 feed advertisement, an official social challenge, a search advertisement, a radio advertisement, an influencer / creator social endorsement, a celebrity / talent social endorsement, a meet and greet, an asset own digital communications featuring brand, a sponsored team clothing or music artist / band clothing featuring brand, an asset onsite brand experience area during event, an asset onsite signage featuring brand, an asset merchandise featuring brand, a brand own created event, a brand permanent experience, a brand learning experience, a brand pop-up store / experience, an augmented reality experience, a virtual reality experience, a metaverse collectable, a text message, a subscription email, a brand official channel online video, a brand official social page, an official website, an official application, an official online store, branded licensed products, branded vehicle (e g., truck, van, car, etc.), standard packaging, special edition packaging, a physical sampling / trial, brand promotion on third-party e-commerce / online grocery, a brand store on third party e- commerce website, a vending machine, a drinks fountain, a retail in-store secondary display, etc. These touchpoints are merely exemplary and any other touchpoints may also be used.

[0015] The touchpoints may also vary7depending on the ty pe of content that is being managed and distributed by the system. For example, the touchpoints for a movie or television series may be different than touchpoints for video game content or advertisement content.

[0016] For a given touchpoint, values associated with the one or more dimensions may be determined. In some instances, the values may be within a fixed range of values, such as a scale of numerical values ranging from 1-5 (however, any other range of values may also be used). That is, values within the range of values may be determined for each of the eight dimensions.

[0017] Once the values are determined for each of the dimensions, the values may then be transformed using a factor applied to each score. For example, a value ofL’ may be multiplied by numerical factor “x,” a value of “2” may be multiplied by numerical factor “y,” etc. The final scores across touchpoints may then be scaled back to a scale ranging from 0-100%, with 100% indicating perfect quality. These final values may represent the quantified metrics that are used as the point of comparison. An example of this is shown in FIG. 4.

[0018] Turning to the figures, FIG. 1 depicts an example use case 100 involving the use of a consolidated media dashboard and a system used to perform automated simulations and content distribution as described herein. The use case 100 begins with scene 102, which81396046 25040-6137 shows that content is being presented to one or more end users (for example, one or more first end users 103, one or more second end users 104, one or more third end users 105, etc.) via one or more different types of touchpoints. In the use case 100, the content may be a broadcast of a live sports event, as an example, however, the content may also be any other type of content that an end user may view and / or interact with, such as a television show, an advertisement, a video game, a poll for information, etc. Although the figure only shows a specific number of end users associated with each touchpoint, this is merely exemplar}', and any other number of end users may also exist.

[0019] A first touchpoint involves the content being presented via a cable television broadcast that is viewed by the one or more first end users 103 via a television 106. A second touchpoint is show n as the content being live streamed content that the one or more second end users 104 may view using an application of a smartphone 107 or other type of device. A third touchpoint is shown as the content being live streamed to one or more third end users 105 via a virtual reality environment that the end users may access via a virtual reality headset 108. The content may also be presented to various end users via any other types of touchpoints described herein or otherwise as well. The depiction of the single television 106. smartphone 107, and virtual reality headset 108 is merely to exemplify types of devices through which the content may be provided to the end users via the various touchpoints. The end users may view and / or interact with the content through any other number of devices (for example, different users of the one or more first end users 103) may view the content using multiple televisions 106, which may be provided at the same or different locations.

[0020] Data associated with the generation and distribution of the content, the touchpoints used to present the content to the end users, the view ership of the content and interaction with the content by the end users (number of users viewing and / or interacting with the content, duration of viewing and / or interaction, etc.), and / or any other types of information that is relevant to the management and distribution of the content may be provided to the system 110. For example, the television 106, smartphone 107, virtual reality headset 107, and / or any other types of devices, systems, etc. associated w ith the touchpoints may automatically provide the data to the system 110, the system 110 may request the data, and / or the data may be obtained by the system 110 in any other suitable manner.

[0021] In embodiments, the data that is obtained may be processed by the system 110 and presented via a dashboard 116 to a user 114. For example, the user 114 may access the dashboard 116 via an application provided on a smartphone 112. The user 114 may also81396046 25040-6137 access the dashboard 116 using any other type of device and / or in any other suitable manner (for example, via a website, etc.). Additional details about the dashboard 116 and the information that may be presented via the dashboard 116 are provided with respect to at least FIGS. 3-12.

[0022] In addition to presenting the data via the dashboard 11 , the system 1 10 may also automatically perform simulations using the existing data. The system 110 may include a machine learning model that may be trained to perform simulations to determine the manner in which the content should be automatically distributed by the system to either the same or different touchpoints in the future. For example, the machine learning model may use historical data to produce simulated metrics associated with distributing the content in the same manner that the content was previously distributed (for example, via the three different touchpoints). The machine learning model may also automatically simulate metrics associated with distributing the content in any number of different ways, including different combinations of touchpoints, among various other parameters that may be considered in the distribution of the content. Further non-limiting examples of such parameters may include, geographical region, demographic audience, duration of time that the content is shown at the touchpoints, etc.

[0023] The system 110 may use the results of the simulations to automatically determine how content should be distributed in the future. In the scenario shown in the use case 100, the system 110 may determine that the first touchpoint and the second touchpoint should be more heavily emphasized for content distribution in the future instead of the third touchpoint. The system 110 may then automatically distribute the content in this manner based on the simulations. This is illustrated in scene 120, in which the system 110 automatically redistributes the content to the first touchpoint (to one or more fourth end users 122) and the second touchpoint (to one or more fifth end users 124) but not the third touchpoint. The system 1 10 may also increase the exposure of the content to the first and second touchpoints. For example, the system 110 may increase the time duration that the content is provided to the first and second touchpoints, increase the number of geographical regions the content is provided to via the first and second touchpoints, etc. Even if the system 110 does not increase the exposure in this manner, by not providing the content to the third touchpoint, the system 110 may be provided benefits in the form of reduced processing and data transmission requirements, reduced costs for content providers, etc.81396046 25040-6137

[0024] In embodiments, the machine learning model and the system 110 may also consider manual user inputs when performing simulations and performing automated content management and distribution. In embodiments, the user 114 may configure parameters of the content management and distribution via the dashboard 116. For example, the user 1 14 may indicate that the content should only be distributed to certain geographic regions, that the cost of the content distribution should remain below a threshold cost, and / or any other parameters that may impact the manner in which the content is distributed. The machine learning model may consider these parameters when determining the manner in which content should automatically be managed and distributed by the system 110.

[0025] Additionally, in some instances, a user (such as user 114) may manually override the determinations by the machine learning model and the system 110, and the process by which the content is distributed may be performed entirely manually based on inputs provided by the user 114. For example, the user 114 may indicate through the dashboard 116 that they desire for content to still be distributed via the third touchpoint even if the machine learning model determines that it would be more optimal from a computational resources or monetary cost perspective (or based on any other parameters) to avoid distributing content via the third touchpoint.

[0026] In embodiments, the machine learning model may initially be trained to perform the simulations using historical data, the historical data may include any of the different types of data describes herein or otherwise. For example, the data may include types of content (and / or other attributes of the content, such as genre, duration, etc.) distributed to end users, the touchpoints through which the content was presented to the end users, the geographical regions in which the content was presented, the results of the content distribution (number of end user views of the content and / or other types of interactions with the content), and / or any other types of relevant data.

[0027] Further, even after the initial training using historical data, the machine learning model may perform iterative self-re-training such that the machine learning model is constantly improving the accuracy of the simulations that are performed. For example, following the subsequent distribution of content in scene 120, additional data may be collected. This data may then be compared to the simulated data and any discrepancies between the actual data and the simulated data may be used by the machine learning model to train itself (that is, the machine learning model may train itself in a supervised manner). For example, if the simulated data predicts that distributing a type of content to end-users81396046 25040-6137 via a first touchpoint should product 10,000 views of the content by the end users, but the actual data indicates that only 5,000 views occurred, then this information may be provided to the machine learning model to re-train the machine learning model to generate more accurate simulations in the future.

[0028] This training process may be performed iteratively as the machine learning model continues to perform simulations and obtain new data. This iterative training and retraining process may provide the benefit of improving the accuracy of the machine learning model over time, resulting in more effective simulations and automated content distribution strategies. In embodiments, this iterative training process may occur until the machine learning model converges towards a threshold level of accuracy in the simulations it outputs.

[0029] FIG. 2 depicts an example method 200 for presenting a consolidated media dashboard and performing automated simulations and content distribution. Some or all of the blocks of the process flows or methods in this disclosure may be performed in a distributed manner across any number of devices or systems (for example, system 110, user device 1401, computing device 1404, computing device 1500, etc.). The operations of the method 200 may be optional and may be performed in a different order.

[0030] At block 202 of the method 200, computer-executable instructions stored on a memory of a system or device, such as, system 110, user device 1401, computing device 1404, computing device 1500, etc., may be executed to receive, from one or more first devices, first viewership data associated with content that is presented to one or more users via one or more first touchpoints. At block 204 of the method 200, computer-executable instructions stored on a memory of a system or device may be executed to simulate, using a machine learning model and based on the first viewership data, simulated second viewership data associated with presenting the content via the one or more first touchpoints or one or more second touchpoints. At block 206 of the method 200, computer-executable instructions stored on a memory of a system or device may be executed to present the first viewership data and the simulated second viewership data via a user interface of a dashboard. At block 208 of the method 200, computer-executable instructions stored on a memoiy of a system or device may be executed to automatically transmit, based on the simulated second viewership data, the content to for presentation via the one or more first touchpoints or one or more second touchpoints.81396046 25040-6137

[0031] FIGS. 3-12 depict various user interfaces for presentation of a consolidated media dashboard. The user interfaces merely provide example illustrations of a dashboard and are not intended to limit the dashboard in any way.

[0032] Beginning with FIG. 3, example users interfaces (for example, user interface 300, user interface 302, user interface 304, and user interface 306) for performing the initial configuration of a consolidated media dashboard are shown. The user interface 300 provides a user with a listing of various types of brands from which the user may select. The dashboard may be configured to present information associated with the brands that are selected by the user. The user interface 302 provides a user with a listing of various locations from which the user may select. The dashboard may be configured to present locationspecific information associated with the brands based on the selections in the user interface 302. For example, if the user selects "Peru " and “China,” then the dashboard may present data associated with those locations for the selected brands. The user interface 304 provides a user with a listing of types of marketing categories from which the user may select. Finally, the user interface 306 provides a user with a listing of various types of content from which the user may select.

[0033] FIG. 4 shows a user interface 400 of the dashboard that presents various types of information to a user. The user interface 400 includes a number of different tiles (for example, first tile 402, second tile 404, third tile 406, fourth tile 408, fifth tile 410, sixth tile 412. and / or any other number of tiles) that present different t pes of information to a user via the dashboard. In embodiments, each tile may include the name of the corresponding metric, the value, and the trending change value based on historical data (for example, the previous year (PY) or any other time period). The example user interface 400 of FIG. 4 shows six example key performance indicators (KPIs) that are presented in each of the tiles, however, this is merely illustrative and any other information may be presented.

[0034] For example, in the specific user interface 400 shown in the figure, the first tile 402 may provide investment information, which may provide an indication of the amount of money spent during a given period of time. A secondary per capita figure may also be included to show how much money was spent per person in the target group. A combination of a higher investment value and a lower per capita value may be more desirable.

[0035] The second tile 404 may provide impacts information, which may provide an indication of the number of impacts achieved in a given period of time. A secondary per81396046 25040-6137 capita figure may also be included to show how many impacts were achieved per person in the target group. A combination of larger impacts and per capita values may be desirable.

[0036] The third tile 406 may provide cost per thousand impacts (CPT) information, which may provide an indication of the amount of money spent to achieve a given number of impacts. A lower CPT value may be more desirable. Cost per thousand is exemplary and cost per any other number is also possible as well.

[0037] The fourth tile 408 may provide average quality information, which may be a function of impacts divided by quality impacts. A larger average quality value may be more desirable. In some instances, the average quality value may be a standardized value that provides a point of comparison between data associated with different types of content. The quality value may be determined based on a combination of a number of impacts generated by the content and a quality of the media associated with the content.

[0038] For a given touchpoint, values associated with one or more dimensions (for example, the eight dimensions including expanse, environment exclusivity7, enquired, exposure, sensorality, engage, shareability, and transcatability, and / or any other dimension and / or combination of dimensions) may be determined. In some instances, the values may be within a fixed range of values, such as a scale of numerical values ranging from 1-5 (however, any other range of values may also be used). That is, values within the range of values may be determined for each of the eight dimensions.

[0039] Once the values are determined for each of the dimensions, the values may then be transformed using a factor applied to each score. For example, a value of ‘ ’ may be multiplied by numerical factor “x,” a value of “2” may be multiplied by numerical factor “y,” etc. The final scores across touchpoints may then be scaled back to a scale ranging from 0-100%, with 100% indicating perfect quality. For example, the user interface 400 provides one example in which a quality score of 8% is show n, which may be a relatively low-quality score on a scale ranging from 0-100%.

[0040] The fifth tile 410 may provide quality7impact information, which may provide an indication of the total number of impacts multiplied by each filtered touchpoint’s quality score. A secondary per capita value may also be included to show how many quality impacts were achieved per person in the target group. A combination of larger quality impacts value and per capita value may be desirable.81396046 25040-6137

[0041] The sixth tile 412 may provide quality cost per thousand impacts (QCPT) information, which may provide an indication of the amount of money spent to achieve a given number of quality impacts. A lower QCPT value may be desirable.

[0042] These tiles may be re-arranged within the user interface 400 automatically by the dashboard or manually by a user. For example, a user may manually drag and drop tiles at different locations within the user interface 400 to adjust the positioning and order of the tiles relative to one another. The dashboard may also automatically provide the tiles in a grid-like arrangement as shown in the figure. In some instances, the dashboard may automatically re-position one or more of the tiles based on any number of different factors. For example, a machine learning model (or any other type of artificial intelligence) may be used to determine which information a user is most likely to desire to view via the dashboard. Based on the output of the machine learning model, the dashboard may automatically re-arrange the tiles, change the size of the tiles, or otherwise modify or reposition the tiles such that this information is made more readily available to a user.

[0043] The information that is presented in each of the tiles may also be automatically determined by the dashboard and / or manually adjusted by the system. That is. although the tiles are shown as providing specific types of information, a user may manually indicate other types of information that they desire to be presented via the tiles. Additionally, the dashboard may automatically determine which information should be presented in a similar manner in which the dashboard may automatically re-position or otherwise modify one or more of the tiles.

[0044] The user interface 400 also includes a toolbar 414 that allows a user to modify the sources of data used by the dashboard to present the information via the tiles. For example, the user configured the dashboard to include data from different locations, different brands, etc. via the user interface 300. The user may also be able to filter the data based on subsets of the locations, brand, etc. using the toolbar 414 to provide the user more granular control over the particular types of data the user desires to view via the dashboard. The toolbar 414 (or another portion of the dashboard) may also allow the user to add additional data sources (for example, brands, locations, etc.) even after the initial configuration shown in the user interface 300 has been completed. While reference is made specifically to “brands,” this is use case specific and the filtering may also be performed on any other types of content or aspects of content based on the specific content that is being presented via the dashboard.81396046 25040-6137

[0045] Additionally, the dashboard may allow for the creation of different user profiles such that different configurations may be saved for later use without requiring a user to modify a current configuration. For example, one profile may be established to present information for a first brand for a first location and a second profile may be established to present information for a second brand for a second location and a third location (any other types of factors may also be adjusted in these profiles). Thus, when a user desires to view information about the second brand, the user may select the second profile. Given that the first profile is saved, if the user desires to return to viewing information about the first brand, the user may simply select the first profile rather than needing to reconfigure the dashboard to present the data that was previously saved to the first profile.

[0046] FIGS. 5-8 show various user interfaces (for example, user interface 500, user interface 600. user interface 700, and user interface 800) that include touchpoint analysis information. These user interfaces may present curated charts that break down the performance of relevant touchpoints based on combinations of the six main KPIs (or other information). A series of curated charts may be presented that provide context into how any level of the touchpoint hierarchy (for example, marketing category’, touchpoint category, and individual touchpoints) is performing based on standalone or combined KPIs. The user interfaces may also provide a table view through which a user may download and / or export data in different ways (for example, via a pivot table, etc.). These user interfaces may generally allow for a user to sort various metrics and analyze information about touchpoints, such as which touchpoints are associated with the greatest investment, which touchpoints have the highest qualify, etc.

[0047] FIG. 6 shows a user interface 600 of the dashboard. Particularly, the user interface 600 shows a bar chart view that may be presented when a user selects a bar chart icon 602 presented via the dashboard. Information may also be presented in bar char format based on any other types of inputs to the dashboard or may be presented automatically without requiring any user input. The bar chart may be sorted by various KPIs (or other information) and / or in any other manner. The user interface 700 of FIG. 7, for example, shows a bar chart providing information associated with the quality impacts KPI. The data may also be filtered using any number of different types of filters. A specific category' may be viewed by double-clicking or otherwise selecting that specific category'.

[0048] FIG. 8 shows another type of chart that may be presented to a user via the dashboard. For example, the user interface 800 shows a "‘bubble’" chart that may provide81396046 25040-6137 information about the relationship between three distinct metric variables. An average line may also be included for a y-axis metric, which may provide a clear differentiation for categories that are performing above or below average. Each category may be color-coded to be readily distinguished from other categories. The categories may also be distinguished in any other manner (for example, different types of shading, etc.).

[0049] FIG. 9 shows a user interface 900 of the dashboard through which simulations may be initiated by a user and the results of such simulations may be presented to a user. For example, a user may select an element 902 presented on the user interface 900 that may cause initiation of the simulations. The user interface 900 may also include a listing 904 of simulations that have already been performed such that a user may select a particular simulation to view information relating to that simulation, such as the parameters used to generate the simulation, the simulated data itself, the date and time at which the simulation was performed, etc.

[0050] In embodiments, the simulations may provide an indication of how a simulated content plan (for example, a plan for distributing content to be presented to end users via a number of touchpoints) may perform based on various parameters, such as the six main KPIs (and / or any other parameters), alongside a table of values and supporting visualizations. In embodiments, the simulations may be performed using the same parameters (for example, the same budget, geographical regions, etc.) that were previously used for the distribution of content via various touchpoints. That is, the simulations may determine a content distribution strategy that optimizes a particular metric associated with the content distribution. For example, the simulations may determine the content distribution strategy that maximizes the number of resulting quality impacts. The simulations may also determine an optimal content distribution strategy based on any other metrics and / or combinations of metrics as well.

[0051] The simulations that produce the simulated data may be performed using a machine learning model, for example. In embodiments, the machine learning model may also automatically perform simulations based on data that is received by the system and the results of the simulations may automatically be presented via the user interface 900 of the dashboard. That is, the simulations may not necessarily require any user input to be performed, but may rather be performed periodically or in real-time.

[0052] In some instances, the machine learning model may also selectively determine parameters to use for simulation based on historical data. For example, the machine learning81396046 25040-6137 model may determine that, in the past, certain types of touchpoints resulted in greater viewership and / or interactions than other touchpoints. Based on this, the machine learning model may perform simulations in which content is simulated to only be distributed via those particular touchpoints. By reducing the number of potential simulations, the machine learning model may reduce computational and time requirements of the simulations.

[0053] As aforementioned, the system may not only perform the simulations using the machine learning model but may also automatically perform content management and distribution based on the simulations without requiring user input as well. For example, based on the results of the simulations, the system may determine the content that should be distributed and the manner in which the content should be distributed (for example, touchpoints used to present the content to end users, geographical regions in which to present the content, days and times during which the content is presented, etc.).

[0054] FIG. 10 shows a user interface 1000 of the dashboard through which a user may manually configure aspects of the simulations produced by the system (for example, system 110, computing device 1304, computing device 1400, etc.). That is, the user may customize at least some aspects of the simulations that are performed. For example, the user interface 1 00 shows a listing of various types of touchpoints for advertisement content (e.g., product packaging, an outdoor display, a retail store display, a social feed advertisement, a cooler, a menu board advertisement, etc.). A user may add a single or group of touchpoints and set the investment, impacts, and / or CPT values (as well as any other parameters) of each touchpoint to gauge the cost to achieve a set number of impacts based on the touchpoints for a future time period. The user may also lock one or more of the touchpoints by selecting a lock icon 1004 associated with that given touchpoint. The user may then select a simulate button 1006 presented via the user interface 1100 to initiate the simulations using the parameters 1004 that were configured by the user.

[0055] As shown in the user interface 1100 of FIG. 11, once the user selects the simulate button 1006 via the user interface 1000, a pop-up box 1102 may be presented. The pop-up box 1102 may allow the user to configure certain objectives associated with content distribution. For example, the pop-up box 1102 shown in the figure includes three configurable parameters, including awareness, engagement, and conversion. The user may input a percentage allocation for each of the parameters, effectively providing a weighting to each of the parameters. The figure shows that the user provides greater weight to awareness and engagement than conversion. For example, the example shown in the figure81396046 25040-6137 may be associated with advertisement content, and the parameters provided in the pop-up box 1102 may relate to product awareness and engagement versus product conversion (e.g., purchases made based on the advertisements that are provided to end users). That is, the user has indicated that product awareness and engagement are more important than product purchases. However, the use of advertisement content is merely exemplary and the pop-up box 1102 may also include other configurable parameters that are applicable to other types of content as well.

[0056] FIG. 12 shows another user interface 1200 providing an example of at least some aspects of the simulation results. In particular, the user interface 1200 shows a ranking of the touchpoints to select for a content distribution plan based on the objective-adjusted quality' scores. After selecting the touchpoints that are to be used in a content distribution plan, a prompt may be presented in which information about key targets that are desirable to achieve by the touchpoints may be provided (e.g. investment, impacts. CPT, etc.). As this information is provided, some values may be updated automatically and the six main KPIs above the table may update accordingly. Once the touchpoints are established, the plan may be automatically implemented by the system (for example based on a user input indicating that the content distribution plan should be performed or without requiring any user input). For example, content may then automatically be distributed to end users via the touchpoints. This entire process of performing simulations and distributing the content to the touchpoints may also be performed automatically without requiring any user input.

[0057] FIG. 13 is an example system 1300 for generating distinct images using a generative model. In one or more embodiments, the system may include one or more user devices 1301 (which may be associated with one or more users 1302), one or more computing devices 1304, and / or one or more databases 1310. However, these components of the system 1300 are merely exemplary and are not intended to be limiting in any way. For simplicity, reference may be made hereinafter to a user device 1301, computing device 1304, database 1310, etc., however, this is not intended to be limiting and may still refer to any number of such elements.

[0058] The user device 1301 may be any type of device, such as a smartphone, desktop computer, laptop computer, tablet, smart television (for example, a television with Internet connectivity', the capability to install applications, etc.), and / or any other ty pe of device. The user device 1301 may include an application 1303 that may allow a user 1302 to interact with any of the systems, devices, etc. to perform any number of different types of actions,81396046 25040-6137 such as viewing and interacting with information presented via a dashboard 1305 and / or any other types of functionality described herein. However, the user does not necessarily need to access the dashboard 1305 via the application 1303. For example, the user 1302 may access the dashboard 1305 via a web browser and / or in any other suitable manner.

[0059] The computing device 1304 may be any type of device (such as a local or remote server for example) used to perform any of the processing described herein. For example, the computing device 1304 may host the dashboard 1305 that is presented to the user 1302 via the application 1303 of the user device 1301 (or via any other mechanism that may be used to access the dashboard 1305). For example, the user interfaces illustrated in FIGS. 3- 12 may be presented to the user 1302 via the dashboard 1305, however, as aforementioned, these user interfaces are merely exemplary and not intended to be limiting.

[0060] Additionally, the computing device 1304 may host one or more machine learning model 1307. The machine learning model 1307 may be any type of model, including, but not limited to, models that perform supervised learning, unsupervised learning, semisupervised learning, reinforcement learning, etc., such as nearest neighbor models, naive bayes models, decision trees, linear regression models, support vector machines, neural networks, etc. Additionally, in some cases, multiple of such models may be employed and the models may work in parallel or in series (for example, the output of one model may be the input of another model) to perform any of the tasks described herein.

[0061] The database 1310 may store any of the data that is used as described herein. For example, the database 1310 may store any of the data that is presented via the dashboard 1305, such as metrics associated with content presentation to end users (e.g., viewership numbers and / or interaction numbers, quality7metrics, content distribution costs, and / or any other types of data), listings of touchpoints via which the content was presented, the types of content itself, simulated data, and / or any other types of data described herein or otherwise. In embodiments, the computing device 1304 may retrieve the data from the database 1310 to present the data via the dashboard 1305 and / or perform simulations using the machine learning model 1307 and / or automatically manage and distribute content.

[0062] In one or more embodiments, any of the elements of the system 1300 (for example, one or more user devices 1301, one or more computing devices 1304, one or more databases 1310, and / or any other element described with respect to FIG. 13 or otherwise) may be configured to communicate via a communications network 1350. The communications network 1350 may include, but not limited to. any one of a combination of81396046 25040-6137 different types of suitable communications networks such as, for example, broadcasting networks, cable networks, public networks (e.g., the Internet), private networks, wireless networks, cellular networks, or any other suitable private and / or public networks. Further, the communications network 1350 may have any suitable communication range associated therewith and may include, for example, global networks (e.g., the Internet), metropolitan area networks (MANs), wide area networks (WANs), local area networks (LANs), or personal area networks (PANs). In addition, communications network 1350 may include any type of medium over which network traffic may be carried including, but not limited to, coaxial cable, twisted-pair wire, optical fiber, a hybrid fiber coaxial (HFC) medium, microwave terrestrial transceivers, radio frequency communication mediums, white space communication mediums, ultra-high frequency communication mediums, satellite communication mediums, or any combination thereof.

[0063] Finally, any of the elements (for example, one or more user devices 1301, one or more computing devices 1304, and / or one or more databases 1310) of the system 1300 may include any of the elements of the computing device 1500 as well (such as the processor 1502, memon' 1504, etc.).

[0064] FIG. 14 is a schematic block diagram of an illustrative computing device 1400 in accordance with one or more example embodiments of the disclosure. The computing device 1400 may include any suitable computing device capable of receiving and / or generating data including, but not limited to, a user device such as a smartphone, tablet, e- reader, wearable device, or the like; a desktop computer; a laptop computer; a content streaming device; a set-top box; or the like. The computing device 1400 may correspond to an illustrative device configuration for the devices of FIGS. 1-13 (such as system 110, smartphone 112, user device 1401, computing device 1304, etc.).

[0065] The computing device 1400 may be configured to communicate via one or more networks with one or more servers, search engines, user devices, or the like. In some embodiments, a single remote server or single group of remote servers may be configured to perform more than one type of content rating and / or machine learning functionality.

[0066] Example network(s) may include, but are not limited to. any one or more different types of communications networks such as, for example, cable networks, public networks (e.g., the Internet), private networks (e.g., frame-relay networks), wireless networks, cellular networks, telephone networks (e.g., a public switched telephone network), or any other suitable private or public packet-switched or circuit-switched81396046 25040-6137 networks. Further, such network(s) may have any suitable communication range associated therewith and may include, for example, global networks (e.g., the Internet), metropolitan area networks (MANs), wide area networks (WANs), local area networks (LANs), or personal area networks (PANs). In addition, such network(s) may include communication links and associated networking devices (e.g., link-layer switches, routers, etc.) for transmitting network traffic over any suitable type of medium including, but not limited to, coaxial cable, twisted-pair wire (e.g., twisted-pair copper wire), optical fiber, a hybrid fibercoaxial (HFC) medium, a microwave medium, a radio frequency communication medium, a satellite communication medium, or any combination thereof.

[0067] In an illustrative configuration, the computing device 1400 may include one or more processors (processor(s)) 1402, one or more memory devices 1404 (generically referred to herein as memory 1404), one or more input / output (I / O) interface(s) 1406, one or more network interface(s) 1408, one or more sensors or sensor interface(s) 1410, one or more transceivers 1412, one or more optional speakers 1414, one or more optional microphones 1416, and data storage 1420. The computing device 1400 may further include one or more buses 1418 that functionally couple various components of the computing device 1400. The computing device 1400 may further include one or more antenna(e) 1434 that may include, without limitation, a cellular antenna for transmitting or receiving signals to / from a cellular network infrastructure, an antenna for transmitting or receiving Wi-Fi signals to / from an access point (AP), a Global Navigation Satellite System (GNSS) antenna for receiving GNSS signals from a GNSS satellite, a Bluetooth antenna for transmitting or receiving Bluetooth signals, a Near Field Communication (NFC) antenna for transmitting or receiving NFC signals, and so forth. These various components will be described in more detail hereinafter.

[0068] The bus(es) 1418 may include at least one of a system bus. a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer-executable code), signaling, etc.) between various components of the computing device 1400. The bus(es) 1418 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth. The bus(es) 1418 may be associated with any suitable bus architecture including, without limitation, an Industry' Standard Architecture (ISA), a Micro Channel Architecture (MCA), an Enhanced ISA (EISA), a Video Electronics Standards Association (VESA) architecture, an Accelerated Graphics Port (AGP) architecture, a Peripheral Component Interconnects81396046 25040-6137(PCI) architecture, a PCI-Express architecture, a Personal Computer Memory Card International Association (PCMCIA) architecture, a Universal Serial Bus (USB) architecture, and so forth.

[0069] The memory 1404 of the computing device 1400 may include volatile memory (memory that maintains its state when supplied with power) such as random access memory (RAM) and / or non-volatile memory (memory that maintains its state even when not supplied with power) such as read-only memory (ROM), flash memory, ferroelectric RAM (FRAM), and so forth. Persistent data storage, as that term is used herein, may include nonvolatile memory. In certain example embodiments, volatile memory may enable faster read / write access than non-volatile memory7. However, in certain other example embodiments, certain types of non-volatile memory' (e.g., FRAM) may enable faster read / write access than certain types of volatile memory'.

[0070] In various implementations, the memory' 1404 may include multiple different types of memory such as various types of static random access memory (SRAM), various types of dynamic random access memory (DRAM), various ty pes of unalterable ROM, and / or writeable variants of ROM such as electrically erasable programmable read-only memory (EEPROM), flash memory, and so forth. The memory 1404 may include main memory' as well as various forms of cache memory' such as instruction cache(s), data cache(s), translation lookaside buffer(s) (TLBs), and so forth. Further, cache memory’ such as a data cache may be a multi-level cache organized as a hierarchy of one or more cache levels (LI, L2, etc.).

[0071] The data storage 1420 may include removable storage and / or non-removable storage including, but not limited to, magnetic storage, optical disk storage, and / or tape storage. The data storage 1420 may provide non-volatile storage of computer-executable instructions and other data. The memory 1404 and the data storage 1420, removable and / or non-removable, are examples of computer-readable storage media (CRSM) as that term is used herein.

[0072] The data storage 1420 may store computer-executable code, instructions, or the like that may be loadable into the memory 1404 and executable by the processor(s) 1402 to cause the processor(s) 1402 to perform or initiate various operations. The data storage 1420 may additionally store data that may be copied to memory' 1404 for use by the processor(s) 1402 during the execution of the computer-executable instructions. Moreover, output data generated as a result of execution of the computer-executable instructions by the81396046 25040-6137 processor(s) 1402 may be stored initially in memory 1404, and may ultimately be copied to data storage 1420 for non-volatile storage.

[0073] More specifically, the data storage 1420 may store one or more operating systems (O / S) 1422; one or more database management systems (DBMS) 1424; and one or more program module(s), applications, engines, computer-executable code, scripts, or the like such as, for example, one or more module(s) 1426. Any of the components depicted as being stored in data storage 1420 may include any combination of software, firmware, and / or hardware. The software and / or firmware may include computer-executable code, instructions, or the like that may be loaded into the memory 1404 for execution by one or more of the processor(s) 1402. Any of the components depicted as being stored in data storage 1420 may support functionality described in reference to correspondingly named components earlier in this disclosure.

[0074] The data storage 1420 may further store various types of data utilized by components of the computing device 1400. Any data storaged in the data storage 1420 may be loaded into the memory 1404 for use by the processor(s) 1402 in executing computerexecutable code. In addition, any data depicted as being stored in the data storage 1420 may potentially be stored in one or more datastore(s) and may be accessed via the DBMS 1424 and loaded in the memory 1404 for use by the processor(s) 1402 in executing computerexecutable code. The datastore(s) may include, but are not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed datastores in which data is stored on more than one node of a computer network, peer-to-peer network datastores, or the like. In FIG. 14, the datastore(s) may include, for example, purchase history information, user action information, user profile information, a database linking search queries and user actions, and other information.

[0075] The processor(s) 1402 may be configured to access the memory 1404 and execute computer-executable instructions loaded therein. For example, the processor(s) 1402 may be configured to execute computer-executable instructions of the various program module(s), applications, engines, or the like of the computing device 1400 to cause or facilitate various operations to be performed in accordance with one or more embodiments of the disclosure. The processor(s) 1402 may include any suitable processing unit capable of accepting data as input, processing the input data in accordance with stored computerexecutable instructions, and generating output data. The processor(s) 1402 may include any type of suitable processing unit including, but not limited to. a central processing unit, a81396046 25040-6137 microprocessor, a Reduced Instruction Set Computer (RISC) microprocessor, a Complex Instruction Set Computer (CISC) microprocessor, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a System- on-a-Chip (SoC), a digital signal processor (DSP), and so forth. Further, the processor(s) 1402 may have any suitable microarchitecture design that includes any number of constituent components such as, for example, registers, multiplexers, arithmetic logic units, cache controllers for controlling read / write operations to cache memory, branch predictors, or the like. The microarchitecture design of the processor(s) 1402 may be capable of supporting any of a variety of instruction sets.

[0076] Referring now to functionality supported by the various program module(s) depicted in FIG. 14, the module(s) 1426 may include computer-executable instructions, code, or the like that responsive to execution by one or more of the processor(s) 1602 may perform functions including, but not limited to presentation of the dashboard as described herein, performing simulations, automatically managing and distributing content, calculating quality metrics, etc.

[0077] Referring now to other illustrative components depicted as being stored in the data storage 1420, the O / S 1422 may be loaded from the data storage 1420 into the memory 1404 and may provide an interface between other application software executing on the computing device 1400 and hardware resources of the computing device 1400. More specifically, the O / S 1422 may include a set of computer-executable instructions for managing hardware resources of the computing device 1400 and for providing common services to other application programs (e g., managing memory allocation among various application programs). In certain example embodiments, the O / S 1422 may control execution of the other program module(s) to dynamically enhance characters for content rendering. The O / S 1422 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non-proprietary operating system.

[0078] The DBMS 1424 may be loaded into the memory 1404 and may support functionality for accessing, retrieving, storing, and / or manipulating data stored in the memory 1404 and / or data stored in the data storage 1420. The DBMS 1424 may use any of a variety7of database models (e.g., relational model, object model, etc.) and may support any of a variety7of query7languages. The DBMS 1424 may access data represented in one or more data schemas and stored in any suitable data repository including, but not limited to,81396046 25040-6137 databases (e.g., relational, object-oriented, etc.), fde systems, flat files, distributed datastores in which data is stored on more than one node of a computer network, peer-to-peer network datastores, or the like. In those example embodiments in which the computing device 1400 is a user device, the DBMS 1424 may be any suitable light-weight DBMS optimized for performance on a user device.

[0079] Referring now to other illustrative components of the computing device 1400, the input / output (I / O) interface(s) 1406 may facilitate the receipt of input information by the computing device 1400 from one or more I / O devices as well as the output of information from the computing device 1400 to the one or more I / O devices. The I / O devices may include any of a variety of components such as a display or display screen having a touch surface or touchscreen; an audio output device for producing sound, such as a speaker; an audio capture device, such as a microphone; an image and / or video capture device, such as a camera; a haptic unit; and so forth. Any of these components may be integrated into the computing device 1400 or may be separate. The I / O devices may further include, for example, any number of peripheral devices such as data storage devices, printing devices, and so forth.

[0080] The I / O interface(s) 1406 may also include an interface for an external peripheral device connection such as universal serial bus (USB), FireWire, Thunderbolt, Ethernet port or other connection protocol that may connect to one or more networks. The I / O interface(s) 1406 may also include a connection to one or more of the antenna(e) 1434 to connect to one or more networks via a wireless local area network (WLAN) (such as Wi-Fi) radio, Bluetooth, ZigBee, and / or a wireless network radio, such as a radio capable of communication with a wireless communication network such as a Long Term Evolution (LTE) network, WiMAX network, 3G network, ZigBee network, etc.

[0081] The computing device 1400 may further include one or more network interface(s) 1408 via which the computing device 1400 may communicate with any of a variety of other systems, platforms, networks, devices, and so forth. The network interface(s) 1408 may enable communication, for example, with one or more wireless routers, one or more host servers, one or more web servers, and the like via one or more of networks.

[0082] The antenna(e) 1434 may include any suitable type of antenna depending, for example, on the communications protocols used to transmit or receive signals via the antenna(e) 1434. Non-limiting examples of suitable antennas may include directional81396046 25040-6137 antennas, non-directional antennas, dipole antennas, folded dipole antennas, patch antennas, multiple-input multiple-output (MIMO) antennas, or the like. The antenna(e) 1434 may be communicatively coupled to one or more transceivers 1412 or radio components to which or from which signals may be transmitted or received.

[0083] As previously described, the antenna(e) 1434 may include a cellular antenna configured to transmit or receive signals in accordance with established standards and protocols, such as Global System for Mobile Communications (GSM), 3G standards (e.g., Universal Mobile Telecommunications System (UMTS), Wideband Code Division Multiple Access (W-CDMA), CDMA2000, etc.), 4G standards (e.g., Long-Term Evolution (LTE), WiMax, etc.), direct satellite communications, or the like.

[0084] The antenna(e) 1434 may additionally, or alternatively, include a Wi-Fi antenna configured to transmit or receive signals in accordance with established standards and protocols, such as the IEEE 802.11 family of standards, including via 2.4 GHz channels (e.g., 802.11b, 802.11g, 802.1 In), 5 GHz channels (e.g., 802.1 In, 802.1 lac), or 60 GHz channels (e.g., 802. Had). In alternative example embodiments, the antenna(e) 1434 may be configured to transmit or receive radio frequency signals within any suitable frequency range forming part of the unlicensed portion of the radio spectrum.

[0085] The antenna(e) 1434 may additionally, or alternatively, include a GNSS antenna configured to receive GNSS signals from three or more GNSS satellites carrying timeposition information to triangulate a position therefrom. Such a GNSS antenna may be configured to receive GNSS signals from any current or planned GNSS such as, for example, the Global Positioning System (GPS), the GLONASS System, the Compass Navigation System, the Galileo System, or the Indian Regional Navigational System.

[0086] The transceiver(s) 1412 may include any suitable radio component(s) for - in cooperation with the antenna(e) 1434 - transmitting or receiving radio frequency (RF) signals in the bandwidth and / or channels corresponding to the communications protocols utilized by the computing device 1400 to communicate with other devices. The transceiver(s) 1412 may include hardware, software, and / or firmware for modulating, transmitting, or receiving - potentially in cooperation with any of antenna(e) 1434 - communications signals according to any of the communications protocols discussed above including, but not limited to, one or more Wi-Fi and / or Wi-Fi direct protocols, as standardized by the IEEE 802.11 standards, one or more non-Wi-Fi protocols, or one or more cellular communications protocols or standards. The transceiver(s) 1412 may further81396046 25040-6137 include hardware, firmware, or software for receiving GNSS signals. The transceiver(s) 1412 may include any known receiver and baseband suitable for communicating via the communications protocols utilized by the computing device 1400. The transceiver(s) 1412 may further include a low noise amplifier (LNA), additional signal amplifiers, an analog- to-digital (A / D) converter, one or more buffers, a digital baseband, or the like.

[0087] The sensor(s) / sensor interface(s) 1410 may include or may be capable of interfacing with any suitable type of sensing device such as, for example, inertial sensors, force sensors, thermal sensors, and so forth. Example types of inertial sensors may include accelerometers (e.g., MEMS-based accelerometers), gyroscopes, and so forth.

[0088] The optional speaker(s) 1414 may be any device configured to generate audible sound. The optional microphone(s) 1416 may be any device configured to receive analog sound input or voice data.

[0089] It should be appreciated that the program module(s), applications, computerexecutable instructions, code, or the like depicted in FIG. 14 as being stored in the data storage 1420 are merely illustrative and not exhaustive and that processing described as being supported by any particular module may alternatively be distributed across multiple module(s) or performed by a different module. In addition, various program module(s), script(s), plug-in(s), Application Programming Interface(s) (API(s)), or any other suitable computer-executable code hosted locally on the computing device 1400, and / or hosted on other computing device(s) accessible via one or more networks, may be provided to support functionality provided by the program module(s), applications, or computer-executable code depicted in FIG. 14 and / or additional or alternate functionality. Further, functionality may be modularized differently such that processing described as being supported collectively by the collection of program module(s) depicted in FIG. 14 may be performed by a fewer or greater number of module(s), or functionality described as being supported by any particular module may be supported, at least in part, by another module. In addition, program module(s) that support the functionality described herein may form part of one or more applications executable across any number of systems or devices in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth. In addition, any of the functionality described as being supported by any of the program module(s) depicted in FIG. 14 may be implemented, at least partially, in hardware and / or firmw are across any number of devices.81396046 25040-6137

[0090] It should further be appreciated that the computing device 1400 may include alternate and / or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the computing device 1400 are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative program module(s) have been depicted and described as software module(s) stored in data storage 1420, it should be appreciated that functionality described as being supported by the program module(s) may be enabled by any combination of hardware, software, and / or firmware. It should further be appreciated that each of the above- mentioned module(s) may, in various embodiments, represent a logical partitioning of supported functionality. This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and / or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality' described as being provided by a particular module may, in various embodiments, be provided at least in part by one or more other module(s). Further, one or more depicted module(s) may not be present in certain embodiments, while in other embodiments, additional module(s) not depicted may be present and may support at least a portion of the described functionality and / or additional functionality. Moreover, while certain module(s) may be depicted and described as sub-module(s) of another module, in certain embodiments, such module(s) may be provided as independent module(s) or as sub- module(s) of other module(s).

[0091] Program module(s), applications, or the like disclosed herein may include one or more software components including, for example, software objects, methods, data structures, or the like. Each such software component may include computer-executable instructions that, responsive to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0092] A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and / or operating system platform. A software component comprising assembly language81396046 25040-6137 instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and / or platform.

[0093] Another example programming language may be a higher-level programming language that may be portable across multiple architectures. A software component comprising higher-level programming language instructions may require conversion to an intermediate representation by an interpreter or a compiler prior to execution.

[0094] Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a script language, a database query or search language, or a report writing language. In one or more example embodiments, a software component comprising instructions in one of the foregoing examples of programming languages may be executed directly by an operating system or other software component without having to be first transformed into another form.

[0095] A software component may be stored as a file or other data storage construct. Software components of a similar type or functionally related may be stored together such as, for example, in a particular directory, folder, or library. Software components may be static (e.g., pre-established or fixed) or dynamic (e.g., created or modified at the time of execution).

[0096] Software components may invoke or be invoked by other software components through any of a wide variety' of mechanisms. Invoked or invoking software components may comprise other custom-developed application software, operating system functionality (e.g., device drivers, data storage (e.g., file management) routines, other common routines and services, etc.), or third-party software components (e.g., middleware, encry ption, or other security software, database management software, file transfer or other network communication software, mathematical or statistical software, image processing software, and format translation software).

[0097] Software components associated with a particular solution or system may reside and be executed on a single platform or may be distributed across multiple platforms. The multiple platforms may be associated with more than one hardware vendor, underlying chip technology, or operating system. Furthermore, software components associated with a particular solution or system may be initially written in one or more programming languages, but may invoke software components written in another programming language.

[0098] Computer-executable program instructions may be loaded onto a specialpurpose computer or other particular machine, a processor, or other programmable data81396046 25040-6137 processing apparatus to produce a particular machine, such that execution of the instructions on the computer, processor, or other programmable data processing apparatus causes one or more functions or operations specified in the flow diagrams to be performed. These computer program instructions may also be stored in a computer-readable storage medium (CRSM) that upon execution may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement one or more functions or operations specified in the flow diagrams. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer- implemented process.

[0099] Additional types of CRSM that may be present in any of the devices described herein may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the information and which can be accessed. Combinations of any of the above are also included within the scope of CRSM. Alternatively, computer-readable communication media (CRCM) may include computer- readable instructions, program module(s), or other data transmitted within a data signal, such as a carrier wave, or other transmission. However, as used herein, CRSM does not include CRCM.

[0100] Although embodiments have been described in language specific to structural features and / or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,"’ ‘‘could," “might,’" or “may.” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and / or steps. Thus, such conditional language is not generally intended to imply that features, elements, and / or steps are in any way required for one or81396046 25040-6137 more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and / or steps are included or are to be performed in any particular embodiment.

Claims

81396046 25040-6137CLAIMSTHAT WHICH IS CLAIMED IS:

1. A system comprising: memory' that stores computer-executable instructions; and one or more processors configured to access the memory and execute the computerexecutable instructions to: receive, from one or more first devices, first viewership data associated with content that is presented to one or more users via one or more first touchpoints; simulate, using a machine learning model and based on the first viewership data, simulated second viewership data associated with presenting the content via the one or more first touchpoints or one or more second touchpoints; present the first viewership data and the simulated second viewership data via a user interface of a dashboard; and automatically transmit, based on the simulated second viewership data, the content to for presentation via the one or more first touchpoints or one or more second touchpoints.

2. The system of claim 1, wherein the one or more processors are further configured to execute the computer-executable instructions to: receive third viewership data associated with the content; determine a difference between the third viewership data and the simulated second viewership data; train the machine learning model based the difference; simulate, using the machine learning model and subsequent to training, simulated fourth viewership data; and automatically transmit, based on the simulated fourth viewership data, the content for presentation.

3. The system of claim 1, wherein simulating the simulated second viewership data is further based on a parameter that is manually modified by a user.

4. The system of claim 1, wherein the one or more processors are further configured to execute the computer-executable instructions to:81396046 25040-6137 present, via the user interface, first information; receive, via the user interface, an input provided by a user; and present, via the user interface, second information that is filtered based on the input provided by the user.

5. The system of claim 1, wherein the one or more processors are further configured to execute the computer-executable instructions to: determine a quality metric, the quality’ metric being a quantified value; and present the quality metric via the dashboard.

6. The system of claim 5, wherein determining the quality metric further comprises: determine a plurality of first numerical scores for a plurality of quality dimensions, wherein the plurality of first numerical scores are within a first range of values.

7. The system of claim 6, wherein determining the quality metric further comprises: determine a plurality of second numerical scores by multiplying one or more weight values by the plurality of first numerical scores; and scale the plurality of second numerical scores to a second range of values.

8. A method comprising: receiving, using one or more processors and from one or more first devices, first viewership data associated with content that is presented to one or more users via one or more first touchpoints; simulating, using a machine learning model and based on the first viewership data, simulated second viewership data associated with presenting the content via the one or more first touchpoints or one or more second touchpoints; presenting the first viewership data and the simulated second viewership data via a user interface of a dashboard; and automatically transmitting, based on the simulated second viewership data, the content to for presentation via the one or more first touchpoints or one or more second touchpoints.81396046 25040-61379. The method of claim 8, further comprising: receiving third viewership data associated with the content; determining a difference between the third viewership data and the simulated second viewership data; training the machine learning model based the difference; simulating, using the machine learning model and subsequent to training, simulated fourth viewership data; and automatically transmitting, based on the simulated fourth viewership data, the content for presentation.

10. The method of claim 8, wherein simulating the simulated second view ership data is further based on a parameter that is manually modified by a user.

11. The method of claim 8, further comprising: presenting, via the user interface, first information; receiving, via the user interface, an input provided by a user; and presenting, via the user interface, second information that is filtered based on the input provided by the user.

12. The method of claim 8, further comprising: determining a quality metric, the quality metric being a quantified value; and presenting the quality metric via the dashboard.

13. The method of claim 12, wherein determining the quality metric further comprises: determining a plurality of first numerical scores for a plurality of quality dimensions, wherein the plurality of first numerical scores are within a first range of values.

14. The method of claim 13, wherein determining the quality’ metric further comprises: determining a plurality of second numerical scores by multiplying one or more weight values by the plurality of first numerical scores; and scaling the plurality of second numerical scores to a second range of values.81396046 25040-613715. A non-transitory computer readable medium including computer-executable instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform operations of: receiving, from one or more first devices, first viewership data associated with content that is presented to one or more users via one or more first touchpoints; simulating, using a machine learning model and based on the first viewership data, simulated second viewership data associated with presenting the content via the one or more first touchpoints or one or more second touchpoints; presenting the first viewership data and the simulated second viewership data via a user interface of a dashboard; and automatically transmitting, based on the simulated second viewership data, the content to for presentation via the one or more first touchpoints or one or more second touchpoints.

16. The non-transitory' computer readable medium of claim 15, wherein the one or more processors are further configured to execute the computer-executable instructions to perform operations of: receiving third viewership data associated with the content; determining a difference between the third viewership data and the simulated second viewership data; training the machine learning model based the difference; simulating, using the machine learning model and subsequent to training, simulated fourth viewership data; and automatically transmitting, based on the simulated fourth viewership data, the content for presentation.

17. The non-transitory computer readable medium of claim 15, wherein simulating the simulated second viewership data is further based on a parameter that is manually modified by a user.

18. The non-transitory computer readable medium of claim 15, wherein the one or more processors are further configured to execute the computer-executable instructions to perform operations of:81396046 25040-6137 present, via the user interface, first information; receive, via the user interface, an input provided by a user; and present, via the user interface, second information that is filtered based on the input provided by the user.

19. The non-transitory computer readable medium of claim 15, wherein the one or more processors are further configured to execute the computer-executable instructions to perform operations of: determining a quality metric, the quality metric being a quantified value; and presenting the quality metric via the dashboard.

20. The non-transitory computer readable medium of claim 19, wherein determining the quality metric further comprises: determining a plurality of first numerical scores for a pl ural i ty of quality dimensions, wherein the plurality of first numerical scores are within a first range of values; determining a plurality of second numerical scores by multiplying one or more weight values by the plurality of first numerical scores; and scaling the plurality of second numerical scores to a second range of values.

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