SYSTEMS AND METHODS FOR EMBEDDING EXTRACTABLE METADATA ELEMENTS WITHIN A CHANNEL-AGNOSTIC LAYER OF AUDIO-VISUAL CONTENT

Embodiments of the present invention provide a system for embedding extractable metadata elements within a channel-agnostic layer of audio-visual content. The system is configured for receiving a content file, determining products displayed in the content file, and delivering resource metadata related to the products displayed in the content file in an interactive and secure manner. The invention may further include various ways in which to automatically display resource metadata in an intuitive manner in conjunction with streaming the content file to a user device via one or more platforms or channels.

BACKGROUND

Conventional audio and visual content systems do not possess the capability to intuitively embed product data such that a user can easily locate and research items they may be interested in. As such, a need exists for improved audio and visual systems that allow users to easily locate products of interest and potentially conduct a resource action in response to the data presented.

BRIEF SUMMARY

The following presents a summary of certain embodiments of the invention. This summary is not intended to identify key or critical elements of all embodiments nor delineate the scope of any or all embodiments. Its sole purpose is to present certain concepts and elements of one or more embodiments in a summary form as a prelude to the more detailed description that follows.

Embodiments of the present invention address the above needs and/or achieve other advantages by providing apparatuses (e.g., a system, computer program product and/or other devices) and methods for embedding extractable metadata elements within a channel-agnostic layer of audio-visual content. The system embodiments may comprise one or more memory devices having computer readable program code stored thereon, a communication device, and one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute the computer readable program code to carry out the invention. In computer program product embodiments of the invention, the computer program product comprises at least one non-transitory computer readable medium comprising computer readable instructions for carrying out the invention. Computer implemented method embodiments of the invention may comprise providing a computing system comprising a computer processing device and a non-transitory computer readable medium, where the computer readable medium comprises configured computer program instruction code, such that when said instruction code is operated by said computer processing device, said computer processing device performs certain operations to carry out the invention.

In some embodiments, the present invention includes the steps of receiving, from a user device, a content file, wherein the content file comprises at least one visual frame; receiving product data for the received content, wherein the product data comprises product identifiers for one or more products displayed within the received content file and one or more timestamps corresponding to when the one or more products are displayed; determining resource data for the one or more products; generating a metadata file containing instructions to automatically trigger the display of the product identifiers and resource data for the one or more products.

In some embodiments, the computer executable instructions cause the computer processor to perform the steps of receiving a request from the user device for displaying the content; and in response to the request, causing the content file and the metadata file to be displayed on the user device.

In some embodiments, causing the content file and metadata file to be displayed on the user device further comprises streaming the content file and metadata file to the user device.

In some embodiments, the metadata is displayed on the user device according to one or more specific timestamps of the one or more products.

In some embodiments, causing the content file and metadata file to be displayed on the user device further comprises displaying a clickable graphical icon overlay over the content file at a specific timestamp range.

In some embodiments, the resource data for the one or more products further comprises product description data, product resource data, and merchant data for the one or more products.

In some embodiments, the resource data further comprises a product price displayed on the user device in a currency according to the geolocation of the user device.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

As described herein, the term “entity” may be a financial institution which may include herein may include any financial institutions such as commercial banks, thrifts, federal and state savings banks, savings and loan associations, credit unions, investment companies, insurance companies and the like. In some embodiments, the entity may be a financial institution that maintains, manages, or provides services to third party entities that sell products, goods, services, or the like to users (e.g., merchants), where the users may or may not be customers of the financial institution. In some embodiments, the entity may be a non-financial institution.

Many of the example embodiments and implementations described herein contemplate interactions engaged in by a user with a computing device and/or one or more communication devices and/or secondary communication devices. A “user”, as referenced herein, may refer to customer or a potential customer of the entity. In some embodiments, the user may not be a customer of the entity. Furthermore, as used herein, the term “user computing device” or “mobile device” may refer to mobile phones, computing devices, tablet computers, wearable devices, smart devices and/or any portable electronic device capable of receiving and/or storing data therein.

Typically, audio visual content provided by audio visual content providers (e.g., streaming platforms) may contain various resources (e.g., products, goods, services, or the like). Currently no system exists that can determine resources depicted in audio visual content, provide additional information associated with the resources, and allow users to seamlessly acquire the resources. The system of the present invention solves these problems as explained in detail below.

FIG.1aprovides a block diagram illustrating a system environment100for embedding extractable metadata elements within a channel-agnostic layer of audio-visual content, in accordance with an embodiment of the invention. As illustrated inFIG.1a, the environment100includes a resource information determination system300, an entity system200, one or more third party systems201, audio visual content providers202, and a computing device system400. One or more users110may be included in the system environment100, where the users110interact with the other entities of the system environment100via a user interface of the computing device system400. In some embodiments, the one or more user(s)110of the system environment100may be customers of an entity associated with the entity system200. In some embodiments, the one or more users110may not be customers of the entity. In some embodiments, the one or more users110may be potential customers of the entity. In some embodiments, the one or more users110may be customers of the third party entity. The one or more third party systems201may be associated with third party entities (e.g., merchants) that provide resources to users110. The audio visual content providers202may be any providers that provide audio visual content to users. In some embodiments, the audio visual content providers202may be audio visual content providing platform, where audio visual content providing platform may be a web based application, mobile application, a streaming service platform, or the like.

The entity system(s)200may be any system owned or otherwise controlled by an entity to support or perform one or more process steps described herein. In some embodiments, the entity is a financial institution. In some embodiments, the entity may be a non-financial institution.

The resource information determination system300is a system of the present invention for performing one or more process steps described herein. In some embodiments, the resource information determination system300may be an independent system. In some embodiments, the resource information determination system300may be a part of the entity system200.

The resource information determination system300, the entity system200, and the computing device system400may be in network communication across the system environment100through the network150. The network150may include a local area network (LAN), a wide area network (WAN), and/or a global area network (GAN). The network150may provide for wireline, wireless, or a combination of wireline and wireless communication between devices in the network. In one embodiment, the network150includes the Internet. In general, the resource information determination system300is configured to communicate information or instructions with the entity system200, and/or the computing device system400across the network150.

The computing device system400may be a system owned or controlled by the entity of the entity system200and/or the user110. As such, the computing device system400may be a computing device of the user110. In general, the computing device system400communicates with the user110via a user interface of the computing device system400, and in turn is configured to communicate information or instructions with the resource information determination system300, and/or entity system200across the network150.

FIG.1billustrates an exemplary machine learning (ML) subsystem architecture1000, in accordance with an embodiment of the invention. The machine learning subsystem1000may include a data acquisition engine1002, data ingestion engine1010, data pre-processing engine1016, ML model tuning engine1022, and inference engine1036. It is understood that the machine learning (ML) subsystem architecture1000may be a sub-system of resource information determination system300, in accordance with various embodiments of the invention.

The data acquisition engine1002may identify various internal and/or external data sources to generate, test, and/or integrate new features for training the machine learning model1024. These internal and/or external data sources1004,1006, and1008may be initial locations where the data originates or where physical information is first digitized. The data acquisition engine1002may identify the location of the data and describe connection characteristics for access and retrieval of data. In some embodiments, data is transported from each data source1004,1006, or1008using any applicable network protocols, such as the File Transfer Protocol (FTP), Hyper-Text Transfer Protocol (HTTP), or any of the myriad Application Programming Interfaces (APIs) provided by websites, networked applications, and other services. In some embodiments, the these data sources1004,1006, and1008may include Enterprise Resource Planning (ERP) databases that host data related to day-to-day business activities such as accounting, procurement, project management, exposure management, supply chain operations, and/or the like, mainframe that is often the entity's central data processing center, edge devices that may be any piece of hardware, such as sensors, actuators, gadgets, appliances, or machines, that are programmed for certain applications and can transmit data over the internet or other networks, and/or the like. The data acquired by the data acquisition engine1002from these data sources1004,1006, and1008may then be transported to the data ingestion engine1010for further processing.

Depending on the nature of the data imported from the data acquisition engine1002, the data ingestion engine1010may move the data to a destination for storage or further analysis. Typically, the data imported from the data acquisition engine1002may be in varying formats as they come from different sources, including RDBMS, other types of databases, S3 buckets, CSVs, or from streams. Since the data comes from different places, it needs to be cleansed and transformed so that it can be analyzed together with data from other sources. At the data ingestion engine1002, the data may be ingested in real-time, using the stream processing engine1012, in batches using the batch data warehouse1014, or a combination of both. The stream processing engine1012may be used to process continuous data stream (e.g., data from edge devices), i.e., computing on data directly as it is received, and filter the incoming data to retain specific portions that are deemed useful by aggregating, analyzing, transforming, and ingesting the data. On the other hand, the batch data warehouse1014collects and transfers data in batches according to scheduled intervals, trigger events, or any other logical ordering.

In machine learning, the quality of data and the useful information that can be derived therefrom directly affects the ability of the machine learning model1024to learn. The data pre-processing engine1016may implement advanced integration and processing steps needed to prepare the data for machine learning execution. This may include modules to perform any upfront, data transformation to consolidate the data into alternate forms by changing the value, structure, or format of the data using generalization, normalization, attribute selection, and aggregation, data cleaning by filling missing values, smoothing the noisy data, resolving the inconsistency, and removing outliers, and/or any other encoding steps as needed.

In addition to improving the quality of the data, the data pre-processing engine1016may implement feature extraction and/or selection techniques to generate training data1018. Feature extraction and/or selection is a process of dimensionality reduction by which an initial set of data is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process. Feature extraction and/or selection may be used to select and/or combine variables into features, effectively reducing the amount of data that must be processed, while still accurately and completely describing the original data set. Depending on the type of machine learning algorithm being used, this training data1018may require further enrichment. For example, in supervised learning, the training data is enriched using one or more meaningful and informative labels to provide context so a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. Data labeling is required for a variety of use cases including computer vision, natural language processing, and speech recognition. In contrast, unsupervised learning uses unlabeled data to find patterns in the data, such as inferences or clustering of data points.

The ML model tuning engine1022may be used to train a machine learning model1024using the training data1018to make predictions or decisions without explicitly being programmed to do so. The machine learning model1024represents what was learned by the selected machine learning algorithm1020and represents the rules, numbers, and any other algorithm-specific data structures required for classification. Selecting the right machine learning algorithm may depend on a number of different factors, such as the problem statement and the kind of output needed, type and size of the data, the available computational time, number of features and observations in the data, and/or the like. Machine learning algorithms may refer to programs (math and logic) that are configured to self-adjust and perform better as they are exposed to more data. To this extent, machine learning algorithms are capable of adjusting their own parameters, given feedback on previous performance in making prediction about a dataset.

To tune the machine learning model, the ML model tuning engine1022may repeatedly execute cycles of experimentation1026, testing1028, and tuning1030to optimize the performance of the machine learning algorithm1020and refine the results in preparation for deployment of those results for consumption or decision making. To this end, the ML model tuning engine1022may dynamically vary hyperparameters each iteration (e.g., number of trees in a tree-based algorithm or the value of alpha in a linear algorithm), run the algorithm on the data again, then compare its performance on a validation set to determine which set of hyperparameters results in the most accurate model. The accuracy of the model is the measurement used to determine which set of hyperparameters is best at identifying relationships and patterns between variables in a dataset based on the input, or training data1018. A fully trained machine learning model1032is one whose hyperparameters are tuned and model accuracy maximized.

The trained machine learning model1032, similar to any other software application output, can be persisted to storage, file, memory, or application, or looped back into the processing component to be reprocessed. More often, the trained machine learning model1032is deployed into an existing production environment to make practical business decisions based on live data1034. To this end, the machine learning subsystem1000uses the inference engine1036to make such decisions. The type of decision-making may depend upon the type of machine learning algorithm used. For example, machine learning models trained using supervised learning algorithms may be used to structure computations in terms of categorized outputs (e.g., C_1, C_2 . . . C_n1038) or observations based on defined classifications, represent possible solutions to a decision based on certain conditions, model complex relationships between inputs and outputs to find patterns in data or capture a statistical structure among variables with unknown relationships, and/or the like. On the other hand, machine learning models trained using unsupervised learning algorithms may be used to group (e.g., C_1, C_2 . . . C_n1038) live data1034based on how similar they are to one another to solve exploratory challenges where little is known about the data, provide a description or label (e.g., C_1, C_2 . . . C_n1038) to live data1034, such as in classification, and/or the like. These categorized outputs, groups (clusters), or labels are then presented to the user input system130. In still other cases, machine learning models that perform regression techniques may use live data1034to predict or forecast continuous outcomes.

It will be understood that the embodiment of the machine learning subsystem1000illustrated inFIG.1bis exemplary and that other embodiments may vary. As another example, in some embodiments, the machine learning subsystem1000may include more, fewer, or different components.

FIG.2provides a block diagram illustrating the entity system200, in greater detail, in accordance with embodiments of the invention. As illustrated inFIG.2, in one embodiment of the invention, the entity system200includes one or more processing devices220operatively coupled to a network communication interface210and a memory device230. In certain embodiments, the entity system200is operated by a first entity, such as a financial institution.

It should be understood that the memory device230may include one or more databases or other data structures/repositories. The memory device230also includes computer-executable program code that instructs the processing device220to operate the network communication interface210to perform certain communication functions of the entity system200described herein. For example, in one embodiment of the entity system200, the memory device230includes, but is not limited to, a resource information determination application250, one or more entity applications270, and a data repository280comprising data accessed, retrieved, and/or computed by the entity system200. The one or more entity applications270may be any applications developed, supported, maintained, utilized, and/or controlled by the entity. The computer-executable program code of the network server application240, the resource information determination application250, the one or more entity application270to perform certain logic, data-extraction, and data-storing functions of the entity system200described herein, as well as communication functions of the entity system200.

The network server application240, the resource information determination application250, and the one or more entity applications270are configured to store data in the data repository280or to use the data stored in the data repository280when communicating through the network communication interface210with the resource information determination system300, and/or the computing device system400to perform one or more process steps described herein. In some embodiments, the entity system200may receive instructions from the resource information determination system300via the resource information determination application250to perform certain operations. The resource information determination application250may be provided by the resource information determination system300. The one or more entity applications270may be any of the applications used, created, modified, facilitated, and/or managed by the entity system200.

FIG.3provides a block diagram illustrating the resource information determination system300in greater detail, in accordance with embodiments of the invention. As illustrated inFIG.3, in one embodiment of the invention, the resource information determination system300includes one or more processing devices320operatively coupled to a network communication interface310and a memory device330. In certain embodiments, the resource information determination system300is operated by an entity, such as a financial institution. In some embodiments, the resource information determination system300is operated by an entity, such as a non-financial institution. In some embodiments, the resource information determination system300is owned or operated by the entity of the entity system200. In some embodiments, the resource information determination system300may be an independent system. In alternate embodiments, the resource information determination system300may be a part of the entity system200.

It should be understood that the memory device330may include one or more databases or other data structures/repositories. The memory device330also includes computer-executable program code that instructs the processing device320to operate the network communication interface310to perform certain communication functions of the resource information determination system300described herein. For example, in one embodiment of the resource information determination system300, the memory device330includes, but is not limited to, a network provisioning application340, a frame extraction application350, a resource identification application360, a search application362, a web crawling application365, a resource data extraction application370, a resource data embedding application380, and a data repository390comprising data processed or accessed by one or more applications in the memory device330. The computer-executable program code of the network provisioning application340, the frame extraction application350, the resource identification application360, the search application362, the web crawling application365, the resource data extraction application370, and the resource data embedding application380may instruct the processing device320to perform certain logic, data-processing, and data-storing functions of the resource information determination system300described herein, as well as communication functions of the resource information determination system300.

The network provisioning application340, the frame extraction application350, the resource identification application360, the search application362, the web crawling application365, the resource data extraction application370, and the resource data embedding application380are configured to invoke or use the data in the data repository390when communicating through the network communication interface310with the entity system200, and/or the computing device system400. In some embodiments, the network provisioning application340, the frame extraction application350, the resource identification application360, the search application362, the web crawling application365, the resource data extraction application370, and the resource data embedding application380may store the data extracted or received from the entity system200, and the computing device system400in the data repository390. In some embodiments, the network provisioning application340, the frame extraction application350, the resource identification application360, the search application362, the web crawling application365, the resource data extraction application370, and the resource data embedding application380may be a part of a single application.

FIG.4provides a block diagram illustrating a computing device system400ofFIG.1ain more detail, in accordance with embodiments of the invention. However, it should be understood that a mobile telephone is merely illustrative of one type of computing device system400that may benefit from, employ, or otherwise be involved with embodiments of the present invention and, therefore, should not be taken to limit the scope of embodiments of the present invention. Other types of computing devices may include portable digital assistants (PDAs), pagers, mobile televisions, entertainment devices, desktop computers, workstations, laptop computers, cameras, video recorders, audio/video player, radio, GPS devices, wearable devices, Internet-of-things devices, augmented reality devices, virtual reality devices, automated teller machine devices, electronic kiosk devices, or any combination of the aforementioned.

Some embodiments of the computing device system400include a processor410communicably coupled to such devices as a memory420, user output devices436, user input devices440, a network interface460, a power source415, a clock or other timer450, a camera480, and a positioning system device475. The processor410, and other processors described herein, generally include circuitry for implementing communication and/or logic functions of the computing device system400. For example, the processor410may include a digital signal processor device, a microprocessor device, and various analog to digital converters, digital to analog converters, and/or other support circuits. Control and signal processing functions of the computing device system400are allocated between these devices according to their respective capabilities. The processor410thus may also include the functionality to encode and interleave messages and data prior to modulation and transmission. The processor410can additionally include an internal data modem. Further, the processor410may include functionality to operate one or more software programs, which may be stored in the memory420. For example, the processor410may be capable of operating a connectivity program, such as a web browser application422. The web browser application422may then allow the computing device system400to transmit and receive web content, such as, for example, location-based content and/or other web page content, according to a Wireless Application Protocol (WAP), Hypertext Transfer Protocol (HTTP), and/or the like.

The processor410is configured to use the network interface460to communicate with one or more other devices on the network150. In this regard, the network interface460includes an antenna476operatively coupled to a transmitter474and a receiver472(together a “transceiver”). The processor410is configured to provide signals to and receive signals from the transmitter474and receiver472, respectively. The signals may include signaling information in accordance with the air interface standard of the applicable cellular system of the wireless network152. In this regard, the computing device system400may be configured to operate with one or more air interface standards, communication protocols, modulation types, and access types. By way of illustration, the computing device system400may be configured to operate in accordance with any of a number of first, second, third, and/or fourth-generation communication protocols and/or the like.

As described above, the computing device system400has a user interface that is, like other user interfaces described herein, made up of user output devices436and/or user input devices440. The user output devices436include a display430(e.g., a liquid crystal display or the like) and a speaker432or other audio device, which are operatively coupled to the processor410.

The user input devices440, which allow the computing device system400to receive data from a user such as the user110, may include any of a number of devices allowing the computing device system400to receive data from the user110, such as a keypad, keyboard, touch-screen, touchpad, microphone, mouse, joystick, other pointer device, button, soft key, and/or other input device(s). The user interface may also include a camera480, such as a digital camera.

The computing device system400may also include a positioning system device475that is configured to be used by a positioning system to determine a location of the computing device system400. For example, the positioning system device475may include a GPS transceiver. In some embodiments, the positioning system device475is at least partially made up of the antenna476, transmitter474, and receiver472described above. For example, in one embodiment, triangulation of cellular signals may be used to identify the approximate or exact geographical location of the computing device system400. In other embodiments, the positioning system device475includes a proximity sensor or transmitter, such as an RFID tag, that can sense or be sensed by devices known to be located proximate a merchant or other location to determine that the computing device system400is located proximate these known devices.

The computing device system400further includes a power source415, such as a battery, for powering various circuits and other devices that are used to operate the computing device system400. Embodiments of the computing device system400may also include a clock or other timer450configured to determine and, in some cases, communicate actual or relative time to the processor410or one or more other devices.

The computing device system400also includes a memory420operatively coupled to the processor410. As used herein, memory includes any computer readable medium (as defined herein below) configured to store data, code, or other information. The memory420may include volatile memory, such as volatile Random Access Memory (RAM) including a cache area for the temporary storage of data. The memory420may also include non-volatile memory, which can be embedded and/or may be removable. The non-volatile memory can additionally or alternatively include an electrically erasable programmable read-only memory (EEPROM), flash memory or the like.

The memory420can store any of a number of applications which comprise computer-executable instructions/code executed by the processor410to implement the functions of the computing device system400and/or one or more of the process/method steps described herein. For example, the memory420may include such applications as a conventional web browser application422, a resource information determination application421, entity application424. These applications also typically instructions to a graphical user interface (GUI) on the display430that allows the user110to interact with the entity system200, the resource information determination system300, and/or other devices or systems. The memory420of the computing device system400may comprise a Short Message Service (SMS) application423configured to send, receive, and store data, information, communications, alerts, and the like via the wireless telephone network152. In some embodiments, the resource information determination application421provided by the resource information determination system300allows the user110to access the resource information determination system300. In some embodiments, the entity application424provided by the entity system200and the resource information determination application421allow the user110to access the functionalities provided by the resource information determination system300and the entity system200.

The memory420can also store any of a number of pieces of information, and data, used by the computing device system400and the applications and devices that make up the computing device system400or are in communication with the computing device system400to implement the functions of the computing device system400and/or the other systems described herein.

FIG.5provides a process flow for embedding extractable metadata elements within a channel-agnostic layer of audio-visual content, in accordance with an embodiment of the invention. As shown in block510, the system receives, from a user device, a content file, wherein the content file comprises at least one visual frame. For instance, the content file might include, in some embodiments, a digital video file in any suitable format for streaming over a network (e.g., MP4., MOV., WMV., AVI., AVCHD., FLV, F4V, MKV, or the like). Next, as shown in block520, the system receives product data for the received content file. In some embodiments, the system may generate product data via an intelligent machine learning engine process, such as by identification of products, items, or objects within the content file via a recurrent convolutional neural network (Mask R-CNN, or the like) using the machine learning architecture as described with respect toFIG.1b. In some embodiments, the product data comprises product identifiers (stock keeping unit or “SKU” codes, universal product codes or “UPC” codes, stock codes, internet links, QR codes, bar codes, or the like) for products displays within the received content file. In some embodiments, the content file further comprises one or more timestamps corresponding to when the one or more products are displayed in the visual content (one or more frames, or the like). In some embodiments, the frames in which the products are displayed may be further determined by use of the machine learning architecture as described inFIG.1b.

Next, as shown in block530, the invention may determine resource data for the one or more products. For instance, the invention may crawl one or more websites, product pages, product catalogue databases, internet search history data, third party databases, marketing databases, cloud servers, or the like, in order to determine resource data for the one or more products. In some embodiments, the resource data may comprise a product price for a specific product. In some embodiments, the product price may be an average price among available, “trusted” online merchants for which the system has a pre-programmed list. In other embodiments, the product price may be a multi-faceted datapoint, such as a price range found across multiple merchants, a price range comparison of new and used items, a promotional price found at a specific merchant or during a specific time of year, or the like. In other embodiments, product price data may not be located, and it may be assumed that the product is unavailable, discontinued, temporarily sold out, or the like, in which case the resource data may simply refer to a channel to search for the item on a used product marketplace. In some embodiments, resource data may be further distilled to include only offers from merchants with which the entity has an ongoing relationship or affiliation. In some embodiments, resource data may be excluded for merchants or product sellers which are not trusted or exist on a pre programmed list of untrusted merchants. In some embodiments, the resource data may be in the form of one or more currency types, such that a product price may be displayed to users in an accurate currency given their IP address, device geolocation, or the like. In some embodiments, the resource data may require conversation based on a known current currency conversation rate. In some embodiments, the resource data may include a last known “lowest” and “highest” price for the item such that a user may be informed of whether or not current supply and demand for the product is affecting its price.

Next, as shown in block540, the system may generate a metadata file containing instructions to automatically trigger the display of the product identifiers and resource data for the one or more products at a corresponding timestamp during playback, streaming, or the like, of the content file. For instance, a user may request, via the user device, via an entity application or the like, to stream the content file from the entity server or a third party server, as shown in block550. In response, the system may stream the content file along with the metadata file to the user device.

As such, the system may provide the content file along with a metadata file containing the product identifiers and resource data. The entity application, third party application, or the like may be programmed to display an overlay via a graphical user interface of the metadata, such that the user may be informed about various products within the content file. In some embodiments, the overlay may be displayed in an interactive manner, such as an informational icon, or the letter “i” to indicate that the user may click on the icon and obtain further information about the product. In some embodiments, the user clicking on the interactive icon may pause the playback of the content file and display further information, such as the resource data and product price data, which may include links to view the items, purchase the items, or automatically add items to at “cart” within the graphical user interface without visiting an outside merchant website. In this way, the user may safely and securely add the items to a cart within the playback environment and may later “check out” using the playback environment as well, without ever needing to visit a third party merchant website. In this way, the entity managing the system of the invention, which may be a trusted financial institution, may be in a unique position to pre-populate resource account data and facilitate a secure transaction without requiring the user to disclose payment information. In some embodiments, the system may rely on previously authenticating the user to access the playback platform as a means for authenticating the user to access their resource accounts and use these accounts to purchase products from the content file.