Method and system for matching video content to podcast episodes

Abstract

Systems and methods are provided for matching videos to podcast episodes. A data store including podcast episode identifiers is accessed. The podcast episode identifiers are associated with one or more podcast episode attributes. A video content item is identified. The video content item includes one or more video content item attributes. Based on the one or more podcast episode attributes and the one or more video content item attributes, a matching podcast episode identifier that matches the video content item is determined. The ranking of one of the video content item or the matching podcast episode identifier is adjusted to reflect the correspondence between the video content item and the matching podcast episode identifier. Information associated with the matching podcast episode identifier is provided to a first user device.

Description

Technical Field

[0001] Aspects and embodiments of the present disclosure relate to matching video content to podcast episodes. Background Art

[0002] Various platforms enable users to listen to audio content such as podcast episodes. For example, users can search for and find audio content using a search engine or through a content sharing platform. Additionally, podcast publishers can use a podcast manager platform to track analytics information about podcasts. The analytics information can include the number of times a podcast episode has started, the average length of time users have listened to a particular podcast episode, and demographic data about podcast listeners. Summary of the Invention

[0003] The following is a simplified overview of the present disclosure to provide a basic understanding of some aspects of the present disclosure. This overview is not an extensive review of the present disclosure. It is neither intended to identify key or important elements of the present disclosure nor to delineate any scope of any particular embodiment of the present disclosure or any scope of the claims. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In some embodiments, a system and method for matching video content items to podcast episodes to enhance a content sharing platform are disclosed. In an embodiment, a data store including one or more podcast episode identifiers and one or more video content item identifiers is accessed. A podcast episode identifier among the one or more podcast episode identifiers can be associated with one or more podcast episode attributes, and a video content item identifier among the one or more video content item identifiers can be associated with one or more video content item attributes. For a matching podcast episode identifier among the one or more podcast episode identifiers, a matching video content item identifier is determined based on one or more podcast episode attributes associated with the matching podcast episode identifier. The ranking of at least one of the matching video content item identifier or the matching podcast episode identifier is adjusted to reflect the correspondence between the matching video content item identifier and the matching podcast episode identifier. Information associated with the matching podcast episode identifier is provided to a first user device.

[0005] In some embodiments, another system and method are disclosed for matching video content items to podcast episodes to enhance a search engine platform. In an embodiment, a data store including one or more podcast episode identifiers and one or more video content item identifiers is accessed. A podcast episode identifier among the one or more podcast episode identifiers can be associated with one or more podcast episode attributes, and a video content item identifier among the one or more video content item identifiers can be associated with one or more video content item attributes. For a matching podcast episode identifier among the one or more podcast episode identifiers, a matching video content item identifier is determined based on the one or more podcast episode attributes associated with the matching podcast episode identifier. The ranking of at least one of the matching video content item identifier or the matching podcast episode identifier is adjusted to reflect the correspondence between the matching video content item identifier and the matching podcast episode identifier. Information associated with the matching podcast episode identifier and associated with the matching video content item identifier is provided to a user device.

[0006] In some embodiments, another system and method are disclosed for matching video content items to podcast episodes to enhance a podcast manager platform. In an embodiment, a request for podcast analysis information for a podcast is received. The podcast can be associated with one or more podcast episodes. An identification of a source including one or more video content items is received. Additionally, one or more podcast episode attributes for the one or more podcast episodes are identified, and one or more video content item attributes for the one or more video content items are identified. Based on the one or more podcast episode attributes and the one or more video content item attributes, a matching video content item that matches a matching podcast episode among the one or more podcast episodes is determined. Analysis information associated with the matching video content item is determined. A response to the request is provided. The response includes the analysis information associated with the matching video content item and the podcast analysis information. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Aspects and embodiments of the present disclosure will be more fully understood from the detailed description given below and from the accompanying drawings of various aspects and embodiments of the present disclosure. However, these descriptions and drawings should not be regarded as limiting the present disclosure to a particular aspect or embodiment, but are only for explanation and understanding.

[0008] Figure 1 An example system architecture in accordance with an embodiment of the present disclosure is illustrated.

[0009] Figure 2 An example of a matching subsystem in accordance with an embodiment of the present disclosure is depicted.

[0010] Figure 3Illustrates an example graphical user interface (GUI) on a client device according to an embodiment of the present disclosure, which illustrates an example podcast target page on a content sharing platform.

[0011] Figure 4 Illustrates an example GUI on a client device according to an embodiment of the present disclosure, which illustrates an example of a content sharing platform including podcast episodes and video content items.

[0012] Figure 5 Illustrates an example GUI of a content sharing platform optimized for audio content according to an embodiment of the present disclosure.

[0013] Figure 6 Illustrates an example GUI of a content sharing platform enhanced for episode content according to an embodiment of the present disclosure.

[0014] Figure 7 Illustrates an example GUI of a lock screen of a user device according to an embodiment of the present disclosure, which shows media controls optimized for listening to audio content.

[0015] Figure 8 Illustrates an example GUI of a search engine platform optimized according to aspects of the present disclosure.

[0016] Figure 9 and Figure 10 Illustrates an example GUI of a podcast manager platform optimized according to aspects of the present disclosure.

[0017] Figure 11 Depicts a flowchart of a method for enhancing a content sharing platform by matching video content items with podcast episodes according to an embodiment of the present disclosure.

[0018] Figure 12 Depicts a flowchart of a method for enhancing a search engine platform by matching video content items with podcast episodes according to an embodiment of the present disclosure.

[0019] Figure 13 Depicts a flowchart of a method for enhancing a podcast manager platform by matching video content items with podcast episodes according to an embodiment of the present disclosure.

[0020] Figure 14 Depicts a block diagram of a computer system operating according to an embodiment of the present disclosure.

[0021] These figures can be better understood when observed in conjunction with the following detailed description. Detailed Description

[0022] Aspects of the present disclosure relate to matching video content to podcast episodes. Podcast creators and / or publishers are able to publish podcast episodes in both audio and video formats. For example, a podcast publisher may publish an episode as an audio file via a podcast publishing service and may publish a video of the same episode via a video hosting service. The video may include a video recording of the podcast host who recorded the episode, and / or may include slides or images related to the episode content.

[0023] In many cases, the content of the video is different from the content of the audio format of the podcast episode. For example, the video of a podcast episode may include visual intros and outros, while the intro / outro of the audio format of the podcast episode may be optimized for listening (e.g., the outro of the video format may include end credits in print format, while the outro of the audio format may include verbally expressed end credits). In some cases, breaks or recaps included in both the audio and video formats may be different. For example, the audio version of a podcast episode may include a break every set number of minutes (e.g., every 15 minutes) and may include a recap after the break to accommodate the publisher's platform (e.g., a radio station). However, the video format of the podcast episode may exclude the break and recap, or may include the break and recap at different time intervals. Additionally, the video and audio formats may not be released on the same date. For example, a podcast episode may first be released in audio format and may be released as a video only if the audio version reaches a certain number of plays.

[0024] Existing content sharing platforms, search engines, and / or podcast managers all operate independently, and if users need to find a matching podcast episode on these different platforms, they must switch between multiple applications. This switching requires a significant amount of time and computing resources. Aspects and embodiments of the present disclosure address the above and other deficiencies or problems by providing techniques for matching video content to podcast episodes across multiple platforms / systems (e.g., content sharing platforms, search engines, and / or podcast managers). Additionally, aspects and embodiments of the present disclosure provide tools that can combine performance metrics, analyze data and information across multiple platforms / systems, and can cover both audio episode formats and video episode formats.

[0025] In some embodiments, to match video content items with podcast episodes (and vice versa), a matching system operating in accordance with aspects of the present disclosure can identify attributes associated with a video content item and attributes associated with a podcast episode. Attributes can include a transcription of the audio of the video content item and / or podcast episode, the duration of the video content item and / or podcast episode, a title, a description, and / or a publication date. Additional attributes not listed here can be identified. The matching system can compare the attributes to generate a match score for each episode-video pair. The episode-video pair having the highest match score can be determined for matching.

[0026] Based on the match, a platform optimization system operating in accordance with aspects of the present disclosure can enhance the experience of podcast publishers by providing comprehensive analysis information that includes data in both audio and video formats from the episode. The platform optimization system can also enhance the experience of users of a search engine platform and / or a content sharing platform by providing search results that include the audio and video formats of the episode (including optimized versions of each format). Additionally, the ranking for generating search results on a search engine platform or a content sharing platform can be enhanced based on the match of the content item with the podcast episode.

[0027] More specifically, by matching the video format of a podcast episode with the audio format of the podcast episode, a podcast analytics provider can provide analysis information associated with both the video format and the audio format, rather than simply for the audio format. For example, the podcast analytics provider can display combined data on the total number of plays of the episode across both audio and video, thus providing a more complete picture to the podcast publisher. Additionally, by matching the video format of a podcast episode with the audio format of the podcast episode, a search engine can enhance the ranking signal by combining performance numbers (such as the number of plays) for each format. Additionally, the search engine ranking signal can be enhanced by combining information associated with both the audio and video formats. For example, the video of a podcast episode may not include host information, while the audio format of the same podcast episode may include host information. By determining a match between the two, the combined information can adjust the ranking of both formats. The search engine can be further enhanced by presenting search results that include combined audio and video items, rather than showing separate experiences for the audio format and the video format.

[0028] In addition, content sharing platforms can benefit from the matching of video and audio formats of podcast episodes. Similar to search engines, content sharing platforms can adjust the rankings of the two formats by combining the performance numbers of the video format with those of the audio format. They can also adjust the rankings by combining information from both formats (e.g., host information). Additionally, the user experience of content sharing platforms can be enhanced by providing a "listen-only" experience to viewers of video content items that match podcast episodes. When a content sharing platform displays a video that matches a podcast episode, an optimized listen-only experience can be provided. The listen-only experience can include, but is not limited to, information about the matching podcast and / or host, previous episode / next episode controls, automatically playing another listen-only video at the end of the video, the ability to subscribe to the podcast, and the ability to donate / contribute to the podcast publisher. Additionally, when a video is matched to a podcast episode, the advertisements presented during the video via the content sharing platform can be enhanced. The content sharing platform can provide advertisements designed to be listened to, rather than embedding general video advertisements.

[0029] Aspects of the present disclosure provide numerous technical advantages, including, for example, a mechanism for matching podcast episodes across different content sharing platforms, search engines, and / or podcast managers, thereby creating a deeper integration among these platforms / systems and reducing the time and computing resources that would otherwise be consumed due to repetitive manual switching (e.g., switching between platforms / systems) done by users. Another technical advantage is the improved and extended functionality of search engine platforms, content sharing platforms, and podcast manager platforms. In particular, aspects of the present disclosure combine ranking signals associated with matched podcast episodes and video content items to provide improved search results. For example, the performance numbers of podcast episodes can be combined with those of the matched video content items to provide more accurate ranking signals for both the podcast episodes and the video content items. By providing more accurate ranking signals, content sharing platforms and / or search engine platforms can provide more accurate results to users, thus avoiding wasting computer resources on providing less accurate search results. Additionally, the content sharing platform experience can be optimized to provide better controls related to the listen-only experience when the user is viewing a video that matches a podcast episode, thereby improving the operation of the content sharing platform.

[0030] Aspects of the above methods and systems are described in detail below by way of example and not limitation.

[0031] Figure 1FIG. illustrates an example system architecture 100 in accordance with an embodiment of the present disclosure. The system architecture 100 (also referred to herein as the "system") includes end-user devices 102A-N, a data store 110, a content sharing platform 120, a search engine platform 130, a podcast manager platform 150, a server machine 140, and a third-party platform 165, each connected to a network 104.

[0032] In an embodiment, the network 104 may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or a wide area network (WAN)), a wired network (e.g., Ethernet), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and / or combinations thereof.

[0033] The end-user devices 102A-N may each include a computing device, such as a personal computer (PC), a laptop computer, a mobile phone, a smartphone, a tablet computer, a netbook computer, an Internet-connected television, etc. In some embodiments, the end-user devices 102A-N may also be referred to as "user devices" or "client devices". Each end-user device may include a content viewer. In some embodiments, the content viewer may be an application that provides a user interface (UI) for a user to view or upload content such as images, video items, podcast episodes, web pages, documents, etc. For example, the content viewer may be a web browser capable of accessing, retrieving, presenting, and / or navigating content served by a web server (e.g., a web page such as a HyperText Markup Language (HTML) page, a digital media item, etc.). The content viewer may render, display, and / or present content to the user. The content viewer may also include an embedded media player (e.g., a player or an HTML5 player) embedded in a web page (e.g., a web page that may provide information about a product sold by an online merchant). In another example, the content viewer may be a stand-alone application (e.g., a mobile application or an app) that allows a user to view digital media items (e.g., digital video items, digital podcast episodes, digital images, e-books, etc.).

[0034] According to aspects of the present disclosure, the content viewer can be a content sharing platform application for a user to record, edit, and / or upload content for sharing on the content sharing platform 120. As such, the content viewer can be provided by the content sharing platform 120 to the end-user devices 102A-N. For example, the content viewer can be an embedded media player embedded in a web page provided by the content sharing platform 120. The content viewer can be a search platform application for a user to search for content using the search engine platform 130. Additionally or alternatively, the content viewer can be a podcast manager application that enables a user to manage podcasts on the podcast manager platform 150.

[0035] Media items 121 can be consumed via the Internet or via a mobile device application such as the content viewer of the end-user devices 102A-N. The media items 121 requested by a user of the content sharing platform 120 can be requested to be presented to the user. As used herein, "media", "media item", "online media item", "digital media", "digital media item", "content", and "content item" can include electronic files that can be executed or loaded using software, firmware, or hardware configured to present digital media items to an entity. In one embodiment, the content sharing platform 120 can use the data store 110 to store the media items 121. In another embodiment, the content sharing platform 120 can use the data store 110 to store the media items 121 or fingerprints as electronic files in one or more formats. The media items 121 can be provided to the user, where providing the media items 121 can include allowing access to the media items 121, transmitting the media items 121, and / or presenting or allowing the presentation of the media items 121.

[0036] In some embodiments, media item 121 can be a video item. A video item is a collection of sequential video frames (e.g., image frames) representing a scene in motion. For example, a series of sequential video frames can be captured continuously or reconstructed later to produce an animation. Video items can be provided in various formats, including but not limited to analog, digital, two-dimensional, and three-dimensional videos. Additionally, a video item can include a movie, a video clip, or any collection of animated images to be displayed in sequence. Further, a video item can be stored as a video file that includes a video component and an audio component. The video component can refer to video data in a video coding format or an image coding format (e.g., H.264 (MPEG-4 AVC), H.264 MPEG-4 Part 2, Graphics Interchange Format (GIF), WebP, etc.). The audio component can refer to audio data in an audio coding format (e.g., Advanced Audio Coding (AAC), MP3, etc.). It can be noted that a GIF can be saved as an image file (e.g.,.gif file) or as a series of images in an animated GIF (e.g., GIF89a format). It can be noted that H.264 can be a video coding format that is a block-based motion-compensated video compression standard for, e.g., the recording, compression, or distribution of video content.

[0037] In some embodiments, media item 121 can be an audio file, such as a podcast episode. The media item 121 as a video item can be a video version of a podcast episode. For example, the video version of a podcast episode can be the video of the host who created the podcast episode, or can be images or a series of images related to the theme of the podcast episode combined with the audio of the podcast episode.

[0038] In some embodiments, data store 110 is a persistent storage that can store media item 121 and data structures for tagging, organizing, and indexing media item 121. Data store 110 can be hosted by one or more storage devices (such as main memory, magnetic or optical storage-based disks, tapes, or hard disk drives, NAS, SAN, etc.). In some embodiments, data store 110 can be a network-attached file server, while in other embodiments, data store 110 can be some other type of persistent storage, such as an object-oriented database, a relational database, etc., which can be hosted by content sharing platform 120 or by one or more different machines coupled to server content sharing platform 120 via network 104.

[0039] In one embodiment, the content sharing platform 120, the search engine platform 130, the podcast manager platform 150, or the server machine 140 can be one or more computing devices (such as rack servers, router computers, server computers, personal computers, mainframe computers, laptop computers, tablet computers, desktop computers, etc.), data storage (e.g., hard disks, memories, databases), networks, software components, and / or hardware components that can be used to provide a user with access to media item 121 and / or provide the media item 121 to the user. For example, the content sharing platform 120 can allow a user to consume, upload, search, approve ("like"), disapprove ("dislike"), or comment on the media item 121. The content sharing platform 120 can also include a website (e.g., a web page) or application backend software that can be used to provide a user with access to the media item 121. As another example, the search engine platform 130 can allow a user to perform an Internet search, which can include searching for the media item 121. The search engine platform 130 can include a website (e.g., a web page) or application backend software that can be used to provide a user with access to the Internet (including the media item 121). As another example, the podcast manager platform 150 can provide podcast analysis information to a user. For example, the user can be a podcast publisher. The podcast manager platform 150 can enable the podcast publisher to view a list of the published podcasts and episodes and can include performance data (e.g., number of plays, play dates, average play length, etc.) for each podcast episode and demographic data information about the listeners.

[0040] In some embodiments, the content sharing platform 120, the search engine platform 130, and / or the podcast manager platform 150 can each be combined into a single platform. In some embodiments, the server machine 140 or any of its components (e.g., the podcast episode scraper 144, the video content item scraper 143, and / or the matching subsystem 142) can be combined with the platforms 120, 130, 150. Each of the platforms 120, 130, 150 can include a platform optimizer 170A-C. The platform optimizer 170A-C can use the results from the matching subsystem 142 to optimize platform operations.

[0041] In embodiments of the present disclosure, a "user" can be represented as a single individual. However, other embodiments of the present disclosure cover a "user" being an entity controlled by a group of users and / or an automated source. For example, a group of individual users united as a community in a social network can be considered a "user". In another example, an automated consumer can be an automated ingestion pipeline of the content sharing platform 120, such as a topic channel.

[0042] The content sharing platform 120 may include multiple channels (e.g., channels A through X). A channel can include one or more media items 121 that are obtainable from a common source or media items 121 that have a common topic, theme, or substance. The media item 121 can be digital content selected by a user, digital content obtainable by a user, digital content uploaded by a user, digital content selected by a content provider, digital content selected by a broadcaster, etc. A channel can be associated with an owner, who is a user who can perform actions on the channel. Different activities can be associated with a channel based on the owner's actions (such as the owner making digital content available on the channel, the owner selecting (e.g., liking) digital content associated with another channel, the owner commenting on digital content associated with another channel, etc.). The activities associated with a channel can be collected in the channel's activity feed. Users other than the channel owner can subscribe to one or more channels that they are interested in. The concept of "subscribing" can also be referred to as "liking", "following", "friending", etc.

[0043] The third-party platform 165 can be used to provide video and / or audio advertisements. Alternatively, the third-party platform 165 can provide other services. For example, the third-party platform 165 can be a video streaming service provider that generates a media streaming service via a communication application for users to play videos, TV shows, video clips, audio, audio clips, and movies on the end-user devices 102A-N via the third-party platform 165. In some embodiments, a content provider can upload or otherwise provide (e.g., via the third-party platform 165) the media item 121 to the content sharing platform 120 for presentation to one or more users.

[0044] In some embodiments, the server machine 140 can include a podcast episode scraper 144, a video content item scraper 143, and a matching subsystem 142. In some embodiments, the podcast episode scraper 144 can identify podcast episodes that have been uploaded to the network. For example, the podcast episode scraper 144 can have a web crawler that crawls the Internet in an organized and automated manner to locate podcast episodes. In some embodiments, the podcast episode scraper 144 can identify podcast episodes that have been uploaded via the content sharing platform 120 and / or the podcast manager platform 150. When locating and / or identifying a podcast episode, the podcast episode scraper 144 can store the podcast episode identifier in the data store 110. The podcast episode identifier can be the URL of the podcast episode, and / or can be an identifier that links to the URL of the podcast episode. In some embodiments, the podcast episode scraper 144 can also extract certain metadata from the identified podcast episodes and store the extracted metadata as attributes in the data store 110.

[0045] The video content item crawler 143 can operate in a similar manner. That is, the video content item crawler 143 can have a web crawler that crawls the Internet in an organized and automated manner to locate video content items. In some embodiments, the video content item crawler 143 can identify video content items that have been uploaded via the content sharing platform 120 and / or the podcast manager platform 150. When locating and / or identifying video content items, the video content item crawler 143 can store video content item identifiers in the data store 110. The video content item identifier can be the URL of the video content item and / or can be an identifier that links to the URL of the video content item. In some embodiments, the video content item crawler 143 can extract metadata from the identified video content items and store the extracted metadata as attributes in the data store 110.

[0046] The matching subsystem 142 can identify matching video content items and podcast episodes. In some embodiments, the matching subsystem 142 can use the extracted metadata and / or other information about the podcast episode identifiers and video content item identifiers stored in the data store 110 to match podcast episodes to video content items. The matching subsystem 142 can store the matching podcast episode identifiers and the matching video content item identifiers in the data store 110. Refer to Figure 2 The matching subsystem 142 and the data store 110 are further described.

[0047] In some embodiments, the matching subsystem 142 is a machine learning module trained to assign a matching score to a pair of podcast episode identifiers and video content item identifiers based on the attributes of each media item. In some embodiments, a labeled input training data set can be used to train the machine learning model. The input training data set can include a subset of paired podcast episode identifiers associated with episode attributes (e.g., audio, transcript, duration, release date, title, description, etc.) that are paired with matching video content item identifiers associated with video content item attributes (e.g., audio, transcript, duration, release date, title, description, etc.). The input training data set can also include a subset of podcast episode identifiers associated with episode attributes that are paired with non-matching video content item identifiers associated with video content item attributes. The input training data for training can be used to train a supervised machine learning model to provide a high score when a video content item matches a podcast episode and a low score when a video content item does not match a podcast episode.

[0048] In some embodiments, platform optimizers 170A-C can use the results of matching system 142 to optimize the platform. In some embodiments, platform optimizer 170A can be used to optimize content sharing platform 120, and / or enhance the user experience of content sharing platform 120. In some embodiments, to optimize content sharing platform 120, platform optimizer 170A can cause the rankings of the matched video content item identifiers and / or the matched podcast episode identifiers to be adjusted to reflect the correspondence between the matched video content item identifiers and the matched podcast episode identifiers. That is, content sharing platform 120 can rank video content items and / or podcast episodes based on certain metrics. These metrics can include, for example, popularity indicators such as the number of recommendations made for the video content item and / or podcast episode, the number of likes for the video content item and / or podcast episode, the number of views of the video content item and / or podcast episode, and the number of shares of the video content item and / or podcast episode. In some embodiments, the video content item and / or podcast episode identifiers can be further ranked based on the topic of the item.

[0049] Once matching subsystem 142 has matched video content item identifiers and podcast episode identifiers, platform optimizer 170A can combine the rankings of the matched video content item identifiers and the matched podcast episode identifiers, for example, by combining the popularity indicators of each item. Content sharing platform 120 can use these combined rankings to provide a more accurate representation of podcast episodes and / or video content items within content sharing platform 120. Platform optimizer 170A can also cause content sharing platform 120 to provide information associated with both the matched podcast episode identifiers and the matched video content item identifiers to users. For example, a user can use content sharing platform 120 to search for videos on a particular topic. Content sharing platform 120 can use the combined rankings generated by platform optimizer 170A to identify one or more video content items related to a particular topic. For example, a video content item that may have been ranked low based only on its popularity indicator can now be ranked higher based on the popularity indicator of its matched podcast episode.

[0050] In addition, in response to a user's search request, content sharing platform 120 can provide the user with both video content items related to the searched topic and matching podcast episodes. When providing the matching podcast episodes, content sharing platform 120 can include audio-specific features and / or podcast-specific information. For example, the audio-specific features can include a "pure listening" option. An example of the "pure listening" feature can include optimized advertisements or promotional items that are more suitable for listening as opposed to advertisements or promotional items that are more suitable for viewing. For example, a user can use content sharing platform 120 to view a video content item with a matching podcast episode. Content sharing platform 120 can present the video content item with the "pure listening" option, and / or can present a video content item optimized for the listening experience. The promotional items presented during the video content item can be optimized for the listening experience. As another example, the podcast-specific information can include a "next / previous episode" feature, and / or host information. Figures 3 - 7 Illustrated is an example content sharing platform 120 graphical user interface enhanced by platform optimizer 170A.

[0051] In some embodiments, platform optimizer 170B can be used to optimize search engine platform 130 and / or enhance the user experience of search engine platform 130. In some embodiments, similar to how platform optimizer 170A optimizes content sharing platform 120, platform optimizer 170B can optimize search engine platform 130 by having the rankings of matching video content items and / or matching podcast episodes adjusted to reflect the correlation between the two. Search engine platform 130 can rank podcast episode identifiers and / or video content item identifiers based on certain metrics, including number of plays. Platform optimizer 170B can combine the number of plays of a matching video content item identifier with the number of plays of a matching podcast episode identifier. The number of plays can be the number of times a video content item associated with a video content identifier has started playing on the content sharing platform, on the search engine platform, and / or on another platform. The number of plays can be the number of times a podcast episode associated with a podcast episode identifier has started playing on the content sharing platform, on the search engine platform, on the podcast manager platform, and / or on another platform.

[0052] When determining whether to respond to a search by returning either or both of a matching video content item and a matching podcast episode, the search engine platform 130 can use the combined number of plays to rank both the matching video content item and the matching podcast episode. For example, a user can use the search engine platform 130 to search the Internet for a specific topic. The search engine platform 130 can determine that the results include a specific video content item. The search engine platform 130 can determine via the platform optimizer 170B that the specific video content item has a matching podcast episode (as determined by the matching subsystem 142). The platform optimizer 170B can adjust the ranking of the video content item based on a combined metric associated with both the video content item and the matching podcast episode. The metric can include, for example, the number of plays and / or a popularity metric.

[0053] The platform optimizer 170B can also be used to enhance the user experience of the search engine platform 130. In some embodiments, when the search results include a video content item with a matching podcast episode (or vice versa, a podcast episode with a matching video content item), the search engine platform 130 can display both the video content item and the podcast episode. Figure 8 An example graphical user interface of the search engine platform 130 enhanced by the platform optimizer 170B is illustrated.

[0054] In some embodiments, the platform optimizer 170C can be used to optimize the podcast manager platform 150 and / or enhance the user experience of the podcast manager platform 150. The podcast manager platform 150 can, for example, provide podcast analytics information to podcast publishers, including, for example, the number of times a podcast episode has been played, the number of plays in the past 30 days, and the average duration of the plays. In some embodiments, to optimize the podcast manager platform 150, the platform optimizer 170C can identify analytics information associated with a matching podcast episode identifier and a matching video content item identifier. In some embodiments, for example, the podcast manager platform 150 can use an API to extract analytics information associated with the matching video content item identifier from the content sharing platform 120.

[0055] In some embodiments, a user of the podcast manager platform 150 (e.g., a podcast publisher) can use the podcast manager platform 150 to request analytics information for a podcast. The user can identify one or more channels A - X of the content sharing platform 120 associated with the user's podcast episodes. The podcast manager platform 150 can identify podcast episode identifiers associated with the user, and can identify video content item identifiers associated with the identified channels. The platform optimizer 170C can use the matching subsystem 142 to match the identified video content item identifiers to the identified podcast episode identifiers. Then, the platform optimizer 170C can determine analytics information associated with the matched podcast episode identifiers and the matched video content item identifiers, and provide the analytics information to the user. An example of a graphical user interface of the podcast manager platform 150 enhanced by the platform optimizer 170C is shown in Figures 9 - 10 is shown.

[0056] In addition to the above description, controls can be provided to the user to allow the user to select whether and when the systems, programs, or features described herein can collect user information (e.g., information about the user's social network, social actions or activities, occupation, user preferences, or the user's current location), and whether to send content or communications to the user from the server. Further, before storing or using certain data, the data can be processed in one or more ways to remove personally identifiable information. For example, the user's identity can be processed so that the user's personally identifiable information cannot be determined, or the user's geographical location can be generalized (such as to a city, zip code, or state level) when location information is obtained so that the user's specific location cannot be determined. Thus, the user can control what information about the user is collected, how that information is used, and what information is provided to the user.

[0057] Figure 2 Illustrated is a matching subsystem according to an embodiment of the present disclosure. The matching subsystem 142 can include an attribute extraction module 203, a comparison score generator 205, and a matching module 207.

[0058] The data store 110 can store a set of podcast episodes 211, video content items 212, a matching score heap 213, a set of matched podcast episodes 214, and a set of matched video content items 215. The podcast episodes 211 can include a list of identified podcast episodes identified by Figure 1 the podcast episode scraper 144. In some embodiments, the podcast episodes 211 store a list of podcast identifiers that reference podcast episodes stored in another data store (not shown). For example, the podcast episodes 211 can be an index of podcast episodes. Similarly, the video content items 212 can include a list of Figure 1A list of the identified video content items identified by the video content item crawler 143. In some embodiments, the video content item 212 stores a list of video content items that reference video content items stored in another data store (not shown). For example, the video content item 212 can be an index of video content items.

[0059] The attribute extraction module 203 can extract attributes from podcast episodes and / or video content items. Attributes can include, but are not limited to, transcriptions of audio, audio content, titles, descriptions, durations, and / or publication dates. Some attributes (such as titles and descriptions) can be stored in podcast episode and / or video content item metadata. Thus, the attribute extraction module 203 can read the metadata and extract the relevant attributes. The attribute extraction module 203 can store the extracted attributes in the data store 110. Other attributes may not be stored in the metadata of the associated media item, in which case the attribute extraction module 203 can use other techniques to identify and extract the relevant attributes. For example, the attribute extraction module 203 can use various transcription techniques to generate a transcription of the media item. In some embodiments, the podcast episode 211 can be a table that stores podcast episode identifiers and associated podcast episode attributes, and the video content item 212 can be a table that stores video content item identifiers and associated video content item attributes.

[0060] The comparison score generator 205 can generate a comparison score for each media item (i.e., for each podcast episode referenced in the podcast episode 211 and / or for each video content item referenced in the video content item 212). In some embodiments, the comparison score generator 205 can identify a first podcast episode identifier in the podcast episode 211. The first podcast episode identifier can be a podcast episode identifier that does not have a matching video content item. That is, the first podcast episode identifier can be a podcast episode identifier that is not included in the matched episodes 215 of the data store 110.

[0061] The comparison score generator 205 can compare the attributes of the extracted first podcast episode identifier with the attributes of the extracted video content item 212. Based on the comparison of the attributes, the comparison score generator 205 can generate a matching score. If the matching score exceeds a threshold score, the first podcast episode identifier and the matching video content item can be added to the matching score heap 213. The matching score heap 213 can store a list of episode identifiers, video content item identifiers, and their matching scores. The matching score heap 213 can be sorted by the matching score value, with the highest matching score value at the head of the heap.

[0062] In some embodiments, the comparison score generator 205 can first compare the transcription attributes of the first podcast episode identifier with the transcription attributes of the video content item 212. In some embodiments, transcription can be the best way to match a video content item to a podcast episode. If the match score based on the transcription meets the match criteria (i.e., exceeds the match score threshold), then the comparison score generator 205 can stop there. In some embodiments, the match score generator 205 can compare the remaining attributes to generate a more comprehensive match score.

[0063] In some embodiments, the matching module 207 can determine the match value for each attribute. Then, the match score can be a weighted average of the match values of the attributes. For example, the matching module 207 can compare the transcription associated with the podcast episode identifier with the transcription associated with the video content item identifier. If the transcriptions match or substantially match (e.g., more than a certain percentage of the transcriptions match, such as 70%), then the matching module 207 can store the match value of the transcription attribute. In some embodiments, the match value for each attribute can be "1" for a match and "0" for a non-match. Similarly, the matching module 207 can compare the title attribute associated with the podcast episode identifier with the title associated with the video content item identifier. If the titles match or substantially match (e.g., more than a certain percentage of the titles match, such as 90%), then the matching module 207 can store the match value of the title attribute (e.g., "1"). If the titles do not match, then the matching module 207 can store the non-match value of the title attribute (e.g., "0"). The matching module 207 can perform similar comparisons for each attribute associated with the podcast episode identifier and the video content item identifier.

[0064] To determine the match score, in some embodiments, the matching module 207 can aggregate the attribute match values. If the match score exceeds a certain threshold, then the matching module 207 can determine that the podcast episode identifier and the video content item identifier match. In some embodiments, the matching module 207 can use the average or weighted average of the attribute match values to determine the match score. For example, the matching module 207 can assign a higher weight to the transcription because a match in the transcription indicates a high likelihood that the podcast episode identifier and the video content item identifier match. As another example, the matching module 207 can assign a lower weight to the release date because a podcast episode and the video content item of the podcast episode can be released on different dates. The matching module 207 can use other techniques to determine the match score for each pair of podcast episode identifier and video content item identifier.

[0065] The matching module 207 can determine when the matching score heap 213 is not empty. When the matching score heap 213 is not empty, the matching module 207 can select the first entry in the matching score heap 213 (i.e., the matching podcast episode identifier and video content item identifier with the highest matching score). In some embodiments, the matching module 207 can determine whether the podcast episode identifier and / or video content item identifier in the first entry of the matching score heap 213 has already been matched by checking to see if the podcast episode identifier and / or video content item identifier is listed in the list of matched episodes 215 or the list of matched videos 217, respectively. If they have not been matched, the matching module 207 can add the podcast episode identifier and video content item identifier in the first entry of the matching score heap 213 to the list of matched episodes 215 or the list of matched videos 217, respectively.

[0066] Figures 3 to 10 Illustrated is an example graphical user interface (GUI) on a client device according to an embodiment of the present disclosure. Figures 3 to 7 Illustrated is an example GUI on a user device, which illustrates content provided by a content sharing platform (such as Figure 1 content sharing platform 120). Figure 8 Illustrated is an example GUI on a user device, which illustrates search results provided by a search platform (such as Figure 1 search engine platform 130). Figures 9 - 10 Illustrated is an example GUI on a user device, which illustrates content provided by a podcast manager platform (such as Figure 1 podcast manager platform 150).

[0067] Figure 3 Illustrated is an example podcast target page 300 on a content sharing platform on a user device according to an embodiment of the present disclosure. In some embodiments, Figure 1 platform optimizer 170A can enhance content sharing platform 120 by providing the podcast target page, as Figure 3 shown. The podcast target page can include podcasts in which the user has expressed interest (e.g., podcasts that the user typically listens to or has listened to, or podcasts that the user has subscribed to or liked). The podcast target page can include podcasts related to topics in which the user has expressed interest. In some embodiments, the podcast target page can include links to podcasts and / or podcast episodes with rankings enhanced by platform optimizer 170A. For example, Figure 3The podcast 302 shown can have a matching video content item. A combined popularity indicator associated with the podcast 302 and its matching video content item can cause the podcast 302 to be listed in the user's podcast destination page. The podcast destination page can group podcasts based on a theme and / or style (such as "inspirational talks" and "talk shows"), as Figure 3 shown.

[0068] Figure 4 FIG. illustrates an example GUI 400 of a content sharing platform that includes both podcast episodes and video content items according to an embodiment of the present disclosure. Included in the Figure 4 shown home screen is a "Good for listening" section 401. In some embodiments, Figure 1 the platform optimizer 170A can enhance the content sharing platform 120 to include links to video content items that have been matched with podcast episodes. The video content items listed in the "Good for listening" section 401 can be ranked according to a combined popularity indicator of the matched video content item and the matched podcast episode.

[0069] Figure 5 FIG. illustrates an example GUI 500 of a content sharing platform optimized for audio content, such as a video content item matched with a podcast episode, according to an embodiment of the present disclosure. In one example, a user can select a podcast episode from the Figure 4 shown "Good for listening" section 401 and the podcast episode can be presented to the user, as Figure 5 shown. The GUI 500 can include information provided by both the matched podcast episode identifier and the matched video content item. For example, the GUI 500 can include the video content item in the top portion of the screen and the bottom portion of the screen can include information extracted from the matched podcast episode. For example, host information can be extracted from the matched podcast episode metadata. The GUI 500 optimized for listening can be used to provide a user with a video content item that has been matched with a podcast episode via the content sharing platform, where instead of showing the video content item in full screen, the video content item can occupy less than half of the screen and the listening controls 502 can be more prominent on the screen.

[0070] Figure 6 FIG. illustrates an example GUI 600 of a content sharing platform enhanced for episode content according to an embodiment of the present disclosure. As Figure 6As shown, the GUI 600 includes a link to the podcast 602 and a video of the podcast episode displayed in the main portion of the screen. By matching the video of the podcast episode and the podcast series associated with the podcast episode, the GUI 600 is capable of displaying additional information associated with the matched podcast. In Figure 6 the example shown, a link 601 to "Continue watching Season 2" of the podcast series can be added to the GUI 600. In the case where the video of the podcast episode is not matched to a podcast episode identifier (and thus to a podcast series), the content sharing platform may not have enough information to include links to other podcast episodes in the series in the GUI 600.

[0071] Figure 7 FIG. illustrates an example GUI 700 of a lock screen that optimizes media controls for listening to audio content such as podcast episodes according to an embodiment of the present disclosure. The lock screen can be displayed on a mobile device when the mobile device is using the content sharing platform to play a video that is matched to a podcast episode. Since the video is matched to a podcast episode, the GUI 700 is capable of displaying an optimized lock screen that shows media controls more suitable for audio content. That is, instead of providing media controls traditionally used for viewing video content items (e.g., a "skip" control), the GUI 700 can provide media controls traditionally used for audio content, such as + / - 10 second seek controls 701.

[0072] Figure 8 FIG. illustrates an example GUI 800 of a search engine platform optimized according to aspects of the present disclosure. In this example, the search results of the search engine combine audio episode search results and video content search results. As Figure 8 shown, the search results can be presented in a carousel that shows mixed content, such as some audio content items (e.g., podcasts or podcast episodes) and some video content items. More specifically, the search results include a carousel 803 that contains links to podcast episodes 801 and links to video content items 802. Traditional search engine platforms may display two carousels, one for podcasts and one for videos, taking up more space on the screen, which can be particularly problematic for smaller mobile device screens.

[0073] Figure 9 FIG. illustrates an example GUI 900 of a podcast manager platform optimized according to aspects of the present disclosure. As shown by the header 902, via the podcast manager platform (e.g., Figure 1The analytics information provided to a user (e.g., a podcast publisher) by the podcast manager platform 150) includes analytics information from both the podcast publisher and the content sharing platform. In an example, a platform optimizer (e.g., Figure 1 the platform optimizer 170C) identifies video content items to match Figure 9 the podcast episodes listed in, and has combined analytics information associated with both the podcast episodes and the matching video content items. Further details can be shown when hovering over a metric. For example, as Figure 9 shown, hovering the mouse over the play count for Ep114. Box 904 shows a breakdown of the play count for Ep114, which includes 48 plays on the podcast publisher platform and 128 plays on the content sharing platform.

[0074] Figure 10 FIG. illustrates an example GUI 1000 that provides additional details regarding the analytics information provided to a user. For example, GUI 1000 includes additional details regarding the play count for Ep144 from Figure 9 . The total number of plays for the podcast episode and the matching video content item is 176, where 48 plays are audio plays and 128 plays are video plays. The number of minutes played is the sum of the total minutes played for both the video content item and the podcast episode. The figure shows the total play count on a timeline.

[0075] Figure 11 , 12 and 13 depict flowcharts of methods 1100, 1200, and 1300 performed in accordance with some embodiments of the present disclosure. Methods 1100, 1200, and 1300 can be executed by processing logic that can include hardware (circuits, dedicated logic, etc.), software (e.g., instructions running on a processing device), or a combination thereof. In one embodiment, some or all of the operations of methods 1100, 1200, and 1300 can be performed by Figure 1 one or more components of the system 100.

[0076] For simplicity of explanation, methods 1100, 1200, and 1300 of the present disclosure are depicted and described as a series of acts. However, acts in accordance with the present disclosure can occur in various orders and / or simultaneously and in conjunction with other acts not presented and described herein. Further, not all of the acts shown may be required to implement methods 400 and 500 in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that methods 1100, 1200, and 1300 can alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be appreciated that methods 1100, 1200, and 1300 disclosed in this specification can be stored on an article of manufacture to facilitate transfer and conveyance of these methods to a computing device. As used herein, the term "article of manufacture" is intended to encompass a computer program accessible from any computer readable device or storage medium.

[0077] Figure 11 FIG. 4 is a flowchart of method 1100 for enhancing a content sharing platform by matching video content items to podcast episodes, in accordance with some embodiments of the present disclosure. At block 1110, processing logic can access a data store storing a plurality of podcast episode identifiers. Podcast episode identifiers among the plurality of podcast identifiers can be associated with one or more podcast episode attributes.

[0078] Podcast episode attributes and / or video content item attributes can include at least one of the following: a transcription of the audio, audio content, title, description, duration, or publication date. Processing logic can identify the attributes by reading metadata associated with the respective podcast episode identifier and / or video content item identifier. For example, the publication date of a podcast episode can be stored in metadata associated with the podcast episode identifier. In some embodiments, processing logic can determine or generate an attribute. For example, processing logic can use transcription techniques to generate a transcription of a podcast episode associated with a podcast episode identifier. Attributes can be similarly obtained from the video content item and / or from video content item metadata associated with the video content item identifier.

[0079] At block 1120, processing logic can identify a video content item. The video content item can include one or more video content item attributes or be associated with one or more video content item attributes. In some embodiments, processing logic can receive an identification of the video content item from a user device. For example, a user using the content sharing platform can upload a video content item to the content sharing platform. Processing logic can identify the video as a video content item and can extract or identify one or more video content item attributes. In some embodiments, processing logic can identify video content items that have been uploaded to the content sharing platform.

[0080] At block 1130, the processing logic can determine a matching podcast episode among a plurality of podcast episode identifiers that matches the video content item based on one or more podcast episode attributes and one or more video content item attributes.

[0081] In some embodiments, to determine the matching podcast episode, the processing logic can compare one or more video content item attributes with one or more podcast episode attributes of each of the plurality of podcast episode identifiers. In some embodiments, for each podcast episode identifier, the processing logic can compare the podcast episode attributes with the video content item attributes. For example, the processing logic can compare the transcript associated with each podcast episode identifier with the transcript associated with the video content item identifier. The processing logic can assign a matching value to each compared attribute. For example, if the transcripts match, the processing logic can assign a matching value of "1" to the transcript attribute; if the titles do not match, the processing logic can assign a matching value of "0" to the title attribute. The determination of whether an attribute matches can include a substantial match. That is, if a certain percentage (e.g., 70%) of the transcript matches, the processing logic can determine that the transcripts match; or if a percentage (e.g., 90%) of the descriptions match, the processing logic can determine that the descriptions match.

[0082] For each of the one or more podcast episode identifiers, the processing logic can determine a matching score based on the comparison. In some embodiments, the matching score can be the sum of the matching values of each compared attribute. In some embodiments, the matching score can be the average (or weighted average) of the matching values of each compared attribute. For example, the processing logic can assign a higher weight to the transcript and a lower weight to the title. The processing logic can identify the matching podcast episode identifier with the highest matching score that meets the matching criteria. The highest matching score can meet the matching criteria by exceeding a minimum matching score threshold.

[0083] In some embodiments, to determine the matching podcast episode, the processing logic can provide one or more video content item attributes as input to a machine learning model. The machine learning model can be trained to identify the matching podcast episode based on one or more podcast episode attributes and video content item attributes.

[0084] At block 1140, the processing logic can cause the ranking of at least one of the video content item or the matching podcast episode to be adjusted to reflect the correspondence between the video content item and the matching podcast episode. In some embodiments, the ranking of the video content item or the matching podcast episode is based on a popularity indicator. Causing the ranking of the video content item or the matching podcast episode to be adjusted to reflect the correspondence between the video content item and the matching podcast episode can include combining the popularity indicator associated with the video content item and the popularity indicator associated with the matching podcast episode.

[0085] At block 1150, the processing logic can provide information associated with a matching podcast episode to a first user device. In some embodiments, in response to receiving a search query from a user device, information associated with a matching podcast episode is provided. The results of the search query can include video content items. Additionally, the results of the search query can include podcast analytics information associated with the matching podcast episode. In some embodiments, the information associated with the matching podcast episode includes host information, previous / next podcast episode controls, and / or a listen-only option.

[0086] Figure 12 A flowchart of a method 1200 for enhancing a search engine platform by matching video content items to podcast episodes, in accordance with some embodiments of the present disclosure, is depicted.

[0087] At block 1210, the processing logic can access a data store that includes one or more podcast episode identifiers and one or more video content item identifiers. The podcast episode identifiers among the one or more podcast episode identifiers can be associated with one or more podcast episode attributes. The video content item identifiers among the one or more video content item identifiers can be associated with one or more video content item attributes.

[0088] The podcast episode attributes and / or the video content item attributes can include at least one of the following: a transcription of the audio, audio content, a title, a description, a duration, or a publication date. The processing logic can identify the attributes by reading metadata associated with the respective podcast episode identifiers and / or video content item identifiers. For example, the publication date of a podcast episode can be stored in the metadata associated with the podcast episode identifier. In some embodiments, the processing logic can determine or generate an attribute. For example, the processing logic can use transcription techniques to generate a transcription of a podcast episode associated with a podcast episode identifier. Attributes can be obtained similarly from the video content item and / or from the video content item metadata associated with the video content item identifier.

[0089] The processing logic can also periodically identify additional podcast episodes and / or additional video content items, and store the associated additional podcast episode identifiers and / or additional video content item identifiers in the data store. In some embodiments, the processing logic can crawl the Internet to identify additional podcast episodes and video content items. In some embodiments, the processing logic can receive additional podcast episodes and / or video content items. For example, a user can upload podcast episodes and / or video content items to the platform, and the processing logic can store the newly uploaded podcast episode identifiers and / or video content item identifiers in the data store.

[0090] At block 1220, the processing logic can determine a matching video content item identifier for a matching podcast episode identifier among one or more podcast episode identifiers based on one or more podcast episode attributes associated with the matching podcast episode identifier. In some embodiments, the processing logic can select one of the plurality of podcast episode identifiers from the data store as the matching podcast episode identifier for which a matching video content item is found. In some embodiments, the data store includes an identifier indicating whether each podcast episode identifier in the data store has a matching video content item identifier. The processing logic can select the matching podcast episode identifier as the first podcast episode identifier in the data store that does not have a matching video content item. In some embodiments, the processing logic can select the matching podcast episode identifier based on a popularity indicator. That is, the processing logic can identify a podcast episode identifier that does not have a matching video content item but has a high popularity indicator (such as a number of plays or likes exceeding a corresponding threshold) as the matching podcast episode identifier.

[0091] In some embodiments, to determine the matching video content item identifier, the processing logic can compare one or more podcast episode attributes associated with the matching podcast episode identifier with one or more video content item attributes associated with one or more video content item identifiers. In some embodiments, for each video content item identifier, the processing logic can compare the podcast episode attributes associated with the matching podcast episode identifier with the video content item attributes. For example, the processing logic can compare the transcript associated with the matching podcast episode identifier with the transcript associated with each video content item identifier. The processing logic can assign a matching value to each compared attribute. For example, if the transcripts match, the processing logic can assign a matching value of "1" to the transcript attribute; if the titles do not match, the processing logic can assign a matching value of "0" to the title attribute. The determination of whether an attribute matches can include substantially matching. That is, if a certain percentage (e.g., 70%) of the transcript matches, the processing logic can determine that the transcripts match; or if a percentage (e.g., 90%) of the descriptions match, the processing logic can determine that the descriptions match.

[0092] The processing logic can determine a matching score for each of one or more video content item identifiers based on the comparison. In some embodiments, the matching score can be the sum of the matching values of each compared attribute. In some embodiments, the matching score can be the average (or weighted average) of the matching values of each compared attribute. For example, the processing logic can assign a higher weight to the transcript and a lower weight to the title. The processing logic can identify the matching video content item identifier having the highest matching score that meets the matching criteria. The highest matching score can meet the matching criteria by exceeding a minimum matching score threshold.

[0093] In some embodiments, to determine a matching video content item identifier, the processing logic can provide one or more video content item attributes as input to a machine learning model. The machine learning model can be trained to identify a matching video content item identifier based on one or more matching podcast episode attributes and video content item attributes.

[0094] At block 1230, the processing logic can cause the ranking of at least one of the matching video content item identifier or the matching podcast episode identifier to be adjusted to reflect the correspondence between the matching video content item identifier and the matching podcast episode identifier. In some embodiments, the ranking of the matching video content item or the matching podcast episode is based on a popularity indicator. Causing the ranking of the video content item or the matching podcast episode to be adjusted to reflect the correspondence between the video content item and the matching podcast episode can include combining the popularity indicator associated with the matching video content item identifier and the popularity indicator associated with the matching podcast episode identifier.

[0095] At block 1240, the processing logic can provide information associated with the matching podcast episode identifier and information associated with the video content item identifier to a user device. In some embodiments, the information associated with the matching podcast episode identifier and the information associated with the matching video content item identifier can be provided in response to receiving a search query from the user device. The result of the search query can include at least one of the matching video content item or the matching podcast episode identifier. For example, search results provided to a user by a platform not enhanced with aspects of the present disclosure that include a particular video content item will include only the video content item and information associated with the video content item. However, by matching video content items to podcast episodes according to aspects of the present disclosure, the search platform can enhance the search results by providing the video content item and the matching podcast episode and associated information to the user device. Thus, information (e.g., host information) that may be associated only with the podcast episode is added to the search results.

[0096] Figure 13 A flowchart of a method 1300 for enhancing a podcast manager platform by matching video content items to podcast episodes, in accordance with embodiments of the present disclosure, is depicted. At block 1310, the processing logic can receive a request for podcast analysis information for a podcast, where the podcast is associated with one or more podcast episodes. In some embodiments, the processing logic can receive the request from a user device implementing the podcast manager platform. A user can request podcast analysis information associated with a particular podcast.

[0097] At block 1320, the processing logic can receive an identification of a source that includes one or more video content items. In some embodiments, a podcast can be associated with one or more specific channels on a content sharing platform. In other embodiments, a user can provide a source that includes one or more video content items associated with a podcast to a podcast management platform.

[0098] At block 1330, the processing logic can identify one or more podcast episode attributes of one or more podcast episodes. At block 1340, the processing logic can identify one or more video content item attributes of one or more video content items. The podcast episode attributes and / or the video content item attributes can include at least one of the following: a transcription of the audio, audio content, a title, a description, a duration, or a release date. The processing logic can identify the attributes by reading metadata associated with the corresponding podcast episode identifier and / or video content item identifier. For example, the release date of a podcast episode can be stored in the metadata associated with the podcast episode identifier. In some embodiments, the processing logic can determine or generate an attribute. For example, the processing logic can use transcription technology to generate a transcription of a podcast episode associated with a podcast episode identifier. Attributes can be similarly obtained from the video content item and / or from the video content item metadata associated with the video content item identifier. In some embodiments, the processing logic can receive an attribute from a user device (e.g., the user can provide an attribute).

[0099] At block 1350, the processing logic can determine a matching video content item that matches a matching podcast episode in one or more podcast episodes based on one or more podcast episode attributes and one or more video content item attributes. In some embodiments, the processing can first identify a matching podcast episode from one or more podcast episodes for which a matching video content item is identified. The processing logic can identify a podcast episode as the matching podcast episode for which a matching video content item is to be identified based on a popularity indicator (i.e., identifying the matching video content item of the most popular podcast episode), or methodically (i.e., selecting the first podcast episode in a list of podcast episodes). The podcast episode selected as the matching podcast episode can be a podcast episode that does not yet have a matching video content item. The processing logic can determine a matching video content item for each podcast episode associated with the identified podcast.

[0100] To determine a matching video content item, the processing logic can determine a matching score for one or more video content items by comparing the corresponding video content item attributes with one or more podcast episode attributes associated with the matching podcast episode. By comparing the attributes of the matching podcast episode with the attributes of each video content item, as described above with reference to Figure 11 and Figure 12Determining the matching score. The processing logic can identify the matching video content item as the video content item with the highest matching score. In some embodiments, the matching score meets the matching criteria by exceeding a threshold matching value. For example, if the highest matching video content item has a matching score below the threshold matching value, the processing logic may not identify it as a match.

[0101] In some embodiments, to determine the matching video content item identifier, the processing logic can provide one or more video content item attributes and matching podcast episode attributes as inputs to a machine learning model. The machine learning model can be trained to identify the matching video content item identifier based on one or more matching podcast episode attributes and video content item attributes.

[0102] At block 1360, the processing logic can determine the analysis information associated with the matching video content item. At block 1370, the processing logic can provide a response to the request, where the response includes the analysis information associated with the matching video content item and podcast analysis information. In some embodiments, the analysis information provided in response to the request can include the combined number of plays associated with each podcast episode and the matching video content item, as Figure 9 and Figure 10 shown in the example GUI of. For example, the analysis information provided in response to the request can also include additional combined information, such as the combined average length of the plays, or the combined number of plays in the previous 30 days. The analysis information can also include demographic data about podcast listeners and video content item viewers.

[0103] Figure 14 is a block diagram illustrating an exemplary computer system 1400 according to an embodiment of the present disclosure. The computer system 1400 can correspond to the server machine 140, the content sharing platform 120, and / or the end-user devices 102A-N described with reference to Figure 1 The computer system 1400 can operate as a server or an endpoint machine in an endpoint server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine can be a television, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a network device, a server, a network router, a switch, or a bridge, or any machine capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by that machine. Further, although only a single machine is illustrated, the term "machine" should also be understood to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0104] Example computer system 1400 includes a processing device (processor) 1402, a main memory 1404 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM), double data rate (DDR SDRAM), or DRAM (RDRAM)), a static memory 1406 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 1418, which communicate with each other via a bus 1440.

[0105] The processor (processing device) 1402 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, the processor 1402 can be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a processor implementing a combination of instruction sets. The processor 1402 can also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The processor 1402 is configured to execute instructions 1405 (e.g., for identifying matching video content items and podcast episodes) to perform the operations discussed herein.

[0106] Computer system 1400 can also include a network interface device 1408. Computer system 1400 can also include a video display unit 1410 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an input device 1412 (e.g., a keyboard and alphanumeric keypad, a motion sensing input device, a touch screen), a cursor control device 1414 (e.g., a mouse), and a signal generation device 1420 (e.g., a speaker).

[0107] The data storage device 1418 can include a non-transitory machine-readable storage medium 1424 (also referred to as a computer-readable storage medium) having stored thereon a set or sets of instructions 1426 (e.g., for optimizing a platform using the matching pairs of the identified video content items and podcast episodes), which instructions embody any one or more of the methods or functions described herein. During execution of the instructions by computer system 1400, the instructions can also reside, completely or at least partially, within the main memory 1404 and / or the processor 1402, which also constitutes a machine-readable storage medium. These instructions can also be transmitted or received over a network 1430 via the network interface device 1408.

[0108] In one embodiment, instruction 1426 includes instructions for identifying matching pairs of podcast episode identifiers and video content item identifiers and for optimizing the platform based on the identified matches. Although computer-readable storage medium 1424 (machine-readable storage medium) is shown as a single medium in the exemplary embodiment, the terms "computer-readable storage medium" and "machine-readable storage medium" should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store a set or multiple sets of instructions. The terms "computer-readable storage medium" and "machine-readable storage medium" should also be understood to include any medium that is capable of storing, encoding, or carrying a set of instructions executable by a machine and that causes the machine to perform any one or more of the methods of the present disclosure. The terms "computer-readable storage medium" and "machine-readable storage medium" should be understood to correspondingly include, but not be limited to, solid-state memory, optical media, and magnetic media.

[0109] Throughout the specification, references to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the phrases "in one embodiment" or "in an embodiment" that appear in various places throughout the specification can, but do not necessarily, refer to the same embodiment, depending on the context. Additionally, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0110] For the terms "comprising," "including," "having," "containing," and their variants and other similar words used in the detailed description or claims, these terms are intended to be inclusive in a manner similar to the term "including" as an open transitional word, without excluding any additional or other elements.

[0111] As used in this application, the terms "component," "module," "system," etc. generally refer to a computer-related entity, either hardware (e.g., circuitry), software, a combination of hardware and software, or an entity related to an operating machine with one or more specific functionalities. For example, a component can be, but is not limited to, a process running on a processor (e.g., a digital signal processor), a processor, an object, an executable program, an execution thread, a program, and / or a computer. For instance, both an application running on a controller and the controller can be components. One or more components can reside in a process and / or an execution thread, and a component can be located on one computer and / or distributed between two or more computers. Additionally, a "device" can take the form of specially designed hardware; general-purpose hardware specialized by executing software thereon that enables the hardware to perform a specific function (e.g., generating points of interest and / or descriptors); software on a computer-readable medium; or a combination thereof.

[0112] The foregoing systems, circuits, modules, etc. have been described with respect to the interactions between several components and / or blocks. It will be appreciated that such systems, circuits, components, blocks, etc. can include those components or designated sub-components, some of the designated components or sub-components, and / or additional components, and in accordance with the various arrangements and combinations described above. Sub-components can also be implemented as components communicatively coupled to other components rather than being included within a parent component (hierarchical). Additionally, it should be noted that one or more components can be combined into a single component that provides aggregated functionality, or divided into several separate sub-components, and any one or more intermediate layers such as a management layer can be provided to communicatively couple to these sub-components in order to provide integrated functionality. Any component described herein can also interact with one or more other components not specifically described herein but known to those skilled in the art.

[0113] Furthermore, the words "example" or "exemplary" are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Instead, the use of the words "example" or "exemplary" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X adopts A or B" is intended to mean any natural inclusive arrangement. That is, if X adopts A; X adopts B; or X adopts both A and B, then in any of the foregoing cases, "X adopts A or B" is satisfied. Additionally, the articles "a" and "an" as used in this application and the appended claims should generally be construed to mean "one or more" unless otherwise specified or clearly indicated to the contrary in the context.

[0114] Finally, the embodiments described herein include collecting data that describes a user and / or user activities. In one embodiment, such data is collected only when the user consents to the collection of such data. In some embodiments, the user is prompted to explicitly allow data collection. Additionally, the user can decide to participate or not participate in such data collection activities. In one embodiment, the data collected is anonymized before any analysis is performed to obtain any statistical patterns such that the identity of the user cannot be determined from the data collected.

Claims

1. A method, comprising: Accessing a data store including a plurality of podcast episode identifiers, wherein a podcast episode identifier among the plurality of podcast episode identifiers is associated with one or more podcast episode attributes; Identifying a video content item, wherein the video content item includes one or more video content item attributes; Based on the one or more podcast episode attributes and the one or more video content item attributes, determining a matching podcast episode identifier among the plurality of podcast episode identifiers that matches the video content item; Causing a ranking of at least one of the video content item or the matching podcast episode identifier to be adjusted to reflect a correspondence between the video content item and the matching podcast episode identifier, wherein causing the ranking of at least one of the video content item or the matching podcast episode identifier to be adjusted includes combining a first set of popularity indicators associated with the video content item and a second set of popularity indicators associated with the matching podcast episode identifier; and Providing information associated with the matching podcast episode identifier to a first user device.

2. The method according to claim 1, wherein, The one or more podcast episode attributes include at least one of the following: audio transcription, audio content, title, description, duration, or publication date; and wherein the one or more video content item attributes include at least one of the following: audio transcription, audio content, title, description, duration, or publication date.

3. The method according to claim 1, wherein The video content item is received from a second user device.

4. The method according to claim 1, wherein Determining the matching podcast episode identifier among the plurality of podcast episode identifiers that matches the video content item includes: Comparing the one or more video content item attributes with the one or more podcast episode attributes associated with each podcast episode identifier among the plurality of podcast episode identifiers; For each podcast episode identifier among the plurality of podcast episode identifiers, determining a matching score based on the comparison; and Identifying the matching podcast episode identifier having the highest matching score, wherein the matching score meets a matching criterion.

5. The method according to claim 1, wherein Determining the matching podcast episode identifier among the plurality of podcast episode identifiers that matches the video content item includes: Providing the one or more video content item attributes as input to a machine learning model that is trained to identify the matching podcast episode identifier based on the one or more podcast episode attributes associated with each podcast episode identifier accessed from the data store and the one or more video content item attributes.

6. The method according to claim 1, wherein In response to receiving a search query from the first user device, providing the information associated with the matching podcast episode identifier, wherein the result of the search query includes the video content item.

7. The method according to claim 6, wherein, The result of the search query includes podcast analysis information associated with the matching podcast episode identifier.

8. The method according to claim 1, wherein The information associated with the matching podcast episode identifier includes: host information, previous / next podcast episode controls, or a pure listening option.

9. The method according to claim 1, wherein The ranking of the video content item is based on a combination of the first set of popularity indicators associated with the video content item and the second set of popularity indicators associated with the matching podcast episode identifier, and the ranking of the matching podcast episode identifier is based on the combination of the first set of popularity indicators associated with the video content item and the second set of popularity indicators associated with the matching podcast episode identifier.

10. A system comprising: a memory; and a processing device communicatively coupled to the memory, the processing device configured to: access a data store including one or more podcast episode identifiers and one or more video content item identifiers, wherein a podcast episode identifier among the one or more podcast episode identifiers is associated with one or more podcast episode attributes, and wherein a video content item identifier among the one or more video content item identifiers is associated with one or more video content item attributes; identify a matching podcast episode identifier from the one or more podcast episode identifiers; for the matching podcast episode identifier, determine a matching video content item identifier based on one or more podcast episode attributes associated with the matching podcast episode identifier; cause the ranking of at least one of the matching video content item identifier or the matching podcast episode identifier to be adjusted to reflect the correspondence between the matching video content item identifier and the matching podcast episode identifier, wherein causing the ranking of at least one of the matching video content item identifier or the matching podcast episode identifier to be adjusted includes combining a first set of popularity indicators associated with the matching video content item identifier and a second set of popularity indicators associated with the matching podcast episode identifier; and provide information associated with the matching podcast episode identifier and associated with the matching video content item identifier to a user device.

11. The system according to claim 10, wherein, The one or more podcast episode attributes include at least one of the following: transcription, audio content, title, description, duration, or publication date; and wherein the one or more video content item attributes include at least one of the following: transcription, audio content, title, description, duration, or publication date.

12. The system according to claim 10, wherein, The processing device is further configured to: periodically identify additional podcast episode identifiers and additional video content item identifiers; and store the additional podcast episode identifiers and the additional video content item identifiers in the data store.

13. The system according to claim 10, wherein Provide the information associated with the matching podcast episode identifier and associated with the matching video content item identifier in response to receiving a search query from the user device, wherein the result of the search query includes one of the following: the matching video content item identifier or the matching podcast episode identifier.

14. The system according to claim 10, wherein The ranking of the matching video content item identifiers or the ranking of the matching podcast episode identifiers is based on a combination of the first set of popularity indicators associated with the matching video content items and the second set of popularity indicators associated with the matching podcast episode identifiers.

15. The system according to claim 10, wherein, To determine the matching video content item identifier for the matching podcast episode identifier among the one or more podcast episode identifiers, the processing device is further configured to: Compare one or more podcast episode attributes associated with the matching podcast episode identifier with one or more video content item attributes associated with the one or more video content item identifiers; For each video content item identifier among the one or more video content item identifiers, determine a matching score based on the comparison; And Identify the matching video content item identifier having the highest matching score, wherein the matching score meets a matching criterion.

16. The system according to claim 10, wherein, To determine the matching video content item identifier, the processing device is further configured to: Provide the one or more video content item attributes and the one or more matching podcast episode attributes as inputs to a machine learning model that is trained to identify the matching video content item identifier based on the one or more matching podcast episode attributes.

17. A non-transitory machine-readable storage medium comprising instructions that cause a processing device to perform operations, the operations including: Receiving a request for podcast analysis information for a podcast, wherein the podcast is associated with one or more podcast episodes; Receiving an identification of a source including one or more video content items; Identifying one or more podcast episode attributes of the one or more podcast episodes; Identifying one or more video content item attributes of the one or more video content items; Based on the one or more podcast episode attributes and the one or more video content item attributes, determining a matching video content item that matches a matching podcast episode among the one or more podcast episodes; Adjusting the ranking of at least one of the matching video content item or the matching podcast episode to reflect the correspondence between the matching video content item and the matching podcast episode, wherein adjusting the ranking of at least one of the matching video content item or the matching podcast episode includes combining a first set of popularity indicators associated with the matching video content item and a second set of popularity indicators associated with the matching podcast episode; Determining analysis information associated with the matching video content item; and Providing a response to the request, wherein the response includes the analysis information associated with the matching video content item and the podcast analysis information.

18. The non-transitory machine-readable storage medium according to claim 17, wherein, The one or more podcast episode attributes include at least one of the following: transcription, audio content, title, description, duration, or release date, and wherein the one or more video content item attributes include at least one of the following: transcription, audio content, title, description, duration, or release date.

19. The non-transitory machine-readable storage medium according to claim 17, wherein, The analysis information and the podcast analysis information associated with the matching video content item include: the number of times the matching video content item has been played and the number of times the matching podcast episode has been played.

20. The non-transitory machine-readable storage medium according to claim 17, wherein, To determine the matching video content item that matches the matching podcast episode in the one or more podcast episodes based on the one or more podcast episode attributes and the one or more video content item attributes includes: determining a matching score for the one or more video content items by comparing corresponding video content item attributes with one or more podcast episode attributes associated with the matching podcast episode; and identifying, for the matching podcast episode, the matching video content item having the highest matching score, wherein the highest matching score meets a matching criterion.

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