Video content conversion

Described are techniques for video conversion for accessibility including a technique comprising determining, using data from at least one camera, that a user is distracted based on a direction of gaze of the user with respect to a display device presenting video content. The technique further comprises converting, by a machine learning model, the video content to audio content in response to determining the user is distracted, wherein the audio content comprises a description of the video content. The technique further comprises outputting, using at least one speaker, the audio content to the user while the user is distracted.

BACKGROUND

The present disclosure relates to content accessibility, and, more specifically, to generating supplemental content corresponding to video content for distracted users.

Video content is increasingly accessible due to the proliferation of user devices and the improvements in streaming technology and wireless communication. As a result, video content can be viewed on phones, tablets, laptops, desktops, televisions, vehicle consoles, and other displays at most times and in most locations. Despite the improved distribution and accessibility of video content, numerous things may distract a viewer from video content. For example, a participant in an online video conference may monitor email during the video conference. These and other examples can cause viewers to be distracted for portions of video content that they are viewing.

Viewing video content while distracted is inefficient. For example, a variety of video content includes audio content that may not fully reflect the information in the video content. For example, during a video conference, a presenter may direct a viewer's attention to a graph or chart but not elaborate on the contents of the graph or chart. For a viewer that is listening to the presenter but not looking at the video content, information is still lost. Accordingly, there is a growing need to improve video content accessibility for users who may be intermittently distracted while viewing the video content.

SUMMARY

Aspects of the present disclosure are directed toward a computer-implemented method comprising determining, using data from at least one camera, that a user is distracted based on a direction of gaze of the user with respect to a display device presenting video content. The method further comprises converting, by a machine learning model, the video content to audio content in response to determining the user is distracted, where the audio content comprises a description of the video content. The method further comprises outputting, using at least one speaker, the audio content to the user while the user is distracted.

Advantageously, the aforementioned method enhances usability of the display device insofar as supplemental audio content based on the video content is selectively provided to the user when the user is distracted from the display device. A second advantage relates to the machine learning model. Using the machine learning model enables a faster and more accurate conversion of the video content to supplemental audio content than may be achieved using static rules. In fact, machine learning and deep learning may be the only tools with the power and flexibility to generate useful supplemental audio content from video content given the wide variety of video content that exists (e.g., presentations, television shows, movies, sporting events, news casts, and so on). A third advantage relates to the direction of gaze of the user as detected by at least one camera in order to determine that the user is distracted. Utilizing the direction of gaze to characterize a distracted viewer of video content is advantageous insofar as it is an accurate and efficient of method detecting distracted users.

Another aspect of the present disclosure based on the above-described method further includes determining the user is distracted by determining the direction of gaze of the user does not intersect the display device for a period of time above a time threshold. Advantageously, this aspect of the present disclosure reduces false-positive indications of a distracted user by ensuring that the direction of gaze of the user does not intersect the display device for at least the time threshold.

Another aspect of the present disclosure based on the above-described method further includes converting the video content to the audio content by identifying salient features of the video content, converting, to audio content, a subset of the salient features that do not match any preexisting audio content in the video content, and interleaving the audio content with the preexisting audio content. Advantageously, this aspect of the present disclosure converts salient features of the video content that have no corresponding preexisting audio content to audio content for a distracted user. In other words, this aspect of the present disclosure reduces the amount of duplicated audio content by only generating audio content for portions of the video content that are considered salient features of the video content and for which there is no preexisting audio content.

Additional aspects of the present disclosure are directed to systems and computer program products configured to perform the methods described above. The present summary is not intended to illustrate each aspect of, every implementation of, and/or every embodiment of the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure are directed toward to content accessibility, and, more specifically, to generating supplemental content corresponding to video content for distracted users. While not limited to such applications, embodiments of the present disclosure may be better understood in light of the aforementioned context.

FIG. 1illustrates a block diagram of a computational environment100including a video conversion system102. The video conversion system102comprises a display device104presenting video content106. The display device104can be any user interface such as, for example, a smartphone, a smart television, a tablet, a laptop, a computer, a vehicle console, a heads up display (HUD), or any other screen capable of electronically displaying video content106. Video content106can be, for example, a presentation, a video conference, a movie, a show, a sporting event, or other content including graphical elements.

Video conversion system102can further include one or more cameras108, one or more speakers110, and/or one or more microphones112. The cameras108can be used to continuously or intermittently monitor a direction of gaze of a user using gaze tracking techniques known by those skilled in the art. For example, cameras108can be configured for optical tracking based on corneal reflections of light (e.g., infrared light). However, other gaze tracking techniques are also within the spirit and scope of the present disclosure such as, but not limited to, eye-attached tracking (e.g., using a custom contact lens (not shown) configured for monitoring gaze detection) and electric potential measurement (e.g., using electrooculogram (EOG) data from electrodes (not shown) surrounding a user's eyes).

Speakers110can be used to present audio content118generated to summarize video content106for a distracted user. Microphones112can be used to receive verbal feedback from a user for updating a machine learning model114or otherwise modifying aspects of the video conversion system102.

The video conversion system102further includes a machine learning model114configured to ingest the video content106and generate supplemental content116. The machine learning model114can be trained using historical video content and historical supplemental content. The supplemental content116can include audio content118and/or a visual summary120. The supplemental content116can be used to convey visual information from the video content106to a user that is distracted (e.g., not looking at the display device104) while they are distracted (e.g., audio content118) and/or after they are distracted (e.g., visual summary120).

In some embodiments, the machine learning model114can include any number of machine learning algorithms such as, but not limited to, natural language processing (NLP), decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity/metric training, sparse dictionary learning, genetic algorithms, rule-based learning, and/or other machine learning techniques.

For example, machine learning model114can be configured to perform machine learning on a training set of historical video content and corresponding historical supplemental content using one or more of the following example techniques: K-nearest neighbor (KNN), learning vector quantization (LVQ), self-organizing map (SOM), logistic regression, ordinary least squares regression (OLSR), linear regression, stepwise regression, multivariate adaptive regression spline (MARS), ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least-angle regression (LARS), probabilistic classifier, naïve Bayes classifier, binary classifier, linear classifier, hierarchical classifier, canonical correlation analysis (CCA), factor analysis, independent component analysis (ICA), linear discriminant analysis (LDA), multidimensional scaling (MDS), non-negative metric factorization (NMF), partial least squares regression (PLSR), principal component analysis (PCA), principal component regression (PCR), Sammon mapping, t-distributed stochastic neighbor embedding (t-SNE), bootstrap aggregating, ensemble averaging, gradient boosted decision tree (GBRT), gradient boosting machine (GBM), inductive bias algorithms, Q-learning, state-action-reward-state-action (SARSA), temporal difference (TD) learning, apriori algorithms, equivalence class transformation (ECLAT) algorithms, Gaussian process regression, gene expression programming, group method of data handling (GMDH), inductive logic programming, instance-based learning, logistic model trees, information fuzzy networks (IFN), hidden Markov models, Gaussian naïve Bayes, multinomial naïve Bayes, averaged one-dependence estimators (AODE), Bayesian network (BN), classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), expectation-maximization algorithm, feedforward neural networks, logic learning machine, self-organizing map, single-linkage clustering, fuzzy clustering, hierarchical clustering, Boltzmann machines, convolutional neural networks, recurrent neural networks, hierarchical temporal memory (HTM), and/or other machine learning techniques. After training the machine learning model114on a training set of historical video content and corresponding historical supplemental content, the machine learning model114can ingest video content106and output supplemental content116, where the supplemental content116can include audio content118and/or visual summary120.

Audio content118can be audio descriptions of otherwise unstated information such as, but not limited to, presentation content (e.g., information on a slide of a presentation), gestures (e.g., hand gestures for emphasis), facial expressions (e.g., smiling, frowning, grimacing, laughing, etc.), motions or movements (e.g., notable player movements during a portion of a sporting event), scene information (e.g., a description of a background in a show, movie, or other presentation), and/or other information that may be useful in conveying the content, context, or subtext of video content106to a user that cannot see the video content106. In some embodiments, audio content118is only generated for portions of video content106that meet at least one of the following criteria: (i) the portion of video content106is considered a salient portion, and/or (ii) the portion of the video content106has no preexisting audio content associated with it.

Salient portions of video content106can be portions that are important or useful for understanding the information, context, tone, perspective, or other explicit or implicit information associated with the video content106. As one example, a chart may be considered a salient portion of the video content106, whereas a presenter adjusting a microphone or taking a drink of water may be considered to not be a salient portion of the video content.

Preexisting audio content can refer to audio content that (i) exists in the original video content106and (ii) adequately describes the video content106to a viewer that cannot see the video content106. For example, an example video content106may contain a statement such as “as shown in the chart, these two factors are correlated.” Thus, while audio content associated with this portion of the video content does exists, it is insufficient to adequately describe the corresponding portion of the video content106to a viewer that cannot see the video content106. For example, the viewer may not know what the factors are, and whether they're positively correlated, negatively correlated, or inversely correlated.

The machine learning model114can be configured to accurately identify salient portions of the video content106and portions of the video content106associated with preexisting audio content as described above.

The visual summary120can be a graphical summary of the information shown in the video content106while the user is distracted (e.g., not looking at the display device104). For example, the visual summary120can include text summarizing the video content106that played while the user was distracted.

In some embodiments, the visual summary120can be displayed to the display device104or a secondary user device122. When the visual summary120is displayed to the display device104, the visual summary120can be displayed in response to determining that the user's direction of gaze has returned to the display device104. When the visual summary120is displayed to the secondary user device122, the visual summary120can be transmitted to the secondary user device122in the form of, for example, a short message service (SMS) message, a text message, an email, a voice message, a message from an application associated with the video conversion system102, or another form. The visual summary120can be transmitted to the secondary user device122using a network such as, but not limited to, the Internet, a cellular network, a short-range network (e.g., Bluetooth®, etc.), or another network.

The secondary user device122can be, but is not limited to, a smartphone, a tablet, a laptop, a heads-up-display (HUD), smartglasses, smart contacts, a vehicle interface console, a smartwatch, or another secondary user device.

Video conversion system102can be implemented in various configurations. For example, machine learning model114can be software downloaded to hardware, such as a laptop, where the laptop includes the display device104(e.g., a screen), the camera108, the speaker110, and the microphone112. In such an example, the video conversion system102is realized by the laptop executing software configured to utilize the laptop as the video conversion system102.

In another example, the video conversion system102can be embodied in a vehicle, where the machine learning model114is software downloaded to, and executable by, hardware associated with the vehicle, and where the vehicle is associated with a display device104, camera(s)108, speaker(s)110, and microphone(s)112. In such an example, the video conversion system102is realized by the vehicle executing software configured to utilize the vehicle as the video conversion system102.

In yet another example, the video conversion system102can be a standalone system that is usable in conjunction with one or more other systems. For example, the video conversion system102can include a display device104, camera(s)108, speaker(s)110, microphone(s)112, and the machine learning model114. In this example, the video conversion system102can be communicatively coupled to a laptop, smartphone, television, or another device capable of presenting video content106.

Finally, although the discussion thus far has discussed a single user, any number of users fall within the spirit and scope of the present disclosure. In one example, a group of individuals, each with headphones communicatively coupled to the video conversion system102, can be simultaneously observing video content106. As individuals in the group intermittently become distracted, supplemental content116such as audio content118can be provided to the distracted individuals via their respective headphones while it is not necessarily provided to other individuals that are not distracted.

Although the aforementioned example relates to a group of people in a similar physical location, that is also not necessarily limiting. For example, a group of individuals can virtually meet using a videoconference, and the video conversion system102can be incorporated into the video conferencing software such that when any of the attendees in the videoconference become distracted from the video content106, supplemental content116such as audio content118can be provided to those distracted individuals.

FIG. 2illustrates a flowchart of an example method200for providing supplemental content116corresponding to video content106to a distracted user, in accordance with some embodiments of the present disclosure. The method200can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a processor, or another configuration of hardware and/or software.

Operation202includes determining that a user is distracted based on a direction of gaze of the user with respect to a display device104presenting video content106. In some embodiments, operation202includes determining that the direction of gaze of the user does not intersect the display device104for a predetermined period of time (e.g., where the predetermined period of time is less than, more than, or equal to three seconds, five seconds, ten seconds, or another amount of time). In some embodiments, the direction of gaze of the user is determined based on camera(s)108using techniques, strategies, and technologies understood by those skilled in the art for determining a direction of gaze of a user.

Operation204includes converting the video content106to supplemental content116using a machine learning model114. In various embodiments, the supplemental content116can include audio content118and/or a visual summary120. In some embodiments, operation204automatically occurs in response to determining that the user is distracted in operation202.

Operation206includes outputting the supplemental content116to the user while the user is distracted. For example, if the supplemental content116includes audio content118, then operation206can include outputting the audio content118via one or more speaker(s)110. As another example, if the supplemental content116includes a visual summary120, then operation206can include outputting the visual summary120to a secondary user device122.

FIG. 3Aillustrates a flowchart of an example method300for stopping the outputting of the audio content118in response to determining the user is no longer distracted, in accordance with some embodiments of the present disclosure. The method300can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a processor, or another configuration of hardware and/or software. In some embodiments, the method300occurs after operation206of the method200ofFIG. 2.

Operation302includes determining that the user is not distracted based on a second direction of gaze of the user with respect to the display device104. In some embodiments, the second direction of gaze of the user is based on data collected from one or more camera(s)108and using techniques, strategies, and technologies known to those skilled in the art of gaze tracking. In some embodiments, operation302includes determining that the second direction of gaze intersects with the display device104for at least a threshold period of time (e.g., where the threshold period of time less, than, more than, or equal to one second, three seconds, five seconds, or a different amount of time).

Operation304includes stopping the outputting of the audio content118in response to determining that the user is no longer distracted. Advantageously, the method300ensures that audio content118is only provided to the user when it is needed, thereby improving the usability of the video conversion system102.

FIG. 3Billustrates a flowchart of an example method310for stopping the outputting of the audio content118in response to determining the user has prioritized another task, in accordance with some embodiments of the present disclosure. The method310can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a processor, or another configuration of hardware and/or software. In some embodiments, the method310occurs after operation206of the method200ofFIG. 2.

Operation312includes determining that the video content106is a secondary priority for the user. For example, operation312can include determining that the user is on another phone call, that the user is driving, that the user is having a face-to-face conversation with another individual, and so on. Accordingly, determining that the video content106is a secondary priority can be derived from information collected from one or more camera(s)108, one or more microphone(s)112, a secondary user device122, and/or other components such as a vehicle navigation system that may be communicatively coupled to the video conversion system102. For example, the one or more camera(s)108can use machine vision to determine that the user is talking on a cell phone (e.g., secondary user device122) while the display device104is presenting video content106. As another example, the microphone(s)112can determine that the user is engaged in a face-to-face conversation with another individual.

Operation314includes stopping the outputting of the audio content118. Operation314relates to stopping the outputting of the audio content118, however, operation314does not necessarily stop the outputting of the visual summary120. In fact, in some embodiments, if the audio content118is stopped, then generation of the visual summary120can be automatically started. Advantageously, the information in the video content106that the user is missing can nonetheless be provided to the user (e.g., by the visual summary120) without disrupting the user (e.g., as would occur if outputting audio content118).

FIG. 3Cillustrates a flowchart of an example method320for updating a machine learning model114based on user feedback, in accordance with some embodiments of the present disclosure. The method320can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a processor, or another configuration of hardware and/or software. In some embodiments, the method320occurs after operation206of the method200ofFIG. 2.

Operation322includes receiving feedback from the user. In some embodiments, the feedback is verbal feedback received by one or more microphone(s)112. In some embodiments, the feedback is responsive to one or more questions presented to the user via the display device104and/or the one or more speaker(s)110. In some embodiments, the feedback is related to adjustable settings such as a user increasing or decreasing characteristics of the supplemental content116.

Operation324includes updating the machine learning model114based on the feedback. In some embodiments, after updating the machine learning model114, the method320returns to operation202or operation204of the method200in order to apply the updates to improve detecting a distracted user (operation202) and/or improve converting the video content106to the supplemental content116(operation204). Advantageously, the method320improves usability and accuracy of the video conversion system102over time by incorporating user feedback into the machine learning model114.

FIG. 4Aillustrates a flowchart of an example method400for converting video content106to audio content118, in accordance with some embodiments of the present disclosure. The method400can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a machine learning model114, a processor, or another configuration of hardware and/or software. In some embodiments, the method400is a sub-method of operation204of the method200ofFIG. 2. Operations402-406can utilize NLP, optical character recognition, machine vision, and/or other techniques, strategies, and technologies.

Operation402includes converting textual content in the video content106to audio content118. For example, operation402can include converting text of a presentation to audio content118. As another example, operation402can include converting streaming text (e.g., ticker-tape text presented at a bottom of a newscast) to audio content118. As yet another example, operation402can include converting a graphical representation to audio content118, where the audio content118can describe chart titles, axis titles, axis metrics, names of data, trends of data, relationships between sets of data, and the like.

Operation404includes converting motions in the video content106to audio content118. Motions can include, for example, gestures, movements, expressions, and the like. Operation404can, for example, generate audio content118describing a gesture made by a presenter for emphasis in the video content106. As another example, operation404can generate audio content118describing movements of several players during a sporting event in the video content106.

Operation406can include converting scene information in the video content106to audio content118. For example, operation406can include generating audio content118describing landscapes, locations, people, times of day, weather, and the like.

FIG. 4Billustrates a flowchart of another example method410for converting video content106to audio content118, in accordance with some embodiments of the present disclosure. The method410can be implemented by, for example a video conversion system102, a computer (e.g., computer500ofFIG. 5), a machine learning model114, a processor, or another configuration of hardware and/or software. In some embodiments, the method410is a sub-method of operation204of the method200ofFIG. 2.

Operation412includes identifying salient features of the video content106. Salient features can include features that are important, critical, or notable for understanding the video content106. Salient features can be detected by, for example, the machine learning model114.

Operation414includes matching a first subset of the salient features to preexisting audio content in the video content106. For example, if a presenter reads a bullet point from a presentation in the video content106, then the text associated with the first bullet point is added to the first subset insofar as audio content already exists for that portion of the video content106(e.g., in the form of the presenter reading the text associated with the bullet point). As previously discussed, preexisting audio content can refer to audio content that (i) exists in the original video content106and (ii) adequately describes the video content106to a viewer who cannot see the video content106. Thus, in the earlier example, if the presenter mentioned the bulleted text but did not elaborate on it, then that portion of the original audio content would not qualify as preexisting audio content insofar as it did not adequately describe the video content106to a viewer who cannot see the video content106.

Operation416includes converting to audio content118a second subset of the salient features that do not match any preexisting audio content in the video content106. Thus, operation416can be useful for generating audio content118associated with salient features of the video content106that have no preexisting audio content.

Operation418includes interleaving the audio content118with the preexisting audio content. Interleaving can refer to a variety of strategies, techniques, and methodologies for coherently incorporating the generated audio content118into the video content106with preexisting audio content. For example, operation418can include inserting the generated audio content118into portions of the video content106that do not have any preexisting audio content. As another example, operation418can include replacing certain original audio content in the video content106with generated audio content118, where the generated audio content118is more descriptive than the original audio content. As yet another example, operation418can include increasing the playback speed of the preexisting audio content in order to create room in the video content106for inserting the generated audio content118. As still another example, operation418can include pausing the video content106on a certain frame (or decreasing the playback speed of the video content) and inserting the generated audio content118into the video content106while it is paused on the certain frame (or playing at the decreased playback speed). These and other techniques, strategies, and methodologies are within the spirit and scope of the present disclosure for interleaving the generated audio content118with the preexisting audio content in the video content106.

FIG. 5illustrates a block diagram of an example computer500in accordance with some embodiments of the present disclosure. In various embodiments, computer500can perform any or all of the method described inFIGS. 2-4and/or implement the functionality discussed in any one ofFIG. 1. In some embodiments, computer500receives instructions related to the aforementioned methods and functionalities by downloading processor-executable instructions from a remote data processing system via network550. In other embodiments, computer500provides instructions for the aforementioned methods and/or functionalities to a client machine such that the client machine executes the method, or a portion of the method, based on the instructions provided by computer500. In some embodiments, the computer500is incorporated into (or functionality similar to computer500is virtually provisioned to) the video conversion system102ofFIG. 1or another aspect of the present disclosure.

Each CPU505retrieves and executes programming instructions stored in memory525or storage530. Interconnect520is used to move data, such as programming instructions, between the CPUs505, I/O device interface510, storage530, network interface515, and memory525. Interconnect520can be implemented using one or more busses. CPUs505can be a single CPU, multiple CPUs, or a single CPU having multiple processing cores in various embodiments. In some embodiments, CPU505can be a digital signal processor (DSP). In some embodiments, CPU505includes one or more 3D integrated circuits (3DICs) (e.g., 3D wafer-level packaging (3DWLP), 3D interposer based integration, 3D stacked ICs (3D-SICs), monolithic 3D ICs, 3D heterogeneous integration, 3D system in package (3DSiP), and/or package on package (PoP) CPU configurations). Memory525is generally included to be representative of a random-access memory (e.g., static random-access memory (SRAM), dynamic random access memory (DRAM), or Flash). Storage530is generally included to be representative of a non-volatile memory, such as a hard disk drive, solid state device (SSD), removable memory cards, optical storage, or flash memory devices. In an alternative embodiment, storage530can be replaced by storage area-network (SAN) devices, the cloud, or other devices connected to computer500via I/O device interface510or network550via network interface515.

In some embodiments, memory525stores instructions560. However, in various embodiments, instructions560are stored partially in memory525and partially in storage530, or they are stored entirely in memory525or entirely in storage530, or they are accessed over network550via network interface515.

Instructions560can be computer-readable and computer-executable instructions for performing any portion of, or all of, the methods ofFIGS. 2-4and/or implement the functionality discussed inFIG. 1. In some embodiments, instructions560can be referred to as a video content conversion protocol, video content conversion instructions, and/or a video content conversion mechanism. Although instructions560are shown in memory525, instructions560can include program instructions collectively stored across numerous computer-readable storage media and executable by one or more CPUs505.

In various embodiments, I/O devices512include an interface capable of presenting information and receiving input. For example, I/O devices512can present information to a user interacting with computer500and receive input from the user.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Embodiments of the present invention can also be delivered as part of a service engagement with a client corporation, nonprofit organization, government entity, internal organizational structure, or the like. These embodiments can include configuring a computer system to perform, and deploying software, hardware, and web services that implement, some or all of the methods described herein. These embodiments can also include analyzing the client's operations, creating recommendations responsive to the analysis, building systems that implement subsets of the recommendations, integrating the systems into existing processes and infrastructure, metering use of the systems, allocating expenses to users of the systems, and billing, invoicing (e.g., generating an invoice), or otherwise receiving payment for use of the systems.

Different instances of the word “embodiment” as used within this specification do not necessarily refer to the same embodiment, but they can. Any data and data structures illustrated or described herein are examples only, and in other embodiments, different amounts of data, types of data, fields, numbers and types of fields, field names, numbers and types of rows, records, entries, or organizations of data can be used. In addition, any data can be combined with logic, so that a separate data structure may not be necessary. The previous detailed description is, therefore, not to be taken in a limiting sense.

Any advantages discussed in the present disclosure are example advantages, and embodiments of the present disclosure can exist that realize all, some, or none of any of the discussed advantages while remaining within the spirit and scope of the present disclosure.

Several non-limiting example embodiments of the present disclosure will now be discussed.

Example 1. A computer-implemented method comprising determining, using data from at least one camera, that a user is distracted based on a direction of gaze of the user with respect to a display device presenting video content; converting, by a machine learning model, the video content to audio content in response to determining the user is distracted, wherein the audio content comprises a description of the video content; and outputting, using at least one speaker, the audio content to the user while the user is distracted.

Example 2. The method of Example 1, wherein determining the user is distracted further comprises determining the direction of gaze of the user does not intersect the display device for a period of time above a time threshold.

Example 3. The method of any of Examples 1-2, wherein converting the video content to the audio content further comprises converting textual content in the video content to audio information in the audio content.

Example 4. The method of any of Examples 1-3, wherein converting the video content to the audio content further comprises converting motions in the video content to audio information in the audio content.

Example 5. The method of any of Examples 1-4, wherein converting the video content to the audio content further comprises converting scene information in the video content to audio information in the audio content.

Example 6. The method of any of Examples 1-5, wherein converting the video content to the audio content further comprises identifying salient features of the video content; converting, to the audio content, a subset of the salient features that do not match any preexisting audio content; and interleaving the audio content with the preexisting audio content.

Example 7. The method of Example 6, wherein interleaving the audio content with the preexisting audio content further comprises inserting the audio content into portions of the video content without any preexisting audio content.

Example 8. The method of Example 6, wherein the interleaving the audio content with the preexisting audio content further comprises increasing a speed of the preexisting audio content in order to create respective portions of the video content without any preexisting audio content and inserting the audio content into the respective portions.

Example 9. The method of Example 6, wherein the interleaving the audio content with the preexisting audio content further comprises pausing the video content and inserting the audio content while the video content is paused.

Example 10. The method of any of Examples 1-9, further comprising determining the user is not distracted based on a second direction of gaze of the user with respect to the display device; and stopping outputting the audio content to the user.

Example 11. The method of any of Examples 1-9, further comprising determining that the video content is a secondary priority to the user; and stopping outputting the audio content.

Example 12. The method of any of Examples 1-11, further comprising receiving feedback from the user in response to outputting the audio content to the user; and updating the machine learning model based on the feedback.

Example 13. The method of Example 12, wherein the feedback comprises verbal feedback received at a microphone.

Example 14. The method of any of Examples 1-13, further comprising generating a textual summary of the video content while the user is distracted; and presenting the textual summary to the user.

Example 15. The method of Example 14, wherein the textual summary is presented on the display device.

Example 16. The method of Example 14, wherein the textual summary transmitted to a personal device associated with the user and presented on the personal device.

Example 17. The method of any of Examples 1-16, wherein the method is performed by a video conversion system according to software that is downloaded to the video conversion system from a remote data processing system.

Example 18. The method of Example 17, wherein the method further comprises metering a usage of the software; and generating an invoice based on metering the usage.

Example 19. A system comprising one or more processors; and one or more computer-readable storage media storing program instructions which, when executed by the one or more processors, are configured to cause the one or more processors to perform a method according to any of Examples 1-18.

Example 20. A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method according to any of Examples 1-18.