Systems and methods for managing visual allocation are provided herein that use models to determine states based on visual data and, based thereon, output feedback based on the determined states. Visual data is initially obtained by a visual allocation management system. The visual data includes eye image sequences of a person in a particular state, such as engaging in a task or activity. Visual features can be identified from the visual data, such that glance information including direction and duration can be calculated. The visual data, information derived therefrom, and/or other contextual data is input into the models, which correspond to states, to calculate probabilities that the particular state that the person is engaged in is one of the modeled states. Based on the state identified as having the highest probability, an optimal feedback, such as a warning or instruction, can be output to a connected devices, systems, or objects.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for managing visual allocation, comprising: at least one memory operable to store one or more models, the one or more models including at least one of: (1) a model corresponding to a plurality of candidate states; and (2) a plurality of models in which each model corresponds to one of the plurality of candidate states; and at least one processor communicatively coupled to the at least one memory, the at least one processor being operable to: receive visual data corresponding to a person engaging in an activity during a continuous period of time; identify a sequence of glances from the visual data and, for each glance in the sequence of glances, identify corresponding glance information, the glance information including a glance direction; classify each of the glances in the sequence of glances into a spatial region from among a set of predetermined spatial regions based on their respective glance information; identify, based on the one or more models, the presence of one or more states of the person while engaging in the activity during the continuous period of time by inputting into each of the stored one or more models model input data including one or more of (1) the classification of the glances in the sequence of glances and (2) the glance information of the glances in the sequence of glances; and output feedback based on the identified one or more states. 2. The system of claim 1, wherein the processor is further operable to: identify one or more eye image sequences in the visual data, the one or more eye image sequences including images of the eye region of the person engaging in the activity during the continuous period of time; and extract visual features from each of the images of the one or more eye image sequences, wherein the sequence of glances and corresponding glance information are identified from the one or more eye image sequences based on the extracted visual features. 3. The system of claim 1, wherein, to identify the presence of one or more states at a given instance during the continuous period of time, the at least one processor is further operable to: calculate, by the one or more models, respective probabilities of the presence of each of the plurality of candidate states based on model input data; and select, as the identified one or more states, one or more of the plurality of candidate states having the highest respective calculated probabilities of presence at the given instance during the continuous period of time. 4. The system of claim 3, wherein the output feedback includes one or more of: (1) the probability of the presence of each of the plurality of candidate states during the continuous period of time; (2) the identified one or more states having the highest respective calculated probabilities of presence during the continuous period of time; and (3) instructions based on the identified one or more states of the person while engage in the activity during the continuous period of time. 5. The system of claim 2, wherein the at least one processor is further operable to identify, for each of the sequence of glances, one or more of a glance duration and a glance transition, the glance transition indicating the glance direction of a next glance in the sequence of glances, wherein the glance information of each of the sequence of glances further includes the one or more of the respective glance duration and glance transition, and wherein the identifying of the one or more states is further based on the one or more of the glance duration and the glance transition of each of the sequence of glances. 6. The system of claim 5, wherein the at least one processor is further operable to identify one or more glance patterns from among the sequence of glances, based on the classification of each of the sequence of glances and/or the glance transitions of each of the sequence of glances, and wherein the identifying the presence of the one or more states is further based on the glance transition patterns input into each of the one or more models. 7. The system of claim 1, wherein the one or more models are Hidden Markov Models. 8. The system of claim 1, wherein each of the one or more eye image sequences correspond to a single individual. 9. The system of claim 8, wherein the one or more eye image sequences in the visual data are captured using one or more cameras. 10. The system of claim 1, wherein the receiving of the visual data and outputting the result are performed in real-time. 11. The system of claim 1, wherein the processor is further operable to receive contextual data from one or more communicatively coupled systems, and wherein the identifying the presence of the one or more states is further based on the contextual data. 12. The system of claim 11, wherein the contextual data includes at least one of environment data, surrounding data, and user data. 13. The system of claim 1, wherein the at least one processor is further operable to: train the one or more models using (1) visual data corresponding to a plurality of people engaging in the activity during continuous periods of time, and (2) state data including one or more states present in the plurality of people while engaging in the activity. 14. A vehicle comprising the system of claim 4, wherein the person engaging in the activity is a driver engaging in the operating of the vehicle, wherein the output feedback includes the instructions based on the identified one or more states, and wherein the instructions include one or more of: (1) instructions to manage the spatial attention or awareness of the driver of the vehicle, (2) instructions causing an impact on control of the vehicle, and (3) instructions to augment an exchange of information with at least one of the vehicle and the driver of the vehicle. 15. The vehicle of claim 14, wherein the instructions to augment an exchange of information with at least one of the vehicle and the driver of the vehicle comprise instructions to suppress an exchange of information to and from the vehicle. 16. A method for managing visual allocation, comprising: storing one or more models, the one or more models including at least one of: (1) a model corresponding to a plurality of candidate states; and (2) a plurality of models in which each model corresponds to one of the plurality of candidate states; and receiving visual data corresponding to a person engaging in an activity during a continuous period of time; identifying a sequence of glances from the visual data and, for each glance in the sequence of glances, identify corresponding glance information, the glance information including a glance direction; classifying each of the glances in the sequence of glances into a spatial region selected from among a set of predetermined spatial regions, based on their respective glance information; identifying, based on the one or more models, the presence of one or more states of the person while engaging in the activity during the continuous period of time, by inputting into each of the stored one or more models model input data including one or more of (1) the classification of the glances in the sequence of glances and (2) the glance information of the glances in the sequence of glances; and outputting feedback based on the identified one or more states. 17. The method of claim 16, further comprising: identifying one or more eye image sequences in the visual data, the one or more eye image sequences including images of the eye region of the person engaging in the activity during the continuous period of time; and extracting visual features from each of the images of the one or more eye image sequences, wherein the sequence of glances and corresponding glance information are identified from the one or more eye image sequences based on the extracted visual features. 18. The method of claim 16, wherein, to identify the presence of one or more states at a given instance during the continuous period of time, the method further comprises: calculating, by the one or more models, respective probabilities of the presence of each of the plurality of candidate states based on model input data; and selecting, as the identified one or more states, one or more of the plurality of candidate states having the highest respective calculated probabilities of presence at the given instance during the continuous period of time. 19. The method of claim 18, wherein the outputting the feedback includes one or more of: (1) determining the probability of the presence of each of the plurality of candidate states during the continuous period of time; (2) determining the identified one or more states having the highest respective calculated probabilities of presence during the continuous period of time; and (3) providing instructions based on the identified one or more states of the person while engage in the activity during the continuous period of time. 20. The method of claim 17, further comprising: identifying, for each of the sequence of glances, one or more of a glance duration and a glance transition, the glance transition indicating the glance direction of a next glance in the sequence of glances, wherein the glance information of each of the sequence of glances further includes the one or more of the respective glance duration and glance transition, and wherein the identifying of the one or more states is further based on the one or more of the glance duration and the glance transition of each of the sequence of glances. 21. The method of claim 20, further comprising: identifying one or more glance patterns from among the sequence of glances, based on the classification of each of the sequence of glances and/or the glance transitions of each of the sequence of glances, wherein the identifying the presence of the one or more states is further based on the glance transition patterns input into each of the one or more models. 22. The method of claim 16, wherein the one or more models are Hidden Markov Models. 23. The method of claim 16, wherein each of the one or more eye image sequences correspond to a single individual. 24. The method of claim 23, wherein the one or more eye image sequences in the visual data are captured using one or more cameras. 25. The method of claim 16, wherein the receiving of the visual data and outputting the result are performed in real-time. 26. The method of claim 16, further comprising: receiving contextual data from one or more communicatively coupled systems, wherein the identifying the presence of the one or more states is further based on the contextual data. 27. The method of claim 26, wherein the contextual data includes at least one of environment data, surrounding data, and user data. 28. The method of claim 16, further comprising: training the one or more models using (1) visual data corresponding to a plurality of people engaging in the activity during continuous periods of time, and (2) state data including one or more states present in the plurality of people while engaging in the activity. 29. A system for managing visual allocation, comprising: at least one memory; and at least one processor communicatively coupled to the at least one memory, the at least one processor being operable to: receive (1) visual data corresponding to a plurality of people engaging in an activity during a continuous period of time, and (2) state data including one or more states present in the plurality of people while engaging in the activity during the continuous period of time; calculate one or more mathematical structures using the visual data; train one or more models corresponding to each of the one or more states, based on one or more of the calculated mathematical structures; store the trained one or more models in the one or more memories; and identify, using the trained one or more models, the presence of at least one of the one or more states using visual data of a person engaging in the activity during a different continuous period of time. 30. The system of claim 29, wherein the mathematical structures include structures representing glances in the visual data in the context of at least one of glance transitions and glance duration. 31. The system of claim 30, wherein the mathematical structures include one or more matrices selected from the group consisting of: (i) a transition counts matrix; (ii) a transition probabilities matrix; (iii) a transition importance mask matrix; (iv) a transition significance matrix; (v) a duration counts matrix; (vi) a time distribution per location percentage matrix; (vii) a time distribution per transition percentage matrix; and (viii) a duration importance mask matrix. 32. The system of claim 31, wherein: the transition counts matrix includes data representing a number of times that the glances in the visual data transition from one glance location to another glance location from among a set of predetermined candidate glance locations; the transition probabilities matrix includes data representing a relative frequency that each of the glances transition to another candidate glance location, calculated by dividing values of the transition count value matrix by the sum of all values in the respective row; the transition importance mask matrix includes data calculated by dividing values of the transition counts matrix by the largest value among the values of the respective row; the transition significance matrix includes data calculated by multiplying the transition probabilities matrix by the transition significance matrix; the duration counts matrix includes data representing a duration of each of the glances; the time distribution per location percentage matrix includes data representing an aggregation of the durations of the glances for each of the candidate glance locations, obtained by normalizing the duration counts matrix by candidate glance location; the time distribution per transition percentage matrix includes data representing the aggregation of the duration of the glances for each of the candidate glance locations relative to all of the candidate glance locations, obtained by normalizing the duration counts matrix by all of the candidate glance locations; and the duration importance mask matrix representing data calculated by normalizing the duration counts matrix to unity. 33. The system of claim 32, wherein the one or more states of the person engaging in the activity during the other continuous period of time is not identified in the visual data of the person.