A method of estimating one or more conditional probabilities may be provided. A method may include determining one or more states based on user input, and determining a similarity measurement between at least one state pair of one or more state pairs. The method may further include determining a likelihood of probability for the at least one state pair of the one or more state pairs. Moreover, the method may include estimating a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of estimating one or more conditional probabilities, comprising: determining one or more states based on user input; determining a similarity measurement between at least one state pair of one or more state pairs; determining a likelihood of probability for the at least one state pair of the one or more state pairs; and estimating a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states. 2. The method of claim 1, wherein the determining one or more states comprises recognizing at least one of one or more emotional states and one or more cognitive states of a user based on the user input. 3. The method of claim 1, wherein the determining one or more states comprises determining one or more states based on the user input comprising text provided by a user. 4. The method of claim 1, wherein the determining a conditional probability comprises determining the conditional probability via a Bayesian inference. 5. The method of claim 1, wherein the determining a similarity measurement between at least one state pair of one or more state pairs comprises determining the similarity measurement via a neural word embedding model. 6. The method of claim 5, wherein the determining the similarity measurement via a neural word embedding model comprises training the neural word embedding model based on one or model parameters. 7. The method of claim 1, wherein the determining a likelihood of probability for the at least one state pair of the one or more state pairs comprises normalizing each state pair over all possible state pairs of the one or more state pairs. 8. The method of claim 1, further comprising estimating a probability of a state of a user transitioning from one state of the at least one state pair to another state of the at least one state pair based on the determined conditional probability for the at least one state pair. 9. One or more non-transitory computer-readable media that include instructions that, when executed by one or more processors, are configured to cause the one or more processors to perform operations, the operations comprising: determining one or more states based on user input; determining a similarity measurement between at least one state pair of one or more state pairs; determining a likelihood of probability for the at least one state pair of the one or more state pairs; and estimating a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states. 10. The computer-readable media of claim 9, wherein the determining one or more states comprises recognizing at least one of one or more emotional states and one or more cognitive states of a user based on the user input. 11. The computer-readable media of claim 9, wherein the determining a conditional probability comprises determining the conditional probability via a Bayesian inference. 12. The computer-readable media of claim 9, wherein the determining a similarity measurement between at least one state pair of one or more state pairs comprises determining the similarity measurement via a neural word embedding model. 13. The computer-readable media of claim 12, wherein the determining the similarity measurement via a neural word embedding model comprises training the neural word embedding model based on one or model parameters. 14. The computer-readable media of claim 9, the operations further comprising estimating a probability of a state of a user transitioning from one state of the at least one state pair to another state of the at least one state pair based on the determined conditional probability for the at least one state pair. 15. The computer-readable media of claim 9, wherein the determining a likelihood of probability for the at least one state pair of the one or more state pairs comprises normalizing each state pair over all possible state pairs of the one or more state pairs. 16. A system, comprising: one or more processors configured to: determine one or more states based on user input; determine a similarity measurement between at least one state pair of one or more state pairs; determine a likelihood of probability for the at least one state pair of the one or more state pairs; and estimate a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states. 17. The system of claim 16, wherein the one or more processors are further configured to estimate a probability of a state of a user transitioning from one state of the at least one state pair to another state of the at least one state pair based on the determined conditional probability for the at least one state pair. 18. The system of claim 16, wherein the one or more processors are further configured to recognize at least one of one or more emotional states and one or more cognitive states of a user based on the user input to determine the one or more states. 19. The system of claim 16, wherein the one or more processors are further configured determine the conditional probability via a Bayesian inference. 20. The system of claim 16, wherein the one or more processors are further configured to determine the similarity measurement via a neural word embedding model.