Patents by Inventor Jorge del Val Santos
Jorge del Val Santos has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11847727Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: GrantFiled: December 21, 2022Date of Patent: December 19, 2023Assignee: ELECTRONIC ARTS INC.Inventors: Jorge del Val Santos, Linus Gisslen, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Publication number: 20230123486Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: ApplicationFiled: December 21, 2022Publication date: April 20, 2023Inventors: Jorge del Val Santos, Linus Gisslen, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Patent number: 11580378Abstract: A computer-implemented method comprises instantiating a policy function approximator. The policy function approximator is configured to calculate a plurality of estimated action probabilities in dependence on a given state of the environment. Each of the plurality of estimated action probabilities corresponds to a respective one of a plurality of discrete actions performable by the reinforcement learning agent within the environment. An initial plurality of estimated action probabilities in dependence on a first state of the environment are calculated. Two or more of the plurality of discrete actions are concurrently performed within the environment when the environment is in the first state. In response to the concurrent performance, a reward value is received. In response to the received reward value being greater than a baseline reward value, the policy function approximator is updated, such that it is configured to calculate an updated plurality of estimated action probabilities.Type: GrantFiled: November 12, 2018Date of Patent: February 14, 2023Assignee: ELECTRONIC ARTS INC.Inventors: Jack Harmer, Linus Gisslén, Magnus Nordin, Jorge del Val Santos
-
Patent number: 11562521Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: GrantFiled: June 22, 2021Date of Patent: January 24, 2023Assignee: Electronic Arts Inc.Inventors: Jorge del Val Santos, Linus Gisslén, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Publication number: 20210319610Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: ApplicationFiled: June 22, 2021Publication date: October 14, 2021Inventors: Jorge del Val Santos, Linus Gisslén, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Patent number: 11049308Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: GrantFiled: April 25, 2019Date of Patent: June 29, 2021Assignee: ELECTRONIC ARTS INC.Inventors: Jorge del Val Santos, Linus Gisslén, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Publication number: 20210036868Abstract: A computer-implemented method for validating a digital signature of at least one node in a peer to peer network comprises the steps of, a) executing a gossip algorithm, b) locally calculating in each node, a trust score s assigned to other node of the network, c) locally validating in each node, each digital signature of the at least one node, based on the value of said trust score s of the network, d) if there is a change in at least one trust endorsement value t of a node, sending the changed trust endorsement to each neighbour node and executing step a), or e) if a new node is added or deleted in the network, executing step a). A data processing system, a computer program product and a computer-readable storage medium for carrying out the steps of the method are also described.Type: ApplicationFiled: April 4, 2019Publication date: February 4, 2021Inventor: Jorge Del Val Santos
-
Publication number: 20200302667Abstract: A computer-implemented method for generating a machine-learned model to generate facial position data based on audio data comprising training a conditional variational autoencoder having an encoder and decoder. The training comprises receiving a set of training data items, each training data item comprising a facial position descriptor and an audio descriptor; processing one or more of the training data items using the encoder to obtain distribution parameters; sampling a latent vector from a latent space distribution based on the distribution parameters; processing the latent vector and the audio descriptor using the decoder to obtain a facial position output; calculating a loss value based at least in part on a comparison of the facial position output and the facial position descriptor of at least one of the one or more training data items; and updating parameters of the conditional variational autoencoder based at least in part on the calculated loss value.Type: ApplicationFiled: April 25, 2019Publication date: September 24, 2020Inventors: Jorge del Val Santos, Linus Gisslén, Martin Singh-Blom, Kristoffer Sjöö, Mattias Teye
-
Publication number: 20190286979Abstract: A computer-implemented method comprises instantiating a policy function approximator. The policy function approximator is configured to calculate a plurality of estimated action probabilities in dependence on a given state of the environment. Each of the plurality of estimated action probabilities corresponds to a respective one of a plurality of discrete actions performable by the reinforcement learning agent within the environment. An initial plurality of estimated action probabilities in dependence on a first state of the environment are calculated. Two or more of the plurality of discrete actions are concurrently performed within the environment when the environment is in the first state. In response to the concurrent performance, a reward value is received. In response to the received reward value being greater than a baseline reward value, the policy function approximator is updated, such that it is configured to calculate an updated plurality of estimated action probabilities.Type: ApplicationFiled: November 12, 2018Publication date: September 19, 2019Inventors: Jack Harmer, Linus Gisslén, Magnus Nordin, Jorge del Val Santos