Patents by Inventor Omar Florez CHOQUE
Omar Florez CHOQUE 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).
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Publication number: 20240095498Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: ApplicationFiled: December 1, 2023Publication date: March 21, 2024Inventors: Omar Florez CHOQUE, Erik Mueller
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Patent number: 11934924Abstract: Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.Type: GrantFiled: March 16, 2022Date of Patent: March 19, 2024Assignee: Capital One Services, LLCInventors: Omar Florez Choque, Anish Khazane, Alan Salimov
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Patent number: 11836601Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: GrantFiled: January 19, 2023Date of Patent: December 5, 2023Assignee: CAPITAL ONE SERVICES, LLCInventors: Omar Florez Choque, Erik Mueller
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Publication number: 20230385553Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.Type: ApplicationFiled: June 1, 2023Publication date: November 30, 2023Applicant: Capital One Services, LLCInventors: Alan SALIMOV, Anish KHAZANE, Omar FLOREZ CHOQUE
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Patent number: 11704500Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.Type: GrantFiled: September 9, 2022Date of Patent: July 18, 2023Assignee: Capital One Services, LLCInventors: Alan Salimov, Anish Khazane, Omar Florez Choque
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Publication number: 20230153578Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: ApplicationFiled: January 19, 2023Publication date: May 18, 2023Inventors: Omar Florez CHOQUE, Erik Mueller
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Patent number: 11586877Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: GrantFiled: August 2, 2021Date of Patent: February 21, 2023Assignee: CAPITAL ONE SERVICES, LLCInventors: Omar Florez Choque, Erik T. Mueller
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Publication number: 20230021052Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.Type: ApplicationFiled: September 9, 2022Publication date: January 19, 2023Applicant: Capital One Services, LLCInventors: Alan SALIMOV, Anish KHAZANE, Omar FLOREZ CHOQUE
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Patent number: 11468241Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.Type: GrantFiled: April 27, 2020Date of Patent: October 11, 2022Assignee: Capital One Services, LLCInventors: Alan Salimov, Anish Khazane, Omar Florez Choque
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Publication number: 20220277229Abstract: Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.Type: ApplicationFiled: March 16, 2022Publication date: September 1, 2022Applicant: Capital One Services, LLCInventors: Omar FLOREZ CHOQUE, Anish KHAZANE, Alan SALIMOV
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Patent number: 11308421Abstract: Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.Type: GrantFiled: January 21, 2019Date of Patent: April 19, 2022Assignee: Capital One Services, LLCInventors: Omar Florez Choque, Anish Khazane, Alan Salimov
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Publication number: 20220067500Abstract: Systems and methods are provided herein for utilizing a knowledge base to improve online automated dialogue responses based on machine learning models. Contextual customer data stored in external memory may be used for retraining a machine learning model to incorporate new observations into the model and to reduce bias and/or improve fairness in associated automated responses without having to retrain an entire memory architecture. The disclosed technology may improve the accuracy of machine learning models by using potentially private contextual customer data to inform the model while eliminating the ability of an intruder to access such data when the model is utilized in cloud-based services.Type: ApplicationFiled: August 25, 2020Publication date: March 3, 2022Inventors: Omar Florez Choque, Rui Zhang, Erik T. Mueller
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Publication number: 20210365763Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: ApplicationFiled: August 2, 2021Publication date: November 25, 2021Inventors: Omar Florez CHOQUE, Erik T. Mueller
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Publication number: 20210312265Abstract: Memory augmented neural networks may use one or more neural encoders to transform input data into distributed representations and a memory module to store the representations with individual addresses. Memory augmented neural networks allow for few-shot learning capabilities because latent representations are persistent between training examples and gradient-based updates affect only certain memory locations via content-based lookups. When a query vector is not found in memory and the memory is full, existing memories that are positively associated with a particular representation may be identified, redundant memories may be aged, and updated memories may be generated. These updated memories retain relevant information acquired during training and reduce redundancy in the memories stored using the memory module, thereby improving the efficiency of data storage and reducing overfitting of data typically encountered with existing neural networks using memory modules.Type: ApplicationFiled: June 16, 2021Publication date: October 7, 2021Inventors: Omar Florez Choque, Anish Khazane, Erik T. Mueller
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Patent number: 11093821Abstract: A system for using hash keys to preserve privacy across multiple tasks is disclosed. The system may provide training batch(es) of input observations each having a customer request and stored task to an encoder, and assign a hash key(s) to each of the stored tasks. The system may provide a new batch of input observations with a new customer request and new task to the encoder. The encoder may generate a new hash key assigned to the new customer request and determine whether any existing hash key corresponds with the new hash key. If so, the system may associate the new batch of input observations with the corresponding hash key and update the corresponding hash key such that it is also configured to provide access to the new batch of input observations. If not, the system may generate a new stored task and assign the new hash key to it.Type: GrantFiled: May 4, 2020Date of Patent: August 17, 2021Assignee: CAPITAL ONE SERVICES, LLCInventors: Omar Florez Choque, Erik T. Mueller
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Publication number: 20210232972Abstract: Exemplary embodiments relate to techniques for integrating common sense into a machine learning (ML) system. In contrast to existing machine learning algorithms that search for statistical correlations between concepts, exemplary embodiments attempt to learn the semantic relationships or causality between the concepts. This may be accomplished by training an algorithm or data structure to learn similar vector representations of words present in the same context (e.g., that are present together in the same sentence). The resulting AI/ML structure may be used to guide the generation of a causal graph having predictive capabilities. This causal graph may represent semantic relationships and/or causation between concepts, and hence may be employed to introduce a degree of common sense in the machine learning system.Type: ApplicationFiled: April 9, 2021Publication date: July 29, 2021Applicant: Capital One Services, LLCInventor: Omar FLOREZ CHOQUE
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Patent number: 11068773Abstract: Memory augmented neural networks may use one or more neural encoders to transform input data into distributed representations and a memory module to store the representations with individual addresses. Memory augmented neural networks allow for few-shot learning capabilities because latent representations are persistent between training examples and gradient-based updates affect only certain memory locations via content-based lookups. When a query vector is not found in memory and the memory is full, existing memories that are positively associated with a particular representation may be identified, redundant memories may be aged, and updated memories may be generated. These updated memories retain relevant information acquired during training and reduce redundancy in the memories stored using the memory module, thereby improving the efficiency of data storage and reducing overfitting of data typically encountered with existing neural networks using memory modules.Type: GrantFiled: July 11, 2019Date of Patent: July 20, 2021Assignee: Capital One Services, LLCInventors: Omar Florez Choque, Anish Khazane, Erik T. Mueller
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Publication number: 20210150368Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method. A method can include receiving observations and a corresponding class label, determining a candidate key based on the observations, determining a current memory state of a memory module based on a similarity of stored keys to the candidate key, generating a measurement vector based on the current memory state, concatenating the candidate key and measurement vector to form a state vector, determining, based on the state vector and an action distribution policy, an action of a plurality of actions such that the determined action maximizes an expected reduction in entropy as compared to the remaining actions of the plurality actions, executing the determined action, determining a value of the determined action, and updated, based on the value, the action distribution policy.Type: ApplicationFiled: November 23, 2020Publication date: May 20, 2021Inventors: Omar Florez Choque, Erik Mueller
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Publication number: 20210150414Abstract: A method for determining machine learning training parameters is disclosed. The method can include a processor receiving a first input. The processor may receive a first response to the first input, determine a first intent, and identify a first action. The processor can then determine first trainable parameter(s) and determine whether the first trainable parameter(s) is negative or positive. Further, the processor can update a training algorithm based on the first trainable parameter(s). The processor can then receive a second input and determine a second intent for the second input. The processor can also determine a second action for the second intent and transmit the second action to a user. The processor can then determine second trainable parameter(s) and determine whether the second trainable parameter(s) is positive or negative. Finally, the processor can further update the training algorithm based on the second trainable parameter(s).Type: ApplicationFiled: January 27, 2021Publication date: May 20, 2021Inventors: Omar Florez Choque, Erik T. Mueller, Zachary Kulis
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Patent number: 10977580Abstract: Exemplary embodiments relate to techniques for integrating common sense into a machine learning (ML) system. In contrast to existing machine learning algorithms that search for statistical correlations between concepts, exemplary embodiments attempt to learn the semantic relationships or causality between the concepts. This may be accomplished by training an algorithm or data structure to learn similar vector representations of words present in the same context (e.g., that are present together in the same sentence). The resulting AI/ML, structure may be used to guide the generation of a causal graph having predictive capabilities. This causal graph may represent semantic relationships and/or causation between concepts, and hence may be employed to introduce a degree of common sense in the machine learning system.Type: GrantFiled: December 5, 2019Date of Patent: April 13, 2021Assignee: Capital One Services, LLCInventor: Omar Florez Choque