Patents by Inventor Erik Mueller
Erik Mueller 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: 20240131802Abstract: An extrusion-based additive manufacturing process including the step of selectively depositing a molten thermoplastic material (P) on a film or sheet made from or containing a polymer blend obtained by melt blending a mixture made from or containing: (A) 60% to 98.8% by weight of a polyolefin; (B) 0.1% to 30% by weight of a compatibilizer; (C) 0.05% to 20% by weight of an amino resin; and (D) 0% to 5% by weight of an additive, wherein the amounts of (A), (B), (C) and (D) are based on the total weight of (A)+(B)+(C)+(D).Type: ApplicationFiled: January 26, 2022Publication date: April 25, 2024Applicants: Basell Polyolefine GmbH, Albert-Ludwigs-Universität FreiburgInventors: Carl Gunther Schirmeister, Erik Hans Licht, Karsten Schmitz, Yannic Kessler, Klaus Klemm, Jürgen Rohrmann, Dieter Langenfelder, Mikhail Dureev, Rolf Muelhaupt, Mirco Müller, Steer Peter, Kolano Benjamin
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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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Publication number: 20240048510Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.Type: ApplicationFiled: September 13, 2023Publication date: February 8, 2024Applicant: Capital One Services, LLCInventors: Scott Karp, Erik Mueller
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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: 20230368778Abstract: Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.Type: ApplicationFiled: June 1, 2023Publication date: November 16, 2023Applicant: Capital One Services, LLCInventors: Oluwatobi OLABIYI, Alan SALIMOV, Anish KHAZANE, Erik MUELLER
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Publication number: 20230335108Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.Type: ApplicationFiled: June 26, 2023Publication date: October 19, 2023Inventors: Gregory W. Zoller, Scott Karp, Sujay Eliphaz Jacob, Erik Mueller, Stephanie Hay, Adam Roy Paynter
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Patent number: 11784947Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.Type: GrantFiled: September 23, 2022Date of Patent: October 10, 2023Assignee: Capital One Services, LLCInventors: Scott Karp, Erik Mueller
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Patent number: 11735157Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.Type: GrantFiled: April 30, 2021Date of Patent: August 22, 2023Assignee: CAPITAL ONE SERVICES, LLCInventors: Gregory W. Zoller, Scott Karp, Sujay Eliphaz Jacob, Erik Mueller, Stephanie Hay, Adam Roy Paynter
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Publication number: 20230262010Abstract: Methods and systems are described for generating dynamic interface options using machine learning models. The dynamic interface options may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by interpreting multi-modal feature inputs. For example, the machine learning model may include a first machine learning model, wherein the first machine learning model comprises a convolutional neural network, and a second machine learning model, wherein the second machine learning model performs a Weight of Evidence (WOE) analysis.Type: ApplicationFiled: April 21, 2023Publication date: August 17, 2023Applicant: Capital One Services, LLCInventors: Minh LE, Erik MUELLER, Rui ZHANG
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Patent number: 11705112Abstract: Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.Type: GrantFiled: April 12, 2021Date of Patent: July 18, 2023Assignee: Capital One Services, LLCInventors: Oluwatobi Olabiyi, Alan Salimov, Anish Khazane, Erik Mueller
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Publication number: 20230188484Abstract: In certain embodiments, intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain a first set of predicted intents of the user. For each intent of the first set of predicted intents, a candidate question may be selected from a question set based on the candidate question matching the intent. In some embodiments, the candidate questions may be simultaneously presented on the chat interface.Type: ApplicationFiled: February 10, 2023Publication date: June 15, 2023Applicant: Capital One Services, LLCInventors: Victor Alvarez MIRANDA, Rui ZHANG, Vinay IGURE, Scott KARP, Erik MUELLER, Tanushree LUKE, Kunlaya SOIAPORN
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Patent number: 11677693Abstract: Methods and systems are described for generating dynamic interface options using machine learning models. The dynamic interface options may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by interpreting multi-modal feature inputs. For example, the machine learning model may include a first machine learning model, wherein the first machine learning model comprises a convolutional neural network, and a second machine learning model, wherein the second machine learning model performs a Weight of Evidence (WOE) analysis.Type: GrantFiled: July 6, 2022Date of Patent: June 13, 2023Assignee: Capital One Services, LLCInventors: Minh Le, Erik Mueller, Rui Zhang
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Publication number: 20230169364Abstract: Disclosed are methods and systems for classifying a webpage or one or more webpage elements. For instance, a method include detecting, by a user device, that a user of the user device has navigated to the webpage using a web browser installed on the user device and classifying, by the user device, the webpage or the one or more webpage elements into a classification by inputting first input data into one or more machine learning models. The method may further include, in response to classifying the webpage or the one or more webpage elements into the classification, causing the user device to display a user interface associated with an electronic application. The user interface may include information identifying the classification of the webpage.Type: ApplicationFiled: November 30, 2021Publication date: June 1, 2023Applicant: Capital One Services, LLCInventors: Erik MUELLER, Zenobia LIENDO, Jonathan BLOCKSOM, Eric MEDIN
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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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Publication number: 20230123592Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.Type: ApplicationFiled: September 23, 2022Publication date: April 20, 2023Applicant: Capital One Services, LLCInventors: Scott Karp, Erik Mueller
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Publication number: 20230062915Abstract: System and method of generating an executable action item in response to natural language dialogue are disclosed herein. A computing system receives a dialogue message from a remote client device of a customer associated with an organization, the dialogue message comprising an utterance indicative of an implied goal. A natural language processor of the computing system parses the dialogue message to identify one or more components contained in the utterance. The planning module of the computing system identifies the implied goal. The computing system generates a plan within a defined solution space. The computing system generates a verification message to the user to confirm the plan. The computing system transmits the verification message to the remote client device of the customer. The computing system updates an event queue with instructions to execute the action item according to the generated plan upon receiving a confirmation message from the remote client device.Type: ApplicationFiled: October 17, 2022Publication date: March 2, 2023Applicant: Capital One Services, LLCInventors: Scott Karp, Erik Mueller, Zachary Kulis
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Patent number: 11582172Abstract: In certain embodiments, intent prediction and dialogue generation may be facilitated. In some embodiments, a chat initiation request may be obtained from a user. The latest activity information associated with the user may be provided to a prediction model to obtain a first set of predicted intents of the user. For each intent of the first set of predicted intents, a candidate question may be selected from a question set based on the candidate question matching the intent. In some embodiments, the candidate questions may be simultaneously presented on the chat interface.Type: GrantFiled: December 6, 2021Date of Patent: February 14, 2023Assignee: Capital One Services, LLCInventors: Victor Alvarez Miranda, Rui Zhang, Vinay Igure, Scott Karp, Erik Mueller, Tanushree Luke, Kunlaya Soiaporn
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Publication number: 20220414684Abstract: Provided herein are systems and methods for using multi-modal regression to predict customer intent to contact a merchant. Multi-modal data including numerical data and unstructured data are extracted from customer interactions with the merchant. Features of the numerical data and the unstructured data are separately extracted and classified using techniques specific to the data types. The features for each type are then separately used to predict probabilities of customer intent. A neural network is used to combine the predictions into a single set of estimates of customer intent. This set of estimates of customer intents is used to estimate a probability that the customer will contact the merchant. The customer is then contacted based on the estimate.Type: ApplicationFiled: June 23, 2021Publication date: December 29, 2022Applicant: Capital One Services, LLCInventors: Minh LE, Rui ZHANG, Erik MUELLER, Victor Alvarez MIRANDA
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Publication number: 20220358396Abstract: Described are methods and systems are for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.Type: ApplicationFiled: May 4, 2021Publication date: November 10, 2022Applicant: Capital One Services, LLCInventors: Minh Le, William Miller, Sara Mikulic, Rui Zhang, Erik Mueller
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Publication number: 20220345425Abstract: Methods and systems are described for generating dynamic interface options using machine learning models. The dynamic interface options may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by interpreting multi-modal feature inputs. For example, the machine learning model may include a first machine learning model, wherein the first machine learning model comprises a convolutional neural network, and a second machine learning model, wherein the second machine learning model performs a Weight of Evidence (WOE) analysis.Type: ApplicationFiled: July 6, 2022Publication date: October 27, 2022Applicant: Capital One Services, LLCInventors: Minh LE, Erik MUELLER, Rui ZHANG