Patents by Inventor Marcos Campos
Marcos Campos 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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Patent number: 11836650Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.Type: GrantFiled: September 14, 2021Date of Patent: December 5, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Keen McEwan Browne, Marcos Campos, Megan Adams, Mark Hammond, Matthew Brown, Tod Frye
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Patent number: 11775850Abstract: The AI engine has a first module that chooses from a library of algorithms to use when automatically assembling and building different learning topologies to solve different concepts making up a resulting AI model. The AI engine may integrate both i) one or more dynamic programming training algorithms and ii) one or more policy optimization algorithms, to build the different learning topologies to solve the different concepts contained with an AI model in order to solve a wide variety of problem types. Each concept contained in the AI model can use a most appropriate approach for achieving a mission of that concept. A learning topology representing a first concept can be built by the first module with a first dynamic programming training algorithm, while a learning topology representing a second concept in the same AI model can be built by the first module with a first policy optimization algorithm.Type: GrantFiled: August 16, 2018Date of Patent: October 3, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Marcos Campos, Aditya Gudimella, Ruofan Kong, Matthew Brown
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Publication number: 20210406774Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.Type: ApplicationFiled: September 14, 2021Publication date: December 30, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Keen McEwan Browne, Marcos Campos, Megan Adams, Mark Hammond, Matthew Brown, Tod Frye
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Patent number: 11164109Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.Type: GrantFiled: June 14, 2018Date of Patent: November 2, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Keen McEwan Browne, Marcos Campos, Megan Adams, Mark Hammond, Matthew Brown, Tod Frye
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Patent number: 11120365Abstract: Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.Type: GrantFiled: June 14, 2018Date of Patent: September 14, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Marcos Campos, Aditya Gudimella, Ross Story, Matineh Shaker, Ruofan Kong, Matthew Brown, Victor Shnayder
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Patent number: 11100423Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.Type: GrantFiled: January 26, 2017Date of Patent: August 24, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, Megan Adams
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Patent number: 10733531Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more servers configured to cooperate with one or more databases including one or more AI-engine modules. The one or more AI-engine modules include an architect module configured to propose an AI model from an assembly code. The assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model on the one or more concept modules in one or more training cycles. The AI engine can be configured to instantiate a trained AI model based on the one or more concept modules learned by the AI model in the one or more training cycles.Type: GrantFiled: January 26, 2017Date of Patent: August 4, 2020Assignee: Bonsai AI, Inc.Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, Megan Adams
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Patent number: 10586173Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.Type: GrantFiled: January 26, 2017Date of Patent: March 10, 2020Assignee: Bonsai AI, Inc.Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, William Guss, Ross Story
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Patent number: 10540613Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.Type: GrantFiled: January 26, 2017Date of Patent: January 21, 2020Assignee: Bonsai AI, Inc.Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, William Guss, Ross Story
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Publication number: 20180357543Abstract: An AI engine configured for measuring training accuracy of one or more AI models over time is disclosed. The AI engine includes, in some embodiments, one or more AI-engine modules including an instructor module, a learner module, and an assessor module. The instructor module is configured to coordinate training for each AI model of the one or more AI models with a corresponding simulator. The learner module is configured to train each AI model with the corresponding simulator on one or more concepts of a mental model defined in a pedagogical programming language. The assessor module is configured to determine when each AI model is sufficiently trained on at least a concept of the mental model by measuring the training accuracy of the AI model over time. The assessor module is also configured to terminate the training of each AI model by ending any simulations of the corresponding simulator.Type: ApplicationFiled: August 16, 2018Publication date: December 13, 2018Applicant: Bonsai AI, Inc.Inventors: Matthew Brown, Marcos Campos, Ruofan Kong
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Publication number: 20180357552Abstract: The AI engine has a first module that chooses from a library of algorithms to use when automatically assembling and building different learning topologies to solve different concepts making up a resulting AI model. The AI engine may integrate both i) one or more dynamic programming training algorithms and ii) one or more policy optimization algorithms, to build the different learning topologies to solve the different concepts contained with an AI model in order to solve a wide variety of problem types. Each concept contained in the AI model can use a most appropriate approach for achieving a mission of that concept. A learning topology representing a first concept can be built by the first module with a first dynamic programming training algorithm, while a learning topology representing a second concept in the same AI model can be built by the first module with a first policy optimization algorithm.Type: ApplicationFiled: August 16, 2018Publication date: December 13, 2018Applicant: Bonsai AI, Inc.Inventors: Marcos Campos, Aditya Gudimella, Ruofan Kong, Matthew Brown
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Publication number: 20180293517Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.Type: ApplicationFiled: June 14, 2018Publication date: October 11, 2018Inventors: Keen McEwan Browne, Marcos Campos, Megan Adams, Mark Hammond, Tod Frye
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Publication number: 20180293498Abstract: Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.Type: ApplicationFiled: June 14, 2018Publication date: October 11, 2018Inventors: Marcos CAMPOS, Aditya GUDIMELLA, Ross STORY, Martineh SHAKER, Ruofan KONG, Matthew BROWN, Victor SHNAYDER
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Publication number: 20170213128Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.Type: ApplicationFiled: January 26, 2017Publication date: July 27, 2017Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, Megan Adams
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Publication number: 20170213155Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.Type: ApplicationFiled: January 26, 2017Publication date: July 27, 2017Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, William Guss, Ross Story
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Publication number: 20170213154Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more servers configured to cooperate with one or more databases including one or more AI-engine modules. The one or more AI-engine modules include an architect module configured to propose an AI model from an assembly code. The assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model on the one or more concept modules in one or more training cycles. The AI engine can be configured to instantiate a trained AI model based on the one or more concept modules learned by the AI model in the one or more training cycles.Type: ApplicationFiled: January 26, 2017Publication date: July 27, 2017Inventors: Mark Isaac Hammond, Keen McEwan Browne, Marcos Campos, Matthew James Brown, Ruofan Kong, Megan Adams
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Patent number: 9152972Abstract: A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.Type: GrantFiled: June 18, 2009Date of Patent: October 6, 2015Assignee: Oracle International CorporationInventors: Francisco V. Casas, Jooyoung John Kim, Krisztian Z. Danko, Peter J. Stengard, Ari Mozes, Marcos Campos
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Patent number: 8775230Abstract: Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.Type: GrantFiled: June 2, 2009Date of Patent: July 8, 2014Assignee: Oracle International CorporationInventors: Francisco V. Casas, Jooyoung John Kim, Krisztian Z. Danko, Peter J. Stengard, Ari Mozes, Marcos Campos
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Publication number: 20100114663Abstract: Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.Type: ApplicationFiled: June 2, 2009Publication date: May 6, 2010Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Francisco V. Casas, Jooyoung John Kim, Krisztian Z. Danko, Peter J. Stengard, Ari Mozes, Marcos Campos
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Publication number: 20100114992Abstract: A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.Type: ApplicationFiled: June 18, 2009Publication date: May 6, 2010Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Francisco V. CASAS, Jooyoung John KIM, Krisztian Z. DANKO, Peter J. STENGARD, Ari MOZES, Marcos CAMPOS