Patents by Inventor Jeremy Elser
Jeremy Elser 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: 20230297582Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: ApplicationFiled: May 30, 2023Publication date: September 21, 2023Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Patent number: 11704325Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: GrantFiled: July 15, 2022Date of Patent: July 18, 2023Assignee: Palantir Technologies Inc.Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Publication number: 20230214690Abstract: A computer-implemented method is provided to predict one or more expected quantities using a machine learning model. The method system may comprise steps to receive a set of data items associated with one or more characteristics, generate or train a machine learning model using the set of data items and associated characteristics, receive one or more sets of simulation parameters from a user indicating a hypothetical scenario and a time period, and generate user interface data. The user interface data may comprise a time-based chart illustrating the respective time periods. The computing system may further apply machine learning model to the set of simulation parameters to predict a set of expected quantities based on the simulation parameters, aggregate one or more types of expected quantities from the set of expected quantities to determine one or more combined quantities, and include in the user interface indications of the one or more combined quantities.Type: ApplicationFiled: March 10, 2023Publication date: July 6, 2023Inventors: Jeremy Elser, Andrew Floren, Aditya Naganath
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Publication number: 20220374454Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: ApplicationFiled: July 15, 2022Publication date: November 24, 2022Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Patent number: 11392591Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: GrantFiled: November 13, 2018Date of Patent: July 19, 2022Assignee: Palantir Technologies Inc.Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Patent number: 10636097Abstract: Systems and methods are provided that allow for generating and applying an improved predictive data model that aggregates two or more models performed sequentially, for the purposes of improving the prediction of overall profitability of individuals or households in a population. The models may be generated by the processing of customer profitability data and third-party population data together. One of the two aggregated models may be an inherently probabilistic, binary model tasked with determining whether an individual is a high-loss individual and using that result to improve the predictive capability of the system.Type: GrantFiled: July 15, 2016Date of Patent: April 28, 2020Assignee: Palantir Technologies Inc.Inventors: Jeremy Elser, Sebastian Caliri, Katherine Sebastian, Dustin Janatpour
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Publication number: 20190079937Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: ApplicationFiled: November 13, 2018Publication date: March 14, 2019Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Patent number: 10127289Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: GrantFiled: August 10, 2016Date of Patent: November 13, 2018Assignee: Palantir Technologies Inc.Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Publication number: 20170052958Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.Type: ApplicationFiled: August 10, 2016Publication date: February 23, 2017Inventors: Lawrence Manning, Rahul Mehta, Daniel Erenrich, Guillem Palou Visa, Roger Hu, Xavier Falco, Rowan Gilmore, Eli Bingham, Jason Prestinario, Yifei Huang, Daniel Fernandez, Jeremy Elser, Clayton Sader, Rahul Agarwal, Matthew Elkherj, Nicholas Latourette, Aleksandr Zamoshchin
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Publication number: 20170024824Abstract: Systems and methods are provided that allow for generating and applying an improved predictive data model that aggregates two or more models performed sequentially, for the purposes of improving the prediction of overall profitability of individuals or households in a population. The models may be generated by the processing of customer profitability data and third-party population data together. One of the two aggregated models may be an inherently probabilistic, binary model tasked with determining whether an individual is a high-loss individual and using that result to improve the predictive capability of the system.Type: ApplicationFiled: July 15, 2016Publication date: January 26, 2017Inventors: Jeremy Elser, Sebastian Caliri, Katherine Sebastian, Dustin Janatpour
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Patent number: 9418337Abstract: Systems and methods are provided that allow for generating and applying an improved predictive data model that aggregates two or more models performed sequentially, for the purposes of improving the prediction of overall profitability of individuals or households in a population. The models may be generated by the processing of customer profitability data and third-party population data together. One of the two aggregated models may be an inherently probabilistic, binary model tasked with determining whether an individual is a high-loss individual and using that result to improve the predictive capability of the system.Type: GrantFiled: July 21, 2015Date of Patent: August 16, 2016Assignee: Palantir Technologies Inc.Inventors: Jeremy Elser, Sebastian Caliri, Katherine Sebastian, Dustin Janatpour
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Publication number: 20140156540Abstract: A system and method for managing a database of buyers includes a server configured to store a buyers database having data related to a plurality of buyers. For each of the plurality of buyers, the data includes buyer identification information for the buyer, purchasing criteria of the buyer, and buyer agent information associated with the buyer. The server is further configured to retrieve and store in the buyers database supplemental information about each buyer based upon the buyer identification information. The server receives a first query of the buyers database from a first remote device, the first query returning first query results which includes the purchasing criteria and buyer agent information for each buyer included in the first query results. The server transmits the first query results to the first remote device.Type: ApplicationFiled: June 7, 2013Publication date: June 5, 2014Applicant: BuyerMLS, LLCInventors: Charles Williams, John L. Heithaus, Brian Preston, Jeremy Elser
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Publication number: 20110218937Abstract: Computer systems and methods enable an individual to simulate real estate/financial transactions over time, provide accounting and cash flow management for management of real estate investments, and track property values for rental properties taking into account tenant creditworthiness (tenant credit score) and the like. The computer system calculates a tenant reliability score (TRS) that can be used when an individual real estate investor is contemplating whether to enter into a lease with a tenant. A lower TRS would alert the landlord that the tenant may not be a good risk which would decrease the expected future value of rental property. A simulator preferably incorporates the TRS into cash flow projections such that the investor could compare different potential tenants and. Financial tracking modules enable the real estate investor to track the equity value of his or her real estate investments, cash flow, and to provide data useful for buy/sell decisions.Type: ApplicationFiled: March 3, 2010Publication date: September 8, 2011Inventor: Jeremy Elser
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Publication number: 20110218934Abstract: Computer systems and methods enable an individual to import information related to real property units under consideration for purchase from a list of available real property units available for sale and to compare the real property units. The computer system calculates flip and rental based income for the imported real property units under consideration for purchase and displays for comparison the real property units available for-sale or for-rent. The system further allows the individual to load the information relating to selected real property units for purchase or for rent into a simulator to predict future financial performance of the real property units. The computer based methods and systems also represent each real property unit available for sale and/or for rent by a pixel on a geographic map, and calculates and displays heat maps for each of the real property unit that use a plurality of colors to signify the value of a parameter (e.g.Type: ApplicationFiled: April 6, 2010Publication date: September 8, 2011Inventor: Jeremy Elser