Patents Assigned to CS Disco, Inc.
  • Patent number: 12265575
    Abstract: Embodiments as disclosed include document analysis systems that may obtain email data collected or obtained from email servers from one or more source systems and build a graph of the emails, where the nodes of the email graph represent data about an email and the edges in the graph between the nodes of the graph are determined based on metadata associated with the emails or the text content of the emails. These email graphs may be quickly and efficiently updated as new email data is obtained such that the document analysis systems may organize emails into conversations for utilization by users in reviewing these emails in context.
    Type: Grant
    Filed: January 23, 2024
    Date of Patent: April 1, 2025
    Assignee: CS DISCO, INC.
    Inventors: Levi Jonathan Bucao, Peter Anthony Lee, Ajay Guyyala
  • Patent number: 12081638
    Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.
    Type: Grant
    Filed: October 9, 2023
    Date of Patent: September 3, 2024
    Assignee: CS DISCO, INC.
    Inventors: Brian M. Carr, James D. Snyder, Robert J. Macaulay
  • Patent number: 12007979
    Abstract: Systems and methods for providing consistent and time aligned data from arbitrary sets of data in a data analytics platform are disclosed. Embodiments of such systems and methods may format datasets comprising data received from various data sources to facilitate the provisioning of time aligned data from these datasets based on a time specified in a query for one or more of those datasets.
    Type: Grant
    Filed: June 15, 2022
    Date of Patent: June 11, 2024
    Assignee: CS DISCO, INC.
    Inventors: Jozsef Szalay, Sergei Kozyrenko
  • Patent number: 11928154
    Abstract: Embodiments as disclosed include document analysis systems that may obtain email data collected or obtained from email servers from one or more source systems and build a graph of the emails, where the nodes of the email graph represent data about an email and the edges in the graph between the nodes of the graph are determined based on metadata associated with the emails or the text content of the emails. These email graphs may be quickly and efficiently updated as new email data is obtained such that the document analysis systems may organize emails into conversations for utilization by users in reviewing these emails in context.
    Type: Grant
    Filed: February 26, 2021
    Date of Patent: March 12, 2024
    Assignee: CS DISCO, INC.
    Inventors: Levi Jonathan Bucao, Peter Anthony Lee, Ajay Guyyala
  • Patent number: 11818232
    Abstract: Systems and methods for data modeling in multi-tenant systems are disclosed. Embodiments allow the customization of data models for data types (e.g., such as documents or the like) through the addition of fields to data models for data types for particular tenants without modification to components of the system by allowing the definition of a data type and its associated fields for each tenant and externalizing the definition of those data types. This metamodel can thus enable the fields of a data type for a particular tenant to be independently modified or updated for that tenant and data type. The data type for an individual tenant at any given point in time can thus be defined by the set of fields (e.g., and field versions) associated with that data type as defined for that tenant in the metamodel at that point in time.
    Type: Grant
    Filed: December 16, 2022
    Date of Patent: November 14, 2023
    Assignee: CS DISCO, INC.
    Inventors: Brian M. Carr, James D. Snyder, Robert J. Macaulay
  • Patent number: 11790017
    Abstract: Embodiments of systems and methods for a search system that is adapted to utilize a family search operator are disclosed. Such a family search operator may return all documents that match the inner expression encompassed by the family search operator or having a family member that matched the inner expression of the family search operator.
    Type: Grant
    Filed: August 27, 2021
    Date of Patent: October 17, 2023
    Assignee: CS Disco, Inc.
    Inventors: Brock Joseph Reeve, Matthew Jefferson Hinze, Jordan Travis Janes
  • Patent number: 11620453
    Abstract: Artificial intelligence based document analysis systems and methods are disclosed. Embodiments of document analysis systems may allow the manipulation of datasets and associated codes by determining representations for these codes or datasets based on a machine learning model. The codes or datasets can then be manipulated using the associated representations.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: April 4, 2023
    Assignee: CS Disco, Inc.
    Inventors: Alan Justin Lockett, Ryan Connor Rollings
  • Patent number: 11573996
    Abstract: Embodiments as disclosed may generate an organizational hierarchy based on embeddings of portions of documents. Embeddings resulting from the embedding of the portions of the documents can be clustered using a hierarchical clustering mechanism to segment the portion space into a set of hierarchical clusters. Documents can be assigned to these clusters based on the presence of a portion of a document within a cluster. In this manner, the documents may themselves be clustered based on the clusters created from portions across the documents of the corpus. The clusters to which a document is assigned may also be ranked with respect to that document. Similarly, documents assigned to cluster can be ranked within the cluster to which they are assigned. Additionally, in certain embodiments, names or snippets for the clusters of the hierarchy may be derived from the portions comprising that cluster.
    Type: Grant
    Filed: August 24, 2021
    Date of Patent: February 7, 2023
    Assignee: CS Disco, Inc.
    Inventors: Ryan Connor Rollings, Verlyn Michael Fischer, Alan Justin Lockett
  • Patent number: 11416685
    Abstract: Artificial intelligence based document analysis systems and methods are disclosed. Embodiments of document analysis systems may allow the reuse of coded datasets defined in association with a particular code by allowing these datasets to be bundled to define a dataset for another code, where that code may be associated with a target corpus of documents. A model can then be trained based on that dataset and used to provide predictive scores for the documents of the target corpora with respect to the code. Furthermore, this code can be applied not just to the target corpus of documents, but additionally can be applied against any other corpora.
    Type: Grant
    Filed: May 22, 2020
    Date of Patent: August 16, 2022
    Assignee: CS DISCO, INC.
    Inventors: Alan Justin Lockett, Verlyn Michael Fischer, Richard Alan Vestal, Jesse Abraham Ramos, Robert Duane Harrington, Brian Daniel Luskey
  • Patent number: 11270225
    Abstract: A machine learning system continuously receives tag signals indicating membership relations between data objects from a data corpus and tag targets. The machine learning system is asynchronously and iteratively trained with the received tag signals to identify further data objects from the data corpus predicted to have a membership relation with the single tag target. The machine learning system constantly improves its predictive accuracy in short time by the continuous training of a backend machine learning model based on implicit and explicit tag signals gathered from a non-intrusive monitoring of user interactions during a review process of the data corpus.
    Type: Grant
    Filed: August 10, 2018
    Date of Patent: March 8, 2022
    Assignee: CS Disco, Inc.
    Inventor: Alan Lockett
  • Patent number: 11126647
    Abstract: Embodiments as disclosed may generate an organizational hierarchy based on embeddings of portions of documents. Embeddings resulting from the embedding of the portions of the documents can be clustered using a hierarchical clustering mechanism to segment the portion space into a set of hierarchical clusters. Documents can be assigned to these clusters based on the presence of a portion of a document within a cluster. In this manner, the documents may themselves be clustered based on the clusters created from portions across the documents of the corpus. The clusters to which a document is assigned may also be ranked with respect to that document. Similarly, documents assigned to cluster can be ranked within the cluster to which they are assigned. Additionally, in certain embodiments, names or snippets for the clusters of the hierarchy may be derived from the portions comprising that cluster.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: September 21, 2021
    Assignee: CS DISCO, INC.
    Inventors: Ryan Connor Rollings, Verlyn Michael Fischer, Alan Justin Lockett
  • Publication number: 20190188564
    Abstract: A non-transitory medium includes code representing processor-executable instructions; the code causes a processor to produce, via a machine learning model, a predicted value of a membership relationship between a data object and a target tag. The code causes the processor to display, via a user interface, the data object and the target tag and indicate a non-empty set of identified sections of one or more attributes of data object supporting the membership relationship between the data object and the target tag. The code also causes the processor to receive a tag signal, via the user interface, indicating one of an acceptance tag signal, a dismissal tag signal, or a corrective tag signal, and re-train the machine learning model based at least in part on the tag signal.
    Type: Application
    Filed: October 22, 2018
    Publication date: June 20, 2019
    Applicant: CS Disco, Inc.
    Inventor: Alan LOCKETT
  • Patent number: 10108902
    Abstract: A non-transitory medium includes code representing processor-executable instructions; the code causes a processor to produce, via a machine learning model, a predicted value of a membership relationship between a data object and a target tag. The code causes the processor to display, via a user interface, the data object and the target tag and indicate a non-empty set of identified sections of one or more attributes of data object supporting the membership relationship between the data object and the target tag. The code also causes the processor to receive a tag signal, via the user interface, indicating one of an acceptance tag signal, a dismissal tag signal, or a corrective tag signal, and re-train the machine learning model based at least in part on the tag signal.
    Type: Grant
    Filed: September 18, 2017
    Date of Patent: October 23, 2018
    Assignee: CS Disco, Inc.
    Inventor: Alan Lockett
  • Patent number: 10062039
    Abstract: A machine learning system continuously receives tag signals indicating membership relations between data objects from a data corpus and tag targets. The machine learning system is asynchronously and iteratively trained with the received tag signals to identify further data objects from the data corpus predicted to have a membership relation with the single tag target. The machine learning system constantly improves its predictive accuracy in short time by the continuous training of a backend machine learning model based on implicit and explicit tag signals gathered from a non-intrusive monitoring of user interactions during a review process of the data corpus.
    Type: Grant
    Filed: June 28, 2017
    Date of Patent: August 28, 2018
    Assignee: CS Disco, Inc.
    Inventor: Alan Lockett