Patents by Inventor Christopher ARCADIA

Christopher ARCADIA 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).

  • Patent number: 11790280
    Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.
    Type: Grant
    Filed: July 19, 2021
    Date of Patent: October 17, 2023
    Assignee: BROWN UNIVERSITY
    Inventors: Brenda Rubenstein, Jacob Karl Rosenstein, Christopher Arcadia, Shui Ling Chen, Amanda Doris Dombroski, Joseph D. Geiser, Eamonn Kennedy, Eunsuk Kim, Kady M. Oakley, Sherief Reda, Christopher Rose, Jason Kelby Sello, Hokchhay Tann, Peter Weber
  • Publication number: 20230027270
    Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.
    Type: Application
    Filed: September 1, 2022
    Publication date: January 26, 2023
    Inventors: Brenda RUBENSTEIN, Jacob Karl ROSENSTEIN, Christopher ARCADIA, Shui Ling CHEN, Amanda Doris DOMBROSKI, Joseph D. GEISER, Eamonn KENNEDY, Eunsuk KIM, Kady M. OAKLEY, Sherief REDA, Christopher ROSE, Jason Kelby SELLO, Hokchhay TANN, Peter WEBER, Dana Jo Biechele-Speziale, Selahaddin GUMUS
  • Publication number: 20220012646
    Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.
    Type: Application
    Filed: July 19, 2021
    Publication date: January 13, 2022
    Inventors: Brenda RUBENSTEIN, Jacob Karl ROSENSTEIN, Christopher ARCADIA, Shui Ling CHEN, Amanda Doris DOMBROSKI, Joseph D. GEISER, Eamonn KENNEDY, Eunsuk KIM, Kady M. OAKLEY, Sherief REDA, Christopher ROSE, Jason Kelby SELLO, Hokchhay TANN, Peter WEBER
  • Patent number: 11093865
    Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.
    Type: Grant
    Filed: June 20, 2019
    Date of Patent: August 17, 2021
    Assignee: Brown University
    Inventors: Brenda Rubenstein, Jacob Karl Rosenstein, Christopher Arcadia, Shui Ling Chen, Amanda Doris Dombroski, Joseph D. Geiser, Eamonn Kennedy, Eunsuk Kim, Kady M. Oakley, Sherief Reda, Christopher Rose, Jason Kelby Sello, Hokchhay Tann, Peter Weber
  • Publication number: 20210166159
    Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.
    Type: Application
    Filed: June 20, 2019
    Publication date: June 3, 2021
    Inventors: Brenda RUBENSTEIN, Jacob Karl ROSENSTEIN, Christopher ARCADIA, Shui Ling CHEN, Amanda Doris DOMBROSKI, Joseph D. GEISER, Eamonn KENNEDY, Eunsuk KIM, Kady M. OAKLEY, Sherief REDA, Christopher ROSE, Jason Kelby SELLO, Hokchhay TANN, Peter WEBER