Samuel Joseph Gross, Age 509401 Topridge Dr, Austin, TX 78750

2022033, Oct 20, 2022

Apr 5, 2022

17/714062

- Menlo Park CA, US
Peter D. Freese - San Francisco CA, US
Samuel S. Gross - Sunnyvale CA, US
Robert Abe Paine Calef - Redwood City CA, US
Arash Jamshidi - Redwood City CA, US

C12Q 1/6886
G16B 30/00
G16H 50/20

Disclosed herein are systems and methods for localization of a disease state (e.g., tissue of origin of cancer) using nucleic acid samples. In an embodiment, a method comprises receiving a plurality of cancer signals of a sample, each cancer signal indicating a probability that the sample is associated with a different disease state of a plurality of disease states. The method determines a first cancer signal having a greatest probability among the plurality of cancer signals. In accordance with a determination that the first cancer signal satisfies a criterion, the method associates the sample with a first disease state. In accordance with a determination that the first cancer signal does not satisfy the criterion, the method determines a second cancer signal having a second greatest probability among the plurality of cancer signals, and associates the sample with the first disease state and a second disease state.

2023008, Mar 23, 2023

Nov 21, 2022

17/991318

- Reston VA, US
Samuel S. GROSS - Manassas VA, US

NOBLIS, INC. - Reston VA

H04L 9/30
H04L 9/08
H04L 9/00

Techniques for determining whether a public encryption key is vulnerable as the result of deficiencies in pseudorandom number generation algorithms are provided. In some embodiments, a system may compile a database of cryptographic information received from a plurality of sources, including databases, and network traffic monitoring tools. RSA public keys extracted from the cryptographic information may be stored in an organized database in association with corresponding metadata. The system may construct a product tree from all unique collected RSA keys, and may then construct a remainder tree from the product tree, wherein each output remainder may be determined to be a greatest common divisor of one of the RSA keys against all other unique RSA keys in the database. The system may then use the greatest common divisors to factor one or more of the RSA keys and to determine that the factored keys are vulnerable to being compromised.

2023001, Jan 19, 2023

Sep 16, 2022

17/932763

- Berkeley CA, US
Samuel Hirscher Gross - Berkeley CA, US

F24C 3/14
A47J 47/16
A47B 77/06
A45F 3/46

A mobile kitchen unit includes a storage container, a first module, and a second module. The first module can include a sink for receiving water. The first module can be positioned within the storage container and mounted on a first set of rails such that it can slide at least partially out from the storage container. The second module can have a stove and propane tank in one example. The second module can be positioned within the first module and mounted on a second set of rails such that the second module can slide at least partially out from the first module. In one example, the first and second modules are independently slidable with respect to one another. In an example, the second module can fit substantially within the first module with the stove and propane tank still attached to the second module.

2021031, Oct 7, 2021

Mar 29, 2021

17/216555

- Menlo Park CA, US
Samuel S. Gross - Sunnyvale CA, US
Joshua Newman - Mountain View CA, US
Joerg Bredno - Menlo Park CA, US
Ognjen Nikolic - Menlo Park CA, US

C12Q 1/6886
G16B 40/20

Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. A multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments and at least one synthetic training sample generated from the biological samples. The analytics system generates the synthetic training sample by sampling fragments from a training sample labeled as cancer and sampling fragments from another training sample labeled as non-cancer. The sampling probability is determined based on a limit of detection of the cancer classifier, e.g., in order to generate synthetic training samples with cancer tumor fraction proximate to the limit of detection.

2021012, Apr 29, 2021

Oct 9, 2020

17/066863

- Menlo Park CA, US
Oliver Claude Venn - San Francisco CA, US
Samuel S. Gross - Sunnyvale CA, US
Robert Abe Paine Calef - Redwood City CA, US

G16B 30/00
G16H 10/40
G16H 50/20
G06N 7/00
G06N 20/00

Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of tissue of origin (TOO) classes. In some embodiments, the plurality of TOO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high tissue signal are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to tissue signal and applies binary threshold cutoffs determined for each stratum.

2020036, Nov 19, 2020

May 13, 2020

15/931022

- Menlo Park CA, US
John F. Beausang - Menlo Park CA, US
Oliver Claude Venn - San Francisco CA, US
Arash Jamshidi - Redwood City CA, US
M. Cyrus Maher - San Mateo CA, US
Qinwen Liu - Fremont CA, US
Jan Schellenberger - Belmont CA, US
Joshua Newman - Mountain View CA, US
Robert Calef - Redwood City CA, US
Samuel S. Gross - Sunnyvale CA, US

G16B 5/20
G06N 20/00
G16B 25/00
G16B 40/00
G16H 10/40
C12Q 1/6809
G06N 5/02
G06F 16/28

In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state.

2020034, Oct 29, 2020

Apr 3, 2020

16/839469

- Menlo Park CA, US
Samuel S. Gross - Sunnyvale CA, US
Seyedmehdi Shojaee - San Francisco CA, US

C12Q 1/6886
G16B 30/10
G16B 5/20

An analytics system marks duplicate fragments from an initial set of fragments from a subject. The analytics system generates a sample state vector for each fragment. Each sample state vector comprises a sample genomic location within a reference genome and a plurality of methylation states for a plurality of CpG sites in the fragment, the methylation states determined to be one of methylated, unmethylated, variant, and ambiguous. The analytics system identifies two fragments with methylation state vectors as being derived from a matching reference location, e.g., sharing a common plurality of CpG sites. The analytics system calculates a modified Hamming distance based on methylation states in the first sample state vector and methylation states in the second sample state vector. Based on the modified Hamming distance, the analytics system marks the first fragment and the second fragment as either duplicate fragments or non-duplicate fragments.

2020026, Aug 20, 2020

Feb 19, 2020

16/794972

- Berkeley CA, US
Samuel Hirscher Gross - Berkeley CA, US

F24C 3/14
A47J 47/16
A45F 3/46
A47B 77/06

A mobile kitchen unit includes a storage container, a first module, and a second module. The first module can include a sink for receiving water. The first module can be positioned within the storage container and mounted on a first set of rails such that it can slide at least partially out from the storage container. The second module can have a stove and propane tank in one example. The second module can be positioned within the first module and mounted on a second set of rails such that the second module can slide at least partially out from the first module. In one example, the first and second modules are independently slidable with respect to one another. In an example, the second module can fit substantially within the first module with the stove and propane tank still attached to the second module.