# US 2025/0095151 A1 · Autonomous cell imaging and modeling system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095151 A1 - Document ID: US20250095151A1 - Status: Published application - Kind: A1 - Application number: 18/972,720 - Filed: 2024-12-06 - Published: 2025-03-20 - Earliest claimed: 2021-11-19 - Assignees: Insitro, Inc. - Applicants: Insitro, Inc. - Inventors: Hervé MARIE-NELLY; Jeevaa VELAYUTHAM; Zachary PHILLIPS; Shengjiang TU - Web page: https://patentgrep.com/patent/US20250095151A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/0016: {involving temporal comparison} - A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication} - G01N 15/1429: Signal processing - G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance - G06T 2207/10016: Video; Image sequence - G06T 2207/10056: Microscopic image - G06T 2207/10064: Fluorescence image - G06T 2207/20036: Morphological image processing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time. ## Claims ### Claim 1 (canceled) ### Claim 2 (canceled) ### Claim 3 (canceled) ### Claim 4 (canceled) ### Claim 5 (canceled) ### Claim 6 (canceled) ### Claim 7 (canceled) ### Claim 8 (canceled) ### Claim 9 (canceled) ### Claim 10 (canceled) ### Claim 11 (canceled) ### Claim 12 (canceled) ### Claim 13 (canceled) ### Claim 14 (canceled) ### Claim 15 (canceled) ### Claim 16 (canceled) ### Claim 17 (canceled) ### Claim 18 (canceled) ### Claim 19 (canceled) ### Claim 20 (canceled) ### Claim 21 (canceled) ### Claim 22 (canceled) ### Claim 23 (canceled) ### Claim 24 (canceled) ### Claim 25 (canceled) ### Claim 26 (canceled) ### Claim 27 (canceled) ### Claim 28 (canceled) ### Claim 29 (canceled) ### Claim 30 (canceled) ### Claim 31 (canceled) ### Claim 32 (canceled) ### Claim 33 (canceled) ### Claim 34 (canceled) ### Claim 35 (canceled) ### Claim 36 (canceled) ### Claim 37 (canceled) ### Claim 38 (canceled) ### Claim 39 (canceled) ### Claim 40 (canceled) ### Claim 41 (canceled) ### Claim 42 (canceled) ### Claim 43 (canceled) ### Claim 44 (canceled) ### Claim 45 (canceled) ### Claim 46 (canceled) ### Claim 47 (canceled) ### Claim 48 (canceled) ### Claim 49 (canceled) ### Claim 50 (canceled) ### Claim 51 (canceled) ### Claim 52 (canceled) ### Claim 53 (canceled) ### Claim 54 (canceled) ### Claim 55 (canceled) ### Claim 56 (canceled) ### Claim 57 (canceled) ### Claim 58 (canceled) ### Claim 59 (canceled) ### Claim 60 (canceled) ### Claim 61 (canceled) ### Claim 62 (canceled) ### Claim 63 (canceled) ### Claim 64 (canceled) ### Claim 65 (canceled) ### Claim 66 (canceled) ### Claim 67 A system for identifying one or more cell culture conditions for progressing one or more live biological cells towards a desired cell state, the system comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors to: perform a plurality of cell culture condition identification cycles on the one or more live biological cells until a condition is met, wherein each cell culture condition identification cycle comprises: obtaining a set of one or more images capturing the one or more live biological cells; determining, from at least a subset of the set of one or more images, a cell state of the one or more live biological cells; identifying a new cell culture condition for progressing the one or more live biological cells towards the desired cell state, by inputting the cell state and the desired cell state into a trained machine-learning model; and applying the new cell culture condition to the one or more live biological cells; and identify the one or more cell culture conditions, based on outcomes of the plurality of cell culture condition identification cycles, for use in progressing live biological cells towards the desired cell state in future cell cultures. ### Claim 68 (depends on 67) The system of claim 67, wherein the one or more live biological cells are a first set of one or more live biological cells that are deposited in a first well, and the plurality of cell culture condition identification cycles is a first plurality of cell culture condition identification cycles, wherein the one or more programs, when executed by the one or more processors, cause the system to: deposit a second set of one or more live biological cells in a second well; perform a second plurality of cell culture condition identification cycles on the second set of one or more live biological cells, wherein the second plurality of cell culture condition identification cycles is different from the first plurality of cell culture condition identification cycles; and prioritize one or more cell culture conditions in the first plurality of cell culture condition identification cycles and the second plurality of cell culture condition identification cycles based on the outcomes of the first plurality of cell culture condition identification cycles and the second plurality of cell culture condition identification cycles. ### Claim 69 (depends on 68) The system of claim 68, wherein a time frame for performing the first plurality of cell culture condition identification cycles overlaps with the time frame for performing the second plurality of cell culture condition identification cycles. ### Claim 70 (depends on 68) The system of claim 68, wherein the first well and the second well belong to the same multi-well plate. ### Claim 71 (depends on 68) The system of claim 68, wherein differences between the first plurality of cell culture condition identification cycles and the second plurality of cell culture condition identification cycles comprise cell culture medium ingredient differences, cell culture temperature differences, cell culture pressure exposure differences, and/or cell culture medium light exposure differences. ### Claim 72 (depends on 67) The system of claim 67, wherein the condition is met when the desired cell state is achieved. ### Claim 73 (depends on 67) The system of claim 67, wherein the condition is met when a predefined number of cell culture condition identification cycles are performed. ### Claim 74 (depends on 67) The system of claim 67, wherein the trained machine-learning model is an active-learning machine-learning model. ### Claim 75 (depends on 74) The system of claim 74, wherein each cell culture condition identification cycle further comprises: prompting a user to provide one or more user inputs about a state of the one or more live biological cells after the new cell culture condition is applied; and retraining the active-learning machine-learning model based on the user inputs. ### Claim 76 (depends on 75) The system of claim 75, wherein the user is an individual. ### Claim 77 (depends on 67) The system of claim 67, wherein the trained machine-learning model is a reinforcement-learning machine-learning model. ### Claim 78 (depends on 77) The system of claim 77, wherein each cell culture condition identification cycle further comprises: determining the cell state of the one or more live biological cells after the new cell culture condition applied; and retraining the reinforcement-learning machine-learning model based on the determined cell state. ### Claim 79 (depends on 67) The system of claim 67, wherein the set of one or more images comprise phase images. ### Claim 80 (depends on 67) The system of claim 67, wherein the set of one or more images are generated based on fluorescence images or autofluorescence images. ### Claim 81 (depends on 67) The system of claim 67, wherein the one or more live biological cells are induced pluripotent stem cells. ### Claim 82 (depends on 81) The system of claim 81, wherein the desired cell state is a non-pluripotent cell state. ### Claim 83 (depends on 67) The system of claim 67, wherein the one or more live biological cells are healthy cells. ### Claim 84 (depends on 83) The system of claim 83, wherein the desired cell state is a diseased cell state. ### Claim 85 (depends on 67) The system of claim 67, wherein the new cell culture condition is a perturbagen. ### Claim 86 (depends on 85) The system of claim 85, wherein the perturbagen is a chemical treatment, a genetic treatment, or any combination thereof. ### Claim 87 (depends on 67) The system of claim 67, wherein the new cell culture condition is a therapeutic agent. ### Claim 88 (depends on 87) The system of claim 87, wherein the therapeutic agent is a chemical treatment, a genetic treatment, or any combination thereof. ### Claim 89 (depends on 67) The system of claim 67, wherein the new cell culture condition is a temperature, pressure, and/or light exposure. ### Claim 90 (depends on 67) The system of claim 67, wherein applying a new type of cell culture condition to the one or more live biological cells is automated by one or more electronic devices. ### Claim 91 (depends on 67) The system of claim 67, wherein the one or more live biological cells are mammalian cells. ### Claim 92 A non-transitory computer-readable storage medium storing one or more programs for identifying one or more cell culture conditions for progressing one or more live biological cells towards a desired cell state, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device having a display, cause the electronic device to perform operations comprising: performing a plurality of cell culture condition identification cycles on the one or more live biological cells until a condition is met, wherein each cell culture condition identification cycle comprises: obtaining a set of one or more images capturing the one or more live biological cells; determining, from at least a subset of the set of one or more images, a cell state of the one or more live biological cells; identifying a new cell culture condition for progressing the one or more live biological cells towards the desired cell state, by inputting the cell state and the desired cell state into a trained machine-learning model; and applying the new cell culture condition to the one or more live biological cells; and identifying the one or more cell culture conditions, based on outcomes of the plurality of cell culture condition identification cycles, for use in progressing live biological cells towards the desired cell state in future cell cultures. ### Claim 93 A method for identifying one or more cell culture conditions for progressing one or more live biological cells towards a desired cell state, comprising: performing a plurality of cell culture condition identification cycles on one or more live biological cells until a condition is met, wherein each cell culture condition identification cycle comprises: obtaining a set of one or more images capturing the one or more live biological cells; determining, from at least a subset of the set of one or more images, a cell state of the one or more live biological cells; identifying a new cell culture condition for progressing the one or more live biological cells towards a desired cell state, by inputting the cell state and the desired cell state into a trained machine-learning model; and applying the new cell culture condition to the one or more live biological cells; and identifying the one or more cell culture conditions, based on outcomes of the plurality of cell culture condition identification cycles, for use in progressing live biological cells towards the desired cell state in future cell cultures.