# US 2021/0330218 A1 · Multi-factor activity monitoring > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0330218 A1 - Document ID: US20210330218A1 - Status: Published application - Kind: A1 - Application number: 17/238,593 - Filed: 2021-04-23 - Published: 2021-10-28 - Earliest claimed: 2020-04-24 - Assignees: Glympse Bio, Inc. - Applicants: Glympse Bio, Inc. - Inventors: James Bowen; Faycal Touti - Web page: https://patentgrep.com/patent/US20210330218A1 ## CPC - A61B 5/1473: invasive, e.g. introduced into the body by a catheter - A61B 5/4842: {Monitoring progression or stage of a disease} - A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - C12Q 1/37: involving peptidase or proteinase - G01N 33/58: involving labelled substances (G01N33/53 takes precedence) - G01N 33/68: involving proteins, peptides or amino acids {(involving lipoproteins G01N33/92)} - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 40/63: for local operation - G16H 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - A61B 5/207: {Sensing devices adapted to collect urine} - C12Q 1/6883: for diseases caused by alterations of genetic material - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES - 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) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Data from activity sensors sensitive to enzymes indicative of various disease states is combined with data from other sources including electronic medical records and clinical data including molecular diagnostic testing. The pooled data can be analyzed to identify patterns indicative of certain outcomes including development of a disease, progression of a disease, or likely therapeutic efficacy of a given treatment for a patient. ## Claims ### Claim 1 A method of providing personalized treatment to a patient comprising: administering an activity sensor cocktail to a patient; analyzing results obtained from administration of the activity sensor cocktail; accessing data obtained from at least one other source; and determining a personalized course of treatment for the patient based on analysis of results from activity sensor cocktail administration and the data obtained from the at least one other source. ### Claim 2 (depends on 1) The method of claim 1, wherein the activity sensor cocktail comprises a plurality of activity sensors each comprising: a carrier comprising one or a plurality of molecular subunits; and a plurality of detectable reporters, each linked to the carrier by a cleavable linker containing the cleavage site of an enzyme, wherein the activity sensor reports activity of one or more enzymes by releasing the reporters upon cleavage by the one or more enzymes. ### Claim 3 (depends on 1) The method of claim 1, wherein the determining step comprises diagnosing a disease. ### Claim 4 (depends on 1) The method of claim 1, wherein the determining step comprises identifying a stage in a disease progression. ### Claim 5 (depends on 1) The method of claim 1, wherein the determining step comprises predicting a response to a therapeutic treatment. ### Claim 6 (depends on 1) The method of claim 1, wherein the at least one other source comprises electronic medical records (EMR). ### Claim 7 (depends on 1) The method of claim 1, wherein the at least one other source comprise molecular diagnostic data. ### Claim 8 (depends on 7) The method of claim 7, wherein the molecular diagnostic data is selected from the group consisting of nucleic acid sequence information, epigenetic information, DNA methylation, and RNA expression data. ### Claim 9 (depends on 1) The method of claim 1, wherein the at least one other source comprises comorbidity information. ### Claim 10 (depends on 1) The method of claim 1, wherein determining step comprises identifying patterns in the results from the activity sensor cocktail administration and the data obtained from the at least one other source indicative of an outcome. ### Claim 11 (depends on 10) The method of claim 10, wherein the patterns are identified through machine learning analysis of data for patients with known outcomes. ### Claim 12 (depends on 1) The method of claim 1, wherein the determining step is performed by a computer comprising a tangible, non-transitory memory coupled to a processor. ### Claim 13 A method for identifying diagnostic indicators in patient data, the method comprising: analyzing results obtained from administration of an activity sensor cocktail to a plurality of patients with known outcomes; accessing data for the plurality of patients obtained from at least one other source; and providing the known outcomes, the results, and the data to a machine learning system; identifying, through machine learning analysis, patterns in the results and the data indicative of one or more of the known outcomes using the machine learning system. ### Claim 14 (depends on 13) The method of claim 13, wherein the activity sensor cocktail comprises a plurality of activity sensors each comprising: a carrier comprising one or a plurality of molecular subunits; and a plurality of detectable reporters, each linked to the carrier by a cleavable linker containing the cleavage site of an enzyme, wherein the activity sensor reports activity of one or more enzymes by releasing the reporters upon cleavage by the one or more enzymes. ### Claim 15 (depends on 13) The method of claim 13, wherein the known outcomes comprise development of a disease. ### Claim 16 (depends on 13) The method of claim 13, wherein the known outcomes comprise progression of a disease. ### Claim 17 (depends on 13) The method of claim 13, wherein the known outcomes comprise a response to a therapeutic treatment. ### Claim 18 (depends on 13) The method of claim 13, wherein the at least one other source comprises electronic medical records (EMR). ### Claim 19 (depends on 13) The method of claim 13, wherein the at least one other source comprises comorbidity information. ### Claim 20 (depends on 13) The method of claim 13, wherein the at least one other source comprise molecular diagnostic data.