8 results about "Digital organism" patented technology

Embodiments directed to a computer-implemented diagnostic system and method to determine various diagnoses for different users, as well as symptom severity and onset of conditions associated with such diagnoses are described. In one example, a computer-implemented method of diagnosing health conditions for users includes identifying a defined linguistic pattern in digital biomarker data of a user. The method further includes prompting the user to perform a defined screening test based at least in part on identifying the defined linguistic pattern. The method further includes monitoring linguistic performance of the user while the user performs the defined screening test. The method further includes determining a diagnosis for the user based at least in part on the defined linguistic pattern, the linguistic performance, and a test result for the user from the defined screening test.

Systems and methods in accordance with embodiments of the present disclosure include the creation and utilization of synthetic eye and head movement datasets that can be used as digital biomarkers for the screening, diagnosis or monitoring of neurologic diseases, including rare neurologic conditions. The synthetic datasets can be used to train deep learning models that can identify and phenotype neurologic diseases based on distinct eye movement patterns. The system and method create synthetic eye movement datasets using generative Al techniques. A pose-guided video generation framework is utilized to produce synthetic eye movement videos. A latent video diffusion mechanism translates segmented mask inputs — simplified representations of eye movements — into realistic visual sequences that mimic real eye movements associated with specific neurologic diseases.

The present disclosure provides a method and system for identifying individual procambarus clarkii of biological characteristics, relating to the technical field of deep learning, comprising: extracting local and global digital biological characteristics of each slice image and a target image, and fusing the local and global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, generating a query and a key through an independent multi-layer perceptron, calculating edge weights through a scaling dot product of the query-key, and obtaining multiple node features after multi-head attention splicing; splicing different node features, generating an attention score for each node based on a gating mechanism aggregation module, and outputting a global representation vector of each group of point sets after global pooling operation; mapping the global representation vector to a fixed dimension through a linear network, and taking it as a final point set embedding, and finally outputting a procambarus clarkii individual identification result.

The invention provides a distributed node re-evolution system for virtual organism multi-modal data. The system is composed of a data source supply and initial issuing end (A end), a distributed recombination and secondary training node (B end), a server end (S end), a database, a property electronic account book and the like. The end A is responsible for collecting user multi-modal biological data and setting a distributed node mode and weight distribution; the B end executes distributed recombination and secondary training; and the S end processes data, trains a model and manages resources. According to the system, firstly, a first-level user creates a project, organism multi-modal data is uploaded to a server, and an initial weight ratio of distributed nodes is set; and the server records the information, reconstructs a three-dimensional model module and generates a voice clone and dialogue model. And the secondary user performs distributed recombination and continuous evolution through the B end to generate an iterable digital human or digital biological bifurcation. The system supports secondary modification and re-evolution of digital persons or digital organisms in ownership, inheritance and transaction of the ownership and continuous evolution of organism data are achieved, and diversified evolution of virtual organisms is promoted through deconstruction, reconstruction, distributed recombination, feedback and retraining.

The disclosed embodiments relate to system, method and apparatus for forming ML sessions so that an AI system can identify, track and diagnose a user through the user's bathroom sessions. As used herein, a session is a period of time during which a user is active in the toilet. The session may include certain inactivity if such inactivity lasts only for a predefined duration. In an exemplary embodiment, a system is configured to capture images of excreta and, using ML algorithms, classifies the toileting session using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further urine or fecal screening is necessary. The disclosed embodiments of ML and image identification through AI requires no user behavior change and provides a link between medical records and specific excreta patterns.

To provide non-invasive and inexpensive digital biomarkers. [Solution] The server of the disease state estimation system stores a database containing data about the user, and as a function, it repeatedly executes the following steps at predetermined intervals for a predetermined number of times: acquiring user identification data (S101), presenting predetermined questions to the user to visualize the state of a predetermined disease (S102), receiving input of answers to the questions from the user (S103), and storing answer data indicating the content of the received answers in the database linked to the user identification data (S104). After a predetermined number of times, it executes the following steps: acquiring answer data stored in the database for a predetermined number of times or more, estimating a predetermined disease state based on the answer data for a predetermined number of times or more, and outputting the estimated result of the predetermined disease state.

Provided herein are systems and methods for creating and measuring digital biomarkers for knee and / or hip osteoarthritis, as well as systems and methods for using such biomarkers in the diagnosis, monitoring, and treatment of knee and / or hip osteoarthritis. In particular aspects, the systems and methods involve using at least one primary device comprising one or more capacitor-pressure sensors configured to generate raw gait data, and a remote processing device configured to generate one or more gait parameters for an individual based on the gait data, and apply one or more trained machine learning models to the gait parameters. In further aspects, systems and methods for training said machine learning models configured to generate a digital biomarker are also disclosed.

The present disclosure provides a system for generating a multi-component senescence index for an individual companion animal, the system including digital biomarkers, biological biomarkers, and subjective assessment methods to predict the phenotypic age and phenotypic age acceleration / deceleration (phenotypic age higher or lower than actual age) of dogs and cats. The disclosure also provides a method for slowing phenotypic aging in a companion animal in need thereof. The method includes determining an index according to one aspect of the present disclosure and providing personalized health, diet, and nutritional measures for a companion animal according to the index of the companion animal. The method can also be considered to be a method for improving accelerated aging of animals. In some embodiments, personalized health measures include diets that decelerate phenotypic aging and / or improve accelerated phenotypic aging.