Researchers Submit Patent Application, “Generating And Traversing Data Structures For Automated Classification”, for Approval (USPTO 20240029888): Patent Application
2024 FEB 08 (NewsRx) -- By a
No assignee for this patent application has been made.
News editors obtained the following quote from the background information supplied by the inventors: “Use of telehealth to deliver healthcare services has grown consistently over the last several decades and has experienced very rapid growth in the last several years. Telehealth can include the distribution of health-related services and information via electronic information and telecommunication technologies. Telehealth can allow for long distance patient and health provider contact, care, advice, reminders, education, intervention, monitoring, and remote admissions. Often, telehealth can involve the use of a user or patient’s personal user device, such as a smartphone, tablet laptop, personal computer, or other device. For example, a user or patient can interact with a remotely located medical care provider using live video, audio, or text-based chat through the personal user device. Generally, such communication occurs over a network, such as a cellular or internet network.
“Remote or at-home healthcare diagnosis can solve or alleviate some problems associated with in-person diagnosis. For example, health insurance may not be required, travel to a testing site is avoided, and diagnosis can be completed at a user’s convenience. However, remote or at-home diagnosis generally still depends on availability of a health provider. Accordingly, there exists a need for automated diagnosis and intervention of a user’s condition to allow users to immediately obtain a diagnosis based on symptoms that they are experiencing.”
As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “For purposes of this summary, certain aspects, advantages, and novel features are described herein. It is to be understood that not necessarily all such advantages may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize the disclosures herein may be embodied or carried out in a manner that achieves one or more advantages taught herein without necessarily achieving other advantages as may be taught or suggested herein.
“All of the embodiments described herein are intended to be within the scope of the present disclosure. These and other embodiments will be readily apparent to those skilled in the art from the following detailed description, having reference to the attached figures. The invention is not intended to be limited to any particular disclosed embodiment or embodiments.
“There are many undiagnosed ailments that harm quality of life, but not severely or acutely enough to by diagnosed and treated by a doctor, or for patients to even seek treatment. These ailments are often the result of many different subsystems of the human body each failing slightly rather than a single big ailment. Examples include fatigue, depression, anxiety, mental cloudiness, weakness, anhedonia, joint pains, etc.
“For instance, a user may have a cough but may not want to see a medical professional, or the user may want to retrieve a diagnosis based on the cough without leaving the comfort of their own home. Or a user may have a sore throat, symptoms of COVID-19 and/or any other mouth or throat related issue or symptoms. However, the user may not want to leave their own home because the user does not feel well, or the user may not want to pay a significant amount of money to see a medical professional without an initial diagnosis.
“To address such issues, a system can use a holistic approach to treating symptoms in a continual and ongoing manner via artificial intelligence. The intervention may be coupled with proctors or other external medical practitioners and may include interventions based on patient response, preference and/or risk tolerance. A system with integrated artificial intelligence and machine learning for health information collection may also be able to automatically perform tasks, such as patient intake, allowing medical professionals to spend their time performing other tasks. The system can also automatically and dynamically generate a diagnosis and a treatment plan based on the automated patient intake. The system can reduce the time required for a medical professional to diagnose a patient and develop a treatment plan further reducing the time the medical professional spends with each patient.
“In specific cases, this system can also be configured to retrieve audio, image and/or movement data from a user device to automatically diagnose a user with an illness based on a cough or cough characteristics detected in the audio, image and/or movement data. The system can compare the cough or cough characteristic to cough or cough characteristics of known illnesses. Based on the diagnosis, the system can automatically provide treatment and/or management recommendations to the user. Additionally, the system can use the user device to provide treatment to the user via audio and/or vibration of the user device. The system can also be configured to capture one or more images of a mouth of the user via a camera of a user device. The system can automatically analyze the one or more images to determine an illness or medical issue of the user.
“Thus, the system can use the determination of an illness or medical issue, demographic information and user health information to automatically diagnose the user with an illness or medical issue without the user leaving the user’s home or seeing a medical professional in person.”
The claims supplied by the inventors are:
“1. A computer-implemented method, the method comprising: receiving, from a user device, a first set of indicators associated with a condition experienced by a user; generating a directed acyclic graph (DAG) for the user, wherein the DAG comprises: a first layer of nodes that each correspond to an indicator, wherein the first layer of nodes comprises a first node; a second layer of nodes that each correspond to a cause, wherein the second layer of nodes comprises a second node, wherein each node of the first layer of nodes is connected to each node of the second layer of nodes by an edge, and wherein the edge between the first node of the first layer of nodes and the second node of the second layer of nodes is associated with a probability that the indicator corresponding to the first node is indicative of the cause corresponding to the second node; and a third layer of nodes that each correspond to a treatment, wherein the third layer of nodes comprises a third node, wherein each node of the second layer of nodes is connected to each node of the third layer of nodes by an edge, and wherein the edge between the second node of the second layer of nodes and the third node of the third layer of nodes is associated with a probability that the treatment corresponding to the third node addresses the cause corresponding to the second node; traversing the DAG to determine a likely cause; traversing the DAG to determine at least one treatment for the likely cause; and generating a custom treatment plan for the user based on: the at least one treatment for the likely cause; and a cost function.
“2. The computer-implemented method of claim 1, further comprising: tracking a second set of indicators from the user following the custom treatment plan; updating the DAG based on the second set of indicators; traversing the updated DAG to determine an updated cause; traversing the updated DAG to determine at least one treatment for the updated cause; and generating an updated treatment plan for the user based on the at least one treatment for the updated cause.
“3. The computer-implemented method of claim 1, wherein the directed acyclic graph comprises a recurrent tripartite connected directed acyclic graph.
“4. The computer-implemented method of claim 1, wherein traversing the DAG comprises a random sample consensus (RANSAC) approach.
“5. The computer-implemented method of claim 1, wherein every node of the DAG is stateful and comprises a presence of an indicator as a percentage.
“6. A non-transient computer readable medium containing program instructions for causing a computer to perform a method comprising: receiving, from a user device, a set of indicators associated with a condition experience by a user; generating a directed acyclic graph (DAG) for the user, wherein the DAG comprises: a first layer of nodes that each correspond to an indicator, wherein the first layer of nodes comprises a first node; a second layer of nodes that each correspond to a cause, wherein the second layer of nodes comprises a second node, wherein each node of the first layer of nodes is connected to each node of the second layer of nodes by an edge, and wherein the edge between the first node of the first layer of nodes and the second node of the second layer of nodes is associated with a probability that the indicator corresponding to the first node is indicative of the cause corresponding to the second node; and a third layer of nodes that each correspond to a treatment, wherein the third layer of nodes comprises a third node, wherein each node of the second layer of nodes is connected to each node of the third layer of nodes by an edge, and wherein the edge between the second node of the second layer of nodes and the third node of the third layer of nodes is associated with a probability that the treatment corresponding to the third node addresses the cause corresponding to the second node; traversing the DAG to determine a likely cause; traversing the DAG to determine at least one treatment for the likely cause; and generating a custom treatment plan for the user based on: the at least one treatment for the likely cause; and a cost function.
“7. The non-transient computer readable medium of claim 6, wherein the method further comprises: tracking a second set of indicators from the user following the custom treatment plan; updating the DAG based on the second set of indicators; traversing the updated DAG to determine an updated cause; traversing the updated DAG to determine at least one treatment for the updated cause; and generating an updated treatment plan for the user based on the at least one treatment for the updated cause.
“8. The non-transient computer readable medium of claim 6, wherein the directed acyclic graph comprises a recurrent tripartite connected directed acyclic graph.
“9. The non-transient computer readable medium of claim 6, wherein traversing the DAG comprises a random sample consensus (RANSAC) approach.
“10. The non-transient computer readable medium of claim 6, wherein every node of the DAG is stateful and comprises a presence of an indicator as a percentage.”
For additional information on this patent application, see: Bryant,
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