Researchers Submit Patent Application, “Method For Providing And Updating Treatment Recommendations”, for Approval (USPTO 20230326594): Patent Application

2023 NOV 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors CAMERON, Michael (Los Angeles, CA, US); FRECHTER, Yiftah (Irvington, NY, US); GUSAK, Ksenya (Truckee, CA, US), filed on April 7, 2022, was made available online on October 12, 2023.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “

“Technical Field

“The present disclosure relates generally to the provision of therapeutic interventions for a range of disorders. In some aspects, the range of disorders may include autism spectrum disorders.

“Related Art

“In practice, the provision of therapeutic interventions for multi-faceted disorders may include separate interventions for addressing different aspects of the disorder(s). Current data collection methodologies, in some aspects, include unstructured data consisting of, e.g., therapist notes and personal identification information for a patient. This unstructured data is generally not usable to derive any insights from large populations of patients using existing data analysis methods. In some aspects, therapy may be provided a relatively-inexperienced therapist that may not be able to integrate information from multiple different assessments to formulate a treatment plan (e.g., a set of recommended treatments and/or assessments). Accordingly, there is an expected benefit to providing a system that provides recommendations based on a holistic analysis of available information across multiple areas of human activity.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “Example implementations described herein include an innovative method for providing treatment recommendations based on a holistic assessment including a set of decision trees. The method may include receiving a first set of responses based on prompts within each decision tree of a set of decision trees, each decision tree of the set of decision trees corresponding to a different aspect of a human activity. The method may further include outputting the prompts within each decision tree of the set of decision trees, at least one prompt being outputted based on one or more responses in the first set of responses. The method may also include determining at least one treatment recommendation based on the first set of responses to the prompts outputted for the set of decision trees. The method may further include outputting the at least one treatment recommendation.

“Example implementations described herein include an innovative apparatus for providing treatment recommendations based on a holistic assessment including a set of decision trees. The apparatus may include a user interface configured to present prompts to a user and receive responses from the user. The apparatus may further include a memory and at least one processor coupled to the memory that, when executing a program stored in the memory, is configured to receive a first set of responses based on prompts within each decision tree of a set of decision trees, each decision tree of the set of decision trees corresponding to a different aspect of a human activity. The at least one processor may further be configured to output the prompts within each decision tree of the set of decision trees, at least one prompt being outputted based on one or more responses in the first set of responses. The at least one processor may also be configured to determine at least one treatment recommendation based on the first set of responses to the prompts outputted for the set of decision trees. The at least one processor may further be configured to output the at least one treatment recommendation.

“Example implementations described herein include an innovative computer-readable medium storing computer executable code for providing treatment recommendations based on a holistic assessment including a set of decision trees. The computer executable code may include instructions for receiving a first set of responses based on prompts within each decision tree of a set of decision trees, each decision tree of the set of decision trees corresponding to a different aspect of a human activity. The computer executable code may further include instructions for outputting the prompts within each decision tree of the set of decision trees, at least one prompt being outputted based on one or more responses in the first set of responses. The computer executable code may also include instructions for determining at least one treatment recommendation based on the first set of responses to the prompts outputted for the set of decision trees. The computer executable code may further include instructions for outputting the at least one treatment recommendation.

“In some aspects, the example implementations may provide improved speed and accuracy of treatment recommendations by relatively-inexperience practitioners (e.g., therapists) by providing guidance through a set of interactive decision trees and integrating the information collected to output at least one treatment recommendation based on a holistic assessment. Furthermore, as discussed in the detailed description below, some aspects provide anonymized data for multiple patients that would otherwise be unavailable for analysis (e.g., due to Health Insurance Portability and Accountability Act (HIPAA) requirements). The anonymized data, in some aspects, may be analyzed by algorithms or may be used to train networks (e.g., neural networks) to further improve the accuracy or efficacy of the treatment recommendation and/or to update the decision trees used to generate the treatment recommendations. The anonymized data may further provide a benefit over locally-stored HIPAA-compliant data by allowing larger data sets that lead to improved performance of machine training of neural networks or other machine-trained algorithms. The anonymized data may also be made available to third-party researchers to perform analysis using components of the system.”

The claims supplied by the inventors are:

“1. A method, comprising: receiving a first set of responses based on prompts within each decision tree of a set of decision trees, each decision tree of the set of decision trees corresponding to a different aspect of a human activity; outputting the prompts within each decision tree of the set of decision trees, at least one prompt being outputted based on one or more responses in the first set of responses; determining at least one treatment recommendation based on the first set of responses to the prompts outputted for the set of decision trees; and outputting the at least one treatment recommendation.

“2. The method of claim 1, wherein the set of decision trees comprises at least one of (1) a dental decision tree relating to one or more of a set of dental issues, a set of dental assessments, or a set of dental interventions, (2) a behavioral decision tree relating to one or more of a set of behavioral issues, a set of behavioral assessments, or a set of behavioral interventions, (3) a sleep disorder decision tree relating to one or more of a set of sleep disorder issues, a set of sleep disorder assessments, or a set of sleep disorder interventions, or (4) a feeding decision tree relating to one or more of a set of nutritional issues, a set of nutritional assessments, or a set of nutritional interventions.

“3. The method of claim 2, wherein the set of decision trees comprises a plurality of decision trees.

“4. The method of claim 1, wherein the at least one treatment recommendation comprises at least one of an additional assessment to be performed or a therapeutic intervention to be implemented.

“5. The method of claim 1, further comprising: outputting an indication of a set of responses upon which determining the at least one treatment recommendation is based.

“6. The method of claim 5, wherein the indication of the set of responses includes a state value associated with at least one variable associated with at least one decision tree in the set of decision trees.

“7. The method of claim 1, wherein the first set of responses is related to a first patient being treated for an autism spectrum disorder and the at least one treatment recommendation is at least one autism treatment recommendation.

“8. The method of claim 7, further comprising: receiving a first indication of a first implemented autism treatment after outputting the at least one autism treatment recommendation; and receiving a second indication of a first outcome associated with the first implemented autism treatment.

“9. The method of claim 8, further comprising: receiving for each of a plurality of additional patients being treated for an autism spectrum disorder: an additional set of responses based on prompts within each decision tree of the set of decision trees; a third indication of a determined autism treatment recommendation for the patient in the plurality of additional patients; a fourth indication of an implemented autism treatment for the patient in the plurality of additional patients after outputting the determined autism treatment recommendation for the patient in the plurality of additional patients; and a fifth indication of an outcome associated with the implemented autism treatment for the patient in the plurality of additional patients; and optimizing at least one decision tree in the set of decision trees by performing a machine learning operation based on the first set of responses, the first indication, the second indication, the sets of responses for the plurality of additional patients, the third indications for the plurality of additional patients, the fourth indications for the plurality of additional patients, and the fifth indications for the plurality of additional patients.

“10. The method of claim 9, wherein optimizing the at least one decision tree comprises updating at least one of (1) a prompt in the at least one decision tree, (2) the at least one prompt outputted based on the one or more responses in the first set of responses, or (3) at least one autism treatment recommendation based on a particular set of responses to a set of prompts within at least one decision tree.

“11. The method of claim 9, wherein each of the second indication and the filth indication are associated with a value measuring a post-treatment state (or attribute) associated with one of the first patient or one of the plurality of additional patients, and performing the machine learning operation comprises performing a machine-learning-based operation to identify recommendations that optimize the value.

“12. The method of claim 1, wherein the first set of responses is related to a first patient, the method further comprising: receiving additional sets of responses related to a plurality of additional patients based on prompts within each decision tree of the set of decision trees; performing, based on the first set of responses and the additional set of responses, a machine learning operation to identify a correlation between at least a first response to a first prompt in a first decision tree in the set of decision trees and at least a second response to a second prompt in a second decision tree in the set of decision trees; and updating at least the first decision tree based on the correlation between the first response and the second response.

“13. The method of claim 12, wherein updating at least the first decision tree comprises updating a recommendation associated with the first response to recommend addressing the second prompt in the second decision tree.

“14. The method of claim 1, wherein the first set of responses is related to a first patient being treated for an autism spectrum disorder and the at least one treatment recommendation is an autism treatment recommendation, the method further comprising: receiving additional sets of responses at a plurality of different times related to the first patient based on prompts within each decision tree of the set of decision trees; performing a machine learning operation to identify a correlation between at least a first response to a first prompt in a first decision tree in the set of decision trees at a first time in the plurality of different times and at least a second response to a second prompt in a second decision tree in the set of decision trees at a second time in the plurality of different times; and updating at least the first decision tree based on the correlation between the first response and the second response.

“15. The method of claim 14, wherein the first time is the second time.

“16. An apparatus comprising: a memory storing a program for providing treatment recommendations based on a holistic assessment including a set of decision trees; and at least one processor coupled to the memory that when executing the program, is configured to: receive a first set of responses based on prompts within each decision tree of a set of decision trees, each decision tree of the set of decision trees corresponding to a different aspect of a human activity; output the prompts within each decision tree of the set of decision trees, at least one prompt being outputted based on one or more responses in the first set of responses; determine at least one treatment recommendation based on the first set of responses to the prompts outputted for the set of decision trees; and output the at least one treatment recommendation.

“17. The apparatus of claim 16, the at least one processor further being configured to: output an indication of a set of responses upon which determining the at least one treatment recommendation is based.

“18. The apparatus of claim 16, wherein the first set of responses is related to a first patient being treated for an autism spectrum disorder and the at least one treatment recommendation is at least one autism treatment recommendation, the at least one processor further configured to: receive a first indication of a first implemented autism treatment after outputting the at least one autism treatment recommendation; receive a second indication of a first outcome associated with the first implemented autism treatment; receive for each patient in a plurality of additional patients being treated for an autism spectrum disorder: an additional set of responses based on prompts within each decision tree of the set of decision trees for the patient in the plurality of additional patients; a third indication of a determined autism treatment recommendation for the patient in the plurality of additional patients; a fourth indication of an implemented autism treatment for the patient in the plurality of additional patients after outputting the determined autism treatment recommendation for the patient in the plurality of additional patients; and a fifth indication of an outcome associated with the implemented autism treatment for the patient in the plurality of additional patients; and optimize at least one decision tree in the set of decision trees by performing a machine learning operation based on the first set of responses, the first indication, the second indication, the sets of responses for the plurality of additional patients, the third indications for the plurality of additional patients, the fourth indications for the plurality of additional patients, and the fifth indications for the plurality of additional patients.”

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For additional information on this patent application, see: CAMERON, Michael; FRECHTER, Yiftah; GUSAK, Ksenya. Method For Providing And Updating Treatment Recommendations. U.S. Patent Application Number 20230326594, filed April 7, 2022 and posted October 12, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230326594)&db=US-PGPUB&type=ids

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