Dynamic source balancing synthesis engine

Abstract

Apparatus and associated methods relate to dynamic source balancing synthesis. In an illustrative example, a dynamic source balancing synthesis engine (DSBSE) may synthesize point-source and / or other data inputs to dynamically and adaptively generate corresponding action(s). The action(s), for example, may be generated based on context inference(s) and corresponding weighting factor(s) of the data input(s) (e.g., multiple inputs). The weighting factor(s) may, for example, be generated based on multi-dimensions (e.g., three or more dimensional) matrices. The matrices may, for example, be dynamically adapted by the DSBSE. The action(s) may, for example, include and / or be transformed into control signal(s) configured to operate one or more connected devices in response to the data inputs. Various embodiments may advantageously provide self-adapting, automatically deactivated multi-modal therapeutic / non-therapeutic delivery systems.

Description

DYNAMIC SOURCE BALANCING SYNTHESIS ENGINECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application Serial No. US 63 / 609,192 titled “Dynamic Therapy Engine and Data Agent,” filed by Ryan Douglas, et al., on Dec. 12, 2023.

[0002] This application incorporates the entire contents of the foregoing application(s) herein by reference.

[0003] The subject matter of this application may have common inventorship with and / or may be related to the subject matter of:• U.S. Application Serial No. 18 / 313,249, titled “Dynamically Controlled Cerebrospinal Fluid Shunt,” filed by Samuel Robert Browd, et al., on May 5, 2023;• U.S. Application Serial No. 63 / 365,407, titled "Distributed Sensing and Control of Cerebrospinal Fluid," filed by Samuel Robert Browd, et al., on May 26, 2022;• U.S. Application Serial No. 63 / 477,158, titled "Central Nervous System Monitoring and Intervention," filed by Samuel Robert Browd, et al., on December 23, 2022;• U.S. Application Serial No. 63 / 477,162, titled "Cerebrospinal Fluid Polarization," filed by Samuel Robert Browd, et al., on December 23, 2022;• U.S. Application Serial No. 63 / 488,412, titled "Dynamic Shunt Systems," filed by Samuel Robert Browd, et al., on March 3, 2023;• U.S. Application Serial No. 63 / 364,253, titled "SHUNT TECHNOLOGY AND THE POTENTIAL FOR A SMART SHUNT," filed by Samuel Robert Browd on May 5, 2022;• U.S. Application Serial No. 63 / 590,313, titled “Neurosurgical Devices and Methods,” filed by Samuel Robert Browd, et al., on Oct. 13, 2023; and• PCT Application Serial No. PCT / US24 / 51466, titled “Dynamic Guided Physician-Patient Interaction Engine,” filed by Samuel Robert Browd, et al., on Oct. 15, 2024.

[0004] This application incorporates herein by reference the entire contents of the foregoing application(s) and of all application(s) for which said application(s) claim priority and / or benefit.TECHNICAL FIELD

[0005] Various embodiments relate generally to health monitoring and / or therapy.BACKGROUND

[0006] Health and wellness of an individual may be represented using structured data from electronic medical records (EMRs). EMRs may include, for example, a digital record of a patient's medical history. EMRs may, by way of example and not limitation, include diagnoses, treatments, lab results, and / or medications. This structured data may, for example, be used in algorithms thatcan analyze patterns and predict outcomes. For example, EMRs may be integrated with machine learning models.

[0007] For example, systems for real-time patient monitoring allow healthcare providers to continuously track patients' vital signs and health indicators. Remote patient monitoring devices enable off-site tracking of patients. Radiology and diagnostic devices integrated with EMRs allow for more seamless access to test results and images.

[0008] Data related to health and wellness may be monitored by wearable devices. Wearables, such as smartwatches and fitness trackers, may, for example, monitor vital signs like heart rate, blood pressure, and / or activity levels. This real-time data collection may be used in systems seeking to provide early detection of anomalies and chronic conditions. For instance, a wearable device can alert a patient and their healthcare provider if it detects irregular heart rhythms.

[0009] For example, continuous glucose monitors track blood sugar levels in real-time, allowing adjustments in insulin therapy. Wearable electrocardiogram monitors detect irregular heart rhythms and other cardiac conditions by recording the heart's electrical activity and transmitting the data to a mobile app for further analysis. Additionally, fitness trackers and smartwatches monitor various health metrics, including heart rate, sleep patterns, and physical activity levels, providing valuable data for tracking overall health, detecting early signs of health issues.SUMMARY

[0010] Apparatus and associated methods relate to dynamic source balancing synthesis. In an illustrative example, a dynamic source balancing synthesis engine (DSBSE) may synthesize pointsource and / or other data inputs to dynamically and adaptively generate corresponding action(s). The action(s), for example, may be generated based on context inference(s) and corresponding weighting factor(s) of the data input(s) (e.g., multiple inputs). The weighting factor(s) may, for example, be generated based on multi-dimensions (e g., three or more dimensional) matrices. The matrices may, for example, be dynamically adapted by the DSBSE. The action(s) may, for example, include and / or be transformed into control signal(s) configured to operate one or more connected devices in response to the data inputs. Various embodiments may advantageously provide self-adapting, automatically deactivated multi-modal therapeutic / non-therapeutic delivery systems.

[0011] The details of various embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG. 1 depicts an example dynamic source balancing synthesis engine (DSBSE) in an illustrative use-case scenario.

[0013] FIG. 2 is a block diagram depicting an example DSBSE data relationship architecture.

[0014] FIG. 3 is a block diagram depicting an illustrative architecture of an example DSBSE.

[0015] FIG. 4 is flowchart depicting an example method of operating a DSBSE.

[0016] FIG. 5 depicts an example source balancing matrix (SBM) that may be used, for example, by a DSBSE.

[0017] FIG. 6 depicts an example operation and / or training model of an example context inference engine, such as may be used with (e.g., in) a DSBSE.

[0018] FIG. 7 depicts an example operation and / or training model of an example dynamic source balancing engine, such as may be used with (e.g., in) a DSBSE.

[0019] FIG. 8 depicts an example operation and / or training model of an example recommendation engine, such as may be used with (e.g., in) a DSBSE.

[0020] FIG. 9 depicts an example operation and / or training model of an example dynamic activation engine, such as may be used with (e.g., in) a DSBSE.

[0021] FIG. 10 depicts an illustrative method of training a model(s), such as disclosed at least with reference to FIGS. 6-9.

[0022] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0023] To aid understanding, this document is organized as follows. First, to help introduce discussion of various embodiments, a dynamic source balancing synthesis engine (DSBSE) is introduced with reference to FIG. 1. Second, that introduction leads into a description with reference to FIGS. 2-3 of some exemplary embodiments of a DSBSE. Third, with reference to FIG. 4, some example methods related, for example, to operation of a DSBSE, are described. Fourth, with reference to FIGS. 5-9, the discussion turns to exemplary embodiments that illustrate components and / or engines that may be part of a DSBSE. Fifth, and with reference to FIG. 9, the discussion turns to exemplary model training methods. Finally, the document discusses further embodiments, exemplary applications and aspects relating to dynamic source balancing synthesis.

[0024] FIG. 1 depicts an example dynamic source balancing synthesis engine (DSBSE) in an illustrative use-case scenario. In the depicted example, device(s) 105 are communi cab ly coupled to a network 125. The device(s) 105 may, for example, include personal devices. For example, the personal devices may include computing devices (computer, smartphone). The personal devices may, for example, include wearables (e.g., smartwatch, fitness tracker). The device(s) 105 may,for example, include medical devices. For example, the medical devices may include monitors (e.g., external monitors, embedded monitors) and / or actuators (e.g., imaging devices, therapy delivery devices, notification devices). The device(s) 105 may, for example, include systems (e.g., social media networks, content delivery platforms, research network systems, weather prediction networks, news / events feeds). For example, the systems may include servers and / or other connected devices (e.g., internet of things (IOT) devices). The device(s) 105 may be operated by and / or operably coupled to one or more users. For example, the users may include healthcare providers 110. The users may, for example, include patients 115 and / or related persons (e.g., caretakers, family, friends). The users may, for example, include other persons 120 (e.g., groups of persons).

[0025] The network 125 is communicably coupled to a DSBSE system 140. For example, the DSBSE system 140 may include a computer(s). In some embodiments, the computer(s) may, for example, be implemented as a server(s). For example, the DSBSE system 140 may be implemented in a distributed computing network. A DSBSE 145 may, for example, be implemented on the DSBSE system 140. For example, the DSBSE 145 may be operating on one or more processor(s) of the DSBSE system 140.

[0026] The DSBSE 145 is communicably coupled to one or more data stores 130. For example, the one or more data stores 130 may include internal data stores. The one or more data stores 130 may, for example, include external data stores (e.g., as depicted). The one or more data stores 130 may contain one or more data objects 135. For example, the one or more data objects 135 may include structured data. The one or more data objects 135 may, for example, include unstructured data. In some implementations, by way of example and not limitation, the one or more data objects 135 may include patient reports (e.g., structured, unstructured such as in natural language). The one or more data objects 135 may include healthcare provider observations. The one or more data objects 135 may include data retrieved from electronic health record (EHR) system(s). The one or more data objects 135 may include data objects (e g., weather reports, event data) retrieved from external providers. The one or more data obj ects 135 may, for example, include data from hardware (e.g., medical devices) such as, for example, one or more of the device(s) 105. For example, the data from hardware may include data from hospital devices (e.g., imaging, therapeutic delivery). The data from hardware may include fitness data, for example, such as from personal devices, such as wearables and / or smartphones. The data from hardware may, for example, include data from personal medical devices (e.g., implanted medical devices, external medical devices). The medical devices may, by way of example and not limitation, include monitoring and / or therapeutic delivery devices.

[0027] In this example, the DSBSE 145 operates on one or more data objects 135 (e g., multiple streams of one or more data objects 135 sources) to generate a synthesized action(s) 165. For example, the DSBSE 145 may generate a control signal(s) configured to implement the action(s) 165. The control signal(s) may, for example, be transmitted to the device(s) 105 to implement the action(s) 165.

[0028] In some embodiments, the DSBSE 145 may, for example, advantageously provide a dynamic therapy engine and / or data agent that integrates quantitative and qualitative data from various sources (e.g., one or more data objects 135). The action(s) 165 may, for example, advantageously provide real-time diagnostic and therapeutic recommendations. The DSBSE 145 may, for example, apply (e.g., generate and apply) a source balancing matrix to the input sources to generate the action(s) 165 based on the qualitative and quantitative nature of data, and the context (e.g., immediate context) of a trigger (e.g., a point-source data object). The DSBSE 145 may, for example, advantageously enable precise and personalized healthcare management.

[0029] As shown, the DSBSE 145 includes a context inference engine 150. The context inference engine 150 may, for example, operate on one or more of the data objects 135 (e.g., a trigger data object(s)) and generate a context inference (e.g., a decision to be made, a time scope, a question to be answered). The DSBSE 145 includes, in this example, a source balancing engine 155. The source balancing engine 155 may, for example, operate on the (e.g., selected) one or more data objects 135 based on the context inference(s) to generate a balanced output of each data object (e.g., relative weightings). The DSBSE 145 as shown includes a recommendation engine 160. The recommendation engine 160 may, for example, operate on the one or more data objects 135 based on the balancing output of the source balancing engine 155 to generate the action(s) 165

[0030] For example, traditional healthcare systems often rely on isolated data points and subjective patient reports, leading to delayed or inaccurate diagnoses. In some embodiments, the DSBSE 145 may, for example, advantageously integrate multiple data sources, including implant-based sensors, biometric data, and / or patient-reported outcomes in generating action(s) 165. For example, the DSBSE 145 may advantageously balance the relative importance of the various data sources based on immediate context. Accordingly, in some implementations the DSBSE 145 may, for example, advantageously provide a comprehensive and / or real-time assessment of a patient's health status.

[0031] As an illustrative example, a DSBSE 145 may be implemented as a dynamic source balancing therapy agent. The agent may, for example, be partially or completely autonomous. The agent may, for example, be configured to monitor and integrate multiple data sources, and generate from those sources personalized therapeutic interventions. In some implementations, the dynamic source balancing therapy agent may, for example, monitor a patient with hydrocephalus. Thepatient may, for example, have an implanted intracranial pressure sensor. The dynamic source balancing therapy agent may, for example, receive quantitative sensor data. The sensor data may, for example, include intracranial pressure measurements. The agent may, for example, receive qualitative data. The qualitative data may, for example, include patient-reported symptoms like headaches. The qualitative data may, for example, include lifestyle data such as activity levels and / or social media interactions. For example, the lifestyle data may be generated and / or inferred from hardware data (e.g., wearables such as a fitness tracker) and / or patient-reported qualitative data. Based on the integrated data sources, the dynamic source balancing therapy agent may provide real-time therapeutic recommendations, such as generating a notification (e.g., action(s) 165) advising immediate medical attention when elevated intracranial pressure is detected. In some implementations, the action(s) 165 may include an alert to healthcare providers 110 (e.g., a physician, an emergency medical service).

[0032] Traditional medical monitoring and therapeutic systems may rely primarily on electronic medical records (EMR) data, which can include outdated information, transcription errors, and provider biases. Additionally, traditional systems may not effectively integrate real-time sensor data with qualitative patient feedback and lifestyle indicators. The dynamic source balancing therapy agent may, for example, advantageously operate on point-source data collection (e.g., in real-time) and apply context-specific dynamic weighting of multiple data streams to generate context-specific action-inducing control signals (e.g., action(s) 165).

[0033] FIG. 2 is a block diagram depicting an example DSBSE data relationship architecture. In this example, the one or more data objects 135 may, for example, include multi-person aggregate data 205. The multi -person aggregate data 205 may, as depicted, include unstructured data The multi-person aggregate data 205 may, as depicted, include structured data. The multi-person aggregate data 205 may, for example, include Electronic Health Records (EHRs), such as aggregated data from multiple patients' medical histories, treatments, and / or outcomes, and / or information on common diagnoses, treatments, and / or their success rates. The multi-person aggregate data 205 may, for example, include population health data, such as, for example, data on the prevalence and incidence of diseases across different demographics, and / or information on health disparities among various population groups. The multi-person aggregate data 205 may, for example, include claims and / or billing data, such as, for example, aggregated insurance claims data showing the types of services used and / or their associated costs, and / or trends in healthcare spending and resource utilization. The multi-person aggregate data 205 may, for example, include pharmacy data, such as, for example, data on prescription patterns, medication adherence, and / or side effects, and / or information on the effectiveness of different drugs and / or treatment protocols.

[0034] The multi-person aggregate data 205 may, for example, include laboratory and / or diagnostic data such as, for example, aggregated results from lab tests and / or diagnostic procedures, and / or trends in test utilization and diagnostic accuracy. The multi-person aggregate data 205 may, for example, include patient-reported outcomes such as, for example, surveys and / or questionnaires completed by patients about their health status and quality of life, and / or data on patient satisfaction with treatments and healthcare services. The multi-person aggregate data 205 may, for example, include wearables and / or remote monitoring data such as, for example, data collected from devices like fitness trackers, smartwatches, and / or remote monitoring systems, and / or information on physical activity, heart rate, sleep patterns, and / or other vital signs. The multi-person aggregate data 205 may, for example, include social determinants of health such as, for example, data on factors like socioeconomic status, education, and / or living conditions, and / or impact of social determinants on health outcomes and / or access to care. The multi-person aggregate data 205 may, for example, include public health data such as, for example, information from public health surveillance systems on disease outbreaks and / or immunization rates, and / or data on environmental factors affecting health, such as pollution levels. The multi-person aggregate data 205 may, for example, include clinical research data, such as, for example, results from clinical trials and / or observational studies, and / or information on new treatments, interventions, and / or their effectiveness. The multi-person aggregate data 205 may, for example, include behavioral health data such as, for example, data on mental health conditions, treatment utilization, and / or outcomes, and / or aggregated information on substance use and / or its impact on health. The multi-person aggregate data 205 may, for example, include genomic data such as, for example, aggregated genetic information from large populations, and / or insights into genetic factors influencing health and disease. The multi-person aggregate data 205 may, for example, include provider performance data such as, for example, data on healthcare providers' performance, including outcomes and / or patient satisfaction, and / or trends in provider practices and / or their impact on patient care. The multi -person aggregate data 205 may, for example, include emergency department utilization data such as, for example, information on the frequency and causes of emergency department visits and / or trends in emergency care utilization and / or outcomes. The multi-person aggregate data 205 may, for example, include cost and resource utilization data such as, for example, data on healthcare costs, resource allocation, and / or efficiency, and / or trends in cost-effectiveness of different treatments and / or interventions. The multi-person aggregate data 205 may, for example, include weather and / or climate data, such as weather trends in a region. The multi-person aggregate data 205 may, for example, include aggregate associations between environmental factors (e.g., biomes, animals, plants, weather,allergens) and healthcare impacts (e g., allergies, medical condition prevalence, changes in outcomes of therapy).

[0035] In this example, the one or more data objects 135 includes patient-specific aggregate data 210. The patient-specific aggregate data 210 may, as depicted, include unstructured data. The patient-specific aggregate data 210 may, as depicted, include structured data. The one or more data objects 135 may, for example, include a wide array of patient-specific aggregate data that can significantly impact health. These data objects may encompass various types of medical, environmental, and social data, which individually and collectively may, for example, influence patient health outcomes.

[0036] Medical data may, for example, include Electronic Health Records (EHRs). For example, EHRs may include patient medical histories, including diagnoses, treatments, and / or outcomes. Laboratory and diagnostic data may, for example, include results from lab tests and / or diagnostic procedures, providing insights into patient health status and disease progression. Pharmacy data may, for example, include information on medication prescriptions, adherence, effects, and / or the effectiveness of various drugs for a particular person. Remote monitoring data may, for example, include patient-specific data from medical devices such as fitness trackers, smartwatches, and / or other remote monitoring systems that may track, by way of example and not limitation, vital signs, physical activity, and / or sleep patterns. The medical data may, for example, include patientspecific genomic data. Genomic data may, for example, include patient-specific genetic information that may, for example, advantageously reveal insights into hereditary conditions and / or predispositions to certain diseases. The medical data may, for example, include behavioral health data which may, for example, include information on mental health conditions, treatment utilization, outcomes, and / or substance use. Patient-specific aggregate environmental data may, for example, include weather and / or climate data. For example, the data may include aggregated information on current, past, and / or planned location(s) of a user (e g., with relative duration and / or frequency of stay). The environmental data may, for example, include information on weather patterns and / or environmental conditions. For example, such data may include pollution levels and / or allergens. The environmental data may include aggregate associations between environmental factors, such as, for example, historical data of associations between the patient and local biomes, animal and / or plant populations and / or associated outcomes with the patient. The data may, for example, include aggregate (e g., historic) patient-specific data on socioeconomic factors, education levels, and / or living conditions. The aggregate data may, for example, include aggregated patient-reported outcomes, healthcare provider performance, and / or patient satisfaction. The aggregate data may include patient-specific cost and resource utilization data, such as, for example, information on healthcare expenditures.

[0037] For example, patient-specific aggregate data 210 may include overall trends and / or insights from aggregated data. However, while some systems rely on aggregate data, the use of aggregate data alone may limit the efficacy of real-time decision making. For example, instantaneous changes may be uncaptured in aggregate data.

[0038] The one or more data objects 135 includes, in this example, patient-specific point-source data 215. The patient-specific point-source data 215 may include, as depicted, unstructured data. The patient-specific point-source data 215 may, as depicted, include structure data. Patient-specific point-source (e.g., real-time) data 215 may, for example, encompass a wide array of sources. Taken together with multi-person aggregate data 205 and / or patient-specific aggregate data 210, the patient-specific point-source data 215 may, for example, advantageously enable a more comprehensive view of a patient's health status. Patient-specific point-source data 215 may, for example, include medical device readings. Such readings may, by way of example and not limitation, include heart rate and / or rhythm, such as from electrocardiograms (ECGs). Readings may, for example, include blood glucose levels, such as from continuous glucose monitors (CGMs). Readings may, for example, include blood pressure readings, such as from smart blood pressure monitors. Readings may, for example, include oxygen saturation levels, such as from pulse oximeters. Readings may, for example, include intracranial pressure measurements, such as from implanted sensors (e.g., in a shunt). Readings may, for example, include respiratory rates, such as from ventilators and / or spirometers.

[0039] Patient-specific point-source data 215 may, for example, include patient observations. Observations may, for example, include self-reported symptoms (e.g., instantaneous reports, time- stamped reports), such as, by way of example and not limitation, pain levels, fatigue, and / or dizziness. Observations may, for example, include medication adherence log entries. Observations may, for example, include dietary intake inputs, such as tracked via nutrition app(s).

[0040] Patient-specific point-source data 215 may, for example, include physical activity and / or exercise intensity, such as from wearables like fitness trackers. For example, the physical activity and / or exercise intensity information may include instantaneous readings (e.g., a sharp decrease in physical activity). Patient-specific point-source data 215 may, for example, include sleep data, such as, for example, changes in patterns and / or quality (e.g., such as detected from smartwatches and / or sleep monitors). Patient-specific point-source data 215 may, for example, include (e.g., changes in) mood and / or mental health status (e g., determined from digital diaries and / or health apps).

[0041] Patient-specific point-source data 215 may, for example, include social media posts. For example, comments on personal wellbeing and / or health status shared on social platforms may be detected. In some implementations, patient-specific point-source data 215 may include generalsocial media posts that may be analyzed, for example, in response to other triggers (e.g., a change in activity, a change in temperature). Social media data may, for example, include engagements (e.g., activities). Social media data may, for example, include discussion (health-related and / or otherwise) in forums, blogs, and / or communities. Patient-specific point-source data 215 may, for example, include photos or videos, such as documenting daily activities, recent stressors (e.g., funeral, wedding, interview) and / or environments.

[0042] Patient-specific point-source data 215 may, for example, include (e.g., instantaneous) location information. For example, location data may be determined from check-ins indicating travel and / or environmental changes. Patient-specific point-source data 215 may, for example, include geolocation data (e.g., from global positioning devices, from images). For example, geolocation data may advantageously reveal exposure to different environmental conditions.

[0043] Patient-specific point-source data 215 may, for example, include environmental changes such as, for example, temperature and / or humidity levels, such as from smart home systems, wearables, and / or smartphones. Patient-specific point-source data 215 may, for example, include air quality and / or pollution levels, such as recorded by public and / or private (e.g., networked, local) environmental sensors. Patient-specific point-source data 215 may, for example, include exposure to allergens or pollutants (e.g., tracked via mobile apps, received via communication systems). Patient-specific point-source data 215 may, for example, include weather conditions (e.g., recent changes), such as, for example, heatwaves and / or cold snaps, wind patterns (e g., corresponding to allergens), and / or barometric pressure changes.

[0044] Real-time data points, for example, when integrated (e.g., with each other and / or multiperson aggregate data 205 and / or patient-specific aggregate data 210) and analyzed, may advantageously provide a dynamic and holistic picture of a patient's health status, enabling the DSBSE 145 to generate timely, personalized action(s) 165 (e.g., medical interventions).

[0045] The DSBSE 145 may, for example, be triggered based on one of more of the incoming one or more data objects 135. For example, a specific patient-specific point-source data 215 may trigger the DSBSE 145. A change in multi-person aggregate data 205 and / or patient-specific aggregate data 210 may, for example, trigger the DSBSE 145. In some implementations, the DSBSE 145 may be periodically (e.g., effectively continuously) triggered (e.g., on a predetermined schedule), such as to detect possible relevant changes in the patient-specific point-source data 215, patient-specific aggregate data 210, and / or multi-person aggregate data 205.

[0046] The output of the recommendation engine 160 (e.g., a control signal(s) associated with the action(s) 165) may, for example, be transmitted to one or more connected device(s) 105. For example, the device(s) 105 may include actuator(s) 220 and / or sensor(s). For example, the actuator(s) 220 and / or sensor(s) 225 may include implanted medical devices. Implanted devicesmay, for example, include pacemakers. Implanted devices may, for example, include cochlear implants. Implanted devices may, for example, include cardioverter defibrillators. Implanted devices may, for example, include pumps (e.g., bacterin pumps, insulin pumps). Implanted devices may, for example, include monitors. Implanted devices may, for example, include shunts (e.g., brain shunts).

[0047] The actuator(s) 220 may, for example, include external devices. External devices may, for example, include CGMs. External devices may, for example, include fitness trackers. External devices may, for example, include blood pressure monitors. External devices may, for example, include wearable neuromodulation therapy devices. External devices may, for example, include imaging devices such as, for example, magnetic resonance imaging (MRI) machines, radiography machines (e.g., computed tomography (CT) scanners) and / or ultrasound machines. External devices may, for example, include ventilators. External devices may, for example, include defibrillators. External devices may, for example, include patient monitoring systems. External devices may, for example, include media devices (e.g., gaming consoles, media delivery systems).

[0048] The device(s) 105 may, for example, include one or more dynamic activation engine(s) 230. A dynamic activation engine(s) 230 may, for example, be integrated in the DSBSE 145. A dynamic activation engine(s) 230 may, for example, be embodied in one or more other device(s) 105 (e g., a personal computing device, a networked server, a medical device). A dynamic activation engine(s) 230 may, for example, advantageously activate and / or deactivate a medical device.

[0049] For example, an engine(s) 230 may respond to the action(s) 165 by activating and / or deactivating a therapy. In some implementations, a therapy may be determined by labeling. For example, delivery of media may be therapeutic when provided with labeling related to an indication(s) of use and / or instructions for therapeutic (e.g., medical treatment) use. Delivery of the same media may, for example, be non-therapeutic when provided with no labeling, or labeling indicating it is not therapeutic. In some implementations, the engine(s) 230 may determine, based on the action(s) 165 that conditions for therapeutic use are not met, and may refrain from activating or deactivate therapeutic labeling provided with a device and / or data object(s) (e g., media such as a game). For example, the engine(s) 230 may deactivate a therapeutic mechanic in a game. For example, the engine(s) 230 may determine, based on the action(s) 165, that conditions for therapeutic use are met, and may activate or continue labeling provided with a device and / or data object(s). The engine(s) 230 may, for example, activate a therapeutic mechanic(s) in a game.

[0050] FIG. 3 is a block diagram depicting an illustrative architecture of an example DSBSE, such as shown in FIGS. 1-2. In this example, the DSBSE system 140 includes a processor 305 operablycoupled to memory module(s) 320. The processor 305 is operably coupled to a communication module(s) 310. The processor 305 is operably coupled to one or more storage module 325.

[0051] The DSBSE 145 may be implemented as processor executable instructions on the one or more storage module 325. For example, the DSBSE 145 may be one or more storage module 325 with and / or operably coupled to the context inference engine 150, the source balancing engine 155, the recommendation engine 160, and / or the dynamic activation engine(s) 230.

[0052] The DSBSE 145 may, for example, collect inputs (e.g., one or more data objects 135) from multiple sources (e g., one or more data stores 130). Without limiting other examples herein, inputs may include quantitative sensor data (e.g., from implanted or wearable medical devices, nonmedical devices). Inputs may, for example, include patient-reported symptoms and / or feedback (e.g., through mobile applications). Inputs may, for example, include lifestyle data (e.g., physical activity, social media interactions, gaming performance). Inputs may, for example, environmental and contextual data.

[0053] In some implementations, the context inference engine 150 and / or the source balancing engine 155 may, for example, process collected data through a matrix-based method(s). For example, the method may include assigning (e.g., manually, automatically) initial weights to different data types based on clinical expertise. The method may, for example, include dynamically adjusting weights based on detected conditions and / or historical outcomes. The method may, for example, include comparing incoming data against patient-specific baselines and / or population norms.

[0054] The recommendation engine 160 may, for example, analyze weighted data to generate action(s) 165 (e g., therapeutic insights). The recommendation engine 160 may, for example, apply machine learning models, such as to identify patterns and / or anomalies. The recommendation engine 160 may, for example, generate risk assessments and / or predictive analytics. The action(s) 165 may, for example, validate conclusions (e g., through multiple model checks).

[0055] The action(s) 165 may, for example, generate personalized interventions. For example, the action(s) 165 may adjust monitoring frequency and / or sensitivity, such as based on detected conditions. The action(s) 165 may, for example, generate real-time alerts and / or recommendations. The action(s) 165 may, for example, coordinate responses with healthcare providers.

[0056] Embodiments may, for example, advantageously prioritize (e.g., by the source balancing engine 155) direct data collection from sensors, patient inputs, and lifestyle monitoring over EHR data. Prioritizing direct (e.g., point-source data vs aggregated summaries) may, for example, reduce data degradation and / or provider bias while enabling real-time therapeutic responses.

[0057] As depicted, the DSBSE system 140 may be operably coupled to one or more one or more one or more data stores 130. The DSBSE system 140 may, for example, be operably coupled tosources of structured and / or unstructured data. The DSBSE system 140 may, for example, be operably coupled to source of multi-person aggregate data 205, patient-specific aggregate data 210, and / or patient-specific point-source data 215. In some implementations, at least some of the one or more data stores 130 may, for example, be internal to the DSBSE system 140. The one or more data stores 130 may include, for example, common repositories 360 and / or personal repositories 365. Common repositories may, for example, include medical literature, popular news and / or articles, search engines, device information, product offerings, and / or scientific literature. Personal repositories may, by way of example and not limitation, include electronic health records, personal records, personal query histories, personal preferences, personal device information (e.g., medical devices, wearables). The repositories may, for example, provide sources of multi-person aggregate data 205, patient-specific aggregate data 210, and / or patient-specific point-source data 215.

[0058] In this example, the one or more data objects 135 stored in the one or more data stores 130 may, for example, include entity profiles 370. The entity profiles 370 may, for example, include patient and / or provider profiles. The entity profiles 370 may, for example, include organizational profiles. The one or more data stores 130, as depicted, includes association(s) 375. The association(s) 375 may, for example, include predetermined (e g., historic) associations between entities (e.g., entity profiles) and / or data objects (e.g., from personal and / or common repositories).

[0059] In some implementations, common repositories may, for example, include anonymous electronic health record (EHR) data. In some implementations, personal repositories may include EHR records (e.g., associated with the patient users and / or the healthcare provider users).

[0060] Illustrative common repositories 360 may include, by way of example and not limitation:• Medical literature databases (e.g., PUBMED)• News databases (e.g., healthcare news, weather news, environmental news, scientific discovery news, business news, news potentially associated with users)• Lay articles and / or discussions (e.g., blogs, forums, lay news sites)• Treatment and / or treatment outcome databases• Clinical studies (e.g., ongoing)• Regulatory databases (e.g., US Food and Drug Administration)• Product offerings• Weather and / or climate data

[0061] Illustrative personal repositories 365 may include, by way of example and not limitation:• Non-anonymized EHR• Wearable device data (e.g., fitness tracker, smart watch, implantable and / or wearable medical device sensor and / or actuator data)• Home automation data / accounts• Device accounts (e.g., smartphone accounts)• Service provider accounts• Personal search records• Personal information in clinical studies• Private sales offers (e.g., special discounts)

[0062] For example, DSBSE 145 may apply one or more self-training model (e.g., unsupervised classifier, neural network model) to the queries, responses, and / or other data (e.g., EMR data, literature) received to generate associations between, by way of example and not limitation, patient attributes, product attributes, healthcare provider attributes, condition (e.g., disease, disorder) attributes, and / or treatment option attributes. The associations may, for example, be dynamically determined. In some implementations, the associations may be stored. A confidence level may be increased or decreased (e.g., associated with a stored association) based on, for example, quantity of associations made in a training data pool. In some embodiments, data may be separated into training data and test data pools. In some implementations, training data may include ongoing data received (e.g., ‘live,’ realtime).

[0063] In some implementations, for example, the DSBSE system 140 may be connected to one or more devices (e.g., device(s) 105). Example devices include those disclosed at least with reference to:• FIGS. 1-4 of U.S. Application Serial No. 18 / 313,249, titled “Dynamically Controlled Cerebrospinal Fluid Shunt,” filed by Samuel Robert Browd, et al., on May 5, 2023 (e.g., including shunt devices and associated devices)• FIGS. 1-9 of U.S. Application Serial No. 63 / 365,407, titled "Distributed Sensing and Control of Cerebrospinal Fluid," filed by Samuel Robert Browd, et al., on May 26, 2022 (e.g., including brain shunt(s), CSF ion exchange device(s), light therapy device(s);• FIG. 1 of U.S. Application Serial No. 63 / 477,158, titled "Central Nervous System Monitoring and Intervention," filed by Samuel RobertBrowd, et al., on December 23, 2022 (e.g., “shunt 105” and associated devices);• Paragraphs [0003-0040] of U.S. Application Serial No. 63 / 477,162, titled "Cerebrospinal Fluid Polarization," filed by Samuel Robert Browd, et al., on December 23, 2022 (e.g., a cerebrospinal fluid (CSF) polarization system (CSFPS) and associated devices);• FIGS. 1-32 of U.S. Application Serial No. 63 / 488,412, titled "Dynamic Shunt Systems," filed by Samuel RobertBrowd, et al., on March 3, 2023 (e g., smart shunt(s), non-invasive shunt diagnostic devices / systems, implant devices); and• PCT Application Serial No. PCT / US2024 / 051288 titled “Neurosurgical Devices and Methods,” and filed by Samuel Robert Browd, et al., on October 14, 2024 (e.g., ventricular implants and / or vertebral implants, such as “stent 505,” “bladder 605,” “structural reinforcement modules 210,” “shunt 705,” associated devices), the entire contents of which applications are incorporated herein by reference. The DSBSE 145 may, for example, generate action(s) 165 directed to the physician based on patient cognitive data (e.g., patient queries) and / or patient device data and / or events. Device data may, for example, be stored in personal and / or common (e.g., after data is de-identified) repositories.

[0064] In some implementations, the DSBSE 145 may, for example, generate action(s) 165 directed to the patient. For example, the DSBSE 145 (e.g., engine 155) may generate a notification action alerting the patient to ask their physician about a change in values detected by sensors (e.g., in implanted and / or monitoring devices such as discussed above), which may be associated with recent headaches.

[0065] FIG. 4 is flowchart depicting an example method of operating a DSBSE. In the method 400, “N” data objects are received in a step 405. A counter variable (“i”) is initialized in a step 410. A context inference is generated from the ith data object in a step 415. Based on the context inference(s), a source balancing factor(s) (e.g., weightings) are generated (e.g., by the source balancing engine 155) for the ith data object in a step 420. This process repeats until it is determined, in a decision point 425, that the Nth data object(s) has been evaluated. In some implementations, data objects may be evaluated simultaneously and / or in a matrix operation (e.g., rather than sequentially).

[0066] A recommendation is generated (e.g., an inferred action) is generated in a step 430, based on the source balancing factors. For example, the recommendation may be (e g., also) generated based on context inference(s) and / or other inputs (e.g., in addition to the source balancing factors). The recommendation may, for example, be in a predetermined data structure(s). For example, the recommendation may include an action(s) (e g., predetermined) to be taken. The recommendation may, for example, include a notification and / or change in an actuator. The recommendation may, for example, include an updated weighting (e.g., for the source balancing engine 155), such as in a source balancing matrix (SBM) (e.g., SBM 500).

[0067] If it is determined, in a decision point 435, that autonomous action is to be taken (and / or that a recommendation is externally approved such as manually), then control signal(s) are generated in a step 440. The control signal(s) are transmitted in a step 445, such as to one or more of the corresponding device(s) 105.

[0068] FIG. 5 depicts an example source balancing matrix (SBM) that may be used, for example, by a DSBSE (such as disclosed at least with reference to FIG. 4). In this example, entries in theSBM 500 are depicted as being associated with a source 505. Entries are shown as being associated with an application 510. Entries are shown as being associated with a qualitative-quantitative factor 515. Entries are shown as being associated with a weighting 520.

[0069] For example, a shunt pressure sensor reading has a qualitative-quantitative factor 515, in this example, of 1 (indicating, for example, inherently quantitative). In the context of a shunt malfunction, the shunt pressure sensor reading is associated with a weighting of 0.85. However, in the context of hypertension, the shunt pressure sensor reading is associated with a lower weighting of only 0.1.

[0070] As another example, a natural language processor (NLP) indication of headache (e.g., from free text input, social media posts, emails, and / or text messages of the patient) may be considered as heavily qualitative (e.g., a factor of 0.2) data point. In the context of determining shunt malfunction, the weighting 520 may be only 0.4, but may be more dispositive in the context of hypertension, with a score of 0.7.

[0071] In some implementations, the weighting 520 and / or the qualitative-quantitative factor 515 may be automatically generated by the DSBSE 145. In some implementations, initial values may be inferred based on existing data (e.g., aggregate data). In some implementations, the values may be (e.g., automatically) updated by the DSBSE 145 on an ongoing (e.g., periodic) basis. Initial values may, for example, be manually provided for some entries. In some implementations, values may be automatically generated (e.g., inferred by a NLP model in the DSBSE 145, such as, in the source balancing engine 155) from common repositories (e.g., scientific literature).

[0072] In some implementations, the context inference engine 150 may infer an appropriate application 510 from incoming data points. In some implementations, the context inference engine 150 may, for example, generate an inferred (e g., natural language) context (e.g., natural language question). The DSBSE 145 (e.g., the context inference engine 150) may (e.g., further) retrieve entries from one or more SBM 500 based on the inferred context. For example, a large language model (LLM) may select entries from a SBM 500 that are above a threshold (e.g., predetermined) inferred match for the inferred context. In some implementations, application 510 and / or 505 may, for example, be structured (e g , predetermined). In some implementations, the application 510 may, for example, be unstructured (e.g., free text). In some implementations, the SBM 500 may be automatically generated (e.g., dynamically, on-the-fly) based on historical data.

[0073] In some implementations, the SBM 500 may, for example, be a multi-dimensional matrix. For example, the multi-dimensional matrix may be at least two dimensions (e.g., as shown). In some implementations, the multi-dimensional matrix may, for example, include at least three dimensions. For example, a 3+ dimensional matrix may provide a time-adaptive weighting, incorporating multiple layers of data analysis and weighting. Such matrices may, for example,operate along three axes: the qualitative-quantitative spectrum of data inputs, the contextual importance of each data point, and the temporal dimension that tracks changes and patterns over time.

[0074] The SBM 500 may, for example, advantageously enable a DSBSE 145 (e.g., source balancing engine 155) to dynamically adjust weightings based on multiple factors simultaneously. For example, in hydrocephalus monitoring, when an implanted sensor shows elevated intracranial pressure, the matrix may associate a high (e.g., highest) weighting to this quantitative measurement. This weighting may, for example, induce the recommendation engine 160 to trigger urgent medical intervention. In some examples, a matrix (e.g., the same matrix) can enable a recommendation engine 160 to recognize situations where qualitative data should take precedence, such as when gaming performance metrics indicate cognitive decline despite normal sensor readings.

[0075] In some embodiments, a temporal dimension(s) of the SBM 500 may, for example, advantageously enable it to track patterns and / or changes over time. This may, for example, be particularly valuable when monitoring gradual changes in patient behavior and / or performance that might not be immediately apparent in instantaneous readings. For instance, the DSBSE 145 might apply a 3+ dimensional matrix (e.g., time-varying) to detect subtle degradations in gaming performance and / or changes in social media interaction patterns that, when analyzed over time, indicate potential health concerns.

[0076] A DSBSE 145 may, for example, advantageously be configured to learn and adapt one or more SBM 500. The matrix may change based on accumulated evidence, adjusting its weightings (e.g., by the DSBSE 145, such as via a separate model(s) and / or the recommendation engine 160) as it learns which combinations of data points are most predictive for specific conditions. This learning process may, for example, be enhanced by emphasizing (e.g., via the source balancing engine 155) ‘clean’ point-source data (e g., patient-specific point-source data 215) over potentially biased historical records (e g., multi-person aggregate data 205, patient-specific aggregate data 210), which may, for example, advantageously enable more accurate and reliable diagnostic capabilities of the DSBSE 145.

[0077] FIG. 6 depicts an example operation and / or training model of an example context inference engine, such as may be used with (e.g., in) a DSBSE. In this example, the context inference engine 150 may, for example, be implemented as a model(s) configured (e g., trained, such as disclosed at least with reference to FIG. 10) to generate context inferences based on received data objects. The context inference engine 150 receives one or more data objects 135. One or more data objects 135 may, for example, include input data, such as sensor readings, natural language inputs, and / or other relevant data points. The one or more data objects 135 may, for example, include patient-specific point-source data 215. The one or more data objects 135 may, for example, include patient-specific aggregate data 210. The one or more data objects 135 may, for example, include multi-person aggregate data 205. Associations 605 may, for example, be generated (e.g., by the context inference engine 150). In some implementations, associations 605 may, for example, be retrieved (e.g., from one or more data objects 135). In some implementations, associations 605 may be provided in a matrix (e.g., SBM 500).

[0078] The context inference engine 150 operates on the one or more data objects 135 (e.g., and associations 605) to generate a context inference(s) 610. The inference(s) 610 may, for example, advantageously provide an interpretation of the data object in relation to surrounding environment and / or conditions. For instance, if the data object includes a sensor reading associated, for example, with elevated heart rate, the context inference may infer a context and / or association related to recent physical activity, stress levels, and / or or other health indicators.

[0079] The engine may, for example, leverage algorithms and / or machine learning models to analyze the data object. These models may, for example, be trained (e.g., on an ongoing basis) on (e.g., vast) datasets, enabling them to recognize patterns and / or correlations that might not be immediately apparent. The inference process may, for example, involve sub-steps, including feature extraction, pattern recognition, and / or anomaly detection. Such steps may, for example, contribute to a comprehensive understanding of the data object's context.

[0080] The context inference engine 150 may, for example, dynamically adapt to new information. For example, as depicted, historic context inference(s) and associated one or more data objects 135 (e.g., historic inputs) may be applied to train the context inference engine 150 (e.g., as disclosed at least with reference to FIG. 10). As more data objects are processed and evaluated, the engine may, for example, continually refine its models and / or inference methods. This ongoing learning process may advantageously maintain and / or increase accuracy and / or relevance of the inference(s) over time, such as adapting, by way of example and not limitation, to changes in the patient's condition and / or external factors.

[0081] In some implementations, the context inference engine 150 can generate natural language questions or prompts based on the inferred context. For example, if a pattern of symptoms suggests the onset of a particular condition (e.g., as determined by the recommendation engine 160), the engine might generate natural language questions to pose to an LLM(s) (e.g., connected as a source of one or more data objects 135). In some implementations, the DSBSE 145 may, for example, prompt the patient (e.g., based on an output of recommendation engine 160) to provide additional information, such as via a questionnaire and / or direct inquiry. Such dynamic implementations may, for example, advantageously enhance the data pool and / or refine the context inference further.

[0082] In some implementations, the context inference engine 150 may, for example, ‘loop’ and / or be cascaded after one or more source balancing engine 155 and / or recommendation engine 160. For example, a context inference engine 150 may operate on outputs from one or more upstream (and / or downstream such as in an iterative loop) source balancing engine 155 and / or recommendation engine 160. Such embodiments may, for example, provide enhanced and / or updated inference(s) 610.

[0083] The context inference engine 150 may, for example, advantageously transform raw data objects into meaningful context inferences, which may advantageously enable the DSBSE to locate appropriate additional data sources, determine appropriate source balancing, make informed recommendations, and / or take appropriate actions. By effectively integrating multiple sources of data and continuously updating its models, the engine may, for example, advantageously provide a robust and dynamic approach to contextual analysis.

[0084] FIG. 7 depicts an example operation and / or training model of an example dynamic source balancing engine, such as may be used with (e.g., in) a DSBSE. In this example, the source balancing engine 155 may, for example, be implemented as a model(s) configured (e.g., trained, such as disclosed at least with reference to FIG. 10) to generate source balancing factor(s) 710 based on received data objects and context inference(s) 610. The factor(s) 710 receives (e.g., selects and / or retrieves) one or more data objects 135. In some implementations, associations 605 may, for example, be retrieved (e g., from one or more data objects 135). In some implementations, associations 605 may be provided in a matrix (e.g., SBM 500).

[0085] The source balancing engine 155 may, for example, retrieve entries from one or more SBM 500. For example, in some implementations, SBM(s) 500 may be incorporated in the source balancing engine 155. In some implementations, SBM(s) 500 may be external to the source balancing engine 155 (e.g., connected as data object(s) 135).

[0086] The dynamic source balancing engine 155 may, for example, be implemented as a model(s) configured (e.g., trained, such as disclosed at least with reference to FIG. 10) to process various data inputs and context inferences. This processing may, for example, include analyzing data objects 135, which may, by way of example and not limitation, include sensor readings, natural language inputs, and / or other relevant data points, to generate factor(s) 710. The context inferences 610 provide an interpretation of the data objects in relation to the surrounding environment and conditions.

[0087] The source balancing engine 155 may, for example, assign different levels of importance to various data inputs, based on the inference(s) 610, such as disclosed at least with reference to FIGS. 4-6. Data inputs may, for example, be qualitative or quantitative, or some mix of the two. Data inputs may have higher or lower relevance to a specific context. In some implementations,source balancing may advantageously surface the most pertinent data to be given corresponding weight in an analysis (e.g., by the recommendation engine 160). Accordingly, such embodiments may, for example, advantageously enhance the accuracy and / or reliability of the action(s) 165.

[0088] For instance, a matrix-based approach may be employed to weight different data inputs dynamically. This approach may, for example, consider qualitative data, such as patient feedback and / or subjective assessments, for example. This approach may, for example, consider quantitative data, such as, by way of example and not limitation, sensor readings and / or statistical metrics. The source balancing engine 155 may apply the matrix(s) to adjust corresponding factor(s) 710 based on the context (e.g., via the context inference(s) 610) in which the data is used.

[0089] For example, in a medical diagnosis scenario, quantitative data like blood pressure and heart rate readings may be assigned higher weights due to their objective nature and direct relevance to the patient's health status. Conversely, qualitative data, such as patient-reported symptoms, may also be given significant weight, especially if the patient's subjective experience is crucial for a comprehensive diagnosis.

[0090] The source balancing engine 155 may, for example, leverage algorithms and machine learning models to continually assess the relevance and reliability of the data inputs. Such embodiments may, for example, dynamically adjust the weights assigned to each data source. This continuous adjustment may, for example, advantageously enable other engines (e.g., the 160??0 to operates on the most relevant and accurate data available, thereby improving the overall quality of the inferences made. For example, if a sensor consistently provides inaccurate readings due to a malfunction, the engine can detect this anomaly and reduce the weight assigned to that sensor's data, while increasing the reliance on more reliable sources.

[0091] In another example, environmental context plays a significant role in source balancing. As an illustrative example, in a scenario where a patient's heart rate is elevated, the context inference engine 150 might initially infer that the patient has engaged in physical activity. However, if qualitative data from a recent questionnaire indicates that the patient has been experiencing high levels of stress, the source balancing engine 155 might adjust the weights to give more importance to the stress-related data, thus refining the factor(s) 710 (e g., and ultimate action(s) 165) to consider both physical and psychological factors.

[0092] The source balancing engine 155 may, for example, be configured to adapt to new information and / or changing conditions For example, the source balancing engine 155 and / or recommendation engine 160 may modify the SBM 500. As more data is collected and processed, the source balancing engine 155 may refine its models (e.g., as disclosed at least with reference to FIG. 10). Accordingly, the most relevant data sources may, for example, be prioritized. Such embodiments may advantageously enhance the accuracy of the factor(s) 710. This robust andadaptive approach to source balancing may, for example, advantageously provide reliable and / or actionable insights across various applications, from healthcare to industrial monitoring and beyond.

[0093] The source balancing engine 155 may, for example, leverage algorithms and / or machine learning models to analyze the inputs (e.g., one or more data objects 135, inference(s) 610, associations 605). These models may, for example, be trained (e.g., on an ongoing basis) on (e.g., vast) datasets, enabling them to recognize patterns and / or correlations that might not be immediately apparent. The inference process may, for example, involve sub-steps, including feature extraction, pattern recognition, and / or anomaly detection. Such steps may, for example, contribute to a more comprehensive understanding of the data object's appropriate weightings.

[0094] In some implementations, the source balancing engine 155 can generate natural language questions and / or prompts based on the inference(s) 610 and / or one or more data objects 135. For example, if a pattern of symptoms suggests the onset of a particular condition (e.g., as determined by the recommendation engine 160), the engine might generate natural language questions to pose to an LLM(s) (e.g., connected as a source of one or more data objects 135). In some implementations, the DSBSE 145 may, for example, prompt the patient (e.g., based on an output of recommendation engine 160) to provide additional information, such as via a questionnaire and / or direct inquiry. Such dynamic implementations may, for example, advantageously enhance the data pool and / or refine the factor(s) 710 further (e.g., by selecting more appropriate SBM 500 and / or entries from a SBM 500).

[0095] In some implementations, the source balancing engine 155 may, for example, ‘loop’ and / or be cascaded after one or more recommendation engine 160 and / or before one or more context inference engine 150. For example, a source balancing engine 155 may operate on outputs from one or more upstream (and / or downstream such as in an iterative loop) context inference engine 150 and / or recommendation engine 160. Such embodiments may, for example, provide enhanced and / or updated factor(s) 710.

[0096] The source balancing engine 155 may, for example, advantageously dynamically adjust the weights of data inputs, thereby enhancing the accuracy and reliability of actions and recommendations. This continuous adjustment may, for example, enable a DSBSE system 140 to prioritize the most pertinent and reliable data sources for a specific context(s). Such an adaptive approach may, for example, advantageously provide actionable insights across various applications.

[0097] FIG. 8 depicts an example operation and / or training model of an example recommendation engine, such as may be used with (e g., in) a DSBSE. The recommendation engine 160 may, for example, operate on the one or more data objects 135 and / or inference(s) 610, as modified by thefactor(s) 710, to generate action(s) 165. The action(s) 165 may, for example, generate control signals (e.g., to induce operation of device(s) 105).

[0098] The recommendation engine 160 may, for example, generate the action(s) 165 based on device param eter(s) 805. For example, device parameters 805 may include permissible operating parameters (e.g., from the device, from a third-party such as a healthcare provider) of one or more device(s) 105.

[0099] The recommendation engine 160 may, for example, generate the action(s) 165 based on recommendation directive(s) 810. The directive(s) 810 may, for example, be stored (e.g., in one or more data stores 130). The directive(s) 810 may, for example, be generated based on data from caretakers. The directive(s) 810 may, for example, be generated based on data from device manufacturers. The directive(s) 810 may, for example, be generated based on data from regulatory authorities. The directive(s) 810 may, for example, be generated based on data from patients. In some implementations, the directive(s) 810 may, for example, be manually generated. In some implementations, the directive(s) 810 may, for example, be automatically generated (e.g., by a recommendation engine 160). As an illustrative example, directive(s) 810 may be generated by creating a prompt and sending it to a model(s) (e.g., an LLM) to operate on data (e.g., one or more data objects 135 such as selected for at least one patient, condition, device, and / or physician, such as in response to an inference(s) 610). The model(s) may, for example, operate on the data to generate a directive based on the prompt. For example, the prompt may include an instruction to generate a recommendation directive (e.g., for a specific physician, for a specific condition, for a specific device, and / or for a specific patient).

[0100] As an illustrative example, in a case of hydrocephalus monitoring, the action(s) 165, when an intracranial pressure sensor reading reaches a critical threshold (such as 35 PSI), the recommendation engine 160 may, for example, immediately triggers multiple simultaneous action(s) 165. For example, the action(s) 165 may include an emergency room referral for the patient. The action(s) 165 may, for example, include immediate notifications to the patient and / or physician. The action(s) 165 may include operating one or more device(s) 105 into elevated monitoring protocols. In this example, the context inference engine 150, source balancing engine 155, and recommendation engine 160 cooperate (e.g., using one or more SBM 500) in an illustration of operating on heavily weighted quantitative data to trigger immediate, high-priority actions.

[0101] As an illustrative example, the DSBSE 145 may, for example, handle more nuanced scenarios, such as, for example, where multiple weighted inputs must be considered together. For instance, when monitoring gaming performance metrics, the recommendation engine 160 may detect cognitive decline through degraded performance over time. In this case, the action(s) 165generated might include preliminary actions to operate one or more devices (e.g., including the DSBSE system 140 itself) into increased monitoring frequency. As ongoing decline is determined, inference(s) 610, factor(s) 710, and / or action(s) 165 may change (e.g., in response to adapted weightings of the SBM 500). Subsequent action(s) 165 may, for example, include physician notifications if the decline persists. Further subsequent action(s) 165 may, for example, eventually include recommendations for clinical evaluation, such as if additional corroborating data points are detected by the recommendation engine 160.

[0102] Temporal patterns may, for example, influence action generation. The DSBSE system 140 may, for example, modify its own (and / or other systems’) monitoring frequency based on detected conditions. For example, when a patient reports a headache, the recommendation engine 160 may generate action(s) 165 causing an increase in frequency of data collection across multiple sensors and / or input types. This adaptive monitoring is an example of a preliminary action that can lead to more significant interventions if corroborating (e.g., concerning) patterns emerge (e.g., as weightings are changed in the SBM 500).

[0103] The recommendation engine 160 may, for example, generate different types of actions based on the confidence level derived from the weighted inputs. For example, with highly confident assessments (e.g., like clear sensor readings above critical thresholds), the recommendation engine 160 may generate action(s) 165 including direct intervention recommendations. For less certain situations, the recommendation engine 160 may generate action(s) 165 including a series of progressive actions. As an illustrative example, the progressive actions may include first increasing monitoring, then suggesting lifestyle modifications, and finally recommending medical consultation if patterns persist. Such examples may, by way of example and not limitation, be advantageously enabled by the adaptive interrelation of the DSBSE 145, context inference engine 150, source balancing engine 155, recommendation engine 160, and SBM 500.

[0104] In mental health applications, for example, the recommendation engine 160 may, for example, generate action(s) 165 based on weighted behavioral data (e.g., from multiple sources). Changes in social media interaction patterns, gaming performance, and / or other lifestyle metrics may result in the recommendation engine 160 generating a sequence of escalating action(s) 165. Such actions may, by way of example and not limitation, progress from simple check-in prompts to the patient, to notifications for caregivers, to urgent intervention recommendations if concerning patterns are detected.

[0105] In some implementations, the recommendation engine 160 may, for example, incorporate feedback loops. For example, after taking an action, the DSBSE system 140 may continue monitoring corresponding data objects 135 (e.g., to assess the effectiveness of the intervention).For example, if the recommendation engine 160 generates a recommendation action(s) 165 of rest for a patient with a headache, the DSBSE system 140 may continue monitoring various inputs to determine if additional actions are needed based on the patient's response to the initial recommendation.

[0106] In some examples, action(s) 165 may, for example, generate control actions 165 based on inputs modified by the factor(s) 710. As an illustrative example, the recommendation engine 160 may operate medication pumps to adjust delivery rates based on a combination of sensor readings, patient reported symptoms, and / or behavioral metrics. These adjustments may, for example, be made within safe predetermined ranges. The DSBSE system 140 may, for example, monitor (e.g., continuously) for adverse effects and / or necessary modifications, such as during and / or after implementation of the action(s) 165.

[0107] In some embodiments, this sophisticated interplay between quantitative sensors, qualitative inputs, and / or temporal patterns, weighted and adjusted in real-time, may, for example, advantageously provide a diagnostic system (e.g., DSBSE system 140) that can better capture the complex nature of medical conditions while avoiding the biases and limitations often found in traditional diagnostic approaches.

[0108] FIG. 9 depicts an example operation and / or training model of a dynamic activation engine 230, such as may be used with (e.g., in) a DSBSE. In this example, the dynamic activation engine(s) 230 may, for example, be implemented as a model(s) configured (e.g., trained, such as disclosed at least with reference to FIG. 10) to generate control signal(s) 915 based on received action(s) 165. The control signal(s) 915 may, for example, be generated based on one or more data objects 135. The control signal(s) 915 may, for example, be generated based on context inference(s) 610. The control signal(s) 915 may, for example, be generated based on factor(s) 710. In some implementations, the control signal(s) 915 may, for example, be generated based on associations 605 (e.g., between one or more inputs, such as depicted). The control signal(s) 915 may, for example, be generated based on parameter(s) 805.

[0109] In an illustrative dynamic therapeutic labeling scenario, the DSBSE system 140 may be configured to control therapeutic state transitions without modifying the underlying application architecture (e.g., of a media delivery system such as a game engine and delivery platform). The labeling system (e.g., dynamic adaptation engine(s) 230) may, for example, operate as a control layer that determines whether an application operates in therapeutic or non-therapeutic modes based on real-time data analysis and recommendations (e.g., action(s) 165 from the recommendation engine 160).

[0110] For example, the labeling mechanism may operate through a call-to-label architecture (e.g., instead of embedded labeling). Call-to-label architecture may, for example, advantageouslyprovide enhanced control over real-time labeling (e.g., of devices and / or content that may be operated and / or delivered in medical and non-medical modes). As an illustrative example, when the DSBSE system 140 receives weighted data from various inputs - such as quantitative sensor measurements, qualitative patient feedback, and / or lifestyle data - it may process these inputs through a dynamic weighting matrix (e.g., by the source balancing engine 155) to generate therapeutic recommendations (e.g., by the recommendation engine 160). Based on these recommendations (e.g., action(s) 165), the labeling mechanism (e.g., the dynamic adaptation engine(s) 230) can dynamically activate or deactivate therapeutic features.

[0111] The dynamic activation engine(s) 230 may, for example, be implemented as a model(s) configured (e.g., trained, such as disclosed at least with reference to FIG. 10) to process various data inputs and context inferences. This processing may, for example, include analyzing data objects 135, which may, by way of example and not limitation, include sensor readings, natural language inputs, and / or other relevant data points, to generate control signal(s) 915. The action(s) 165 operate one or more device(s) 105 in response to action(s) 165.

[0112] The dynamic activation engine(s) 230 may, for example, leverage algorithms and / or machine learning models to analyze the inputs (e.g., one or more data objects 135, inference(s) 610, associations 605, action(s) 165, factor(s) 710, parameter(s) 805). These models may, for example, be trained (e.g., on an ongoing basis) on (e.g., vast) datasets, enabling them to recognize patterns and / or correlations that might not be immediately apparent. The inference process may, for example, involve sub-steps, including feature extraction, pattern recognition, and / or anomaly detection. Such steps may, for example, contribute to a more comprehensive understanding of the data object's appropriate weightings.

[0113] In some implementations, the dynamic activation engine(s) 230 can generate natural language questions and / or prompts based on the inputs (e.g., action(s) 165, data objects 135). For example, if an action(s) 165 is directed to increasing delivery of a fluid to a certain amount (e.g., as determined by the recommendation engine 160), the engine may generate natural language questions to pose to an LLM(s) (e.g., connected as a source of one or more data objects 135). For example, the response may be used to determine appropriate control signal(s) (e g., signal and / or data formats, calibration values).

[0114] In some implementations, the dynamic activation engine(s) 230 may, for example, ‘loop’ and / or be cascaded before one or more context inference engine 150, source balancing engine 155, and / or recommendation engine 160. For example, a dynamic activation engine(s) 230 may operate on outputs from one or more upstream engines to generate outputs that are then further modified by downstream engines. Such embodiments may, for example, provide enhanced and / or updated control signal(s) 915 (e.g., based on updated action(s) 165).

[0115] FIG. 10 depicts an illustrative method of training a model(s), such as disclosed at least with reference to FIGS. 6-9. A method 1000 may, for example, be performed by a processor(s) (e.g., processor 205) executing a program(s) of instructions retrieved from a data store(s) (e.g., data source(s) 135). The method 1000 includes, at a step 1005, receiving historical data (e.g., data sources 135, such as disclosed at least with reference to FIGS. 1-3 and 6-9). At a step 1010, corresponding historical data from same and / or other sources (e.g., associations, body(ies) of knowledge, and / or other data sources, such as disclosed elsewhere herein such as at least with respect to FIGS. 6-9, respectively) are determined and retrieved.

[0116] At a step 1015, the retrieved data is divided into a first set of data used for training and a second set of data used for testing. At a step 1020, a model (e.g., a model(s) of the DSBSE 145, the context inference engine 150, the source balancing engine 155, the recommendation engine 160, and / or the dynamic activation engine(s) 230) is applied to the training data to generate a trained model (e.g., neural network model, classifier, large language model). The trained model is applied to the testing data, in a step 1025, to generate test output(s) (e.g., such as disclosed at least with reference to FIGS. 6-9). The output is evaluated, in a decision point 1030, to determine whether the model is successfully trained (e.g., by comparison to a predetermined training criterion(s)). The predetermined training criterion(s) may, for example, include a maximum error threshold. The criterion may, for example, include an expert(s) review threshold (e.g., percentage accuracy, percentage correct, binary accurate / not accurate, complete / incomplete). The criterion may, for example, include match with actual outcome(s) (e.g., percentage matched). For example, if a difference between the actual output (the test data) and the predicted output (the test output) is within a predetermined range, then the model may be regarded as successfully trained. If the difference is not within the predetermined range, then the model may be regarded as not successfully trained. At a step 1035, the processor may generate a signal(s) requesting additional training data, and the method 1000 loops back to step 1030 If the model is determined, at the decision point 1030, to be successfully trained, then the trained model may be stored (e.g., in the storage module 325), in a step 1040, and the method 1000 may, for example, end.

[0117] In some embodiments, such as depicted, the method may, for example, include dynamic training and / or updating of the model(s). For example, the method 1000 may include a decision point 1045 in which a model update (e.g., re-training) trigger is monitored for (e g., periodically). When the trigger is detected (e g., accuracy drift, additional data available, time period reached), then the method 1000 may return to step 1005.

[0118] Although various embodiments have been described with reference to the figures, other embodiments are possible.

[0119] As an illustrative example, a 10-year-old patient with an implanted sensor may, for example, report a headache. The DSBSE system 140 may, for example, receive (e.g., collect, retrieve) data from the sensor. The recommendation engine 160 may, for example, determine that the data indicates elevated intracranial pressure. The DSBSE system 140 may, for example, also receive (e.g., retrieve, query, infer) qualitative data related to the patient, such as sleep patterns and / or stress levels. Based on the integrated data, the DSBSE 145 (e.g., the recommendation engine 160), for example, may determine that the shunt has failed and generate a action(s) 165 recommending immediate medical attention. For example, the DSBSE system 140 may generate an action(s) 165 (e g., control signal(s)) and transmit to a device(s) 105 (e.g., the physician’s smartphone, an EHR) notifying the patient's physician and providing real-time updates on the patient's condition.

[0120] As an illustrative example, in an intracranial pressure monitoring scenario, a patient implanted sensor may, for example, continuously transmit pressure readings to a monitoring application. The DSBSE system 140 may, for example, process this data (e.g., by applying a weighting matrix such as via source balancing engine 155), comparing the weighted outputs against established thresholds and patient-specific baselines. When pressure readings exceed safe levels, the dynamic adaptation engine(s) 230 may, for example, activate a cascade of therapeutic actions (e.g., autonomously, in response to action(s) 165). The actions may, for example, include emergency alert protocol. The actions may, for example, include (e.g., automated) notifications (e.g., to healthcare providers). The actions may, for example, include operating a patient interface (e.g., of an app, of a game, of a display of reading content) to emergency mode with clear instructions (e g., to contact medical help, to take emergency measures to reduce intracranial pressure until help can arrive, to initiate a shunt clearing operation). The DSBSE system 140 may, for example, maintain heightened monitoring frequency until pressure normalizes, at which point it may, for example, gradually deactivate emergency features while maintaining standard monitoring protocols.

[0121] As an illustrative example, in a device (e g., device(s) 105) validation scenario (e.g., a mobile device such as a personal device and / or a portable medical device), the DSBSE system 140 may, for example, implement continuous verification of device performance parameters. For example, parameters may include sensor accuracy, data transmission reliability, and / or application integrity. When a device is determined (e g., by the source balancing engine 155 and / or recommendation engine 160) to exhibit validation issues - such as inconsistent sensor readings and / or compromised data security - the recommendation engine 160 and / or dynamic adaptation engine(s) 230 may execute a graceful degradation protocol. For example, action(s) 165 may be generated configured to first attempt to maintain essential therapeutic functions while disablingpotentially compromised features. If validation issues persist, the action(s) 165 may be configured to initiate a full therapeutic deactivation sequence, such as, for example, reverting the application to its base non-therapeutic state (e.g., while preserving collected data and maintaining basic monitoring capabilities). Such embodiments may, for example, advantageously enhance safety of ‘smart’ and / or portable devices. Such embodiments may, for example, advantageously enable ‘smart’ and / or portable devices to be brought to market with reduced regulatory burden, increased speed to market, and / or decreased cost. Accordingly, access to personalized healthcare may, for example, advantageously increase.

[0122] In some embodiments configured, for example, for mental health applications, the DSBSE system 140 may, for example, apply behavioral analysis algorithms (e.g., by the context inference engine 150, by the recommendation engine 160). For example, the DSBSE 145 may monitor indicators of anxiety and / or emotional state. These indicators may, by way of example and not limitation, include direct patient inputs, activity patterns, sleep data, and / or social interaction metrics. When the weighted analysis (e.g., of the DSBSE 145, such as the context inference engine 150, source balancing engine 155, and / or recommendation engine 160) results in an action(s) 165 corresponding to increasing anxiety levels, the recommendation engine 160 and / or engine(s) 230 may, for example, activate therapeutic features. The actions may, for example, be deployed in a staged approach such as: first enabling guided breathing exercises, then activating cognitive behavioral therapy modules, and potentially escalating to crisis intervention protocols if needed. The DSBSE system 140 may, for example, maintain non-therapeutic status for general wellness features like activity tracking and sleep monitoring.

[0123] As an illustrative example, a DSBSE system 140 configured for pain management applications may, for example, process multiple data streams including patient-reported pain levels, movement patterns, medication timing, and / or physiological indicators. Based on this comprehensive analysis, it can activate (e.g., by the action(s) 165 and / or dynamic adaptation engine(s) 230) one or more therapeutic layers. For example, the therapeutic layers may (e.g., first) include basic pain monitoring and / or educational content. The therapeutic layers may, for example, progress to guided relaxation techniques. The therapeutic layers may, for example, further progress enabling full pain management protocols (e.g., medication reminders, direct healthcare provider communication). Each level of therapeutic activation may, for example, advantageously be precisely controlled through the engine(s) 230 based on the action(s) 165 generated (e g., as the SBM 500 adaptation results in progressively modified factor(s) 710). Such embodiments may, for example, advantageously enable personalized pain management strategies.

[0124] As an illustrative example, in a healthcare partner compliance scenario, a DSBSE system 140 may, for example, maintain continuous monitoring of partner interaction patterns, prescriptionpractices, and / or patient outcome metrics. The DSBSE 145 may, for example, generate action(s) 165 in response to one or more compliance issues. For example, if a partner shows unusual prescription patterns, the DSBSE 145 may generate action(s) 165 restricting certain therapeutic features (e.g., blocking and / or requiring third-party verification of certain prescriptions), while maintaining basic functionality. In cases of serious compliance violations, for example, the DSBSE system 140 may operate to execute a coordinated deactivation across all associated devices while ensuring proper patient care transition protocols are followed. This granular control enables the system to maintain regulatory compliance while protecting patient safety.

[0125] In a regulatory compliance application, for example, the dynamic adaptation engine(s) 230 may, for example, advantageously provide a clear delineation between therapeutic and non- therapeutic states. For example, when therapeutic labeling is deactivated, an application (e.g., on a device(s) 105) may function as a standard non-therapeutic app. The continued accessibility of the app (e.g., a game, reading content, interaction app) in a non-therapeutic mode may, for example, be particularly valuable in maintaining compliance while allowing for broad distribution of the base application. This increased compliance and distribution may, for example, advantageously enhance the therapeutic impact in cases when the therapeutic mode is activated.

[0126] Such architecture examples may, for example, advantageously support efficient deployment of multiple therapeutic applications while maintaining strict control over therapeutic features through a labeling mechanism (e.g., by the dynamic adaptation engine(s) 230). The ability to dynamically adjust labeling based on real-time data and validation status may, for example, advantageously provide a robust framework for managing therapeutic interventions while ensuring regulatory compliance and / or patient safety.

[0127] Although an exemplary system has been described with reference to the figures, other implementations may be deployed in other industrial, scientific, medical, commercial, and / or residential applications.

[0128] In various embodiments, some bypass circuits implementations may be controlled in response to signals from analog or digital components, which may be discrete, integrated, or a combination of each. Some embodiments may include programmed, programmable devices, or some combination thereof (e.g., PLAs, PLDs, ASICs, microcontroller, microprocessor), and may include one or more data stores (e.g., cell, register, block, page) that provide single or multi-level digital data storage capability, and which may be volatile, non-volatile, or some combination thereof. Some control functions may be implemented in hardware, software, firmware, or a combination of any of them.

[0129] Computer program products may contain a set of instructions that, when executed by a processor device, cause the processor to perform prescribed functions. These functions may beperformed in conjunction with controlled devices in operable communication with the processor. Computer program products, which may include software, may be stored in a data store tangibly embedded on a storage medium, such as an electronic, magnetic, or rotating storage device, and may be fixed or removable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

[0130] Although an example of a system, which may be portable, has been described with reference to the above figures, other implementations may be deployed in other processing applications, such as desktop and networked environments.

[0131] Temporary auxiliary energy inputs may be received, for example, from chargeable or single use batteries, which may enable use in portable or remote applications. Some embodiments may operate with other DC voltage sources, such as 9V (nominal) batteries, for example. Alternating current (AC) inputs, which may be provided, for example from a 50 / 60 Hz power port, or from a portable electric generator, may be received via a rectifier and appropriate scaling. Provision for AC (e.g., sine wave, square wave, triangular wave) inputs may include a line frequency transformer to provide voltage step-up, voltage step-down, and / or isolation.

[0132] Although particular features of an architecture have been described, other features may be incorporated to improve performance. For example, caching (e.g., LI, L2, ...) techniques may be used. Random access memory may be included, for example, to provide scratch pad memory and or to load executable code or parameter information stored for use during runtime operations. Other hardware and software may be provided to perform operations, such as network or other communications using one or more protocols, wireless (e.g., infrared) communications, stored operational energy and power supplies (e.g., batteries), switching and / or linear power supply circuits, software maintenance (e.g., self-test, upgrades), and the like. One or more communication interfaces may be provided in support of data storage and related operations.

[0133] Some systems may be implemented as a computer system that can be used with various implementations. For example, various implementations may include digital circuitry, analog circuitry, computer hardware, firmware, software, or combinations thereof. Apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and methods can be performed by a programmable processor executing a program of instructions to perform functions of various embodiments by operating on input data and generating an output. Various embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0134] Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, which may include a single processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, by way of example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (applicationspecific integrated circuits).

[0135] In some implementations, each system may be programmed with the same or similar information and / or initialized with substantially identical information stored in volatile and / or nonvolatile memory. For example, one data interface may be configured to perform auto configuration, auto download, and / or auto update functions when coupled to an appropriate host device, such as a desktop computer or a server.

[0136] In some implementations, one or more user-interface features may be custom configured to perform specific functions. Various embodiments may be implemented in a computer system that includes a graphical user interface and / or an Internet browser. To provide for interaction with a user, some implementations may be implemented on a computer having a display device. The display device may, for example, include an LED (light-emitting diode) display. In some implementations, a display device may, for example, include a CRT (cathode ray tube). In some implementations, a display device may include, for example, an LCD (liquid crystal display). A display device (e g., monitor) may, for example, be used for displaying information to the user. Some implementations may, for example, include a keyboard and / or pointing device (e.g., mouse, trackpad, trackball joystick), such as by which the user can provide input to the computer.

[0137] In various implementations, the system may communicate using suitable communication methods, equipment, and techniques. For example, the system may communicate with compatibledevices (e.g., devices capable of transferring data to and / or from the system) using point-to-point communication in which a message is transported directly from the source to the receiver over a dedicated physical link (e.g., fiber optic link, point-to-point wiring, daisy-chain). The components of the system may exchange information by any form or medium of analog or digital data communication, including packet-based messages on a communication network. Examples of communication networks include, e g., a LAN (local area network), a WAN (wide area network), MAN (metropolitan area network), wireless and / or optical networks, the computers and networks forming the Internet, or some combination thereof. Other implementations may transport messages by broadcasting to all or substantially all devices that are coupled together by a communication network, for example, by using omni-directional radio frequency (RF) signals. Still other implementations may transport messages characterized by high directivity, such as RF signals transmitted using directional (i.e., narrow beam) antennas or infrared signals that may optionally be used with focusing optics. Still other implementations are possible using appropriate interfaces and protocols such as, by way of example and not intended to be limiting, USB 2.0, Firewire, ATA / IDE, RS-232, RS-422, RS-485, 802.11 a / b / g, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributed data interface), token-ring networks, multiplexing techniques based on frequency, time, or code division, or some combination thereof. Some implementations may optionally incorporate features such as error checking and correction (ECC) for data integrity, or security measures, such as encryption (e.g., WEP) and password protection.

[0138] In various embodiments, the computer system may include Internet of Things (loT) devices. loT devices may include objects embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data. loT devices may be in-use with wired or wireless devices by sending data through an interface to another device. loT devices may collect useful data and then autonomously flow the data between other devices.

[0139] Various examples of modules may be implemented using circuitry, including various electronic hardware. By way of example and not limitation, the hardware may include transistors, resistors, capacitors, switches, integrated circuits, other modules, or some combination thereof. In various examples, the modules may include analog logic, digital logic, discrete components, traces and / or memory circuits fabricated on a silicon substrate including various integrated circuits (e.g., FPGAs, ASICs), or some combination thereof. In some embodiments, the module(s) may involve execution of preprogrammed instructions, software executed by a processor, or some combination thereof. For example, various modules may involve both hardware and software.

[0140] In an illustrative aspect, a system may include at least one data store including a program of instructions. The system may include at least one processor operably coupled to the data storesuch that, when the processor executes the program of instructions, the processor causes operations to be performed to induce device response based on dynamic balancing of input sources. The operations may include determine a context inference by the at least one processor applying a context inference engine to data inputs corresponding to a user, the data inputs including at least one point-source data input. The operations may include determine, based on the context inference, a context-specific weighting for each of the data inputs by the at least one processor applying a dynamic source balancing engine to the data inputs. The operations may include determine a context-specific recommended action of at least one connected device by the at least one processor applying a recommendation engine to the data inputs weighted by the context-specific weightings.

[0141] Determining the context-specific weighting for each of the data inputs may include retrieving weightings from a multi-dimensional matrix based on the context inference.

[0142] The multi-dimensional matrix may include at least three dimensions. The at least three dimensions may include time. The multi-dimensional matrix may include associations between data sources and: a corresponding qualitative-quantitative factor, and a corresponding weighting.

[0143] The context-specific weighting may, for example, vary with time.

[0144] The system may include a dynamic activation engine configured to selectively activate the at least one connected device in response to the context-specific recommended action. The dynamic activation engine may control medical labeling presented on the at least one connected device.

[0145] The at least one connected device may include an implanted device. The at least one connected device may include a shunt. The at least one connected device may include a personal computing device. The at least one connected device may include a wearable device.

[0146] The operations may include generate from the context-specific recommendation action, by the at least one processor, a control signal configured to operate the at least one connected device.

[0147] The context-specific recommended action may include providing a notification via the at least one connected device. The context-specific recommended action may include operation of at least one physically moving actuator of the at least one connected device.

[0148] In an illustrative aspect, operations described above may, for example, be implemented as one or more computer-implemented methods performed by at least one processor.

[0149] In an illustrative aspect, some embodiments may include one or more computer program product (CPP) including a program of instructions tangibly embodied on a non-transitory computer readable medium wherein, when the instructions are executed on a processor, the processor causes operations described above to be performed.

[0150] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, advantageous results may be achieved if thesteps of the disclosed techniques were performed in a different sequence, or if components of the disclosed systems were combined in a different manner, or if the components were supplemented with other components. Accordingly, other implementations are contemplated within the scope of the following claims.

Claims

CLAIMS1. A system comprising: at least one data store comprising a program of instructions; and, at least one processor operably coupled to the data store such that, when the processor executes the program of instructions, the processor causes operations to be performed to induce device response based on dynamic balancing of input sources, the operations comprising: determine a context inference by the at least one processor applying a context inference engine to a plurality of data inputs corresponding to a user, the plurality of data inputs comprising at least one point- source data input; determine, based on the context inference, a context-specific weighting for each of the plurality of data inputs by the at least one processor applying a dynamic source balancing engine to the plurality of data inputs; and, determine a context-specific recommended action of at least one connected device by the at least one processor applying a recommendation engine to the plurality of data inputs weighted by the context-specific weightings.

2. The system of claim 1, wherein determining the context-specific weighting for each of the plurality of data inputs comprises retrieving weightings from a multi-dimensional matrix based on the context inference.

3. The system of claim 2, wherein the multi-dimensional matrix comprises at least three dimensions.

4. The system of claim 3, wherein the at least three dimensions comprise time.

5. The system of any of claims 2-4, wherein the multi-dimensional matrix comprises associations between data sources and: a corresponding qualitative-quantitative factor, and a corresponding weighting.

6. The system of any of claims 2-5, wherein the context-specific weighting varies with time.

7. The system of any of claims 1-6, further comprising a dynamic activation engine configured to selectively activate the at least one connected device in response to the context-specific recommended action.

8. The system of claim 7, wherein the dynamic activation engine controls medical labeling presented on the at least one connected device.

9. The system of any of claims 1-8, wherein the at least one connected device comprises an implanted device.

10. The system of any of claims 1-9, wherein the at least one connected device comprises a shunt.

11. The system of any of claims 1-10, wherein the at least one connected device comprises a personal computing device.

12. The system of any of claims 1-11, wherein the at least one connected device comprises a wearable device.

13. The system of any of claims 1-12, the operations further comprising generate from the contextspecific recommendation action, by the at least one processor, a control signal configured to operate the at least one connected device.

14. The system of any of claims 1-13, wherein the context-specific recommended action comprises providing a notification via the at least one connected device.

15. The system of any of claims 1-14, wherein the context-specific recommended action comprises operation of at least one physically moving actuator of the at least one connected device.

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