RELATED APPLICATIONS
None
BACKGROUND & FIELD OF THE INVENTION
Field of the Invention
The System, Method and computer program serves to make the process of drug rehabilitation and drug abuse abatement easier, more efficient, and more connected through multiple resources for the patients helps and needs and multiple databases and software systems to make analysis and predictions of the patients progress given the resources and different possible courses that the patient could take.
Multiple methods and services can be connected to an accessible application with an additional feature of predictive analytics on neuro-cranial structures and future symptoms or behaviour paths which can be accessed and utilized for a more precise service. Peer-to-Peer connection, data transfer between the network nodes for collecting data and evaluating it will prove for an enhanced experience.
Background of the Invention
Modern age drug rehab and abatement procedures only involve physical and medical methods in centres to psychologically ‘correct’ victims, often done without care, treatment or exactment. Diagnosis, provisions, synopsis, and evaluation is all done in one school of thought and centre without any existing depth-ful analysis or retrospect and no personal care for specifics. Automation and formation of a digital network that could provide easy access and utilization of resources in a calculated manner for a quicker, more efficient and more successful process (as opposed to a statistically high amount of drug abusers falling back into their old lifestyle due to current ‘only’ rehabilitation methodologies).
Thus, the current human process of administration over rehabilitation can be largely controlled by accurate algorithms that provide the perfect combination of requirements. The current state of long, painful, psycho-inducing and body intensive method of abating drug abuse and educating everyone involved in a meaningful manner will be largely beneficial.
SUMMARY
Brief Summary of the Invention
The computer program product, system and method comprises of a centrally connected mobile application having a social networking, mapping, online ordering, medical-servicing, diagnosing, and AI based monitoring and advising (like Apple's SIRI) feature for the drug abuser to utilize during the complete abatement journey.
The application will have an easy access UI which is intuitive and containing access links to all network resources for appropriate use of the patient, along with weekly reports of actions, diagnosis, advised lifestyle and specifications, chat links to advisors, doctors, and similar minded people (previous drug addicts, current drug addicts), and synthesised map routes to ‘good’ spots, such as chemists, hospitals, police centres, or any other daily help spaces needed, with a enhanced helpline for each spots to the user.
This data set can be fine-tuned by user based weekly responses to NLP computed questionnaires (based on user diagnostics during app runtime ad advisor/doctor reports). This data is also kickstarted by extracting data from the user profile during application launch on local devices.
In real-time, as the procedure goes on, the patient can utilize any of the resources for their use, often at advisor discretion, to make the process more streamlined. Using mapping applications to get prescribed medicine or visit rehab centres for emergencies and so on, using delivery services for food, medicine and doctor orders, and using synthesized social media for positive motivation and ‘survivor’ stories can be really spearheading as a process (these 3 features are given in example among the many others).
The drug-reward system is a negative feedback loop that biologically rewards the abuser with more of dopamine for each drug intake exponentially. A key motivator of this system would be rewards of any sort for the material lacking in the personal life of the abuser, which is a statistically high cause for them to abuse drugs in the first place. Good track of treatment may be rewarded with such things, with appropriate funding.
With the network of services to use set, the question of resources required per user in total to be stockpiled, looked after, monitored, appropriately distributed, and a management network of funding companies (in reference or not in reference to the provided materials). This network is handled by all possible data being directly fed in to company directory for organized distribution of required goods and availability of services always.
Timely analysis of brain structure will be an integral role in advise towards medicine, resources, and so on. A cumulative scan with multiple methodologies will be taken from the patient, and will be extracted for image data. This data will be analysed using image processing, while accessing symptom data to find any correlation, and use synapse based analysis along with this image analysis data to predict future conditions, render present diagnosis, and link analysed condition to the situation of the full body (internal and external features down to the genetic composition).
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1: Overlaying the complete network of the application central, from user processes to all other aiding factors
FIG. 2: Describing the Data Structure of the τ functionality in detail in context of the total data structure for a brief understanding of synaptic analysis
FIG. 3: Showcasing the application UI (home screen only) to picture out the real-time product ‘face’
FIG. 4: Describing the Hardware System of the t functionality in detail in context of the total data structure for a brief understanding of the monitoring ‘node’
SPECIFICATIONS—DETAILED DESCRIPTION
Referencing FIG. 1: The process of application launch starts with the drug abuser (user of application) logging into the downloaded application on any device and initially visiting a chemist shop for user entry based (profile and questionnaire) purchases (that are free and sponsored) which lead to a diagnostic test taken by the user. The materials used for the diagnostic test vary by users data, and some aspects are mandatory (such as the set of brain scans which are used in the neurocranial analysis in algorithm τ). This data after processing by sponsored diagnosis agencies are fed in specific to the users profile and is viewable by the user anytime.
Referencing FIG. 1: The application provides focused Map services from google APIs, where the focusing and filtering of appropriate and important places for the user (such as ER centers and so on) are highlighted and optimized for travel. The filtering, focusing, and data optimization is done by the 1 Φ unction. The application also has links for contact and registry into rehab centers, for emergencies and possible advised treatments. Psychological counseling can be offered as a first measure treatment to rehab as well by similar authorities and by advising/personal need.
Referencing FIG. 1: The application also has social media based links to previous drug addicts who escaped the vicious cycle and other addicts who are currently suffering. This special feature of connecting with these people may offer anecdotal advise or support or moral/ethical standpoints to each user. The features of the social media feature would be that of a standard one, like Instagram (with motivational picture sharing and filtering and monitoring for negative material regarding drugs, and chatting features to get in touch with).
Referencing FIG. 1: Φ function filtered media, news, reports and so on can also be viewed to suit the positive needs of the user, based on preferences, and user activity as well. These sources will provide needed support to the user in combating drug abuse.
Referencing FIG. 1: From the entirety of the internet, there will be an extracted database ‘γ’ of drug abuse related, drug symptom related, technical, rehab/medical center related data in a matrix for access into the application. This data is different from the map data, social media data and the rehab center or resource data. This data fed into the application is used for user info that is more streamlined to the focus of the application (more knowledge based and for AI to finetune user data in accordance to existing data).
Referencing FIG. 1: For resource distribution to users, typical ordering services like amazon's will be implemented with a stockpile that is monitored for quality. Periodic Monitoring of the resources, of the progress of the user, and of 3rd party use and advise/medical help is also monitored (function t), along with advertisements of products that are catered to user's use (function θ, based on current AI based determination systems). These resources may be upped in value for a reward based system where progress in abating drugs is regarded with user's choice of commodities (optional by user) (function π) which may further boost recovery period.
Referencing FIG. 1: Forming the network of companies for resources, product and service based aspects can be done by finding conglomerates, single or any legal category of companies fitting in the agreement for the said part of the application, where the company has added shares and revenue from the sales of goods and services, relays monitoring and quality control and customer service information, and this can be fed into the application for actions to be taken.
Referencing FIG. 2: The τ function as part of calculated time based diagnosis involves gathering scan data from a set of brain scans, from fMRI to EEG as shown in the figure. The information is extracted that is related to drugs physiological and/or chemical alterations. Using the user data entries of genetic history and genetic data of the person, the computation is performed to produce results.
Referencing FIG. 2: The computational function to provide deterministic analysis uses all drug related symptom data and tries to match the descriptions with image based understanding by NLP and uses recursive ML to render all extracted data against the network of gathered data and across all possible combinations of the data. The results are then computed, and stored in a single file having the analysis of the time based input set ready.
Referencing FIG. 2: The file comprises of 2 separate things: current conditional analysis (from NLP as a key feature) and future conditional predictions using ML based predictive analytics. This is cross referenced against the possible routes towards ailment to determine possibilities of other effects along the route and of other added benefits, which are then compiled against, and a best set is extracted and displayed as a UI on the ‘suggestive treatment’ area. The copies of result data is in user and related personnel applications. The application has a ‘login as’ feature where each category of affiliate can login for use.
Referencing FIG. 3: The structure of monitoring resources, conditions of affiliated places in the main network in FIG. 1 and of all the types of users can be done by a generally linked monitoring system in the application network. Digital logins of any kind: purchases, app logins and so on are marked, and so are sensor based or camera base differences, such as rehab visits, stockpile expiry, location and amount and so on, which comprised of monitoring data.
Referencing FIG. 3: This data is then analyzed for fits in company decided parameters to compare it and form a weekly report of quality of monitoring, monitoring periods and so on. Further mathematical analysis of the same can produce some statistical analysis of the monitoring of all involved nodes in the network in FIG. 1. This is then relayed to the advisor in companies to adjust policies.
Referencing FIG. 4: The home screen of the application is seen where there is clear access to all the listed aspects and features for instant use. The side-tab can be used for note-making, quick personalized access or for quick routing to previous actions. Appropriate links to other sources/websites/or company leased features will be hosted in accordance.
Referencing FIG. 4: The home screen for different types of users (non-abusers) will be different. For advisors, the side-tab will host a list of linked patients for analysis and their respective histories in each of the respective application features, along with a weekly report and appointment scheduling feature.
Referencing FIG. 4: Regarding diagnostics and medical support, a similar fashion of layout to the advisors will follow but with more analyzed details and access to possible treatment routes.
Referencing FIG. 4: For monitoring agencies, more emphasis on monitored data in the t function will be displayed instead of possible treatment routes and appointment schedules and more analyzed user details.
While specific ideas and embodiments have been illustrated and described, numerous modifications come to mind without significantly departing from the spirit of the invention, and the scope of protection is only limited by the scope of the accompanying claims.
Patent Application
20210043322
