Excerpt – US Patent US11,107,588 B2

Reading time 8 minutes

Updated – September 23, 2023

– Gal HONEST, Maier window –

Methods and systems to prioritize treatments, vaccinations, tests and/or activities while respecting the privacy of the individual

Below is an – automatically translated – excerpt from the above-mentioned US patent US11,107,588 B2, which is currently circulating on social media. The patent is said to cover an invention that is used for cell tower communication with people who have been injected with graphene (graphene oxide).

The patent refers to four other patent applications, including three from Israel (277083, 276665* and 276648*), as well as UAE (United Arab Emirates) P6001304/2020, which, however, does not seem to be found on the Internet.
* rejected due to unanswered correspondence

In fact, the patent, like the Israeli one, does not describe any technology that uses graphene in any way; the term “graphene oxide” is not found in either patent!

However, the US patent is still worth reading insofar as it disabuses those who take the opportunistic view “... I have nothing to hide ...”.

Whether it's a cell phone, credit card or use of online GPS map services, such data reveals far more than one would initially assume. They allow the creation of exact movement and encounter profiles in a temporal order and thus provide data that enable selective monitoring, separation and measures that, similar to the social credit system known in CN, allow influence on the behavior of each individual.

Excerpt (italic text):

“Actual individual geolocation data

In some embodiments, each individual's actual measured geolocation data is monitored to assess their potential to serve other individuals. In some embodiments, people who exhibit high levels of movement during the day in areas where other people are present receive a high score. In some embodiments, each individual's actual geolocation data is combined with one or more of:

• 1. Electronic devices, for example the location of their own cell phones provided by GPS
• 2. Using facial recognition technology based on one or more of: a) video surveillance data received from available sources, e.g. E.g. street cameras, ATMs, private surveillance cameras in shops, buildings and houses, etc.; b) social media.
• 3. Digital activity, e.g. B. Credit card usage, IP address used when using a computer or electronic device, antennas that receive data during a telephone call.

Optionally or additionally, such actual geolocation data is used instead of or in addition to the actual identification of contact between individuals.

Historical medical information of the individual

In some embodiments, historical medical data of each individual is assessed to provide an assessment. For example, as mentioned above, people with chronic cough get a high score because they may have a higher chance of transmitting the infectious disease/virus/pathogen. In some embodiments, individuals with a background medical condition that increases the likelihood of transmitting the disease receive a high score.

Current medical information of the individual

In some embodiments, during the pandemic, any new medical data pertaining to each individual will be monitored to determine whether the new data indicates a change in the individual's medical status with respect to their potential to infect others. If a person is diagnosed with chronic cough under the example above, their score will increase (e.g., in general and/or per contact).

Information from third parties about the individual

In some embodiments, third party information is evaluated by people informing others to determine whether the information needs to influence the evaluation. For example, if a third party informs that a person who showed low movement data and received a low score actually makes a lot of movements once the information is verified, the score will change accordingly. The opposite also applies, for example, to a third party who has been informed that a person who showed high movement data and received a high score is actually staying at home. Once the information is verified, the score may change accordingly.

Special mandatory app

In some embodiments, in light of the pandemic, the government may order citizens to install a special application on their smartphones (or other smart devices such as tablets, smart watches and smart glasses, etc.) to assist the government with the logistics of the vaccination process. In some embodiments, the government (or other entity) makes such special smart devices available to the public. In some embodiments, the app and/or smart device is configured to inform of the user's location at all times via Bluetooth and to communicate (e.g.) with neighboring smart devices to assess interactions between users, for example the proximity between users, the movement of users, etc.). In some embodiments of the invention, existing software may be used, e.g. For example, if both Android and based mobile phones have software (e.g. as an operating system service) that can detect the proximity of others, such software can be used or enhanced to provide functionality as described herein.

In some embodiments, such an app may be used to provide information about how many unique people the user meets. For example, a particular user may meet many people, but they are always the same people. While another user may meet fewer people, everyone is a different person. In some embodiments, the second user may receive a higher score and therefore be treated first. In some embodiments, such app and/or smart devices are also used to evaluate the progress of the vaccination procedures and the effectiveness of the vaccination procedure. In some embodiments, individual data received from each user is coupled with their health information (sick, vaccinated, recovered, etc.) to further evaluate the progress of the vaccination process and the effectiveness of the vaccination process. Optionally, if the people met by a user are vaccinated or otherwise considered immune, these contacts may not count and/or be weighted lower.

In some embodiments, the app is also used to send personalized communications to users, e.g. B. to be vaccinated. In some embodiments, certain actions are taken in light of the information received from the app, such as: B. Send a message to the user to increase their awareness of rules of conduct during the pandemic, come and get vaccinated, to avoid certain places where there is a high risk of infection.

Special voluntary app

In some embodiments, in light of the pandemic, the population is encouraged to install a special app in which those who install the app are rewarded. In some embodiments, the reward is to be given priority.

Monitoring behavior of the subject

In some embodiments, the subject's behavior is monitored in relation to security features performed by the subject, e.g. B. wearing a mask (, e.g., analyzing images taken during a call or another screen of the mobile phone ), washing his hands (, e.g., analyzing water sounds or movements through a smartwatch ), where the social distancing (e.g., based on Bluetooth power levels and/or NFC detection), switching between multiple storage locations, etc. In some embodiments, these are monitored using the same devices/methods as noted above.

Exemplary evaluation method

In some embodiments, each individual in a population (e.g., across 100, 1000, 10,000, and/or 100,000 individuals) receives a score that defines each individual's potential level of overdispersion. In some embodiments, the scores are defined as a number of contacts (see herein), and the number of contacts counted is between about 10 and about 100, optionally from about 100 to about 1000, optionally from about 1000 to about 10,000 , for example 100, 400, 1000, 2000, 10000 or medium or larger numbers. In some embodiments, a high value defines a high potential for over-scattering, while a low value defines a low potential for over-scattering. To facilitate explanations of the invention, a rating scale of 0 to 100 is used. It is understood that other scales may be used, such as: B. Heat map evaluation, decimal order scales, etc., are all included within the scope of the invention. In some embodiments of the invention, the score is open. For example, in some embodiments of the invention, the score is normalized to other values. Normalization does not have to be linear. In some embodiments of the invention, the score is a scalar. For example, in some embodiments of the invention, the score is multidimensional, including an overspread potential dimension and a variability of the behavioral dimension). For example, the score is multidimensional, including an overspread potential dimension and a variability of the behavioral dimension). for example, the score is multidimensional, including superspreader potential and behavioral dimension variability).

In some embodiments, the score is calculated using weighted scoring models in which one or more factors and/or components are scored according to the received information data. Now refer to FIG. 3 shows a schematic flowchart of a method for calculating a weighted score according to some embodiments of the invention.

In some embodiments, the system receives informational data about a subject 302., In some embodiments, the information data is searched according to the source of the information data 304., for example electronic information 306., from smartphones, cameras, credit card information, etc. 308., geographical information, for example from GPS or cell towers 310., government information, for example from the Census Bureau or EMR (electronic medical records) 312., human information, for example from other people, which calls for a provision of the information about other people and one or more of the factors and / or components mentioned above. In some embodiments, the system then calculates a weighted rating of each piece of information, optionally according to a predetermined criterion 314., In some embodiments, the system then generates an overall rating from the various weighted scores, optionally according to a predetermined criterion 316., In some embodiments, the system then provides a list with a treatment order that is then used to actually treat the population 318.

In some embodiments, the score includes a variety of components, for example the predicted probability that a subject will transmit an infectious disease/virus/pathogen, the predicted probability that a subject will contract an infectious disease/virus/pathogen, relative Health risk to a subject if that subject has an infectious disease/virus/pathogen, harm to society if the subject contracts an infectious disease/virus/pathogen; one or more of the above optionally with regard to data about physical proximity to other people.

In some embodiments, physical proximity data of a subject is calculated with other subjects by using one or more of:

• 1. The number of subjects with whom the subject may be in contact;
• 2. The subject's potential and/or actual distance from the other subjects;
• 3. The length of time of the subject's possible and/or actual encounter with the other subjects.

In some embodiments of the invention, the score is updated for and/or after each contact event. In some embodiments of the invention, the update is at the end of the day, potentially aggregating multiple meetings with the same person. Optionally or additionally, the score is updated per series of contact events. In some embodiments of the invention, the score is calculated after all contact events have been collected, for example based on an analysis of a contact network to identify individuals who, if vaccinated, will best stop the infection. Such an analysis can be performed by simulating the contact network and trying different vaccination regimens and/or removing different people and/or groups of people.

From the score to the treatment

In some embodiments, once the assessment of each individual is achieved, or optionally the assessment of a high number of individuals in the population, a list is created with the order in which each individual will receive the treatment. In some embodiments, the list is optionally divided by groups, e.g. B. All people who scored between 100 and 90 are grouped into group A, which receives the treatments first. Then all people who scored between 90 and 80 are grouped into group B, which receives the treatments second, and so on.

Information to the public

In some embodiments, once the list is created, individuals are informed when and where to go and receive the treatments, for example via email, dedicated apps in their cell phones, through the media, etc.

Exemplary simulations

In some embodiments, models and simulations are run in dedicated computers to, for example, evaluate the possible progress of treatments and the likely timing of achieving herd immunity and/or select values for various parameters. In some embodiments, simulations include inserting one or more actual data received from people into simulated data from/from people (if necessary to run likely scenarios). In some embodiments, assessments and models use one or more neural networks, machine learning, and specialized simulations.

In some embodiments, the simulations consider and model the likelihood that the treatments will (work or not) work on the individual.

In some embodiments, the simulations consider and model the type of population that a particular subject may potentially encounter and the potential population that those individuals may later encounter. For example, teachers who meet many children receive a higher simulated score because if and when the children are infected by the teacher, the children return home and potentially infect their families. While for example, a doctor working in a prison would potentially receive a lower simulated score because the incarcerated people in prison do not leave and are unlikely to infect anyone. (The infection is kept alone in prison).

In some embodiments, simulations are performed to evaluate parameter values used to identify a superspreader and possibly how to distinguish them from regular individuals.„