Patent Publication Number: US-2023162546-A1

Title: System to evaluate and manage people entering a building based on infection risk data, environmental data, and building entrant health data

Description:
BACKGROUND 
     The present disclosure relates to building management systems. More specifically, the present disclosure relates to building management systems that employ an access control system to make entry decisions for potential building occupants. 
     In most building systems, potential building occupants are only evaluated to determine if they are a valid member of the building&#39;s access directory at a certain time. Existing building systems generally are not configured to use access control systems to determine if increased health risks are posed by a person attempting to enter a building. 
     SUMMARY 
     This summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices or processes described herein will become apparent in the detailed description set forth herein, taken in conjunction with the accompanying figures, wherein like reference numerals refer to like elements. 
     One implementation of the present disclosure is a method for providing access into a building. The method includes receiving an indication that a potential building occupant is requesting access into the building. The method further includes providing input data into a probabilistic model, the input data including current health data of the potential building occupant obtained in response to receiving the indication. The method further includes analyzing, via the probabilistic model, relationships between a plurality of variables within the probabilistic model to determine an entry decision for the potential building occupant, each of the plurality of variables are representative of subsets of the input data. The method further includes, in response to the entry decision permitting the potential building occupant to enter the building, providing a control signal to a security system to permit access to the building for the potential building occupant. 
     In some embodiments, analyzing the relationships between the plurality of variables within the probabilistic model includes determining the subsets of the input data and dependencies between the subsets of the input data, wherein the plurality of variables represent the subsets of the input data and the relationships between the plurality of weighted represent the dependencies between the subsets of the input data, determining a joint probability distribution between two or more of the plurality of variables, using the joint probability distribution to determine a probability of the potential building occupant having an infectious disease, and determining the entry decision for the potential building occupant. 
     In some embodiments, the input data further includes infectious risk data and profile data from a profile associated with the potential building occupant. In some embodiments, the subsets of the input data include at least one of local infection rate, contact tracing status, vaccination status, and temperature. 
     In some embodiments, the probabilistic model is a Bayesian network model. In some embodiments, the plurality of variables are each weighted based on a likelihood that each of the plurality of variables would affect a probability of the potential building occupant having an infectious disease. 
     In some embodiments, the method further includes, in response to the entry decision permitting the potential building occupant to enter the building, determining a location within the building to send the potential building occupant, providing audible or visual signals within the building to guide the potential building occupant to the location, providing instructions to a mobile device of the potential building occupant via a mobile application to guide the potential building occupant to the location. 
     In some embodiments, the method further includes, in response to the entry decision permitting the potential building occupant to enter the building, determining a testing center within the building to send the potential building occupant, providing audible or visual signals within the building to guide the potential building occupant to the testing center, and, in response to determining that the potential building occupant has tested negative for a contagious disease, providing access to the building for the potential building occupant. 
     In some embodiments, receiving the indication that the potential building occupant is requesting access into the building includes receiving a request from the potential building occupant via a mobile application to enter the building. 
     Another implementation of the present disclosure is a controller for providing access into a building. The controller includes a processing circuit including one or more processors and memory, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include receiving an indication that a potential building occupant is requesting access into the building, providing input data into a probabilistic model, the input data including current health data of the potential building occupant obtained in response to receiving the indication, analyzing, via the probabilistic model, relationships between a plurality of variables within the probabilistic model to determine an entry decision for the potential building occupant, each of the plurality of variables are representative of subsets of the input data, and, in response to the entry decision permitting the potential building occupant to enter the building, providing a control signal to a security system to permit access to the building for the potential building occupant. 
     In some embodiments, analyzing the relationships between the plurality of variables within the probabilistic model includes determining the subsets of the input data and dependencies between the subsets of the input data, wherein the plurality of variables represent the subsets of the input data and the relationships between the plurality of variables represent the dependencies between the subsets of the input data, determining a joint probability distribution between two or more of the plurality of variables, using the joint probability distribution to determine a probability of the potential building occupant having an infectious disease, and determining the entry decision for the potential building occupant. 
     In some embodiments, the input data further includes infectious risk data and profile data from a profile associated with the potential building occupant. In some embodiments, the subsets of the input data include at least one of local infection rate, contact tracing status, vaccination status, and temperature. 
     In some embodiments, the probabilistic model is a Bayesian network model. In some embodiments, the plurality of variables are each weighted based on a likelihood that each of the plurality of variables would affect a probability of the potential building occupant having an infectious disease. 
     In some embodiments, the processing circuit is further configured to, in response to the entry decision permitting the potential building occupant to enter the building, determine a location within the building to send the potential building occupant, provide audible or visual signals within the building to guide the potential building occupant to the location, provide instructions to a mobile device of the potential building occupant via a mobile application to guide the potential building occupant to the location. 
     In some embodiments, the processing circuit is further configured to, in response to the entry decision permitting the potential building occupant to enter the building, determine a testing center within the building to send the potential building occupant, provide audible or visual signals within the building to guide the potential building occupant to the testing center, and, in response to determining that the potential building occupant has tested negative for a contagious disease, provide access to the building for the potential building occupant. 
     In some embodiments, receiving the indication that the potential building occupant is requesting access into the building includes receiving a request from the potential building occupant via a mobile application to enter the building. 
     Another implementation of the present disclosure is one or more non-transitory computer readable media having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to implement operations. The operations include receiving an indication that a potential building occupant is requesting access into the building, providing input data into a probabilistic model, the input data including current health data of the potential building occupant obtained in response to receiving the indication. The operations include analyzing, via the probabilistic model, relationships between a plurality of variables within the probabilistic model to determine an entry decision for the potential building occupant, each of the plurality of variables are representative of subsets of the input data. The operations include, in response to the entry decision permitting the potential building occupant to enter the building, providing a control signal to a security system to permit access to the building for the potential building occupant, determining a location within the building to send the potential building occupant, and providing audible or visual signals within the building to guide the potential building occupant to the location. 
     In some embodiments, analyzing the relationships between the plurality of variables within the probabilistic model includes determining the subsets of the input data and dependencies between the subsets of the input data, the plurality of variables represent the subsets of the input data and the relationships between the plurality of variables represent the dependencies between the subsets of the input data, determining a joint probability distribution between two or more of the plurality of variables, using the joint probability distribution to determine a probability of the potential building occupant having an infectious disease, and determining the entry decision for the potential building occupant. 
     In some embodiments, the input data further includes infectious risk data and profile data from a profile associated with the potential building occupant and the subsets of the input data include at least one of local infection rate, contact tracing status, vaccination status, and temperature. 
     In some embodiments, the probabilistic model is a Bayesian network model and the plurality of variables are each weighted based on a likelihood that each of the plurality of variables would affect a probability of the potential building occupant having an infectious disease. 
     In some embodiments, the one or more processors are further configured to, in response to the entry decision permitting the potential building occupant to enter the building, determining a testing center within the building to send the potential building occupant, providing audible or visual signals within the building to guide the potential building occupant to the testing center, and, in response to determining that the potential building occupant has tested negative for a contagious disease, providing access to the building for the potential building occupant. 
     In some embodiments, receiving the indication that the potential building occupant is requesting access into the building includes receiving a request from the potential building occupant via a mobile application to enter the building. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a drawing of a building equipped with a HVAC system, according to an exemplary embodiment. 
         FIG.  2    is a block diagram of an entrance security system for a building, which can be implemented in the building of  FIG.  1   , according to an exemplary embodiment. 
         FIG.  3    is a block diagram of an access control system (ACS) controller, which can be implemented in the entrance security system of  FIG.  2   , according to an exemplary embodiment. 
         FIG.  4 A  is a simplified diagram of a probabilistic network for making entry decisions, which can be implemented in the ACS controller of  FIG.  3   , according to an exemplary embodiment. 
         FIG.  4 B  is a detailed diagram of a probabilistic network for making entry decisions, which can be implemented in the ACS controller of  FIG.  3   , according to an exemplary embodiment. 
         FIG.  5    is a diagram of an entrance gate security system providing selective entry decisions for potential building occupants, which can be implemented in the system of  FIG.  2   , according to some embodiments. 
         FIG.  6    is a diagram of a security system using contactless routing, which can be implemented in the system of  FIG.  2   , according to some embodiments. 
         FIG.  7    is a diagram of a security system using contactless routing, which can be implemented in the system of  FIG.  2   , according to some embodiments. 
         FIG.  8    is a diagram of a security system using contactless routing, which can be implemented in the system of  FIG.  2   , according to some embodiments. 
         FIG.  9    is a diagram of a security system using contactless routing, which can be implemented in the system of  FIG.  2   , according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Referring generally to the FIGURES, systems and methods for providing safe and efficient access throughout a building for potential building occupants (PBOs) are shown, according to some embodiments. To mitigate health risks in an environment, such as mitigating the spread of an infectious disease (e.g., COVID-19, etc.), it can be desirable for a building management system (BMS) to evaluate and manage requests of PBOs to enter a building, and to have the BMS make entry decisions based on infectious risk data, environmental data, and/or current health data of the PBO. 
     In some embodiments, the BMS employs an access control system (ACS) that is configured to dynamically determine entry decisions for PBOs. This can include providing infectious risk data (e.g., local infection rate, current COVID-19 standards, etc.), environmental data (e.g., weather, method of commute, etc.), and current health data of the PBO (e.g., vaccination status, contract trancing status, temperature, etc.) to a model within the ACS that is configured to make a dynamic entry decision on whether to grant access to the building for the PBO. For example, the model can be a probabilistic model, such as a Bayesian network model, that uses relationships between different variables (e.g., temperature, vaccination status, etc.) to make an inference about the safety risk of granting access to the user (e.g., inferring the chances of the PBO having COVID-19, etc.). The model may be trained on system data, where data may be binary, with probability scores attached to each state. Additionally, some of the data may be pre-processed or pre-classified by a system admin, who manually adjusts the weighting of relationships in the system. For example, certain employee roles may be considered to carry a higher infection risk, which may then increase the likelihood that an employee is sent for a test. Advantageously, this technique can provide faster and more accurate health decisions by considering multiple health variables and the relationships thereof. 
     In some embodiments, the ACS may be configured to also or separately provide dynamic routing for PBOs from when they have been granted access into the building to their desired location, and anywhere in between. For example, a PBO is granted access into the building after the ACS makes an entry decision that the PBO is safely allowed to enter the building (e.g., based on decisions made by a probabilistic model, etc.). The ACS is also aware of where the PBO needs to go within the building (e.g., the PBO is going to their office and the ACS knows their office location, the PBO requested to go to a location via a mobile application connected to the ACS, etc.), and provides A/V signals to safely guide the PBO to the location. These can include light signals (e.g., illuminating arrows on the floor to guide the PBO, etc.), audio signals (e.g., verbal instructions provided over an intercom, etc.), and/or video signals (e.g., a video is provided to the mobile device of the PBO via a mobile application guiding the PBO to the location, etc.). Advantageously, this technique can provide a contactless routing system, which can increase social distancing to reduce risks of infection among building occupants. 
     Building Site 
     Referring now to  FIG.  1   , a perspective view of a building  10  is shown. Building  10  is served by a building management system (BMS). A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     The BMS that serves building  10  includes a HVAC system  100 . HVAC system  100  may include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building  10 . For example, HVAC system  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  may provide a heated or chilled fluid to an air handling unit of airside system  130 . Airside system  130  may use the heated or chilled fluid to heat or cool an airflow provided to building  10 . In some embodiments, waterside system  120  is replaced with a central energy plant such as central plant  200 , described with reference to  FIG.  2   . 
     Still referring to  FIG.  1   , HVAC system  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  may use boiler  104  and chiller  102  to heat or cool a working fluid (e.g., water, glycol, etc.) and may circulate the working fluid to AHU  106 . In various embodiments, the HVAC devices of waterside system  120  may be located in or around building  10  (as shown in  FIG.  1   ) or at an offsite location such as a central plant (e.g., a chiller plant, a steam plant, a heat plant, etc.). The working fluid may be heated in boiler  104  or cooled in chiller  102 , depending on whether heating or cooling is required in building  10 . Boiler  104  may add heat to the circulated fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. Chiller  102  may place the circulated fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulated fluid. The working fluid from chiller  102  and/or boiler  104  may be transported to AHU  106  via piping  108 . 
     AHU  106  may place the working fluid in a heat exchange relationship with an airflow passing through AHU  106  (e.g., via one or more stages of cooling coils and/or heating coils). The airflow may be, for example, outside air, return air from within building  10 , or a combination of both. AHU  106  may transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU  106  may include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid may then return to chiller  102  or boiler  104  via piping  110 . 
     Airside system  130  may deliver the airflow supplied by AHU  106  (i.e., the supply airflow) to building  10  via air supply ducts  112  and may provide return air from building  10  to AHU  106  via air return ducts  114 . In some embodiments, airside system  130  includes multiple variable air volume (VAV) units  116 . For example, airside system  130  is shown to include a separate VAV unit  116  on each floor or zone of building  10 . VAV units  116  may include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building  10 . In other embodiments, airside system  130  delivers the supply airflow into one or more zones of building  10  (e.g., via air supply ducts  112 ) without using intermediate VAV units  116  or other flow control elements. AHU  106  may include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU  106  may receive input from sensors located within AHU  106  and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU  106  to achieve setpoint conditions for the building zone. 
     Access Control System Overview 
     Referring now to  FIG.  2   , a block diagram of security system  200  is shown, according to some embodiments. System  200  may be implemented within building  10  and may be configured to implement security measures within building  10 , such as access to building  10 . System  200  is shown to include potential building occupant (PBO)  202 , entrance  204 , camera subsystem  206 , building entrance locking mechanism (“mechanism”)  210 , access control system (ACS) controller (“controller”)  212 , and infectious risk data  214 . 
     PBO  202  may be any type of individual entering, or planning to enter, building  10 . This can include building employees, previously employed employees, students and/or visitors to building  10 . In some embodiments, PBO  202  is attempting to gain access into building  10 . For example, PBO  202  contacts controller  212  via a mobile application, and transmits this request to controller  212  via the mobile application. In some embodiments, the mobile application provides updates to PBO  202  on the status of gaining entrance into building  10  and can provide updates/reasons on the decided entry decision. 
     Camera subsystem  206  may be configured to obtain real-time data of PBO  202  prior, during, and/or after the PBO&#39;s request to pass through entrance  204  is received. For example, in response to controller  212  receiving an indication that PBO  202  is requesting accesses through entrance  204 , a temperature sensor may obtain the temperature of PBO  202 . In some embodiments, subsystem  206  is also configured to provide static images and/or video feed to controller  212  of PBO  202 . This may occur when PBO  202  is within range of cameras within subsystem  206 , after they have entered building  10  and passed through entrance  204 , and any time in between. 
     In some embodiments, controller  212  is configured employ access control into and/or out of entrance  204  using one or more probabilistic models. In the event of an entry decision indicating that PBO is allowed into building  10 , controller  212  may provide control signals to building entrance locking mechanism  208  to allow PBO  202  to enter through entrance  204 . This may include opening entrance gates that are typically locked, and permitting the PBO  202  to gain access after a virtual lock is disengaged. In other embodiments, this may include allowing a code on the mobile application of PBO  202  to be received (e.g., scanned, entered, etc.) into a terminal at or near entrance  204  that thereby permits access through entrance  204 . 
     While the systems and methods disclosed herein are generally referring to probabilistic models (e.g., graphical models, probabilistic graphical models, structured probabilistic model, undirected graphical models, cyclic directed graphical models, etc.), and in particular Bayesian network models, these are merely meant to be exemplary and should not be considered limiting. Other types of dynamic decision-making tools can be used and/or implemented, such as deterministic models, statistical models, deep learning models, and other models implanting types of artificial intelligence. It is worth noting that any and all data taken, recorded, and/or determined during the processes disclosed herein can be stored and used for future analytics. 
     While not shown in  FIG.  2   , controller  212  may also be configured to provide control signals to audio, visual, or electrical subsystems that can be configured to guide PBO  202  (i.e., after gaining access to building  10  through entrance  204 ) to a desired (e.g., per a request from PBO  202 , etc.) and/or suggested (e.g., decided by controller  212 , etc.) location within building  10 . In some embodiments, controller  212  can also be configured to guide PBO  202  (i.e., after being denied access to building  10 ) to a location that mitigates subsequent health risks, such as a nearby testing location or a hospital. In some embodiments, controller  212  is configured to perform both types of guiding processes. 
     Controller  212  may be configured to receive the temperature and/or video data from subsystem  206 , along with a series of other data points. These data points may be provided by the PBO  202  (e.g., vaccination status, current feelings of sickness, etc.) and/or may be queried from a profile of the PBO  202  in a database coupled controller  212 . For example, controller  212  includes a database of profiles corresponding to employees working at building  10 . Controller  212  queries the profile database to determine a variety of information associated with PBO  202 , such as their vaccination history, preferred method of commuting to work, contact tracing information, COVID-19 status, and health information. 
     While the systems and methods disclosed herein are generally referring to the entrance of building  10 , any location in which access may be barred by security measures can implement the systems and methods disclosed herein. For example, an entrance into a large space (e.g., cafeteria, auditorium, etc.), or a section of a building that requires stricter security protocols. Controller  212  is described in greater detail below with reference to  FIG.  3   . 
     Referring now to  FIG.  3   , detailed block diagram of controller  212  is shown, according to some embodiments. Controller  212  is shown to includes communications interface  320  and processing circuit  302  including processor  304  and memory  306 . Processing circuit  302  can be communicably connected to communications interface  320  such that processing circuit  302  and the various components thereof can send and receive data via communications interface  320 . Processor  304  can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. 
     Communications interface  320  can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications. In various embodiments, communications via communications interface  320  can be direct (e.g., local wired or wireless communications) or via a communications network (e.g., a WAN, the Internet, a cellular network, etc.). For example, communications interface can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, communications interface  320  can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, communications interface  320  can include cellular or mobile phone communications transceivers. 
     Memory  306  (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present disclosure. Memory  708  can be or include volatile memory or non-volatile memory. Memory  306  can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. According to an example embodiment, memory  306  is communicably connected to processor  304  via processing circuit  302  and includes computer code for executing (e.g., by processing circuit  302  and/or processor  304 ) one or more processes described herein. 
     In some embodiments, controller  212  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments controller  212  can be distributed across multiple servers or computers (e.g., that can exist in distributed locations).  FIG.  3    is shown to include controller  212 , external risk database  322 , and mechanism  208 , and memory  306  is shown to include data collector  308 , predictive model  310  including Bayesian network  312 , occupant profiles  314 , routing signal generator  316 , and control signal generator  318 . 
     Data collector  308  may be configured to receive any and all information provided to controller  212 . While data collector  308  is generally collecting data that includes some or all of health data of PBO  202 , infectious risk data, and environmental data, other data and/or data sets can be considered and analyzed to perform the processes herein. Data collector  308  may be configured to receive several data sets and provide the data predictive model manager  310 . In some embodiments, as shown in  FIG.  3   , manager  310  may be configured to query and/or store data in occupant profiles  314  without passing through data collector  308 . 
     Data collector  308  may be configured to receive occupant data or current health data associated with one or more PBO  202 . In some embodiments, current health data of PBO  202  includes any data or information pertaining the past or present health of the PBO  202 . For example, the current health data of PBO  202  includes temperature, current vaccination status, previous vaccination information, feelings of sickness, tests statuses, self-reporting information, method of commute to building  10 , contact tracing information, or any combination thereof. 
     Data collector  308  may also receive infectious risk data, which can include specific information about present diseases or pandemics. For example, the infectious risk data can include updated reports/mandates/protocols from government bodies pertaining to COVID-19, local infection rates, local guidelines, company guidelines, social distancing requirements, updates relating to COVID-19 studies, updated testing protocols, or any combination thereof. Data collector  308  may also receive environmental data, which can include data affecting the spread of infectious diseases. For example, the environmental data can include local weather data, wind speeds, building air filtration information (e.g., indicating how airborne pathogens would travel within building  10 ), risk levels within building  10 , or any combination thereof. 
     Predictive model manager  310  may be configured to receive data from data collector  308  as inputs into a model. In some embodiments, the model is a probabilistic model that includes Bayesian network  312  and is configured to determine a likelihood of risk for PBO  202  entering building  10 . Manager  310  may implement a model that uses the different data subsets (e.g., local infection rate, COVID status, contact tracing, etc.) as variables within the model, and mathematically determines their conditional dependencies on one another based on their relationships. This can allow manager  310  to compute the probabilities of certain scenarios. In the embodiments described herein, manager  310  may be configured to compute the probability of PBO  202  having an infectious disease, or computing the probability of PBO  202  being a risk to others if allowed in building  10 . The methods in which manager  310  may use these variables and the relationships thereof to compute probabilities and make decisions is described in greater detail below with reference to  FIG.  4   . 
     Routing signal generator  316  may be configured to receive the risk decisions or other decisions from manager  310  and provide, if necessary, guidance to a decided location for PBO  202 . For example, generator  316  receives an indication that that PBO  202  is allowed to enter building  10  and is granted full access. Routing signal generator  316  then generates audible and/or visual signals to guide PBO  202  to their office. While PBO  202  may know the path to their office, the guidance may guide them on a path that maximizes social distancing to mitigate health risks. 
     In another example, routing generator  316  receives an indication that PBO  202  is allowed to enter building  10 , but needs to be tested in the in-building testing center prior to being granted full access throughout building  10 . As such, generator  316  generates audible and/or visual signals to guide PBO  202  to the in-building testing center. Controller  212  may then receive an indication whether PBO  202  has passed an infectious disease test and, in response to determining that PBO  202  does not have the infectious disease, proceed with granting them full access to building  10 . 
     Control signal generator  318  may be configured to receive risk decisions from manager  310  and to generate control signals to unlock mechanism  208  in response to manager  310  determining that PBO  202  should be allowed to enter building  10 . In some embodiments, control signal generator electronically unlocks a gate and/or lock at or near entrance  204 . In other embodiments, control signal generator  318  may provide a wireless signal to a mobile device of PBO  202  via a mobile application, which can unlock/generate an access code (e.g., QR code, numerical code, security code, etc.). Then, PBO  202  may use the mobile device to access entrance  204 . 
     Access Control Processes Using Probabilistic Modeling 
     Referring now to  FIG.  4 A , a diagram  400  of a network model is shown, according to some embodiments.  FIG.  4 A  is shown to include “commute by bus” node  402 , “no vaccination” node  404 , “high temperature” node  406 , and “access granted” node  408 , along with edges connecting the multiple nodes. In some embodiments, diagram  400  is graphical model with multiple nodes  402 - 408 , and edges (i.e., the lines connecting the nodes). The graphical model me be representation of a Bayesian network, where the conditional probability of node  408  may be determined based on parent nodes (i.e., node  406 ) or grand-parent nodes (i.e., nodes  402 ,  404 ). For the following equations described herein, random variables may be referred to in capital letters (e.g., “A”), while values of variables may be referred to in lowercase letters (e.g., 
     The definition of conditional probability may be defined as the following: 
     
       
         
           
             
               
                 
                   
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     which states that the probability of a given b is equal to the joint probability of a and b, divided by the probability of b. For example (not shown in  FIG.  4 A ), a is the event that PBO  202  has COVID-19, and b is the event that PBO  202  has a temperature greater than 99°. Therefore, Eq. (1) would state that the probability of PBO  202  having COVID-19, given the fact that PBO  202  has a temperature greater than 99°, is equal to the probability of that PBO  202  has COVID-19 (“a”) and has a temperature greater than 99° (“b”), divided by the probability that PBO  202  has a temperature greater than 99° (“b”). 
     Similarly, the probability of b, given a, may be defined using Bayes Rule: 
     
       
         
           
             
               
                 
                   
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     Where the probability of b given a is equal to the probability of a and b times the probability of b, divided by the probability of a. Additionally, the joint probability distribution of nodes  402 - 408  within diagram  400  may be defined as: 
       P(C, V, T, G)=P(V)P(C)P(T|C, V) P(G|T)   Eq. (3)
 
     Where the joint probability P(C, V, T, G) is equal to the probability of V times the probability of C times the probability of T given C or V times the probability of G given T. In some embodiments, controller  212  may use this Bayesian network (e.g., diagram  400 ) to make probabilistic determinations. 
     For example, controller  212  may want to determine (e.g., perform an inference of) the probability of access being granted for PBO  202  (e.g., node  408 ), given that PBO  202  commuted by bus (e.g., node  402 ). This may be defined as: 
     
       
         
           
             
               
                 
                   
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     Where the unconsidered variables T, V are summed, and the probability of access being granted is divided out for each of the probabilities for each node  402 - 408 . Eq. (5) may be calculated by controller  212  to determine a probability of access being granted to PBO  202  given PBO  202  commuting by bus. If the determined probability is over a predetermined threshold (e.g., 5 percent, 10 percent, 20 percent, 50 percent, etc.), controller  212  may determine that that PBO  202  is required to take a test for an infectious disease and test negative prior to entering building  10 . 
     It is worth noting that the variables used in diagram  400  are merely meant to be exemplary and should not be considered limiting. For example, node  408  may be replaced with “positive for COVID-19” and controller  212  wants to determine the probability of PBO  202  testing positive for COVID-19 give PBO not being vaccinated (e.g., P(V)). Multiple other nodes may considered, as shown in greater detail with reference to  FIG.  4 B . 
     Referring now to  FIG.  4 B , a detailed diagram  450  of a network model is shown, according to some embodiments. Diagram  450  may be part of Bayesian network model  312  of controller  212 , and is configured to make probability decisions for multiple variables. These variables may be based on a set of assumptions regarding likely factors that would affect PBO  202  likelihood of having COVID-19 (e.g., their temperature, as represented by node  454 ), according to some embodiments. As mentioned above, the connecting lines (e.g., edges) represent the relationships between the variables (e.g., nodes). 
     In some embodiments, rules based on these variables are represented in Bayesian network model  312  as nodes contributing to joint probability calculations that score target nodes, whether the user should be admitted, and/or sent for a test (represented by node)  476 . For example, a high local infection rate  460  can dynamically increase the likelihood that an employee with a higher than average temperature is instructed to go to a testing center for a test. Bayesian network model  312  may be trained on previous system data (e.g., where data may be binary, etc.) with probability scores attached to each state. Additionally, some of the data may be pre-processed or pre-classified by a system administrator, who manually adjusts the weighting of relationships in the system. For example, certain employee roles may be considered to carry a higher infection risk, which may then increase the likelihood that an employee is sent for a test at a testing center. 
     In some embodiments, entry decision node  478  represents the possible entry decisions, as outlined above. Three other variables may represent the primary sources of data in the in the system as they represent the main sources of data for the entry decision node  478 . These include the COVID status node  464 , the employee health node  466  and the risk factor node  468 , according to some embodiments. Based on the accumulated data provided to these variables, the risk is assessed, and an entry decision may be calculated. Multiple examples of this process are outlined below. 
     In one non-limiting example, a self-reporting health application (represented by node  472 ) is completed by all employees may be used to flag infection risks. An employee that reports multiple symptoms of an infectious disease on the app may prompt the system to send the employee for an on-site test. The employee takes a test and the test results prove negative. Based on these results, the system deems the employee a low infection risk and allows entry. 
     In another non-limiting example, live thermal cameras in the entrance area of a building can report the temperature of building entrants. Tracking software can then associate that temperature with the building entrant and use this data to profile the health of the building entrant. The weighting of this data can also be altered depending on other data sources, such as the outside air temperature, the method of commute of the building entrant, the employee role etc. For example, if the outside air temperature is cold, but the local infection rate is high and the building entrant is reporting a high temperature reading, then the system may deny entry or send the building entrant for a test. 
     In another non-limiting example, an employee may return to his office building after a business trip to a country that has a high infection rate. The system admin has implemented a company policy that all employees returning from ‘infection hotspots’ abroad need to work from home for two weeks. The system denies entry to the employee until the work from home period expires. 
     In another non-limiting example, PBO  202  has been vaccinated against COVID-19 through a national vaccination program. The building entrant validates the status of their vaccination  106  through an app on their phone at an entry point. The system may connect to a national database to verify the presented data, if available. The system notes that the vaccination occurred nine months previously. The user admin has configured the system so that only building entrants vaccinated in the previous six months are allowed entry when the local infection rate is high. The local infection rate  105  is this instance is low. The system deems the building entrant a low risk and allows entry. 
     In another non-limiting example, two employees of a company live in the same area and both commute to building  10  via a bus (represented by node  456 ). One employee reports as positive for an infectious disease through the company&#39;s self-reporting health application. The other employee is sent for a test by the controller  212 , for the next week, every morning. Even though the second employee does not exhibit any other symptoms, controller  212  flags a risk due to their shared method of commute and the locations of their home address. 
     In another non-limiting example, a student in a school tests positive for an infectious disease after being sent for a test by the access control system. All other students in the same class as this student (employee role could be adapted for different settings) are sent for a test upon arrival by the access control system. Two other students test positive and are denied entry. All other students test negative and are allowed entry. 
     Using a probabilistic model, such as Bayesian network model  312 , to make probabilistic determinations may provide helpful decision making for a variety of situations. Multiple scenarios are shown below for reference. 
     In one non-limiting example, thermal cameras may record a high temperature for an employee working in a warehouse. The local infection rate of a disease, which includes a high temperature as a symptom, is  100  cases per day. An ordinary decision tree may feature a tree, with a pre-defined startpoint and endpoint, and yes/no decisions at each variable, such as the following: role—&gt;temperature—&gt;infection rate—&gt;Symptoms—&gt;Access decision. However, a Bayesian network may provide for a dynamic relationship between the variables and contributes to a joint probability calculation. For example, a certain employee role, such as warehouse worker, may decrease the likelihood of being sent for a test as a higher temperature may be considered normal. In the same instance, a higher local infection rate may increase the likelihood of being sent for a test as a warehouse worker may have higher interaction with delivery personnel. In this manner the weighting of relationships between multiple variables is calculated across the network and a comprehensive joint probability decision is calculated. This also allows for a more nuanced and informed access decision. 
     In another non-limiting example, thermal cameras record a high temperature in an employee. The method of commute of the employee is bicycle. The weighting of the temperature node is decreased due to the relationship with the employee&#39;s method of commute, therefore access is granted. 
     In another non-limiting example, an employee has been vaccinated as part of a government vaccination program. The employee has also been tested for a disease, such as COVID-19, two days previously. The employee is reporting symptoms such as fever and sore throat, which are typically deemed symptoms of concern. However, other nodes in the Bayesian network Vaccination status, test status and perhaps local infection rate, dynamically decrease the weighting of the Employee self-reporting node, and so despite symptoms, the access system allows entry. 
     Contactless Routing Diagrams 
     Referring generally to  FIGS.  5 - 9   , diagrams of an entrance lobby of building  10  with controller  212  performing contactless routing for PBO&#39;s is shown, according to some embodiments.  FIGS.  5 - 9    and the processes shown therein may be performed separately or together, either partially or entirely, by controller  212 . It is worth noting that while the various digital displays, elevators, cameras, desks, pathways, and other components may be described using different reference numbers, the components may be partially or entirely similar across the multiple diagrams. For example, turnstiles  512 - 518  as shown in  FIG.  5    may be substantially similar to turnstiles  610 ,  612  as shown in  FIG.  6   . 
     Referring now to  FIG.  5   , a diagram  500  of an entrance lobby to building  10  is shown, according to some embodiments. Diagram  500  is shown to include access control turnstiles  512 - 518 , reception desk  510 , waiting rooms  504 , digital displays  508 , elevators  502 , and thermal imaging camera  506 . A person, for example person  522 , may attempt to gain entry by presenting their ID card at a turnstile, for example turnstile  506 . People  524  may queue to gain access to turnstiles  512 - 518 . Floor markers, such as floor marker  520 , may be used to indicate where people should stand in the queue to maintain appropriate social distancing. Turnstiles  512 - 518  may also be assigned specific purposes to maintain appropriate social distancing. For example, turnstiles  512  and  518  may be used for entry into the building, while turnstiles  514  and  516  may be use for exit from the building, or for people who are not required to queue, such as people making deliveries. 
     Referring now to  FIG.  6   , operation of controller  212  when entry is permitted for PBOs is shown, according to some embodiments. Persons  614  and  616  present their ID cards to turnstiles  610  and  612  respectively. ACS  200  can access their individual risks and grants access to both. Person  614  is directed to elevator  602 , which enables them to follow direct route  608 . Similarly, person  612  is directed to elevator  604 , which enables them to follow direct route  606 . In both cases the doors of elevators  602  and  604  may open automatically to enable frictionless entry. 
     Referring now to  FIG.  7   , operation of controller when a person is directed for testing is shown, according to some embodiments. Person  406  may present their ID card to turnstile  710 . ACS  200  may then access their individual risks and determines that a test is required. Person  406  may then be directed to one of waiting rooms  702 ,  704 , or  708 , depending on which is currently available. Person  405  may then be presumed to take direct route  706 . Finally, person  712  may self-administer a test or wait for a suitably qualified person to arrive to administer the test. 
     Referring now to  FIG.  8   , an example of returning a test result is shown, according to some embodiments. Person  808  may remain in waiting room  806 . After the test results are available, the results and further instructions are presented to person  808  on digital display  810 , according to some embodiments. In the case of a negative result, person  808  may be directed to elevator  802  along route  804 . In the case of a positive result, person  808  may be directed to exit the building through turnstile  816 . A digital display on turnstile  814  may direct person  818  to move backwards to maintain required social distancing. Both the doors on elevator  802  and turnstile  816  may open automatically when required so that person  808  can exit without touching any of the surfaces. 
     Referring now to  FIG.  9   , an example of how controller  212  manages overlapping routes when directing people is shown, according to some embodiments. Person  916  may need to make a delivery to reception desk  904 . Route  908  for person  908  may overlap with route  906  that person  914  would take to reach elevator  902 . ACS  200 may open turnstile  910  but keep turnstile  912  closed to prevent a potential infringement of social distancing rules. Depending on the physical arrangement of the space, turnstile  912  may remain closed until person  916  completes their delivery and leaves through turnstile  910 . 
     Configuration of Exemplary Embodiments 
     As utilized herein, the terms “approximately,” “about,” “substantially”, and similar terms are intended to have a broad meaning in harmony with the common and accepted usage by those of ordinary skill in the art to which the subject matter of this disclosure pertains. It should be understood by those of skill in the art who review this disclosure that these terms are intended to allow a description of certain features described and claimed without restricting the scope of these features to the precise numerical ranges provided. Accordingly, these terms should be interpreted as indicating that insubstantial or inconsequential modifications or alterations of the subject matter described and claimed are considered to be within the scope of the disclosure as recited in the appended claims. 
     It should be noted that the term “exemplary” and variations thereof, as used herein to describe various embodiments, are intended to indicate that such embodiments are possible examples, representations, or illustrations of possible embodiments (and such terms are not intended to connote that such embodiments are necessarily extraordinary or superlative examples). 
     The term “coupled” and variations thereof, as used herein, means the joining of two members directly or indirectly to one another. Such joining may be stationary (e.g., permanent or fixed) or moveable (e.g., removable or releasable). Such joining may be achieved with the two members coupled directly to each other, with the two members coupled to each other using a separate intervening member and any additional intermediate members coupled with one another, or with the two members coupled to each other using an intervening member that is integrally formed as a single unitary body with one of the two members. If “coupled” or variations thereof are modified by an additional term (e.g., directly coupled), the generic definition of “coupled” provided above is modified by the plain language meaning of the additional term (e.g., “directly coupled” means the joining of two members without any separate intervening member), resulting in a narrower definition than the generic definition of “coupled” provided above. Such coupling may be mechanical, electrical, or fluidic. 
     The term “or,” as used herein, is used in its inclusive sense (and not in its exclusive sense) so that when used to connect a list of elements, the term “or” means one, some, or all of the elements in the list. Conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, is understood to convey that an element may be either X, Y, Z; X and Y; X and Z; Y and Z; or X, Y, and Z (i.e., any combination of X, Y, and Z). Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to each be present, unless otherwise indicated. 
     References herein to the positions of elements (e.g., “top,” “bottom,” “above,” “below”) are merely used to describe the orientation of various elements in the FIGURES. It should be noted that the orientation of various elements may differ according to other exemplary embodiments, and that such variations are intended to be encompassed by the present disclosure. 
     The hardware and data processing components used to implement the various processes, operations, illustrative logics, logical blocks, modules and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose single- or multi-chip processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, or, any conventional processor, controller, microcontroller, or state machine. A processor also may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some embodiments, particular processes and methods may be performed by circuitry that is specific to a given function. The memory (e.g., memory, memory unit, storage device) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present disclosure. The memory may be or include volatile memory or non-volatile memory, and may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. According to an exemplary embodiment, the memory is communicably connected to the processor via a processing circuit and includes computer code for executing (e.g., by the processing circuit or the processor) the one or more processes described herein. 
     The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions. “Non-transitory” excludes only mere signals in space, and includes all other forms of computer-readable storage media. 
     Although the figures and description may illustrate a specific order of method steps, the order of such steps may differ from what is depicted and described, unless specified differently above. Also, two or more steps may be performed concurrently or with partial concurrence, unless specified differently above. Such variation may depend, for example, on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations of the described methods could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps. 
     It is important to note that the construction and arrangement of various systems (e.g., system  100 , system  200 , etc.) and methods as shown in the various exemplary embodiments is illustrative only. Additionally, any element disclosed in one embodiment may be incorporated or utilized with any other embodiment disclosed herein. Although only one example of an element from one embodiment that can be incorporated or utilized in another embodiment has been described above, it should be appreciated that other elements of the various embodiments may be incorporated or utilized with any of the other embodiments disclosed herein.