Patent Publication Number: US-2023135997-A1

Title: Ai monitoring and processing system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/274,233, filed on Nov. 1, 2021, and to U.S. Provisional Patent Application No. 63/325,338, filed on Mar. 30, 2022, both of which are incorporated herein by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     The disclosure relates to Artificial Intelligence (AI) monitoring and processing systems and methods, in particular, AI monitoring and processing systems and methods for monitoring and processing the collection of data to ensure that collection and health rules are followed, and that personal identification information of the data collection subjects is protected. 
     BACKGROUND 
     There is often a need for the collection of various types of data that can be later used for many purposes. For example, one type of data that is useful to collect is voice data that is collected as different data collection subjects read a prepared story or have a conversation with each other. The collected voice data can then be used to implement or improve voice recognition applications or audio translation applications. Likewise, other types of data about a data collection subject such as facial or other body data may also be collected for use in the implementation or improvement of facial recognition, retinal scan, or figure print applications. Further, other types of data not directly related to the human body may be collected from the data collection subjects such as data about a data collection subject&#39;s personal preferences or lifestyle or data about his or her job or educational level. Thus, any type of data may be collected as needed. 
     As may be appreciated, in order to properly collect the data so that it can be used for its intended purpose, the person or entity that designs the data collection process will specify rules and protocols that need to be followed to ensure that the data is properly collected. In addition, the environment of the location where the data is collected may also need to be controlled so that adverse conditions do not impede the collection of the data. 
     In some instances, there is often the need for two or more data collection subjects to be in the same location when the data is being collected. For example, two data collection subjects may be involved in a conversation with each other. However, the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) in 2019, and the ensuing pandemic of 2020, have increased and highlighted the importance of ensuring that health and safety protocols are followed when data collection subjects work within close proximity to each other. 
     In addition, in recent years there has been an increased emphasis on the need to protect the personal identity of people involved in various activities. In the context of data collection, it may be important to ensure that the identity of the data collection subjects is protected so that the identity is not discoverable by other parties. For example, many governmental entities now have laws that require that all personal identifiable information (PII) be removed so that the data collection subject cannot be identified by access to the collected data. In jurisdictions without such PII laws, the data collection subject may not agree to perform the data collection with PII protections simply because he or she does not wish to be identified from the data or fears such identification could be used for a malicious purpose. 
     In view of the above, there is a need for an improved AI monitoring system and method for data collection that is able to provide feedback and warnings that ensure the data collection rules and protocols are followed, that health and safety protocols are followed, and that any PII in the collected data is removed. 
     SUMMARY 
     One embodiment disclosed herein provides for an Artificial Intelligence (AI) system for monitoring and/or processing a data collection process involving one or more data collection subjects. The system may include an AI module. The AI module is configured to instantiate in a computer-readable hardware storage device that follows a health protocol check module configured to check if one or more health safety rules and protocols are being satisfied by the one or more data collection subjects during the data collection process, an environmental condition check module configured to check if data collection rules or protocols are being satisfied by the one or more data collection subjects during the data collection process, and a Personal Identification Information (PII) module configured to remove any PII of the one or more data collection subjects from the data collected during the data collection process. 
     Another embodiment disclosed herein provides for a method for an Artificial Intelligence (AI) system to monitor a data collection process. The method includes sending one or more instructions to one or more data collection subjects, the one or more instructions indicating one or more health safety rules and protocols, and/or one or more data collection rules or protocols that are to be satisfied by one or more data collection subjects during the data collection process, sending a warning message to the one or more data collection subjects when it is determined that the one or more data collection subjects are violating or more of the health safety rules and protocols and/or one or more data collection rules or protocols, and receiving feedback from the one or more data collection subjects that the violation has been corrected. 
     A further embodiment disclosed herein provides for an AI system for monitoring a data collection process involving one or more data collection subjects. The AI monitoring system includes a monitoring camera configured to measure distances between one or more data collection subjects, one or more data collection devices at a data collection location, and one or more objects of interest, a video communication client configured to access a video communication program or video conferencing platform and to communicate with a video communication program or video conferencing platform of a remote computing system, and a client configured to render the distances measured by the monitoring camera in real-time such that a user of the remote computing system is able to receive real-time input from the data collection location. 
     A further embodiment makes full use of the capabilities of communication programs or video conferencing platforms. A computing system of a data collection subject at a remote location which can host a communication program or video conferencing platform uses the communication program or video conferencing platform to stream real-time video to a computing system at the location of a data collection coordinator. An AI smart-sensing plugin at the computing system of the data collection coordinator process the incoming streamed data to: 1) detect markers, objects, and people, 2) extract sensor values through a display; perform measurements; and 4) render detection/extraction/measurement results on the video frame and feed this video frame back to the computing system of the data collector using the communication program or video conferencing platform hosted by the computing system of the data collector. This configuration simplifies the preparation and cost of the data collection site as the data collection subject only needs to have a computing system that supports the communication program or video conferencing platform, and the data collection coordinator only needs to send markers and sensors to the data collection subject. 
     In the embodiments disclosed herein, the different AI modules such as an AI monitoring module, an AI processing module, and an AI smart-sensing plugin module can be placed in a computing system of a data collection subject, or they can be placed in a computing system of a data collection coordinator. In other embodiments, the different AI modules may be placed in the cloud. In still other embodiments, some of the AI modules may be placed in the computing system of the data collection subject and some may be place in the computing system of the data collection coordinator. In further embodiments, some of the AI modules may be placed in a combination of the data collection subjects computing system, the data collection coordinators computing system, and the cloud. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present disclosure will become better understood regarding the following description, appended claims, and accompanying drawings. 
         FIG.  1    is a diagram of an AI monitoring and processing system according to an embodiment of the disclosure. 
         FIGS.  2 A- 2 C  illustrate an operation of an AI monitoring module according to an embodiment of the disclosure. 
         FIG.  3    is a diagram of an interaction mode between the AI monitoring module and a data collection subject according to an embodiment of the disclosure. 
         FIGS.  4 A- 4 C  illustrate an example embodiment of an interaction between the AI monitoring module and a data collection subject when performing a health protocol check. 
         FIGS.  5 A- 5 C  illustrate an example embodiment of an interaction between the AI monitoring module and a data collection subject when performing an environmental/condition check. 
         FIG.  6    illustrates an operation of an AI processing module according to an embodiment of the disclosure. 
         FIGS.  7 A and  7 B  illustrate an example embodiment of the interaction between the AI processing module when performing a PII removal process. 
         FIG.  8    illustrates a monitoring system that renders a remote collection location in real time. 
         FIG.  9    illustrates a monitoring system that utilizes the capabilities of communication programs or video conferencing platforms 
     
    
    
     DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS 
     A. Overview 
     A better understanding of different embodiments of the disclosure may be had from the following description read with the accompanying drawings in which like reference characters refer to like elements. 
     While the disclosure is susceptible to various modifications and alternative constructions, certain illustrative embodiments are in the drawings and are described below. It should be understood, however, there is no intention to limit the disclosure to the specific embodiments disclosed, but on the contrary, the intention covers all modifications, alternative constructions, combinations, and equivalents falling within the spirit and scope of the disclosure. 
     It will be understood that unless a term is expressly defined in this application to possess a described meaning, there is no intent to limit the meaning of such term, either expressly or indirectly, beyond its plain or ordinary meaning. 
     B. Various Embodiments and Components for Use Therewith 
     Artificial Intelligence (AI) monitoring and processing system and method embodiments are described herein. An AI monitoring and processing system and method according to the disclosed embodiments advantageously provides a way for data collection that is able to provide feedback and warnings that ensure that data collection rules and protocols are followed, that health and safety protocols are followed, and that any PII in the collected data is removed. The system and method can operate in substantially real-time, and the system may monitor the collection of the data by processing one or more captured images locally or remotely. 
       FIG.  1    is a diagram of an AI monitoring and processing system  100  (hereinafter also referred to simply as “system  100 ) according to an embodiment of the present disclosure. The system  100  may include one or more image capture devices  110 . The one or more image capture devices  110  may be any suitable image capture device, such as a digital camera configured for capturing one or more images or one or more videos, each video comprising a plurality of frames. In one embodiment, the one or more image capture devices  110  may be a 3D depth camera configured to use range imaging to capture the distance to points in a scene to thereby generate images in 3D. Accordingly, the embodiments disclosed herein are not limited to any particular type of image capture device. 
     The one or more image capture devices  110  may be configured with an attachment component so as to be mountable in any suitable position to any suitable surface such as a wall, desk, or ceiling. The attachment component may be any suitable component, such as hardware components including a wall or ceiling mount that can attach using one or more screws or other components. The attachment component may comprise one or more lockable joints cooperating with one or more body segments that allow the camera to be pivoted or swiveled to a desired position. For example, the one or more lockable joints can be unlocked to pivot the attachment component such that the camera  110  points toward towards a data collection subject and the objects surrounding the data collection subject. 
     In embodiments, the system  100  may comprise one or more sensors  120 . The one or more sensors  120  may be mounted on suitable surfaces, such as a wall, ceiling, or otherwise that allow for the sensors to monitor the data collection subject. For example, in some embodiments a sensor  120  may be an infrared temperature sensor that monitors the temperature of the data collection subject or data collection subjects. In other embodiments, a sensor  120  may be a sensor that monitors physical properties of the location or other health properties of the data collection subject or data collection subjects. Accordingly, the embodiments disclosed herein are not limited to any number of sensors  120  or by any type of sensors  120 . 
     In some embodiments, the sensor  120  may be a location determination sensor that is configured to monitor and ascertain the location of the system  100  during the data collection process. For example, the location determination sensor  120  may be, but is not limited to, a GPS sensor that is able to ascertain the location, a Bluetooth sensor that connects to a device such as a cell phone of a data collection subject that has a GPS sensor that is able to ascertain the location, or a sensor with Wi-Fi capability that is able to ascertain the location from the Internet or other network. Thus, in such embodiments the location of the data collection process can be monitored to ensure that the data collection process happens in a desired location. For example, the entity overseeing the data collection process may desire for the data collection process to occur in the North-East of the United States. The use of the location determination sensor  120  allows for monitoring to ensure that the data collection process occurs in the North-East of the United States. In some embodiments, permission from the data collection subjects will be obtained before the location is monitored so as to comply with applicable privacy rules in the location where the data collection process occurs. 
     In embodiments, the system  100  may comprise various data collection equipment  130 . For example, the data collection equipment  130  may include a microphone, a computer such as a laptop computer, speakers, tablet computing systems, visual displays, and the like for collecting and recording various types of data or for providing the data to the data collection subject. The data collection equipment  130  may also include various types of light sources that ensure that the data collection process is able to occur. 
     In embodiments, the one or more image capture devices,  110 , the sensors  120 , and the data collection equipment  130  may be operatively connected to each other by a communication module  140 , which may be any suitable connection modality, including a wired connection or a wireless connection. The communication module  140  may deliver the captured images from the one or more image capture devices  110 , and/or detection signals from the sensors  120  and/or to the data collection equipment  130 . 
     A power source  150  may be provided for the one or more image capture devices  110 , the sensors  120 , the data collection equipment  130 , the communication module  140 , and/or a user interface  160 , and may comprise a battery power source or a wired connection to an existing power source, such as a power outlet in a restroom facility. 
     The user interface  160  may comprise any suitable user interface for communicating with the data collection subject. In embodiments, the user interface  160  may comprise an electronic display such as an electroluminescent (ELD) display, a liquid crystal display (LCD), a light emitting diode (LED) display, a plasma display, a quantum dot display, a touch screen such as a resistive touch screen, a surface capacitive touch screen, a projected capacitive touch screen, a surface acoustic wave (SAW) touch screen, an infrared (IR) touch screen, or any other suitable electronic display. The user interface  160  may comprise one or more user input options or modalities, such as buttons or a keyboard, which allow the data collection subject to input information such as identification information. 
     The user interface  160  may be configured to display instructions to the data collection subject that indicates how the data collection should proceed. The user interface  160  may also be configured to provide warnings to the data collection subject when the instructions have not been properly followed or when a health or safety violation has occurred. The user interface  160  may also be configured to provide feedback to the data collection subject and to allow the data collection subject to provide feedback to the system  100 . 
     In one embodiment, the instructions, the warnings, and/or the feedback that are provided to the data collection subject via the user interface  160  come from or are controlled by an AI module as will be explained in more detail to follow. In another embodiment, the instructions, the warnings, and/or the feedback that are provided to the data collection subject via the user interface  160  come from or are controlled by a data collection coordinator using a video conferencing application associated with the user as will also be explained in more detail to follow. In still a further embodiment, the instructions, the warnings, and/or the feedback that are provided to the data collection subject via the user interface  160  may come from or are controlled from the AI module and/or the data collection coordinator. 
     The system  100  may comprise a non-transitory computer-accessible or computer-readable storage medium or storage  105  including instructions  105 A stored thereon in a non-transitory form for operating the AI monitoring and processing system and a method according to the embodiments described herein. The instructions  105 A, when executed, can cause one or more processors  170  to conduct one or more of the steps described herein, and to utilize an artificial intelligence module instantiated in the one or more processors  170  to determine whether the health protocols have been properly followed and the data collection environments/conditions are properly configured as will be explained in more detail to follow. 
     The storage  105  may also store one or more data collection rules or protocols  106  that are configured to define how an optimum process for collecting the data. For example, in the case of a data collection process for collecting voice data from the data collection subject, the data collection rules or protocols  106  may define a desired distance between a microphone or other equipment  130  and the data collection subject, a desired light condition for the location, or other desired data collection parameters. In some embodiments, the data collection rules or protocols  106  may be entered into the storage  105  by the organization or entity that is hosting the data collection process using a non-illustrated interface or connection. 
     The storage  105  may also store one or more health safety rules and protocols  107  that are configured to specify certain health rules that should be followed to ensure that the data collection process is safe for the data collection subjects. For example, the health safety rules and protocols  107  may specify that two or more data collection subjects should maintain a social distance of six feet or that they should wear a mask to prevent the spread of COVID-19. In some embodiments, the health safety rules and protocols  107  may be entered into the storage  105  by the organization or entity that is hosting the data collection process using a non-illustrated interface. In still other embodiments, the health safety rules and protocols  107  may be obtained from a public health organization such as the World Health Organization (WHO) or the Centers for Disease Control (CDC). 
     The system  100  may be configured to provide feedback to the data collection subject through the user interface  160 . The feedback may comprise a message or warning that one or more of the health and safety rules and protocols  107  have been violated such as a data collection subject is too close to another data collection subject or is not wearing his or her mask. The feedback may also comprise a message that indicating the collection rules or protocols  106  and if something needs to be changed in order to comply with these. For example, the message may indicate that the light source needs to be turned up or that the microphone needs to move closer to the data collection subject so that data collection can be optimized. 
     The system  100  may analyze the captured images for improving the privacy and security of the image capture and assessment process to ensure that the data collection subjects PII is removed. For example, the system  100  may be configured to identify and blur or remove human faces from at least one frame of a plurality of frames of a captured video. The system  100  may also be configured to alter any recorded voice data so that is unrecognizable as coming from a given data collection subject or to blur any identifiable text that could be used to identify the data collection subject and/or the location of the data collection process. 
     As mentioned, the processor  170  may instantiate an artificial intelligence module such as an AI monitoring module  210 , an AI processing module  610 , and/or an AI smart-sensing plugin module  835  or  935 . The artificial intelligence module may comprise one or more computer vision algorithms configured to train and/or apply, for example, a machine learning model and/or a statistical algorithm for determining static features of video frames, human faces in video frames, and to assess the ideal configuration and duration of the data collection process. The machine learning model and/or the statistical algorithm may be trained before the system  100  is deployed and/or may continue to be trained upon successive users of the system  100 . 
     The artificial intelligence module may further advantageously obtain and/or determine a set of metrics generated from the data collection process. The set of metrics may comprise in embodiments an amount of time spent on each step of the procedure. The set of metrics may be used to train the artificial intelligence module to determine a duration and configuration of steps that can reliably lead to an optimum data collection process. The analysis may be done in real-time or after a delay, and may be performed locally or remotely as suitable. 
     The artificial intelligence module may identify and track a data collection subject, using a computer vision modality, such as a facial detection module, a pose estimation module, an object detection module, an object classification module, an object identification module, an object verification module, an object landmark detection module, an object segmentation module, a tracking module, a video annotation module, or any other suitable AI modality. 
     In embodiments, the facial detection module and/or object detection and classification modules may be used to identify and blur or remove static features and/or human faces or any other suitable parts of an image. The system may advantageously store the captured images and/or videos on the storage medium in the edited form, thereby excluding that any permanent image of a user&#39;s face or any other private or sensitive imagery is stored on the system. 
       FIG.  2 A  illustrates an embodiment  200  of the operation of AI monitoring and processing system  100 . As shown in the figure, the processor  170  instantiates an AI monitoring module  210  that performs various AI modalities and which may be considered an example of an AI module of an AI system. For example, as shown at  201 , the AI monitoring module  210  is configured to operate as a health protocol check module  220  that performs a health and safety protocol check, and which can be considered a submodule of the AI monitoring module  210 . The AI monitoring module  210  may use one or more of the image capture devices  110 , the sensors  120 , and the data collection equipment  130  to check if one or more of the health and safety rules and protocols  107  are being followed by a data collection subject  250  during a data collection process. In the embodiment, as shown at  202 , the health protocol check module  220  may check if the data collection subjects are wearing a mask as shown at  221 , may check if the data collection subjects are maintaining a proper social distance as shown at  222 , and may check if any number of additional health protocols are being followed as illustrated by the ellipses  223 . The other health protocol checks  223  may include, but are not limited to a temperature check or a check to see if the data collection subjects are coughing or are congested. 
       FIG.  2 B  illustrates the operation of the AI monitoring module  210  when determining a social distance  270  between a collection subject  260  and a collection subject  261 . It will be appreciated that the distance  270  may be a specific distance, such as six feet, or it may be an acceptable range, such as 5-7 feet. As shown, the image detection device  110 , which in the embodiment is a 3D depth camera, is located near a center or zero location in an X,Y,Z coordinate system. The camera  110  measures a first distance from the camera to the collection subject  260 , who in the embodiment is located at location X 1 , Y 1 , Z 1  in the X,Y,Z coordinate system. The camera also measures a second distance from the camera to a collection subject  261 , who in the embodiment is located at location X 2 , Y 2 , Z 2  in the X,Y,Z coordinate system. 
     During operation, the 3D depth camera  110  collects data at multiple points on the data collection subject  260  and  261 , by for example, using multiple light sensors of the camera to capture the multiple points on the data collection subjects. This collected data in then provided to the AI monitoring module  210  as shown at  271 . 
     The AI monitoring module  210  uses various AI modalities to determine an average of the distance  270  between the data collection subjects  260  and  261 . For example, the average distance can be determined by adding the total of the collected points and then diving by the total. Alternatively, a median can be determined so as to remove any points that do not appear to be valid due to a bad reading, sensor, or the like. The average distance may be determined in other ways. As will be explained in more detail to follow, the AI monitoring module  210  is then able to determine if the distance  270  satisfies a distance specified by a health protocol  107  as being acceptable. 
     In some embodiments, the data collection subjects  260  and  261  may have a marker (unillustrated) attached to them that is used by the camera  110  for focusing where to collect the data points. However, in many instances the data collection subjects  260  and  261  may not desire to have such marker attached to them. Accordingly, in such embodiments the AI monitoring module  210  may perform various body segmentation techniques on the data points collected by the camera  110  using nodes that extend between the body parts of the data collection subjects. Such techniques allow the AI module to focus on the desired parts of the data collection subjects. For example, in some embodiments the camera  110  may be able to collect data from the entire body the data collection subjects. In other embodiments, for example where only the upper half of the body or the face are detectable, then the collection points may focus on only the upper half of the body or on the face. Accordingly, the distance between the center of the faces of the data collection subjects, the distance between the center of the upper portion of the bodies of the data collection subjects, or the distance between the center of the entire bodies of the data collection subjects may be determined. 
     Returning to  FIG.  2 A , as shown at  203 , the AI monitoring module is also configured to operate as an environmental condition and/or data collection configuration  230  that is configured to perform an environmental/condition check, and which can be considered a submodule of the AI monitoring module  210 . The AI monitoring module  210  may use one or more of the image capture devices  110 , the sensors  120 , and the data collection equipment  130  to check if one or more of the data collections rules or protocols  106  are being followed during the data collection process. In the embodiment, as shown at  204 , the environmental condition and/or data collection configuration check module  230  may check if the data collection process is being performed indoors or outdoors as shown at  231 , may check the lighting condition of the location where the data collection process is occurring as shown at  232 , may check the distance or position between one or more of the data collection equipment  130  like a microphone and the data collection subject as shown at  233 , and may check if any number of additional data collection rules or protocols are being followed as illustrated by the ellipses  234 . The other collection rules or protocols  224  may include, but are not limited to, placement of one or more of the data collection equipment  130  in the location of the data collection process, the temperature of the location of the data collection process, the sound conditions, and the ability to measure the environment noise levels in the location of the data collection process, and if there is enough or the right kind of data collection equipment  140  present during the data collection process. Although shown as one module, in some embodiments the environmental condition and/or data collection configuration check module  230  may be a configured as a separate environment condition check module and a separate data collection configuration check module, both of which may be considered as submodules of the AI monitoring module  210 . 
       FIG.  2 C  illustrates the operation of the AI monitoring module  210  when determining a distance  280  between a collection subject  260  and a microphone, which is an example of data collection equipment  130  and also a distance  281  between the data collection subject  260  and another piece of data collection equipment  130 . It will be appreciated that the distances  280  and  281  may be a specific distance, such as six feet, or it may be an acceptable range, such as 5-7 feet. As shown, the image detection device  110 , which in the embodiment is a 3D depth camera, is located at a center or zero location in an X,Y,Z coordinate system. The camera  110  measures a first distance from the camera to the collection subject  260 , who in the embodiment is located at location X 1 , Y 1 , Z 1  in the X,Y,Z coordinate system. The camera also measures a second distance from the camera to the microphone  130 , which in the embodiment is located at location X 2 , Y 2 , Z 2  in the X,Y,Z coordinate system. The camera also measures a third distance from the camera to the data collection equipment  130 , which in the embodiment is located at location X 3 , Y 3 , Z 3  in the X,Y,Z coordinate system. 
     During operation, the 3D depth camera  110  collects data at multiple points on the data collection subject  260 , the microphone  130 , and the other data collection equipment  130 , by for example, using multiple light sensors of the camera to capture the multiple points. This collected data in then provided to the AI monitoring module  210  as shown at  272 . 
     As shown in the figure, the microphone  130  includes a marker  283  and the other data collection equipment  130  includes a marker  284 . In embodiments, the markers  283  and  284  may be 1D or 2D barcodes or QR codes, or any other suitable type of marker. The camera  110  uses the markers  283  and  284  when measuring the distance between the camera and the microphone  130  and the camera and the other data collection equipment  130 . As explained in relation to  FIG.  2 B , the data collection subject  260  is unlikely to have any marker and so the AI monitoring module  210  may perform various body segmentation techniques on the data points collected by the camera. However, in some embodiments the data collection subject  260  will place a marker on himself or herself and such maker can be used by the cameral  110  when determining the distance between the data collection subject  260  and the microphone  130  and/or the data collection equipment  130 . 
     The AI monitoring module  210  uses various AI modality to determine an average of the distance  280  between the collection subject  260  and the microphone  130 . In addition, the AI monitoring module  210  uses various AI modality to determine an average of the distance  281  between the collection subject  260  and the other data collection equipment  130 . Further, the AI monitoring module  210  uses various AI modality to determine an average of the distance  283  between the microphone  130  and the other data collection equipment  130 . For example, the average distance can be determined by adding the total of the collected points and then diving by the total. Alternatively, other robust average or statistical calculations, such as a median calculation, can also be determined so as to remove or at least minimize the effects of any outlier points that do not appear to be valid due to a bad reading, sensor, or the like. These average distances may be determined using other reasonable average and other statistical calculations. As will be explained in more detail to follow, the AI monitoring module  210  is then able to use the distances between the data collection subject  260  and the microphone  130  or the other data collection equipment  130  or the distance between the microphone  130  and the other data collection equipment  130  to determine if an environmental/condition has been satisfied. 
       FIG.  3    illustrates an embodiment  300  of an interaction between the AI monitoring module  210  and a data collection subject  340 , who may correspond to the data collection subject  260  or  261 . As illustrated, the AI monitoring module  210  is able to cause the system  100  to provide an instruction  310  to the data collection subject  340 . In one embodiment, the instruction  310  may instruct the data collection subject  340  about the rules and protocols that should be followed during the data collection process. As described above, these rules and protocols may be specified by the data collection rules or protocols  106 . For example, in the embodiment the instruction  310  may instruct the data collection subject  340  that a microphone for voice collection should be placed at a desired distance from the data collection subject so as to optimize the voice collection. 
     In another embodiment, the instruction  310  may instruct the data collection subject  340  about the health safety rules and protocols that should be followed during the data collection process. As described above, these rules and protocols may be specified by the health safety rules and protocols  107 . For example, in the embodiment the instruction  310  may instruct the data collection subject  340  that proper social distancing should be maintained whenever another data collection subject is in the same location. 
     After receiving the instruction  310 , the data collection subject  340  is able to use the user interface  160  to provide feedback  330  to the system  100  indicating that the instruction  310  is understood. For example, the data collection subject  340  may provide feedback  330  that indicates that he or she has placed the microphone at the desired location or that he or she will maintain proper social distancing when needed. 
     The AI monitoring module  210  is also able to cause that the system  100  to provide a warning  320  when one of the data collections rules and protocols and/or one of the health and safety rules and protocols have not been followed. For example, in one embodiment if the data collection subject  340  has moved the microphone too close to him or her, then the system  100  may provide the warning  320  that indicates that the microphone is needs to be moved to the desired distance. In another embodiment, the system  100  may provide a warning  320  that the data collection subject  340  is too close to another data collection subject and thus has not maintained proper social distancing. In still other embodiments, the system  100  may provide both a warning  320  that indicates the microphone needs to be moved to a desired distance and a warning  320  that indicates that data collection subject  340  has not maintained proper social distancing. 
     After receiving the warning  320 , the data collection subject  340  may provide feedback  330  indicating that the cause of the warning  320  has been corrected. In some embodiments, this may be done by providing feedback using the user interface  160 . In other embodiments, this may be done automatically by the action of the data collection subject  340 . For example, in the embodiment where the warning  320  indicates that the microphone is too close, the action of the data collection subject  340  in moving the microphone to the desired distance may be considered feedback  330  by the system  100 . 
     This process of sending instructions  310  and/or warnings  320  and receiving feedback  330  may be repeated as often as needed. It will be appreciated that although  FIG.  3    shows the warning  320  as being separate from the instruction  310 , this is for ease of illustration only since the warning is a specific type of instruction. Accordingly, it will be appreciated that the warning  320  is actually a subset of the instruction  310 . 
       FIGS.  4 A- 4 C  illustrate a use case example of the interaction between the system  100  including the AI monitoring module  210  and a data collection subject  340  when preforming a health protocol check during a data collection process. As shown in  FIG.  4 A , a data collection process is being performed where data is collected from the data collection subject  340  and a second data collection subject  341  at the same location. The data collection subject  340  may receive an instruction  310  from the system  100  specifying that the data collection subject  340  should maintain a proper social distance  410  from the data collection subject  341 . The data collection subject  340  may provide feedback  330  indicating that he or she understands using the user interface  160 . Thus, as shown in the figure, the data collection subjects are shown as being separated by the proper social distance  410 . Although not illustrated, in some embodiments the data collection subject  341  may also receive an instruction  310  in the same manner as the data collection subject  340 . 
     In some embodiments, the feedback  330  from the data collection subject  340  may be a voice input, pressing one or more buttons on a keyboard, using an attached mouse, or other reasonable input. In some embodiments, feedback  330  from the data collection subject  340  may be in the form of a preprinted maker such as an Aruco marker or a QR code. In such embodiments, the data collection subject  340  would hold up the preprinted maker to a camera to indicate that he or she understood the instructions. In some embodiments, the feedback  330  may be used in a flow control process. Thus, once the data collection subject  340  has followed the first step in a particular instruction, he or she may input a voice command, press a button on the keyboard, use the computer mouse, or show the marker to indicate that the first step is complete. The user would then move onto the second step and would again input a voice command, press a button on the keyboard, use the computer mouse, or show the marker to indicate that the second step is complete. This process could be continued until the instruction was completed. 
       FIG.  4 B  shows that the data collection subject  340  and the data collection subject  341  have moved closer to each other so that they are separated by a distance  420 , which is closer than the proper social distance  410 . Accordingly, the system  100  may send a warning  320  to the data collection subject  340  indicating that he or she is too close to the data collection subject  340  and that he or she needs to move back to the proper social distance  410 . Although not illustrated, in some embodiments the data collection subject  341  may also receive a warning  320  in the same manner as the data collection subject  340 . 
       FIG.  4 C  shows that the data collection subject  340  and the data collection subject  341  have moved so that they are again separated by the proper social distance  410 . The data collection subject  340  may provide feedback  330  using the user interface  160  indicating that he or she is again separated from the data collection subject  341  by the proper social distance. As discussed above, the feedback  330  may alternatively be automatic based on the fact that the data collection subject moved in response to the warning  320 . Although not illustrated, in some embodiments the data collection subject  341  may also provide feedback  330  in response to the warning  320  in the same manner as the data collection subject  340 . 
       FIGS.  5 A- 5 C  illustrate a use case example of the interaction between the AI monitoring system  100  including the AI monitoring module  210  and a data collection subject  340  when performing an environmental/condition check. As shown in  FIG.  5 A , a data collection process is being performed where data is collected from the data collection subject  340  using a specific data collection equipment  130  such as a microphone for collecting voice data. The data collection subject  340  may receive an instruction  310  from the system  100  specifying that the data collection equipment  140  should be located at a location or distance  510  in relation to the data collection subject so that the data may be optimally collected. The data collection subject  340  may provide feedback  330  indicating that he or she understands. Thus, as shown in the figure, the data collection equipment  130  is located at the location or distance  510 . 
       FIG.  5 B  shows that the data collection equipment  130  has moved to a different location or distance  520  relative to the data collection subject  340  which is different from the location or distance  510 . Accordingly, the system  100  may send a warning  320  to the data collection subject  340  indicating that he or she needs to move or alternatively needs to move the data collection equipment  130  so that the location or distance  510  is maintained. 
       FIG.  5 C  shows that the data collection equipment  130  is again located at the location or distance  510  in relation to the data collection subject. The data collection subject  340  may provide feedback  330  indicating this using the user interface  160 . As discussed above, the feedback  330  may alternatively be automatic based on the fact that the data collection subject moved or moved the data collection equipment  130  in response to the warning  320 . 
     The embodiments discussed in relation to  FIGS.  3 - 5    discussed use cases where the data collection subject received the warning  320  that the specified distance or range of distances was violated. As discussed, this led to at least one of the data collection subjects moving so the specified distance or range of distances was again maintained. It will be appreciated that the system  100  may also monitor the distances (i.e., distance  270 ,  280 ,  281 ,  410 ,  510 ) even in cases where there is no violation of the specified distance. In addition, the system  100  may monitor the other health and safety protocols such as mask wearing  221  and other environment condition checks such as indoor/outdoor  231  and light condition  232  even in cases where there is no violation of these protocols. In such embodiments, the system  100  may record or store the monitored data in the storage  105  or some other storage. For example, if the data collection subject  260  and the data collection subject  261  remain within a specified distance  270  or range of distances  270 , the system records this in the storage  105 . In this way, it is possible to maintain a record that the various health and safety protocols  107  and the data collection rules or protocols  106  were followed during the data collection process. 
     In other embodiments, details about the location of the data collection process may be recorded or stored in the storage  105 . In addition, details about the data collection subjects such as their age, their gender, or where they live may also be recorded or stored in the storage  105 . In this way, a record of who participated on the data collection process and where the data collection process occurred may be kept for later statistical analysis of the data collection process and to ensure that a broad range of data collection subjects participated in the data collection process. 
     In some embodiments, the data collected during the data collection process may need to be stored, for example in storage  105 . In such embodiments, there is a risk that the stored data may include personal identifiable information (PII) such as facial, voice, or other body related PII information or textual related PII that may become known. Accordingly, the AI monitoring and processing system  100  is configured to provide processing of the collected data to remove, to blur, or to otherwise alter any collected PII. In some embodiments, the processing of the collected data to remove, to blur, or to otherwise alter any collected PII may occur onsite (i.e., at the location of the data collection process). Providing the onsite processing of the data collected during the data collection process means that the processing of the collected data to remove, to blur, or to otherwise alter any collected PII happens before the data is stored in the storage  105  or is sent to an offsite data storage. Thus, if the storage  105  (or other storge storing the collected data) is later hacked or otherwise accessed in an unauthorized manner, the stored data should not include any PII as this will have already been removed, blurred, otherwise altered, thus providing enhanced security to the stored collected data. In other embodiments, the processing of the collected data to remove, to blur, or to otherwise alter any collected PII may occur at the storage  105  or it may occur at some location or time in the data collection process. Thus, the embodiments disclosed herein contemplate both onsite and non-onsite processing of the collected data to remove, to blur, or to otherwise alter any collected PII. 
       FIG.  6    illustrates an embodiment  600  of the operation of AI monitoring and processing system  100  when operating to remove or to hide any collected PII. As shown in the figure, the processor  170  instantiates the AI processing module  610  that performs various AI modalities, and which may be instantiated in the processor  170  and may be considered an example of an AI module of an AI system. The AI processing module may be considered a submodule of another AI module in some embodiments. For example, as shown at  601 , the AI processing module  610  is configured to operate as a PII removal module  620  that removes, blurs, or otherwise alters any collected PII. 
     As shown at  602 , the AI processing module  610  is configured to cause the system  100  to perform PII removal. In the embodiment, as shown at  602 , the AI processing module  610  blurs or removes facial features as shown at  631 , blurs or removes any textual data that includes PII as shown at  632 , blurs, removes or alters voice data so that the source is not recognized as shown at  633 , and blurs or removes any additional PII as illustrated by the ellipses  634 . The other PII  634  may include, but is not limited to, tattoos, birth marks, or other identifying body features. In addition, AI processing module  610  is able to blur, remove, or otherwise alter any features in the location where the data collection process occurs that may not be considered PII. For example, there may be a desire to blur, remove, or otherwise alter the background of an image so as to focus on the data collection subject or to blur, remove, or otherwise alter furniture, wall pictures, books, or the like that could be used to identify the location of the data collection process. Accordingly, the embodiments disclosed herein allow flexibility for the system to blur, remove, or otherwise alter any features of the capture images, whether the features include PII or non-PII features. 
       FIG.  7 A  illustrates a use case of the AI processing module  610  performing PII removal. As illustrated on the left side of  FIG.  7 A  at  710 , during the data collection process the one or more image capture devices  110  may record the facial features of a data collection subject  705 , who may correspond to the data collection subjects previously discussed. To ensure that any such PII is removed from the captured images, the AI processing module  610  may cause the system  100  to perform the PII removal process as shown at  720 . This results in the blurring or removal  740  of the facial features as shown at  730 . In other embodiments, it may be desirable to keep the face of the data collection subject shown, as this may be helpful when collecting voice data, while blurring the background so as to protect privacy. 
       FIG.  7 B  illustrates another use case of the AI processing module  610  performing PII removal. As illustrated on the left side of  FIG.  7 B  at  760 , during the data collection process the one or more image capture devices  110  may record text  750  that includes PII. In the embodiment, the text  750  reveals the name and address of the data collection subject  705 . To ensure that any such PII is removed from the captured images, the AI processing module  610  may cause the system  100  to perform the PII removal process as shown at  770 . This results in the blurring or removal  780  of the text as shown at  790 . 
       FIG.  8    illustrates an alternative embodiment of the system  100  that is configured more specifically for interaction between a collection subject and a collection coordinator who is remote from the collection subject. It will be appreciated, however, that the embodiment of  FIG.  8    can include all the elements and functionality of the system  100  previously described in addition to the elements and functionality that will be explained in reference to  FIG.  8   . In particular, the embodiment of  FIG.  8    may include the AI monitoring module  210  and the processing module  610 . Thus, the embodiment of  FIG.  8    may provide a user with the option of using the elements and functionality of the embodiments previously described and/or using the elements and functionality of  FIG.  8    as will now be explained. 
     As illustrated, embodiment of  FIG.  8    includes a collection subject  810 , who may correspond to the data collection subjects previously described. In addition, the embodiment includes a collection coordinator  820 . The collection subject may be located at a collection location  801  and the collection coordinator may be location at a monitor location  802 . In the embodiment, the collection location  801  may be considered a remote collection location since it is remote from the location of the collection coordinator  820 . For example, the remote collection location  801  may be in Dallas, Tex. while the monitor location may be in Seattle, Wash. 
     In the embodiment, the collection subject  810  may be provided with a computing system  830 . In the embodiment, the computing system  830  may be a laptop computer or a tablet computing system, although other types of computing systems may also be used. In the embodiment, a monitoring camera  840 , which may correspond to one of the image capture devices  110  discussed previously, may be integrated in the computing system  830 . Having the monitoring camera  840  integrated with the computing system  830  advantageously helps in the setup of the data collection process as will be explained in more detail to follow. However, the monitoring camera  840  need not be integrated with the computing system  830  as there may instances where a detached camera may be beneficial. In still other embodiments, the computing system  830  may include the integrated monitoring camera  840  and may also be connected to one or more other image capture devices  110  as circumstances warrant. 
     The computing system  830  may include a processor such as the processor  170  including the AI monitoring module  210 , a power source such as the power source  150 , and a communication module such as the communication module  140 . In some embodiments, the storage  105  may be included as part of the computing system  830 . In some embodiments, the computing system  830  may include one or more of the sensors  120 . 
     As illustrated, the remote location  801  may include various preprinted markers  803 ,  804 , and  805 . It will be appreciated that there may be any number of additional preprinted markers (not illustrated) included in the remote location  801  as circumstance warrant. In embodiments, the markers  803 ,  804 , and  805  may be 1D or 2D barcodes or QR codes, or any other suitable type of marker and may correspond to the markers  283  and  284 . In one embodiment, the collection coordinator  820  may provide the computing system  830 , the various preprinted markers  803 ,  804 , and  805 , and perhaps one or more sensors  120  (if not included as part of the computing system  830 ) to the collection subject  810 . The collection subject may then set-up the remote collection location  801  by placing the various preprinted markers  803 ,  804 , and  805  (an any additional markers) in desired locations. For example, as shown in the embodiment, the marker  803  may be placed on a TV  811  and the marker  805  may be placed on a door or wall  813 . In addition, the marker  804  may be placed on a data collection device  812 , which may correspond to the data collection equipment  130  previously discussed. In the embodiments, the collection subject  810  may place a preprinted marker  806 , which may be a 1D or 2D barcode or QR code, on himself or herself. The preprinted marker  806  may then be used in the same manner as the other markers to help determine a distance between the collection subject  810  and the other marked objects in the remote collection location  801 . 
     In operation, the monitoring camera  840  is able to measure a distance between the various markers  803 ,  804 ,  805 , and  806 . For example, since the markers  803 ,  804 ,  805 ,  806  are preprinted, the computing system  830  may come preloaded with a size for each of the preprinted markers  803 ,  804 ,  805 , and  806  before any data collection occurs. Using this preloaded size, the processor  170  of the computing system  830  are able to determine a distance between each of the various preprinted markers  803 ,  804 ,  805 , and  806 . In other words, the relative size of the various preprinted markers  803 ,  804 ,  805 , and  806  will change as the markers are moved a further distance from the monitoring camera  840  and thus the distance can be determined since the computing system  830  knows the size of the preprinted markers  803 ,  804 ,  805 , and  806 . 
     It will be appreciated that in the embodiment where the preprinted markers are used, the monitoring camera  840  need not be a 3D depth camera. Rather, since the computing system  830  knows the size of the preprinted markers, the monitoring camera  840  may be a web camera or other camera integrated into the computing system  830  that only has to measure a distance between the various markers  803 ,  804 ,  805 , and  806 . Of course, in embodiments where the collection subject  810  does not wear the preprinted marker  806 , or in embodiments where one or more of the preprinted markers  803 ,  804 , and  805  are not provided, then the camera can be a 3D depth camera that is able to determine the distance between the data collection subject and the other markers in the manner previously described. In some embodiments, the computing system  830  will use both an integrated web camera and a 3D depth camera as circumstances warrant. Thus, the embodiments disclosed herein contemplate scenarios where the computing system  830  implements various kinds of cameras  840 . 
     It will be appreciated that it may often be difficult for the collection subject  810  to set up the remote collection location  801  properly if he or she only has written instructions. In addition, it may also be difficult to set up the remote collection location  801  properly if the collection subject must interpret the instructions  310  and the warnings  320  generated by the AI monitoring module  210  as previously described. One solution to this problem would be for the collection coordinator  820  to come to the collection location  801  and personally direct the set-up of the collection location. However, if, as in the present embodiment, the collection location  801  is remote from the monitor location  802 , such personal direction is not possible. 
     Accordingly, the embodiments provide for a real time video link between the collection subject  810  and the collection coordinator  820 . As shown, the computing system  830  may include a UI  831 , that may correspond to the UI  160 . In addition, the computing system  830  may include an AI smart-sensing plugin module  835 , which may be instantiated in the processor  170  and may be considered an example of an AI module of an AI system. In operation, the AI smart-sensing plugin module  835  operates the monitoring camera  840  and the other sensor hardware of the computing system and is configured to render a view of the remote collection location so that a distance between two of the markers can be determined. Further, the computing system  830  includes a video communication client  836 . In operation, the video communication  836  is configured to access a video communications program or video conferencing platform such as Zoom by Zoom Video Communications, Microsoft Teams, Google Meetings, or any other suitable communications program or video conferencing platform incorporating both audio and visual capabilities and to use the video communication program or video conferencing platform to provide the data such as the distance between two of the markers to be provided to a computing system  850  of the collection coordinator  820  using a communication network  832 . The communication network  832  may be a wireless network that uses the 4G or 5G communication standard (or any other reasonable standard) or it may be a wired network such as the Internet. 
     The computing system  850  of the collection coordinator  820  may include monitoring software  851  and a video communication client  852 . In operation, the video communication client  852  is configured to access a video communications program or video conferencing platform such as Zoom by Zoom Video Communications, Microsoft Teams, Google Meetings, or any other suitable communications program or video conferencing platform incorporating both audio and visual capabilities that corresponds to the video communication program or video conferencing platform accessed by the video communication client  836  of the computing system  830 . Thus, the computing system  830  and the computing system  850  are able to communicate in real time using the compatible video communication program or video conferencing platform. 
     In operation, the monitoring client software  851  is configured to render a view of the remote collection location  801  on a UI  855  of the computing system  850 . As illustrated in  FIG.  8   , the UI  855  shows the collection subject  810 , the TV  811 , the data collection device  812 , the door or wall  813 , and the markers  803 ,  804 , and  805 . In addition, the monitoring client software  851  renders the data such as a distance between two of the markers in the view of the UI  855 . For example, the distance  853  from the marker  804  of the data collection device  812  to the marker  805  of the door or wall  813  is shown as being 250 cm. Likewise, the distance  854  from the marker  804  of the data collection device  812  to the maker  803  of the TV  811  is shown as being 200 cm. Thus, the distances  853  and  854  are shown to the collection coordinator  820  in real time. 
     Accordingly, during set up of the remote collection location  801 , the collection subject  810  and the collection coordinator  820  may communicate with each other in real time using their respective video communication clients. In addition, the collection coordinator  820  is able to monitor in real time as the collection subject  810  sets up the remote collection location  801 . Since the collection coordinator  820  can see the distances between the markers in real time as described, he or she can direct the collection subject in real time to ensure that the distances between the markers are within the desired parameters discussed above. It will be appreciated that the data collection  810  may also use the user interface  831  using a voice command, one or more key buttons, a computer mouse, an Aruco marker, or a QR code as previously described to communicate with the computing system in addition to communicating with the collection coordinator. The user interface  831  may also be used in a flow control process as previously described. 
     In addition, once the actual data collection process begins, the collection coordinator  820  can continuously monitor the remote collection location and the collection subject. If the collection subject  810  moves too close too or too far away from the data collection device  812 , this will be shown in the UI  855 . At such time, the collection coordinator  820  can speak with the collection subject  810  in real time and ask the collection subject  810  to move as needed so that the distance is once again within the desired parameter. That is, the data collection subject may move in the manner discussed previously in relation to  FIGS.  4 A- 4 C and  5 A- 5 C . 
     Although not illustrated, the UI  855  may show additional environmental conditions as measured by one or more sensors  120  in addition to or alternative to the distances that have been discussed. For example, in an embodiment a sensor  120  may measure the ambient noise of the remote collection location  801  or may measure the lighting of the remote collection location  801 . These measures values may then be shown in real time on the UI  855  to the collection coordinator  820 . If the measured values are outside of desired ranges, the collection coordinator  820  can ask the collection subject  810  to make changes as needed so that the environmental conditions are within the desired ranges. 
     In some embodiments, the UI  831  of the computing system  830  may show a QR code  833  that is generated by the computing system  850 . In operation, the collection subject  810  can scan the QR code  833  and can then be taken to a remote website where the collection coordinator  820  may provide further instructions and information related to the data collection process as needed to the collection subject since it may not be possible to provide all necessary information during the video communication. In addition, the QR code  833  may provide additional security as it can be used by the collection subject to verify that the video coordinator is a valid coordinator. In other words, if the QR code  833  is valid, then the collection subject can have confidence that the collection coordinator and the data collection process are valid. 
     Likewise, in some embodiments the UI  855  may show a QR code  856  that is viewable by the collection coordinator  820  and generated by the computing system  830 . In operation, the QR code  856  may be used to by the collection coordinator to validate that he or she is actually viewing the remote collection location  801 . For example, there may be instances where a collection subject  810  may try to spoof the remote location  801  so that the collection subject does not need to follow the health protocols and/or the environmental protocols. If the QR code  856  is valid, then the collection coordinator can have confidence he or she is viewing the remote collection location  801  in real-time. Further, the QR code  856  may lead the collection coordinator to a website where the collection subject is able to provide further information as needed. 
       FIG.  9    illustrates an alternative embodiment of the system  100  that is configured more specifically for interaction between a collection subject and a collection coordinator who is remote from the collection subject. It will be appreciated, however, that the embodiment of  FIG.  9    can include all the elements and functionality of the system  100  previously described in addition to the elements and functionality that will be explained in reference to  FIG.  9   . In particular, the embodiment of  FIG.  9    may include the AI monitoring module  210  and the AI processing module  610 . Thus, the embodiment of  FIG.  9    may provide a user with the option of using the elements and functionality of the embodiments previously described and/or using the elements and functionality of  FIG.  9    as will now be explained. 
     As illustrated, the embodiment of  FIG.  9    includes a collection subject  910 , who may correspond to the data collection subjects previously described. In addition, the embodiment includes a collection coordinator  920 . The collection subject may be located at a collection location  901  and the collection coordinator may be located at a monitor location  902 . In the embodiment, the collection location  901  may be considered a remote collection location since it is remote from the location of the collection coordinator  920 . For example, the remote collection location  901  may be in Dallas, Tex. while the monitor location may be in Seattle, Wash. 
     In the embodiment, the data collection coordinator  920  may be provided with a computing system  950 , which may be any reasonable computing system such as a laptop computer. The computing system  950  may include the processor  170  that instantiates the AI monitoring module  210  and the AI processing module  610  as previously discussed. The computing system  950  may also include a power source such as the power source  150 , and a communication module such as the communication module  140 . In some embodiments, the storage  105  may be included as part of the computing system  950 . 
     In the embodiment, the collection subject  910  may be provided with a computing system  930 . The computing system  930  may be a laptop computer or a tablet computing system, although other types of computing systems may also be used. In this embodiment, the computing system  930  may not include the AI monitoring module  210  and the AI processing module  610  so as to save on the cost of the computing system, although there may be embodiments where the AI monitoring module  210  and the AI processing module  610  are included in the computing system  930 . Thus, in this embodiment the processing of the data by the AI modules will typically occur only in the computing system  950  of the data collection coordinator  920 . 
     The computing system  930  includes a video communication client  936 , which may be part of the communication module  140  of the computing system. In operation, the video communication client  936  is configured to access a video communications program or video conferencing platform such as Zoom by Zoom Video Communications, Microsoft Teams, Google Meetings, or any other suitable communications program or video conferencing platform incorporating both audio and visual capabilities and to use the video communication program or video conferencing platform to stream video to the computing system  950  of the collection coordinator  920  using a communication network  932 . The communication network  932  may be a wireless network that uses the 4G or 5G communication standard (or any other reasonable standard) or it may be a wired network such as the Internet. 
     The computing system  950  of the collection coordinator  920  may include a video communication client  952  and an AI smart-sensing plugin module  935 , which may be instantiated in the processor  170  and may be considered an example of an AI module of an AI system. In operation, the video communication client  952  is configured to access the video communications program or video conferencing platform such as Zoom by Zoom Video Communications, Microsoft Teams, Google Meetings, or any other suitable communications program or video conferencing platform incorporating both audio and visual capabilities that corresponds to the video communication program or video conferencing platform accessed by the video communication client  936  of the computing system  930 . Thus, the computing system  930  and the computing system  950  are able to communicate in real time using the compatible video communication program or video conferencing platform that is supported by both of the computing systems. 
     In the embodiment, a monitoring camera  940 , which may correspond to one of the image capture devices  110  discussed previously, may be integrated in the computing system  930 . That is, the monitoring camera  940  may be the camera that is built into many laptop computers and tablet computers. Having the monitoring camera  940  integrated with the computing system  930  advantageously helps in the setup of the data collection process as will be explained in more detail to follow. However, the camera  940  need not be integrated with the computing system  930  as there may instances where a detached camera may be beneficial. In still other embodiments, the computing system  930  may include the integrated camera  940  and may also be connected to one or more other image capture devices  110  as circumstances warrant. 
     As illustrated, the remote location  901  may include various preprinted markers  903 ,  904 , and  905 . It will be appreciated that there may be any number of additional preprinted markers (not illustrated) included in the remote location  901  as circumstance warrant. In embodiments, the markers  903 ,  904 , and  905  may be 1D or 2D barcodes or QR codes, or any other suitable type of marker and may correspond to the markers  283  and  284 . In one embodiment, the collection coordinator  920  may provide the various preprinted markers  903 ,  904 , and  905 , and perhaps one or more sensors  120  (with display) to the collection subject  910 . 
     The collection subject  910  may then set-up the remote collection location  901  by placing the various preprinted markers  903 ,  904 , and  905  (and any additional markers) in desired locations. For example, as shown in the embodiment, the marker  903  may be placed on a TV  911  and the marker  905  may be placed on a door or wall  913 . In addition, the marker  904  may be placed on a data collection device  912 , which may correspond to the data collection equipment  130  previously discussed. In the embodiments, the collection subject  910  may place a preprinted marker  906 , which may be a 1D or 2D barcode or QR code, on himself or herself. The preprinted marker  906  may then be used in the same manner as the other markers to help determine a distance between the collection subject  910  and the other marked objects in the remote collection location  901 . 
     In operation, the monitoring camera  940  is able to capture a video of the various markers  903 ,  904 ,  905 , and  906  and to stream the video to the computing system  950 . Since the markers  903 ,  904 ,  905 ,  906  are preprinted and thus have a predefined size, the computing system  950  may store the predefined size for each of the preprinted markers  903 ,  904 ,  905 , and  906  before any data collection occurs. Using this stored size, the processor  170  of the computing system  950  is able to determine a distance between each of the various preprinted markers  903 ,  904 ,  905 , and  906  based on the video provided by the monitoring camera  940 . In other words, the relative size of the various preprinted markers  903 ,  904 ,  905 , and  906  will change as the markers are moved a further distance from the monitoring camera  940  and thus the distance can be determined since the computing system  950  knows the size of the preprinted markers  903 ,  904 ,  905 , and  906 . 
     It will be appreciated that in the embodiment where the preprinted markers are used, the monitoring camera  940  need not be a 3D depth camera. Rather, since the computing system  950  knows the size of the preprinted markers, the monitoring camera  940  may be a web camera or other camera integrated into the computing system  930  that only has to capture a video the various markers  903 ,  904 ,  905 , and  906 . 
     It will also be appreciated that it may often be difficult for the collection subject  910  to set up the remote collection location  901  properly if he or she only has written instructions. In addition, it may also be difficult to set up the remote collection location  901  properly if the collection subject must interpret the instructions  310  and the warnings  320  generated by the AI monitoring module  210  as previously described. One solution to this problem would be for the collection coordinator  920  to come to the collection location  901  and personally direct the set-up of the collection location. However, if, as in the present embodiment, the collection location  901  is remote from the monitor location  902 , such personal direction is not possible. 
     Accordingly, the embodiments provide for the compatible video communication program or video conferencing platform that is supported by both of the computing systems of the collection subject  910  and the collection coordinator  920  to stream data between the collection subject  910  and the collection coordinator  920 . In operation, the AI smart-sensing plugin module  935  of the computing system  950  operates the video stream from computing system  930  and causes data, such as a distance between two of the markers, to be determined. In addition, the AI smart-sensing plugin module  935  is configured to render a view of the remote collection location  901  and feed the video stream back to the video communication client  936 , so that the collection subject  910  can see the measured distance. As illustrated in  FIG.  9   , the feedback video stream shows the collection subject  910 , the TV  911 , the data collection device  912 , the door or wall  913 , and the markers  903 ,  904 , and  905 . In addition, the AI smart-sensing plugin module  935  renders the data such as a distance between two of the markers is in the view of the video communication client  936 . For example, a distance  953  from the marker  904  of the data collection device  912  to the marker  905  of the door or wall  913  is shown as being 250 cm. Likewise, the distance  954  from the marker  904  of the data collection device  912  to the maker  903  of the TV  911  is shown as 200 cm. Thus, the distances  953  and  954  are shown to the data collection subject  910  and the collection coordinator  920  in real time. 
     Accordingly, during set up of the remote collection location  901 , the collection subject  910  and the collection coordinator  920  may communicate with each other in real time using their respective video communication clients. In addition, the collection coordinator  920  is able to monitor in real time as the collection subject  910  sets up the remote collection location  901 . Since the collection coordinator  920  can see the distances between the markers in real time as described, he or she can direct the collection subject in real time to ensure that the distances between the markers are within the desired parameters discussed above. 
     In addition, once the actual data collection process begins, the collection coordinator  920  can continuously monitor the remote collection location and the collection subject. If the collection subject  910  moves too close too or too far away from the data collection device  912 , this will be shown in the video feedback. At such time, the collection coordinator  920  can speak with the collection subject  910  in real time and ask the collection subject  910  to move as needed so that the distance is once again within the desired parameters. That is, the data collection subject may move in the manner discussed previously in relation to  FIGS.  4 A- 4 C and  5 A- 5 C . 
     Although not illustrated, the video feedback may show additional environmental conditions as measured by one or more sensors  120  in addition to or alternative to the distances that have been discussed. For example, in an embodiment a sensor  120  may measure the ambient noise of the remote collection location  901  or may measure the lighting of the remote collection location  901  and show the measurement result through an LED display. That is, in the embodiment of  FIG.  9    the sensors  120  may be placed in various spots in the remote collection location  901  and may all include LED screens of a size sufficient to be captured by the monitoring camera  940  and provided to the computing system  950  over the video stream. These measures values may then be read in real time to the collection coordinator  920  from the video stream. If the measured values are outside of desired ranges, the collection coordinator  920  can ask the collection subject  910  to make changes as needed so that the environmental conditions are within the desired ranges. 
     In some embodiments, the AI smart-sensing plugin module  935  of the computing system  950  may show a QR code  956  in the video feedback that is generated by the computing system  950 . In operation, the collection subject  910  can scan the QR code  956  and can then be taken to a remote website where the collection coordinator  920  may provide further instructions and information related to the data collection process as needed to the collection subject since it may not be possible to provide all necessary information during the video communication. In addition, the QR code  956  may provide additional security as it can be used by the collection subject to verify that the video coordinator is a valid coordinator. In other words, if the QR code  956  is valid, then the collection subject  910  can have confidence that the collection coordinator  920  and the data collection process are valid. 
     Not necessarily all such objects or advantages may be achieved under any embodiment of the disclosure. Those skilled in the art will recognize that the disclosure may be embodied or conducted to achieve or optimize one advantage or group of advantages as taught without achieving other objects or advantages as taught or suggested. 
     The skilled artisan will recognize the interchangeability of various components from different embodiments described. Besides the variations described, other known equivalents for each feature can be mixed and matched by one of ordinary skill in this art to construct or use AI monitoring system for data collection using the principles of the present disclosure. 
     Although the AI monitoring system for data collection has been disclosed in certain preferred embodiments and examples, it therefore will be understood by those skilled in the art that the present disclosure extends beyond the disclosed embodiments to other alternative embodiments and/or uses of the AI monitoring system for data collection and method for using the same and obvious modifications and equivalents. It is intended that the scope of the present AI monitoring system for data collection disclosed should not be limited by the disclosed embodiments described above, but should be determined only by a fair reading of the claims that follow.