Analysis of handling parameters for transporting sensitive items using artificial intelligence

Propagation of pathogens is reduced by configuring internet of things (IoT) sensors along a supply chain of package; and analyzing the packages in the supply chain using the IoT sensors to determine handling requirements of products. The packages can be tracked with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors measuring interactions with the packages that parties in the supply chain are handling the packages in accordance with the handling requirements. Product distribution can be stopped through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

BACKGROUND

The present disclosure generally relates to computer analysis of transporting methods for items, and more particularly to artificial intelligence enabled analysis of transporting methods in relation to sensitivities of items shipped, including contamination of the items.

When customer orders any product from online shopping portal, then product is delivered through appropriate supply chain route/stages. For example, either the product is to be manufactured for delivery or needs to be assembled for delivery of the product is already available for delivery. That is, before the product is delivered to customer location, the product is handled in multiple places, either by an automated machine, or by human.

Preventing contamination spreading is one of the major activities while delivering the product to customer location. Contamination can spread through droplet, touch, or in contact with contaminated fluid, gas etc. Novel Coronavirus is the latest global threat we have come across. Similarly, there can be different types of pathogens which can come in contact with packages as they are being delivered to a customer location.

SUMMARY

In accordance with one aspect of the present disclosure, a method for preventing cross-propagation of pathogens is described that includes configuring internet of things (IoT) sensors along a supply chain of package. Analyzing the packages in the supply chain using the IoT sensors to determine handling requirements of the products within the package. Tracking the packages with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors measuring interactions with the packages that parties in the supply chain are handling the packages in accordance with the handling requirements. The method may further include stopping product distribution through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

In another aspect, a system is described for preventing propagation of pathogens. In one embodiment, the system may include a hardware processor; and a memory that stores a computer program product. The computer program product when executed by the hardware processor, causes the hardware processor to configure internet of things (IoT) sensors along a supply chain of package, and analyze packages in the supply chain using the IoT sensors to determine handling requirements of the products within the package. The system may also include instructions to provide that the hardware processor track the packages with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors that are measuring interactions with the packages that the parties in the supply chain are handling the packages in accordance with the handling requirements. The system may further stop product distribution through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

In another aspect, a computer program product for preventing propagation of pathogens comprising a computer readable storage medium having computer readable program code embodied therewith, the program instructions executable by a processor to cause the processor to configure internet of things (IoT) sensors along a supply chain of package, and analyze packages in the supply chain using the IoT sensors to determine handling requirements of the products within the package. The computer program product may also include instructions to provide that the hardware processor track the packages with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors that are measuring interactions with the packages that the parties in the supply chain are handling the packages in accordance with the handling requirements. The computer program product can further stop product distribution through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

DETAILED DESCRIPTION

The methods, systems, and computer program products described herein relate to methods and systems for preventing cross contamination or propagation of pathogen with package delivery. Contamination by virus, bacteria, fungal infection etc. can spread, i.e., be transmitted, during human or machine touch, human droplet, or if the product is travelled through contaminated area or placed in the contaminated location. For example, when a motherboard of a laptop is contaminated, the contamination can spread from the laptop manufacturing location to distributor to seller to customer. In another example, if the delivery person is infected, because of his sneezing, the to-be-delivered-product may get contaminated. When a food item is delivered with a book, but the food item is spoiled and leaking, or fume is coming out from the spoiled food item, it may contaminate the book, and that, in turn can contaminate the people reading the book.

The methods, systems and computer program products that are describe herein can provide a way by which cross contamination can be prevented across the supply chain, and contamination free products only can move across the supply chain until product is delivered to customer. Disclosed is an artificial intelligence (AI), blockchain memory and internet of things (IoT) based system and method to predict the contamination of the products at various handing stages in the supply chain until the delivery to the customer, and then dynamically derive and execute appropriate prevention steps for preventing the cross-contamination in the supply chain. The methods, systems and computer program products are now described in greater detail with reference toFIGS.1-7.

FIG.1is an illustration of an example environment, in which the systems and methods of the present disclosure can prevent cross contamination and/or propagation of pathogens during package delivery.FIG.2is a flow/block diagram showing one embodiment a method for preventing cross contamination and/or propagation of pathogens during package delivery.FIG.3is an illustration of a block diagram depicting one embodiment of a system for preventing cross contamination and/or propagation of pathogens during package delivery.

FIG.1is an illustration of an example environment, in which the systems and methods of the present disclosure can prevent cross contamination and/or propagation of pathogens during package delivery. The term “pathogen” can be a bacterium, virus, or other microorganism that can cause disease. In some embodiments, the systems and methods can be employed to decrease the spread of COVID-19. Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)(virus). The disclosed system and method predict the contamination based on the following factors, such as resources, weather and environment, and regions. “Resources” refers to who have handled the product and product components, including both human and machine and any material handling device like conveyor, drone, truck etc. “Weather and Environment” is a measurement of the temperature, humidity, and all the other ambient factors. “Regions” may refer to where the product and components go through.

The disclosed system and method recommend the prevention steps that can include:1) Putting product movement on hold, or stopping the product movement;2) Route change for the upcoming product movements; and3) Decontamination, as an additional stage in the supply chain.

FIG.1is an illustration of an example environment, in which the systems and methods of the present disclosure can prevent cross contamination and/or propagation of pathogens during package delivery. The system100for preventing cross-contamination or propagation of pathogen with package delivery may be an artificial intelligence (AI), Blockchain and IoT based system and method to predict the contamination of the products at various handing stages in the supply chain until the delivery to the customer, and then dynamically derive and execute appropriate prevention steps for preventing the cross-contamination in the supply chain.

The disclosed system and method recommend the prevention steps like1. Putting product movement on hold, or stopping the product movement2. Route change for the upcoming product movements3. Decontamination, as an additional stage in the supply chain

The method for preventing cross-contamination or propagation includes that if any package that is to be delivered is recognized or predicted to be contaminated, and if appropriate decontamination steps or cross contamination prevention steps are not applied, then the proposed system will be applying appropriate lock on the impacted product/component of product/material handling system (e.g. conveyor) partially or completely, so that the human or machine will not be allowed to handle the product in any product handling node, and will be preventing cross contamination.

For example,FIG.1illustrates one example of an example environment including a supply chain200. The supply chain may include manufacturing201and storage202for product203. The supply chain200may further include a distribution point206at which the product is loaded from manufacturing201and/or storage202into a long distance shipping vehicle207, such as a truck. AlthoughFIG.1illustrates that the long distance shipping vehicle207is a truck, it is not intended that the present disclosure be limited to only this type of vehicle, as aircraft and trains are also suitable shipping vehicles. Long distance refers to distances in travel that would typically include highway passage when over roadways. Moving product from one town to an adjacent town is an example of long distance shipping. More particularly, this type of loading at the distribution point206is particularly for loading a plurality of packages, i.e., packages of product203, into a shipping vehicle having a destination, such as a town and/or county, at which the plurality of packages can be individually distributed to their specific delivery address. Once, the shipping vehicle207reaches their destination, the product203can be redistributed for shipping to the individual address, which is their shipping destination. In the example, two local shipment routes are depicted. One of the local shipping routes is by manned delivery, e.g., people delivering the goods. A first stage of the manned delivery may be motorized man deliver208. This can be followed by personal delivery209. The second shipping route can be by machine, e.g., a drone205type delivery. It is noted that the shipping routes depicted inFIG.1are only examples, and it is not intended that the present disclosure be limited to only these examples.

The manufacturing201can be any facility or grouping of equipment for producing the product203. The product203being delivered through the shipping routes200may be any type of goods. For example, the product203can be an electrical component, e.g., motherboard of a computer. The product203can also be food item. It is noted that these are only two examples of the types of products that can be tracked through the supply chain200.

A system100is configured for preventing cross-contamination or propagation of pathogen with package delivery in accordance with the products203being tracked through the supply chain200. The system100may include artificial intelligence (AI), Blockchain memory211and IoT based system210and can employ a method to predict the contamination of the products at various handing stages in the supply chain200until the delivery to the customer, and then dynamically derive and execute appropriate prevention steps for preventing the cross-contamination in the supply chain.

Any goods that are produced in one location and delivered to a second location may be tracked using the methods and systems of the present disclosure. The products203are tracked to determine whether the products203are contaminated, e.g., have come in contact with a pathogen204, and/or likely to be contaminated, and whether they have been treated for decontamination and/or to stop the spread of the pathogen204. Tracking can include a plurality of sensors and/or cameras210positioned along the supply chain200to track the products203, and measure interaction with products203that can spreads pathogens204Decontamination212can include ultrasound or infrared or any chemical process that can kill pathogens. Cross contamination prevention steps can include users, e.g., human users, not using an internet of things (IoT) enabled mask or gloves213while handling of the product (PPE).

In one example, if IoT enabled systems, such as systems in communication with the IOT enabled masks or gloves, predicts that any product203is loaded on the delivery vehicle207is cross-contaminated, then the delivery vehicle207awill not start. In one example, the IoT enabled system for preventing cross-contamination or propagation of pathogen with package delivery can send a signal to the vehicle engine (of the delivery vehicle207ato be stopped) that either one or more loaded delivery product203is contaminated, or vehicle body is contaminated and accordingly the engine will not be starting. This example is not limited to surface transportation. If contamination is measured for a package that is to be delivered using automated systems, e.g., robot and/or drone205, then the system100will recognize the package and will not be loading any package which is predicted as possible contaminated or will not be picking by the automated system.

Still referring toFIG.1, in some embodiments, if the system100predicts appropriate decontamination steps is applied on the loaded product, then the vehicle will be allowed to start, and IoT enabled smart city will be recommending appropriate contamination free route (for truck or drone), so that during transportation, the vehicle or delivery products are not contaminated. Decontamination212can include ultrasound or infrared or any chemical process that can kill pathogens. The smart city can publish the geofencing boundary of containment zone restricted zone etc. and accordingly the proposed system will be planning for route.

Smart Contract rule for decontamination will be validating the IoT feed210to identify appropriate decontamination steps are applied on the product is being delivered or product handling device etc, and accordingly ledger will be ensuring product is being delivered is contamination free in every handling node. For different types of contamination needs different types of decontamination step, spoiled food-based contamination vs COVID-19 contamination.

Still referring toFIG.1, in some embodiments, the system100for preventing cross-contamination or propagation of pathogen204with package delivery employs blockchain211and IoT210enabled systems (e.g. associated to product package, product handling module, either human or machine etc.) for tracking how any product203or components of products203are handled at different product produce handling nodes (like assembling, packaging, transporting etc). Accordingly, the system100can identify in which step of the activity needs decontamination procedure so that while delivering the product to the customer, the proposed system can ensure a contamination free product and also package is delivered to customer.

While waiting to receive a product through the supply chain200, the customer (i.e., customer awaiting the product203) can visualize if the product203is to be received is contamination free, or is possibly contaminated because of improper handling. In some embodiments, the user, i.e., customer for product being shipped, can receive from the system100a notice214including tracking number for the shipment of the product, with indications validating the progress of the product203through the supply chain200in blockchain to identify if the product that is being received is contamination free, i.e., free of pathogens204. The notice214may be sent from the system100to a mobile computing device215of the user, in which the notice is displayed on a display screen of the mobile computing device215. In some examples, the mobile computing device215may be a smart phone, tablet computer, laptop computer, etc. It is not required that the mobile computing device215be a mobile device, as desktop type devices are also applicable for displaying the notice214.

In embodiments, in which the products203are to be received with an automated method (like robot will be dropping the product to customer house when customer is not present, or drone based delivery), then the automated receiving unit will be validating if the product is to be received is contamination free, otherwise the product will not be received by the order receiving unit.

In some embodiments, the workers handling the products in different handling node of the supply chain200, can wear Augmented Reality glass, and accordingly the AR glass can recognize the unique identifier of the product203can visualize while product or handling device is contaminated.

FIG.2is a flow/block diagram showing one embodiment a method for preventing cross contamination and/or propagation of pathogens during package delivery, in accordance with some embodiments of the present disclosure. In some embodiments, the method can begin with, in response to receiving permission from a user for data collection, registering users with the system100for preventing cross-contamination or propagation of pathogen with package delivery. In some embodiments, the registration at block 1 can be performed once during the time at which the user registers for delivery service, such as during an initial purchase of the product203.

To the extent that implementations of the system100for system100for preventing cross-contamination or propagation of pathogen with package delivery collect, store, or employ personal information provided by, or obtained from, individuals (for example, current locations of the user, historical word usage, etc.), such information shall be used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information may be subject to consent of the individual to such activity, for example, through “opt-in” or “opt-out” processes as may be appropriate for the situation and type of information. Storage and use of personal information may be in an appropriately secure manner reflective of the type of information, for example, through various encryption and anonymization techniques for particularly sensitive information.

FIG.3illustrates a system100for preventing cross-contamination or propagation of pathogen with package delivery. The system may include a registry101for users. The registry101includes an interface for receiving permissions for parties operating within the supply chain200, as well as at least one form of memory for maintaining a database of registry information including what permissions have been granted and withdrawn. For example, the manufacturing unit201, as well as the other supply chain participants, e.g., warehouse202, sign up for the service, as the service requires monitoring resources and processes of the participants.

The method may continue with equipping facilities along the supply chain with internet of things (IOT) sensors210,213that provide data that can be used to track the movement, and handling of the product203at block 2 of the method depicted inFIG.2. By tracking the product203, the system100is able determine whether an exposure with a pathogen204has occurred; whether the exposure results in a like transmission; and how the package is handled after potential exposure, including whether decontamination steps have occurred. Referring toFIGS.1-3, the system100for preventing cross-contamination or propagation of pathogen with package delivery includes at least one interface102to the IOT sensors210,213. The interface may be wireless.

In some examples, the IOT sensors210,213that can be used to track the packages203can include video/thermal camera. Monitoring cam done through cameras210. Audio monitoring can be performed through microphone enabled devices. As will be discussed below, with audio monitoring, speech-to-text and natural language processing can be used to determine if there has been any contamination of package. In some examples, thermal cameras can detect human touch on any object. For example, when the object, such as a package203, is touched, the human body temperature will be transmitted to the product (package203). The transfer of temperature from the human body to the package can be identified with thermal camera. Further, when a human worker wears a mask, and hand gloves, then the IoT sensors210,213will be able to identify if the worker has properly worn the nose mask and hand gloves. In some other embodiments, the product containers, e.g., packages203, have sensor to track movement, or any other related properties like the application of pressure and/or changes in temperature.

As noted, the nose mask213also can have IoT sensor, and similarly, hand gloves213also can have IoT sensors. The nose mask and/or hand gloves with IoT sensors can generate self-power with piezoelectric crystals, while wearing, hand movement etc., these piezoelectric crystals will be generating self-power for the sensors. The mask and hand gloves213with the IoT sensors can be used to track the packages203, as well as to track how the packages are being handled, etc. The mask and gloves213may be in communication with the system100for preventing cross-contamination or propagation of pathogen with package delivery via the IoT sensor interface102depicted inFIG.3.

In some embodiments, the material handling equipment (MHEs) have sensors to identify if appropriate decontamination steps are applied at the loading areas (also referred to as gripping area). The decontamination steps can include chemical washes212. The material handling system, machines etc., will include decontamination policies. For example, every day the material handling system should be decontaminated.

Referring toFIG.2, in a following step at block 3, the packages203may be tracked using the IoT sensors210,213, which as noted above can be integrated into the packages203themselves, as well as being integrated (e.g., the use of IoT based cameras and sensors) into the environments at which the packages203are being handled in the supply chain200.

Block 4 of the method depicted inFIG.2includes classifying the packages according to their handling requirements. The system100for preventing cross-contamination or propagation of pathogen with package delivery may employ deep learning that is applied to classify handling into proper/improper/double-check classes. Referring toFIG.3, the system100for preventing cross-contamination or propagation of pathogen with package delivery includes product classification engine103that employs artificial intelligence to analyze the data collected from the IoT sensors210,213and determine the types of products203that are being handled, and if any product can spoil, like food, chemical etc., and can contaminate. The system100for preventing cross-contamination or propagation of pathogen with package delivery has an interface having different types of sensors, like thermal sensor, smell sensor, proximity sensor, movement sensors etc.

For example, a video camera may take an image of a package having a description on it, or bar code, and the image is then analyzed by the includes product classification engine103. The product classification engine103may employ artificial intelligence utilizing computer software programs that analyze the images using machine vision. Machine vision is a series of algorithms, or mathematical procedures, which work like a flow-chart or series of questions to compare the object seen with stored reference images of objects in different angles, positions and movements. Many other questions are possible, such as the degree to which the object is reflective, the degree to which it is steady or vibrating, and the smoothness with which it moves. Combining all of the values from the various questions, an overall ranking is derived which gives the A.I. the probability that an package203matches a package type stored in a product handling knowledge base55. This type of A.I. is known as “rule-based”. In some embodiments, the computer vision module103aincludes at least one hardware processor for executing a series of instructions for analyzing the images taken by the IoT sensor, and comparing the images to comparison objects from the data saved in an image database55correlating products to handling procedures.

In some embodiments, identifying products having handling procedures from packages using the IoT sensor may include extracting text from the packages203and analyzing the text using natural language processing. In some embodiments, the IoT sensors may include microphones. The microphones may capture verbal descriptions of the packages203, which his then converted to text, and then analyzed using natural language processing. Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Natural language processing frequently involves, natural language understanding, and natural language generation. The data from the text correlating to the package is then compared to products within the product handling knowledge base55. Matches result in a product having handling requirements that are needed to followed in order for the shipments to proceed through the supply chain200.

Referring toFIG.3, the system100for preventing cross-contamination or propagation of pathogen with package delivery includes product classification engine103that can include both the computer vision module103aand the natural language processing module103b. It is noted that computer vision and natural language processing are not the only mechanisms by which packages203being measured using IoT sensors are correlated to products. Any sensor for measuring a characteristic may be considered for determining the identity, i.e., type of product, that is being shipped in a package203.

The handling knowledge base55may be a database of potential products to be shipped. The database may include instructions on how products are to be handled to ensure that pathogens are not transmitted during shipping. The handling knowledge base55may be a database that is stored in cloud memory. In some embodiments, the handling knowledge base55grows by including instructions for product handling from prior product analysis for products shipped through the supply chain200, and tracked by the system. In some embodiments, the product classification engine may also include a web crawler. In scenarios in which the products corresponding to the packages203being tracked by the IOT sensors are identified, but do not match a matching product in the handling knowledge base55, the web crawler may be used to retrieve product handling instructions from data on the internet.

The material handling equipment (MHEs), e.g., decontamination212, are then enabled to perform different types of decontamination steps like ultrasound decontamination, infrared, warm air, chemical cleaning etc. Decontamination212can also include simple proper handling, e.g., gloves and face masks213. The IoT enabled system will be tracking how the handling machine is touching different portions, and accordingly, the system can identify if any area is contaminated.

Referring toFIG.2, the method may continue with the system using the IoT sensors210,213to determine whether the packages203are being handled in accordance with their handling requirements. More specifically, block 5 ofFIG.2includes the packages being tracked using IoT sensors to determine if handling requirements are met through supply chain200. In some examples, R-CNN (Region Based Convolutional Neural Network) based analysis is performed to detect touched areas of the packages203. Region Based Convolutional Neural Networks are a family of machine learning models for computer vision and specifically object detection.

An Artificial Neural Network (ANN)—also referred to simply as a neural network—is a computing system made up of a number of simple, highly interconnected processing elements (nodes), which process information by their dynamic state response to external inputs. ANNs are processing devices (algorithms and/or hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior.

In machine learning, a convolutional neural network (CNN) is a type of artificial neural network in which the connectivity pattern between its nodes (neurons) is inspired by the organization of the animal visual cortex, whose individual neurons are arranged to respond to overlapping regions tiling a visual field. Convolutional networks mimic biological processes and are configured as variations of multilayer perceptrons designed to use minimal amounts of preprocessing while processing data, such as digital images. A region-based convolutional neural network (RCNN) is a CNN that has been trained to identify regions of digital image data where an object of interest might be present with a certain degree of certainty (or a certain level of confidence).

Still referring toFIG.2, at block 5, the systems determines whether the package is being handled in accordance with their handling guidelines. For example, while material, e.g., packages203, are being handled, the proposed system100use thermal camera and other camera feeds to identify if there is any human touch, and which objects were in the proximity. There can be a smart contact rule to identify if any area is contaminated, like with human touch, presence of bad smell, dirty items, etc. Based on the smart contact rule, the proposed system will be identifying if any object/item is contaminated, or, if there is any human touch involved.

Referring toFIG.3, the system100to track the packages, and determine whether the protocols for proper handling are followed may include a package handling confirmation engine105that employs a convolutional neural network, e.g., region-based convolutional neural network (RCNN)105a, that works with the IOT sensors210,213to confirm proper handling of the packages203. The package handling confirmation engine105can perform the functions for block 5 of the method depicted inFIG.2.

The RCNN is a type of artificial neural network. One element of ANNs is the structure of the information processing system, which includes a large number of highly interconnected processing elements (called “neurons”) working in parallel to solve specific problems. ANNs are furthermore trained using a set of training data, with learning that involves adjustments to weights that exist between the neurons. An ANN is configured for a specific application, such as pattern recognition or data classification, through such a learning process.

Referring now toFIG.4, a generalized diagram of a neural network is shown. Although a specific structure of an ANN is shown, having three layers and a set number of fully connected neurons, it should be understood that this is intended solely for the purpose of illustration. In practice, the present embodiments may take any appropriate form, including any number of layers and any pattern or patterns of connections therebetween.

ANNs demonstrate an ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be detected by humans or other computer-based systems. The structure of a neural network is known generally to have input neurons302that provide information to one or more “hidden” neurons304. Connections308between the input neurons302and hidden neurons304are weighted, and these weighted inputs are then processed by the hidden neurons304according to some function in the hidden neurons304. There can be any number of layers of hidden neurons304, and as well as neurons that perform different functions. There exist different neural network structures as well, such as a convolutional neural network, a maxout network, etc., which may vary according to the structure and function of the hidden layers, as well as the pattern of weights between the layers. The individual layers may perform particular functions, and may include convolutional layers, pooling layers, fully connected layers, softmax layers, or any other appropriate type of neural network layer. Finally, a set of output neurons306accepts and processes weighted input from the last set of hidden neurons304.

This represents a “feed-forward” computation, where information propagates from input neurons302to the output neurons306. Upon completion of a feed-forward computation, the output is compared to a desired output available from training data. The error relative to the training data is then processed in “backpropagation” computation, where the hidden neurons304and input neurons302receive information regarding the error propagating backward from the output neurons306. Once the backward error propagation has been completed, weight updates are performed, with the weighted connections308being updated to account for the received error. It should be noted that the three modes of operation, feed forward, back propagation, and weight update, do not overlap with one another. This represents just one variety of ANN computation, and that any appropriate form of computation may be used instead. In the present case the output neurons306provide analysis of whether a package has been handled correctly according to the data provided from the input of the IoT sensors.

To train an ANN, training data can be divided into a training set and a testing set. The training data includes pairs of an input and a known output. During training, the inputs of the training set are fed into the ANN using feed-forward propagation. After each input, the output of the ANN is compared to the respective known output. Discrepancies between the output of the ANN and the known output that is associated with that particular input are used to generate an error value, which may be backpropagated through the ANN, after which the weight values of the ANN may be updated. This process continues until the pairs in the training set are exhausted. In some embodiments, the streaming plan generator303trains to match search items extracted from definitions for requirements used in the requirement management tool to source code that is stored in repositories.

After the training has been completed, the ANN may be tested against the testing set, to ensure that the training has not resulted in overfitting. If the ANN can generalize to new inputs, beyond those which it was already trained on, then it is ready for use. If the ANN does not accurately reproduce the known outputs of the testing set, then additional training data may be needed, or hyperparameters of the ANN may need to be adjusted.

ANNs may be implemented in software, hardware, or a combination of the two. For example, each weight308may be characterized as a weight value that is stored in a computer memory, and the activation function of each neuron may be implemented by a computer processor. The weight value may store any appropriate data value, such as a real number, a binary value, or a value selected from a fixed number of possibilities, that is multiplied against the relevant neuron outputs. Alternatively, the weights308may be implemented as resistive processing units (RPUs), generating a predictable current output when an input voltage is applied in accordance with a settable resistance.

The ANN depicted inFIG.4may be employed in the region-based convolutional neural network (RCNN)105aof the package handling confirmation engine105. The package handling confirmation engine105can track the mobility of packages, handling of package, and will proactively and re-actively be identifying which package might be contaminated or might not be contaminated. If it is found that a package203is contaminated, the package handling confirmation engine105can then work through the system100in sending notification to the transportation system207, and/or manual loaders/material handling equipment (MHE) to stop handling the packages, (e.g, or place them aside). Referring toFIG.2, at block 6, the method may continue with informing the parties within the supply chain200of a package203that has not been handled in accordance with procedures to stop the spread of a pathogen204.

In some embodiments, the package handling confirmation engine105will first check to see if a package203has been contaminated. Following contamination, the package handling confirmation engine105can also check if decontamination212steps have also been performed212. This is consistent with the type of package203as identified by the system, and the proper handling instructions matched by the system100to the type of package203. In some embodiments, a blockchain ledger, e.g., block chain memory211, will be tracking every step of package handling and accordingly be capturing if every stage is contamination free. In some embodiments, to provide a ledger that is immutable blockchain memory is applied. A “blockchain” is a growing list of records, called blocks, which are linked using cryptography. In some examples, each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree).

Referring back toFIGS.1-3, the IOT enabled system100around the handling areas (also referred to as gripping areas) of the supply chain200of the MHE can identifying if the gripping area is contaminated. If the gripping area is contaminated, and said MHE grips any product, then the product will be considered contaminated. For example, if a person is handling the product in the supply chain200and/or a machine handles any product in the supply chain200and there is a potential contamination without applying any decontamination steps, then the IoT enabled system will identify the product203as contaminated at the next stage like packaging and loading (in the delivery vehicle). For example, inFIG.1, the delivery vehicle identified by reference number207awas scheduled to receive a package203that has been identified as potential contaminated, and was not decontaminated, the system100sends a signal to the delivery vehicle207ato stop the vehicle from starting. This stops the delivery of the contaminated package203.

Referring toFIG.2, at block 6, the method can continue with informing parties within supply chain200of packages that have not been handled in accordance with requirements to stop the spread of pathogens.

In some embodiments, if manual handling is performed, manual handling will be analyzed to see if the worker is wearing IoT enabled mask and hand gloves213. The IoT sensors210,213will track the package203handled without any mask or hand gloves, and accordingly will be identifying the package203as being contaminated. The system100also is tracking if proper decontamination212is applied on the mask or hand gloves213before handling of the product.

In one embodiment, the transportation vehicle207awill not start if the packages23loaded into the vehicle include packages203that are contaminated. In this example, the system100sends a signal that disables the starter to the motor vehicle. Referring toFIG.3, the system100for reducing cross contamination or the spread of pathogens204can include an output42. The output42can be in communication wirelessly with the vehicles207for transport. In scenarios, in which the packages203loaded onto the vehicles are not contaminated, the system100does not send a disabling signal, and the vehicles may be driven towards their delivery destination in the supply chain200. For example, the long distance transportation vehicles207may continue towards motorized man delivery208, man delivery209or an unmanned type delivery, such as drones205. The system continues to monitor the supply chain200to ensure that the packages203are property handled at the stages of motorized man delivery208, man delivery209and/or unmanned delivery, e.g., drone type delivery205.

In some embodiments, vehicles, e.g., the vehicle207adisabled for containing contaminated packages203loaded therein, can be started, if any package that has been identified as contaminated, is removed from the disabled vehicle207aor is treated for decontamination, e.g., by decontamination steps212. Upon removal or decontamination of the contaminated packages203, a signal from the system100can be sent to the previously disabled vehicle to reactivate the vehicle. For example, a previously disabled ignition or started system, can have its functionality restored.

As noted above, deep learning will be used in combination with video/image analysis for determining potential handling that can result in contamination, as well as potential decontamination steps. This can be instituted at every stage of delivery. This can be achieved through integration between IoT servers belonging to the smart vehicle and the smart premise (where product and product components have been handled). In some instances, IoT enabled system connected with smart city system will recommend appropriate contamination free roads, so that during transportation the vehicle or delivery products are not contaminated. In some embodiments, while the product, e.g., packages203, is being handled or kept idle, the IoT enabled system100will be tracking if human or any animal/birds has performed mobility around the area, (with thermal camera). More specifically, the system will continue to track the stationary package203to determine if anything is coming into contact with the stationary package203. The IoT sensor feed received at the IoT sensor interface102can be analyzed to identify the proximity objects and the possible distances. The proposed system100can identify if any device or material handling device is contaminated, because of human touch or because of animal/birds etc. that travelled through a contaminated area, and then into direct contact with or close proximity to, the stationary package203. Based on the rule defined, e.g., with data provided by the Artificial intelligence (AI) based product classification engine103, and executed by the package handling confirmation engine105, the system100for reducing cross contamination can establish if any product or product component got contaminated, and can identifying be applying appropriate lock in the device so that it does not create further cross contamination.

Referring toFIG.1, in some embodiments, in addition to the system100for preventing cross contamination providing an output42that disables elements of the supply chain200, the output42may also send messages to the customers of the products within the packages203being shipped. For example, while receiving the product, the customer can visualize if the product is to be received is free from contamination, and accordingly can accept or reject the delivery. This can be accomplished through the user's device215, e.g., a user's mobile device, such as smart phone or tablet computer, or a user's non-mobile device, e.g., desktop computer.

The methods, systems and computer program products described herein, can be employed by logistics companies handling B2C shipments whereby the end-to-end supply chain is transparent in terms of providing real time and data transparency regarding tracking of the shipment and physical handling at all transport nodes from start of packing the shipment till door delivery at customer premises.

In some instances, for the end customer the methods, systems and computer program products described herein can be significant and act similar to a health pass to accept the packages post the disinfection verification using their own mobile application to confirm the sanctity of the verification of packages, and decide whether receiving the tracked packages, or auto rejecting the tracked packages, or with advice of receiving same after following steps of disinfection process and revalidation.

For the carriers/retailers/automobile agents the methods, systems and computer program products described herein can serve as a value-added service to their customers, by provided verified disinfected package information.

It is noted that the above example depicted inFIG.1represents shipments of products through a shipping supply line200, e.g., shipping products from manufacturing facilities and/or warehouses to customers purchasing the products. The methods, systems and computer program products are not limited to only this example.

In another application, the system for reducing cross contamination100may be applied to hospitals having infectious disease center. Users entering the infectious disease center must wear protective gear when entering the room. As they leave, the protective gear is put in a special room. The system for reducing cross contamination100may be applied to this scenario.

In yet another hospital application, an orderly may be pushing a gurney through the halls. In this application, the system for reducing cross contamination100send instructions to augmented reality (AR( ) glasses worn by the orderly to instruct the orderly to clean the gurney cleaned prior to bringing it back to another portion of the hospital, such as the emergency room (ER), for the next patient.

The system for reducing cross contamination100may also be applied to the food preparation/restraint industry. For example, as food is delivered and prepared, the trays are tagged with RFID tags, and their movement is tracked to make sure that cold and hot plates are not stored together. The status of the cold and hot plates can be tracked using a set of AR glasses in combination with the system for reducing cross contamination100, as described inFIG.1. In this example, when one tray is put in the wrong location, all of the trays in that location are flagged in the augmented reality setting as red until they are disposed of and put through a dishwasher.

FIG.3is an illustration depicting one embodiment of a system100for preventing cross contamination and/or propagation of pathogens during package delivery. In one embodiment, the system100for preventing propagation of pathogens includes a hardware processor13; and a memory14that stores a computer program product, which, when executed by the hardware processor, causes the hardware processor to configure internet of things (IoT) sensors along a supply chain of package; and analyze the packages in the supply chain using the IoT sensors to determine handling requirements of the products within the package. The system100can also track the packages with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors measuring interactions with the packages that parties in the supply chain are handling the packages in accordance with the handling requirements; and stop product distribution through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

FIG.5illustrates a processing system400used by or comprised by the system100ofFIG.3for reducing cross contamination, in accordance with the methods and systems described above inFIGS.1-3. The bus102interconnects the plurality of components for the system100described above with the components depicted in the computer system400depicted inFIG.5.

WhileFIG.5shows the computer system400as a particular configuration of hardware and software, any configuration of hardware and software, as would be known to a person of ordinary skill in the art, may be utilized for the purposes stated supra in conjunction with the particular computer system100ofFIG.4. For example, the memory devices94and95may be portions of a single memory device rather than separate memory devices.

In one embodiment, the present disclosure provides a non-transitory computer readable storage medium that includes a computer readable program for identifying the status of object within a region. The non-transitory computer readable program when executed on a computer causes the computer to perform the steps of designating at least one piece of equipment to be tracked; recording a history for the piece of equipment; and detecting when the piece of equipment is present within a region having a set of object restrictions. The non-transitory computer readable program when executed on a computer also causes the computer to perform the steps of comparing the set of object restrictions for the region to the history that is recorded for the piece of equipment that is present in the region to calculate a placement score with a hardware processor that indicates whether the history of the equipment is a match for the region that the equipment is present in; and projecting a sensory signal to the equipment in an augmented reality setting, the sensory signal indicating whether the history of the equipment is a match for the region that the equipment is present in.

It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment (e.g., Internet of thing (IOT)) now known or later developed. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provision computing

capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.

Service Models are as follows:

Deployment Models are as follows:

Service level management84provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment85provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

Workloads layer89provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and for a cognitive recognition model to maximize the business impact96in hardware devices in accordance withFIGS.1-7.