阅读说明：本技术 通过人工智能的暴露最小化响应 (Minimizing response to exposure through artificial intelligence ) 是由 K·穆拉里达拉 A·杰达 P·平托 于 2020-01-30 设计创作，主要内容包括：一种用于操纵资源以使混沌环境中的暴露伤害最小化的人工智能系统,其包括自主代理装置、远程电子传感器和中央服务器。中央服务器用于：在第一时间窗口期间从一个或多个远程电子传感器接收第一组传感器读数,其中,传感器读数记录混沌环境中的一个或多个变量的值；接收临界时间间隔和时间间隔的最大允许风险暴露,其中,在临界时间间隔期间混沌环境可能会影响资源中的一个或多个资源；从混沌环境和混沌环境内的资源来确定临界时间间隔期间的加权总风险暴露；确定加权总风险暴露超过最大允许风险暴露；以及使自主代理装置操纵一个或多个资源以降低加权总风险暴露。(An artificial intelligence system for manipulating resources to minimize exposed damage in chaotic environments includes an autonomous agent device, a remote electronic sensor, and a central server. The central server is used for: receiving a first set of sensor readings from one or more remote electronic sensors during a first time window, wherein the sensor readings record values of one or more variables in the chaotic environment; receiving a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum allowable risk exposure for the time interval; determining a weighted total risk exposure during the critical time interval from the chaotic environment and resources within the chaotic environment; determining that the weighted total risk exposure exceeds the maximum allowable risk exposure; and causing the autonomous agent device to manipulate one or more resources to reduce the weighted total risk exposure.)

1. An artificial intelligence system for manipulating resources to minimize exposure hazards in chaotic environments, comprising:

one or more autonomous proxy devices; and

a central server comprising a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the processor to:

receiving a first set of sensor readings from one or more remote electronic sensors during a first time window, wherein the sensor readings record values of one or more variables in the chaotic environment;

receiving a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum allowable risk exposure for the time interval;

determining, based on the first set of sensor readings, a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment;

determining that the weighted total risk exposure exceeds the maximum allowable risk exposure; and

cause the one or more autonomous agents to manipulate the one or more resources to reduce the weighted total risk exposure in response to determining that the weighted total risk exposure exceeds the maximum allowed risk exposure.

2. The system of claim 1, wherein the non-transitory memory stores instructions that, when executed by the processor, further cause the processor to:

receiving a second set of sensor readings from the one or more remote electronic sensors during a second time window, the second set of sensor readings recording changes in the one or more variables; and is

Updating the weighted total risk exposure during the critical time interval based at least in part on the second set of sensor readings.

3. The system of claim 2, wherein the non-transitory memory stores instructions that, when executed by the processor, further cause the processor to:

after manipulating the one or more resources to reduce the weighted total risk exposure and updating the weighted total risk exposure, iteratively manipulating the one or more resources and updating the weighted total risk exposure until the weighted total risk exposure does not exceed the maximum allowed risk exposure.

4. The system of claim 1, wherein the determination of the weighted total risk exposure is based at least in part on an integral of a risk function with respect to a time from a current time instant to an end of the critical time interval.

5. The system of claim 4, wherein the risk function is based on an assumption of random walk behavior in the one or more variables.

6. The system of claim 5, wherein the risk function is based at least in part on a complementary error function.

7. The system of claim 1, wherein the one or more autonomous proxy devices manipulate the one or more resources by preventing network messages from being transmitted over the network.

8. The system of claim 1, wherein the one or more autonomous proxy devices manipulate the one or more resources by transmitting a network message to a remote computing device.

9. The system of claim 1, wherein the one or more autonomous agent devices manipulate the one or more resources by generating messages for receipt by a human user.

10. The system of claim 1, wherein the one or more autonomous agents manipulate the one or more resources by activating an alarm visible or audible to a human user.

11. An artificial intelligence method for manipulating resources to minimize exposure hazards in chaotic environments, comprising:

receiving a first set of sensor readings from one or more remote electronic sensors during a first time window, wherein the sensor readings record values of one or more variables in the chaotic environment;

receiving a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum allowable risk exposure for the time interval;

determining, based on the first set of sensor readings, a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment;

determining that the weighted total risk exposure exceeds the maximum allowable risk exposure; and

cause the one or more autonomous agents to manipulate the one or more resources to reduce the weighted total risk exposure in response to determining that the weighted total risk exposure exceeds the maximum allowed risk exposure.

12. The method of claim 11, wherein the non-transitory memory stores instructions that, when executed by the processor, further cause the processor to:

receiving a second set of sensor readings from the one or more remote electronic sensors during the second time window, the second set of sensor readings recording changes in the one or more variables; and is

Updating the weighted total risk exposure during the critical time interval based at least in part on the second set of sensor readings.

13. The method of claim 12, wherein the non-transitory memory stores instructions that, when executed by the processor, further cause the processor to:

after manipulating the one or more resources to reduce the weighted total risk exposure and updating the weighted total risk exposure, iteratively manipulating the one or more resources and updating the weighted total risk exposure until the weighted total risk exposure does not exceed the maximum allowed risk exposure.

14. The method of claim 11, wherein the determination of the weighted total risk exposure is based at least in part on an integral of a risk function with respect to a time from a current time instant to an end of the critical time interval.

15. The method of claim 14, wherein the risk function is based on an assumption of random walk behavior in the one or more variables.

16. The method of claim 15, wherein the risk function is based at least in part on a complementary error function.

17. The method of claim 11, wherein the one or more autonomous proxy devices manipulate the one or more resources by preventing network messages from being transmitted over the network.

18. The method of claim 11, wherein the one or more autonomous proxy devices manipulate the one or more resources by transmitting a network message to a remote computing device.

19. The method of claim 11, wherein the one or more autonomous proxy devices manipulate the one or more resources by generating messages for receipt by a human user.

20. The method of claim 11, wherein the one or more autonomous agent devices manipulate the one or more resources by activating an alarm visible or audible to a human user.

Technical Field

The present application relates to artificial intelligence methods and systems, and more particularly to methods and systems for artificial intelligence to analyze sensor data from chaotic environments and to modify the behavior of autonomous systems in response to changes in the environment.

Background

In many engineering and computing applications, the system must be designed to take into account certain risk tolerances. Buildings are often designed to survive not only at typical average wind speed levels throughout the day, but also under so-called "hundred year storms" which may occur on any given day, but are statistically unlikely to occur more frequently than once every hundred years. Online service advertising for critical business or information sharing is said to have "five nine" availability (i.e., active 99.999% of the time, with less than six minutes of downtime per year), and needs to be able to handle a completely unpredictable surge in network traffic without dropping incoming requests for connections.

Accordingly, there remains a need in many computing applications and other fields to better anticipate system changes and reallocation of resources to mitigate the hazards of extreme changes that may or may not occur immediately. This expectation may be facilitated by: the use of distributed sensor systems is increased as part of the "internet of things" and software is added to be incorporated into traditional "dumb" devices to create autonomous vehicles, "smart" thermostats, and other "smart" systems, appliances, and devices with greater understanding of and ability to respond to their operating environment.

Disclosure of Invention

An artificial intelligence system for manipulating resources to minimize exposure hazards in chaotic environments includes one or more autonomous proxy devices and a central server. The central server includes a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the processor to: receiving a first set of sensor readings from one or more remote electronic sensors during a first time window, wherein the sensor readings record values of one or more variables in the chaotic environment; receiving a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum allowable risk exposure for the time interval; determining a weighted total risk exposure during the critical time interval from the chaotic environment and resources within the chaotic environment based on the first set of sensor readings; determining that the weighted total risk exposure exceeds the maximum allowable risk exposure; and in response to determining that the weighted total risk exposure exceeds the maximum allowable risk exposure, causing one or more autonomous agents to manipulate one or more resources to reduce the weighted total risk exposure.

Also disclosed is an artificial intelligence method for manipulating resources to minimize exposure hazards in chaotic environments, the method comprising: receiving a first set of sensor readings from one or more remote electronic sensors during a first time window, wherein the sensor readings record values of one or more variables in the chaotic environment; receiving a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum allowable risk exposure for the time interval; determining a weighted total risk exposure during the critical time interval from the chaotic environment and resources within the chaotic environment based on the first set of sensor readings; determining that the weighted total risk exposure exceeds the maximum allowable risk exposure; and in response to determining that the weighted total risk exposure exceeds the maximum allowable risk exposure, causing one or more autonomous agents to manipulate one or more resources to reduce the weighted total risk exposure.

Drawings

FIG. 1 depicts a computing system for receiving sensor readings from a chaotic environment and directing an autonomous agent in response to changes in the chaotic environment;

FIG. 2 depicts a conceptual diagram of risk exposure from a hazard over a period of time;

FIG. 3 depicts a method by which an artificial intelligence system processes incoming sensor data and boots an agent in a chaotic environment; and

FIG. 4 depicts a general purpose computing device for performing various features described above.

Detailed Description

FIG. 1 depicts a computing system for receiving sensor readings from a chaotic environment and directing an autonomous agent in response to changes in the chaotic environment.

Many engineering, computing, and social systems are affected by the chaotic environment in which they operate. Buildings, bridges, blocks and other engineering projects are built in areas that may be affected by hurricanes, wildfires or other environmental hazards; the networked computing device operating on a network having unpredictable surges of network traffic or rerouting of network traffic due to broken network links; utility and private services must provide services to a distributed group of consumers who may need to access the services at any time; companies may provide valuable assets for trading in markets where the prices of the assets are constantly changing, and wrongly timed quotes may be economically wasteful.

The cost of interference from chaotic systems can be realized in incremental or marginal effects (e.g., as wildfires advance, individual homes are at risk, as traffic increases, network functionality smoothly decreases, or power access from a stressed grid flashes, etc.) and/or sudden catastrophic losses (e.g., a hurricane-induced bridge collapse, a server completely disabled by a denial of service attack, or a grid completely darkens, etc.).

Turning now to the elements of fig. 1, the central server 100 receives sensor data from a plurality of remote electronic sensors 105 via a network 110, the remote electronic sensors 105 observing or relaying data from a chaotic environment in which one or more important resources are present. The central server also transmits instructions to a plurality of electronic computing device agents 115 via the network 110, the electronic computing device agents 115 being capable of acting directly or indirectly to protect important resources from damage caused by the chaotic environment.

The network 110 may be, for example, the general internet, a local wireless network, an ethernet network or other wired network, a satellite communication system, or any other means of connecting the sensors 105 to the central server 100 and the central server to the agents 115 for data transfer. Further, the network 110 may not be a single network as shown, rather than a plurality of separate networks; for example, the central server 100 may have a plurality of near-end sensors 105 to which it is attached by a wired connection, a plurality of near-end sensors 105 to which it is connected via a Wi-Fi network, and/or a plurality of extreme remote sensors 105 to which it is connected via a satellite. The connection may avoid the use of a network altogether and use direct wire or wireless transmission to send data to and from the central server 100. As shown in fig. 1, the arrows show the intended direction of data flow to and from the network.

The sensors 105 may be any type of electronic sensor that registers data from a chaotic environment external to the central server 100. Example sensor types for particular embodiments may include, but are not limited to, a camera, thermometer, GPS tracking device, or other geo-locating device, a sensor of motion/distance/acceleration/orientation of an object to which the sensor is attached or of a remote object observed by the sensor, or a communication module that receives electronic data communications from a source.

Agents 115(agents) may be any form of computing device (or module incorporated into a device not normally used as a computing device for controlling the device) that enables resources to be reallocated, transported, moved, created, destroyed, or otherwise manipulated in a manner that minimizes damage from the chaotic environment interacting with the resources. For example, the agent 115 may be a computing device to perform the following, namely: control automation systems within a building, trigger physical alerts, drive drone aircraft or autonomous vehicles, route network traffic, generate messages for display on physical devices associated with human users, or perform other actions associated with "smart appliances" or other automation systems.

The following describes a number of possible pairings of sensors 105 and agents 115 to achieve a particular purpose.

In an example embodiment, listening devices 105 at a power plant may receive signals from smart power meters 105 at multiple homes and businesses that draw power from the power plant. As power consumption increases, the risk of a power loss due to insufficient power generation or a power outage due to sudden component failure may also increase. The plant automation system 115 may determine whether to start additional turbines, prioritize power output to certain output channels, or otherwise manipulate the power generation resources and the network supplying the output power to reduce the risk of a power loss or outage.

In another example embodiment, the video streaming service may have a firewall or edge network device 105 that receives and routes multiple requests to stream certain video files. Each particular customer may start watching a movie or episode for half an hour or more, which must continue to be smoothly provided for the duration of the time period even if additional customers start logging in and requesting other video data, although some customers will log out before the complete movie or episode is completed. The Content Delivery Network (CDN) management system 115 may examine the current utilization of the network and distribute copies of the movie files to secondary CDN servers to minimize the risk that bandwidth will be fully used and to enable additional customers to access the data or to experience interruptions in the viewing experience for the original customer.

In another example embodiment, an autonomous vehicle with cameras, ranging and other sensors 105 may be traveling along a road with some other vehicles, pedestrians or other objects in the vicinity. The server 100 may need to continuously assess multiple possibilities of serious accidents, such as the possibility that a car a given distance away will turn into the path of the autonomous vehicle before the autonomous vehicle can avoid, or the possibility that a pedestrian walking towards the road will continue to enter the road rather than stopping sideways and instructing the vehicle control system 115 to change its path or speed to reduce the risk of an accident.

In another example embodiment, a weather satellite, anemometer, or Doppler radar system 105 may detect weather systems or hazards such as hurricanes, wildfires, or tornadoes that may be near a neighborhood, city, or other dwelling point. A determination may need to be made regarding the total cost of evacuating in response to a hazard versus the expected cost of not evacuating, which must take into account the possibility that the hazard will never reach the human residence. Similarly, a vehicle, vessel, or other asset may need to move in response to an upcoming hail, sandstorm, or other weather event that may or may not actually affect a given location. The alert system or notification system 115 may be configured to trigger if and only if the potential hazards of non-evacuation meet a predefined risk threshold, or may instruct the autonomous vehicle control system 115 to drive the vehicle to a given location if the risk of damage or disruption to the vehicle becomes unacceptably high based on current weather conditions.

In another example embodiment, the sensor 105 may include a firewall or router at the edge of the computer network and reporting the incoming number of network packets, while the proxy 115 may include a server, router, or firewall in a cluster of servers. The system can monitor current network utilization and calculate the risk that a denial of service attack will occur, and will be able to disable the system in its current configuration. In response, it may activate more servers to handle the attack or slow down the inflow of network traffic until the risk of network failure is sufficiently reduced.

In another example embodiment, the sensor 105 may include a GPS tracker on multiple animals, or a camera that records the position of an animal in a natural reserve. Predators or regional herbivores such as lions or elephants in the protected area may move somewhat randomly around people in the current protected area. The central server may continuously assess the risk that the animal will encounter a person before the person leaves the protected area and have a notification system, alarm or personal mobile computing device 115 of the person alert the person of the possibility of encounter and suggest a path that minimizes the risk of encounter.

In another example embodiment, the sensor 105 may include a device at a stock exchange or other market that reports a current bid or ask price for one or more assets. The agent 115 may include a computing device capable of sending buy or sell orders to the market or recalling buy or sell orders from the market, or may include a firewall device capable of preventing such a computing device from successfully sending a buy or sell order to the market. In response to exposure to an unacceptable level of asset-based risk (such as selling an asset at a fixed price or purchasing an order for an asset at any price available in a market where the price of the asset rapidly increases), the trader's ability to trade may be automatically stopped by the computing device itself, or the trader may be notified of an exception so the trader may proceed with more caution.

In all of the above systems, if the system takes no action, it is valuable to determine the total risk exposure, and this determination is used to decide whether to warrant action to minimize or eliminate the risk of system exposure.

Fig. 2A and 2B depict conceptual diagrams of risk exposure from a hazard over a period of time.

In the simple case (fig. 2A), where the expected time until an adverse event occurs is normally distributed, it is expected that the graph 205, which sees the probability that an event will occur exactly at time t, takes the form of a conventional bell-shaped curve. The plot 210 of the cumulative probability of an event occurring at or before time t will therefore have an S-shape because it comprises the sum of all the points on the plot 205 that occur before it in time.

If it is assumed that the cost of an event affecting a resource is independent of the time at which the event occurred, the cumulative risk exposure based on that resource is simply proportional to the cumulative probability 210 at the end of any time window under consideration.

Conversely, if the timing of an event is related to determining the hazard of that event, the resource-based cumulative risk exposure will be the hazard function multiplied by the integral of the probability function-that is, for each time instant considered, the sum of the probabilities of the occurrence of a hazard at that time instant is multiplied by the amount of hazard that would occur if a hazard occurred.

For example (as shown in fig. 2B), a community may be considered to incur injury equal to X if hit by a hurricane while evacuating, but equal to 10X if hit by a hurricane while residing. If evacuation is commanded, the hazard function 215 will decrease as people are evacuated over time, and even though the curve 205 of the probability of an event at time t does not change, the hazard at a given time multiplied by the probability of an event at a given time will appear very different from the probability curve alone, and thus the graph of weighted cumulative exposure 220 will behave differently, with a sharper initial increase and a faster balance. The difference between the cumulative exposure if evacuation is commanded and the separately calculated cumulative exposure if evacuation is not commanded may be the basis for the automated system to determine to alert the occupants or indicate that security personnel should begin evacuation.

Weighted risk exposures may be calculated for various adverse events (e.g., hurricanes) and/or for various resources (e.g., each settlement point that a hurricane may attack), and summed to determine a total weighted risk exposure for action or inaction within a given chaotic environment.

FIG. 3 depicts a method for an artificial intelligence system to process incoming sensor data and boot an agent in a chaotic environment.

First, the system may receive (from an external server, a saved profile, an entry for a human user, or some other source) a critical time window and a maximum allowable weighted risk exposure to consider (step 300). For example, a weather evacuation system may be configured to look only one week ahead due to the uncertainty of data beyond the time window. A system that monitors the price of an asset may not be of interest for any period of time after the market has been closed for a given day.

Next, the system receives sensor data regarding the environment and resources from the sensors 105 (step 305). In some implementations, the system can be preconfigured with information regarding expected behavior of elements of the environment or expected behavior of the resource. In other embodiments, the system may receive sensor data over a period of time and model the behavior of those elements and resources.

For example, for elements of a chaotic environment that experience random walk-like behavior (such as movement of animals in protected areas, or change in value of assets in the market), in this case, the system may determine the standard deviation of the volatility of the changes in the system to help determine the probability that an element will change by a given amount during a given time interval.

In another example, chaotic environments may experience periodic swings, such as increased use of streaming services or power utility at night and decreased use at night. The system can determine the period of such a cycle and use it during future cycles to predict system changes.

After at least an initial amount of sensor data is received and processed, the central server determines a total weighted risk exposure for the system (step 310).

As described above, this determination may be made based at least in part on integrating all t, P (events occurring at or before t) before the end of the critical time window times the cost function of the events at time t.

The system may assume that a given environmental variable (such as the location of a hazard along a given axis or the value of an asset on a certain scale) experiences a random walk-like behavior, and thus the probability of a variable covering a given distance over time is not proportional to the distance, but instead decreases at a faster rate than linear proportionality. In a preferred embodiment, the "distance" between two environment values a and b should not be calculated as | a-b |, but as (a-b)². Additionally, In some embodiments, the sensed values may be modified prior to calculation, such as determining the distance between Ina and Inb rather than the distance between a and b.

Various embodiments may also take into account natural fluctuations in the underlying system whose environmental variables are being sensed. Dividing the distance by a measure of volatility, such as a value proportional to the standard deviation of the sensed variable value over a period of time, would more accurately represent the fact that: random walks in faster-paced and more chaotic systems are more likely to cover distances in a given time interval than random walks in slower-paced systems. The standard deviation may be calculated from the number of seconds, minutes or days or a predetermined number of sensor readings prior to the calculation of the current risk exposure, regardless of the time interval in which those readings are included. The standard deviation may be set to a predetermined default value when the amount of data or the conciseness of the previous time window available makes the standard deviation more affected by noise in the data and less likely to reflect the actual future changes in the variable over the upcoming time window.

In a preferred embodiment including a squared distance term and a volatility tolerance as described above, if the random walk-like behavior has a standard deviation σ, where the current mean is z₀Then the cumulative probability that the environment variable reaches a certain value z at the exact time t is equal to

Given this assumption, the integral of the function over the total remaining time interval of length T (i.e., the cumulative probability that an event will occur at some point during the interval) is

Where ERFC is the complementary error function. If the cost function of an event is constant with respect to time, the total weighted risk exposure will therefore be equal to

Where k denotes each resource, and C_kRepresenting the cost to the resource if the event occurs.

Other probability functions may be used to estimate the probability of a change in the system that does not appear to experience random walk behavior. The probability functions may also be integrated over time to determine shortcuts to the cumulative probability of an event and to determine the overall weighted risk exposure.

The cost associated with a given resource may be, for example, an estimate of the cost of repairing a physical item, as well as an abstract cost of pain, distress, or loss of life, an abstract cost of loss of reputation of a consumer-oriented system when the consumer relies on the consumer-oriented system, or a purely economic loss due to engaging in an unknowingly trade in a market environment. In many cases, the cost may be an expected cost for a class of events, which may itself have a wide range of possible actual costs.

After determining the overall weighted risk exposure, the system determines whether the overall weighted risk exposure exceeds the stored maximum allowable weighted exposure (step 315).

If there is not an excessive risk exposure, the system returns to receiving sensor data during a new time period (step 305) and recalculates whether the risk exposure has changed in response to updated data from the sensor 105. In some embodiments, recalculating the total number of calculations involved in the total risk exposure may be burdensome, and the system may only recalculate the total weighted risk exposure when a given environmental variable has changed by at least some minimum threshold amount since the last time the total weighted risk exposure was calculated. Additionally, in some embodiments, the system may round, truncate, or otherwise pre-process the environment variable data prior to recalculation in order to simply recalculate or determine that it is not yet necessary.

On the other hand, if there is excessive risk exposure, the system may transmit a message or instruction to the agent 115 (step 320) to begin minimizing risk by moving the resource, notify a human user of the risk of the resource, or otherwise reconfigure the resource to minimize risk from the environment, as described in the embodiments above. After transmitting the message, the system returns to observing the sensor data (step 305 and subsequent steps) and determining if there is still an excessive risk exposure and further action by the agent is required.

In particular embodiments directed to minimizing exposure within a stock market environment, additional considerations and actions may be taken in determining exposure or response to the exposure.

For example, the order itself may be modified, such as changing quotes, quote volume, status (quote, accept, or fill), in addition to changes in the price of the asset to which the order relates. An "order book" containing all outstanding orders may be maintained and updated as orders are provided, modified, or filled out, thereby affecting the total exposure an entity may have on one or more stock exchanges. Software maintaining the order book may need to keep the order book accurate despite the status of the reports updated and the complex chain of modifications, such as orders that are offered, modified, accepted, modified again, and filled in part from one source and in part from another. This task may be further complicated by messages being received out of order, such as receiving confirmation of order modification before notifying a request to modify an order, so that incoming messages may be queued until the context needed to understand them is also received.

The company's total order book may be used to estimate the company's total financial risk at a given time-for example, if the company has outstanding bids to purchase assets at a given price, but the market price has dropped below the given price, and other traders are allowed to earn money and obtain free money substantially at the company's expense. Using the above mathematical calculations, incomplete offers may be weighted by the probability that they will actually be satisfied, which will be inversely related to the distance between the current price of the offer and the current price of the market. When market prices fluctuate or offer prices are modified, the company's total risk exposure may be recalculated to determine if action needs to be taken to reduce the exposure. Examples of actions may include automatically generating modification instructions for one or more outstanding orders and transmitting the instructions to one or more exchanges that have placed the orders; automatically generating a notification for the human user that the increased financial risk level has been reached and that additional attention should be exercised; or even prevent (through control of the communication interface of the computer for the transaction) sending additional orders to the exchange until the total risk exposure has decreased or the human user has authorized resumption of the transaction.

FIG. 4 is a high-level block diagram of a representative computing device that may be used to implement the functions of the various features and processes described herein, e.g., the central server 100, sensors 105, or autonomous agents 115. The computing device may be described in the general context of computer-system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types.

As shown in fig. 4, the computing device is shown in the form of a special purpose computer system. Components of the computing device may include, but are not limited to, one or more processors or processing units 900, a system memory 910, and a bus 915 that couples various system components including the memory 910 to the processors 900.

Bus 915 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

The processing unit 900 may execute computer programs stored in the memory 910. Any suitable programming language may be used to implement the routines of particular embodiments, including C, C + +, Java, assembly language, and the like. Different programming techniques such as procedural or object oriented may be employed. The routines may execute on a single computing device or multiple computing devices. Further, multiple processors 900 may be used.

The computing device typically includes a variety of computer system readable media. Such media may be any available media that is accessible by a computing device and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 910 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)920 and/or cache memory 930. The computing device may also include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 940 may be provided for reading from and writing to non-removable, nonvolatile magnetic media (not shown and commonly referred to as a "hard drive"). Although not shown, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk such as a CD-ROM, DVD-ROM, or other optical media may be provided. In this case, each may be connected to the bus 915 by one or more data media interfaces. As will be further depicted and described below, memory 910 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments described in this disclosure.

By way of example, and not limitation, a program/utility 950 having a set (at least one) of program modules 955, as well as an operating system, one or more application software, other program modules, and program data, may be stored in memory 910. Each of the operating system, one or more application programs, other program modules, and program data, or some combination thereof, may include an implementation of a networked environment.

The computing device may also communicate with: one or more external devices 970 such as a keyboard, pointing device, display, etc.; one or more devices that enable a user to interact with a computing device; and/or any device (e.g., network card, modem, etc.) that enables the computing device to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 960.

Additionally, as described above, the computing device may communicate with one or more networks, such as a Local Area Network (LAN), a general Wide Area Network (WAN), and/or a public network (e.g., the internet) via the network adapter 980. As depicted, the network adapter 980 communicates with the other components of the computing device via the bus 915. It should be understood that although not shown, other hardware and/or software components may be used in conjunction with the computing device. Examples include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archive storage systems, among others.

The description of various embodiments of the present invention has been presented for purposes of illustration but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles of the embodiments, the practical application, or technical improvements to the technology found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

The present invention may be a system, method and/or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions embodied thereon for causing a processor to perform various aspects of the present invention.

The computer readable storage medium may be a tangible device that can retain and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device such as a punch card or a raised pattern in a recess having instructions recorded thereon, and any suitable combination of the foregoing. A computer-readable storage medium as used herein should not be interpreted as a transitory signal per se such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a corresponding computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing device/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing device/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, an electronic circuit comprising, for example, a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), can perform aspects of the present invention by utilizing state information of computer-readable program instructions to execute the computer-readable program instructions to personalize the electronic circuit.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer programs according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having the instructions stored therein comprise an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.