WO2020101693A1 - Markmesafe - Google Patents

Abstract

The system and method may leverage transaction data in a payment system to determine the extent of an emergency situation.

Description

MarkMeSafe

BACKGROUND

[0001] When emergencies occur, often obtaining accurate information regarding to scope of the emergency is a challenge. As electricity and electronic communication may be compromised, it may be difficult to obtain information as would be normally expected. Further, trying to determine if a specific individual is ok can be of great stress as the lack of communication may allow imaginations to run wild.

SUMMARY

[0002] The system and method may leverage transaction data in a payment system to determine the extent of an emergency situation. By reviewing past transaction patterns and comparing patterns over the emergencies to patterns before toe

emergency, the scope of toe emergency may be determined. In addition, the system may trade transactions for individuals to determine whether the individual is ok after the emergency as indicated by the individual making a transaction.

BRIEF DESCRIPTION OF THE DRAWINGS

[0001] The invention may be better understood by references to the detailed description when considered in connection with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the printip!es of the invention. In the figures, like reference numerals designate corresponding parts throughout the different views.

[0002] Fig. 1 shows an illustration of an exemplary payment system for determining if an account holder is safe;

[0003] Fig. 2A shows a first view of an exemplary payment device for use with the system of Fig. 1 ;

[004] Fig. 2B shows a second view of an exemplary payment device for use with the system of Fig. 1;

[005] Fig. 3 shows an exemplary machine learning architecture; [000] Fig. 4 shows an exemplary artificial intelligence architecture;

[007] Fig. 5 is a flowchart of a method for determining whether a person is safe after a disaster; of Fig. 1;

[008] Fig. 6 shows an exemplary computing device that may be physically configured to execute the methods and include the various components described herein;

[009] Fig. 7 shows an illustration of a city with transaction analysis displayed;

[0010] Fig, 8 shows an illustration of a location of an emergency and the

transactions that have been attempted since the emergency and areas where no transactions have occurred;

[0011] Fig. 9 shows an illustration of a location of an emergency and tile

transactions that have been attempted since the emergency and areas where few transactions have occurred;

[0012] Fig. 10 shows an input display to inquire about a card holder; and

[0013] Fig. 11 shows an input display to inquire about a card holder and the results of the inquiry including the merchant, the location and the time.

[0014] Persons of ordinary skill in the art will appreciate that elements in the figures are illustrated for simplicity and clarity so not all connections and options have been shown to avoid obscuring the inventive aspects. For example, common but well- understood elements that are useful or necessary in a commercially feasible

embodiment are not often depicted in order to facilitate a less obstructed view of these various embodiments of the present disclosure. It will be further appreciated that certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. It will also be understood that tine terms and expressions used herein are to be defined with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein.

SPECIFICATION

[0015] The present invention now will be described more folly with reference to the accompanying drawings, which form a part hereof, arid which show, by way of illustration, specific exemplary embodiments by which the invention may be practiced. These illustrations and exemplary embodiments are presented with the understanding that the present disclosure is an exemplification of the principles of one or more inventions and is not intended to limit any one of the inventions to the embodiments illustrated. The invention may be embodied in many different forms and should not be construed as limited to foe embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will folly convey the scope of foe invention to those skilled in foe art. Among other things, foe present invention may be embodied as methods, systems, computer readable media, apparatuses, components, or devices. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. The hardware may be local, may be remote or may be a combination of local and remote. The following detailed description Is, therefore, not to be taken in a limiting sense.

[0016] At a high level, the system and method may use credit and debit card transactions within the area span prior to the disaster to get insight on the whereabouts of affected individuals. The transaction data which is stamped with location information of the merchant may be accessed only by the nominated emergency contacts or by authorized contacts seeking to find a specific person. The system and method may also create an aggregated view based on the number of transactions, which happened before and after the disaster to get an estimate on the number of people affected by the disaster, which can be leveraged to provide relief facilities. More specifically, by comparing the transaction volume before and after the disaster, users may gain a better understanding of the estimated number of people affected by the disaster and also give users a real-time idea of how tilings are developing after the disaster. These facilities may be accessed via a website or an app. In addition, the registered emergency contact(s) may be notified with text messages when an authenticated transaction, such as a debit card with a PIN, a credit card which requires a PIN or Zip Code or electronic wallet/token based systems which require biometric authentication, may be performed after the occurrence of the disaster, hence marking the individual safe. The data within the MarkMeSafe application can be exposed as an API, which can be used by Govt, agencies during disaster rescue and relief planning along with other data tike mobile phone geolocation, etc.

[0017] From a technical standpoint, emergency situations cause stress as normal communication channels are often broken. However, transaction data may be able to be used as a technical solution to determine if a person or plurality of people are unharmed and are making transactions. Further, the API to access the transaction data may be useful to reduce errors and improve accuracy. In addition, the transaction data may be analyzed to determine the extent of the emergency in the area and the type of emergency that may have occurred.

[0018] Fig 1 generally illustrates one embodiment of a payment system 100 for determining whether an emergency has occurred in an area and whether individuals are safe after the emergency. The system 100 may include a computer network 102 that links one or more systems and computer components. In some embodiments, the system 100 includes a user computer system 104, a merchant computer system 106, a payment network system 108, and a transaction analysis system which may embody artifidal intelligence 110.

[0019] The network 102 may be described variously as a communication link, computer network, internet connection, etc. The system 100 may indude various software or computer-executable instructions or components stored on tangible memories and specialized hardware components or modules that employ the software and instructions to identify related transaction nodes for a plurality of transactions by monitoring transaction communications between users and merchants. [0020] The various modules may be implemented as computer-readable storage memories containing computer-readable instructions (i.e., software) for execution by one or more processors of the system 100 within a specialized or unique computing device. The modules may perform the various tasks, methods, blocks, sub-modules, etc., as described herein. The system 100 may also include both hardware and software applications, as well as various data communications channels for

communicating data between the various specialized and unique hardware and software components.

[0021] Networks are commonly thought to comprise the interconnection and interoperation of hardware, data, and other entities. A computer network;, or data network, is a digital telecommunications network which allows nodes to share

resources. In computer networks, computing devices exchange data with each other using connections, i.e., data links, between nodes. Hardware networks, for example, may indude clients, servers, and intermediary nodes in a graph topology. In a similar fashion, data networks may indude data nodes in a graph topology where each node indudes related or linked information, software methods, and other data. It should be noted that the term“server” as used throughout this application refers generally to a computer, other device, program, or combination thereof that processes and responds to the requests of remote users across a communications network. Servers serve their information to requesting“dients." The term“client" as used herein refers generally to a computer, program, other device, user and/or combination thereof that is capable of processing and making requests and obtaining and processing any responses from servers across a communications or data network. A computer, other device, set of related data, program, or combination thereof that fadlitates, processes information and reguests, and/or furthers the passage of information from a source user to ? destination user is commonly referred to as a“node.* Networks generally fadlitate the transfer of information from source points to destinations. A node specifically tasked with furthering the passage of information from a source to a destination is commonly called a“router." There are many forms of networks such as Local Area Networks (LANs),

Pico networks, Wide Area Networks (WANs), Wireless Networks (WLANs), etc. For example, the Internet is generally accepted as being an interconnection of a multitude of networks whereby remote dients and servers may access and interoperate with one another.

[0022] A user computer system 104 may include a processor 145 and memory 146. The user computing system 104 may include a server, a mobile computing device, a smartphone, a tablet computer, a Wi-Fi-enabled device, wearable computing device or other personal computing device capable of wireless or wired communication, a thin client, or other known type of computing device. The memory 146 may include various modules including instructions that, when executed by the processor 145 control the functions of the user computer system generally and integrate the user computer system 104 into the system 100 in particular. For example, some modules may include an operating system 150A, a browser module 150B, a communication module 150C, and an electronic wallet module 150D. In some embodiments, tine electronic wallet module 150D and its functions described herein may be incorporated as one or more modules of tee user computer system 104. In other embodiments, tee electronic wallet module 150D and its functions described herein may be incorporated as one or more sub-modules of the payment network system 108. In some embodiments, a responsible parly 117 is in communication with tee user computer system 104.

[0023] In some embodiments, a module of tee user computer system 104 may pass user payment data to other components of tee system 100 to facilitate determining a real-time transaction analysis determination. For example, one or more of the operating system 150A, a browser module 150B, a communication module 150C, and an electronic wallet module 150D may pass data to a merchant computer system 106 and/or to the payment network system 108 to facilitate a payment transaction for a good or service. Data passed from the user computer system 104 to other components of the system may indude a customer name, a customer ID (e.g., a Personal Account Number or“PAN”), address, current location, and other data.

$0024] The merchant computer system 106 may indude a computing device such as a merchant server 129 induding a processor 130 and memory 132 including components to facilitate transactions with tee user computer system 104 and/or a payment device 200 (Fig. 2) via other entities of the system 100. In some embodiments, the memory 132 may indude a transaction communication module 134. The transaction communication module 134 may indude instructions to send merchant messages 134A to other entities (e.g., 104, 108, 110) of the system 100 to indicate a transaction has been initiated with toe user computer system 104 and/or payment device 200 induding payment device data and other data as herein described. The merchant computer system 106 may indude a merchant transaction repository 142 and instructions to store payment and other merchant transaction data 142A within the transaction repository 142. The merchant transaction data 142A may only correspond to transactions for products with the particular merchant or group of merchants having a merchant profile (e.g., 164B, 164C) at toe payment network system 108.

[0025] The merchant computer system 106 may also include a product repository

143 and instructions to store product data 143A within the product repository 143. For each product offered by the merchant computer system 106, the product data 143A may include a product name, a product UPC code, an item description, an item category, an item price, a number of units sold at a given price, a merchant ID, a merchant location, a customer location, a calendar week, a date, a historical price of the product, a merchant phone number(s) and other information related to toe product. In some embodiments, the merchant computer system 106 may send merchant payment data corresponding to a payment device 200 (Fig. 2) to toe payment network system 108 or other entities of toe system 100, or receive user payment data from the user computer system 104 in an electronic wallet-based or other computer-based transaction between the user computer system 104 and the merchant computer system 106.

[0026] The merchant computer system 106 may also include a fraud module 152 having instructions to facilitate determining fraudulent transactions offered by the merchant computer system 106 to toe user computer system 104. Thus, toe

transaction volume analysis and location information may be accurate.

[0027] The fraud API 152A may indude instructions to access one or more backend components (e.g., the payment network system 108, toe artifidal intelligence engine 110, etc.) and/or toe local fraud module 152 to configure a fraud graphical interface 1528 to dynamically present and apply toe transaction analysis data 144 to products or services 143A offered by the merchant computer system 106 to the user computer system 104. A merchant historical fraud determination module 152C may indude instructions to mine merchant transaction data 143A and determine a list of past fraudulent merchants to obtain historical fraud information on the merchant.

[0028] The payment network system 108 may include a payment server 156 inducting a processor 158 and memory 160. The memory 160 may indude a payment network module 162 induding instructions to facilitate payment between parties (e.g., one or more users, merchants, etc.) using the payment system 100. The module 162 may be communieab!y connected to an account holder data repository 164 induding payment network account data 164A.

[0029] The payment network account data 164A may indude any data to facilitate payment and other funds transfers between system entities (e.g., 104, 106). For example, the payment network account date 164A may indude account identification data, account history data, payment device data, etc. The module 162 may also be communicab!y connected to a payment network system transaction repository 166 induding payment network system global transaction date 166A.

[0030] The global transaction data 166A may indude any data corresponding to a transaction employing the system 100 and a payment device 200 (Fig. 2). For example, the global transaction data 166A may indude, for each transaction across a plurality of merchants, data related to a payment or other transaction using a PAN, account identification data, a product or service name, a product or service UPC code, an item or service description, an item or service category, an item or service price, a number of units sold at a given price, a merchant ID, a merchant location, a merchant phone numbers), a customer location, a calendar week, and a date, corresponding to the product data 143A for the product that was the subject of the transaction or a merchant phone number. The module 162 may also indude instructions to send payment messages 167 to other entities and components of the system 100 in order to complete transactions between users of the user computer system 104 and merchants of the merchant computer system 106 who are both account holders within the payment network system 108. [0031] The artificial intelligence engine 110 may include one or more instruction modules including a transaction analysis module 112 that, generally, may include instructions to cause a processor 114 of a transaction analysis server 116 to functionally communicate with a plurality of other computer-executable steps or sub-modules, e.g., sub-modules 112A, 112B, 112C, 112D and components of the system 100 via the network 102. These modules 112A, 112B, 112C, 112D may indude instructions that, upon loading into the server memory 118 and execution by one or more computer processors 114, dynamically determine transaction analysis data for a product 143A or a merchant 106 using various stores of data 122A, 124A in one more databases 122, 124. As an example, sub-module 112A may be dedicated to dynamically determine transaction analysis data based on transaction data associated with a merchant 106.

[0032] With brief reference to Figs. 2A and 2B, an exemplary payment device 200 may take on a variety of shapes and forms. In some embodiments, the payment device 200 is a traditional card such as a debit card or credit card. In other embodiments, the payment device 200 may be a fob cm a key chain, an NFC wearable, a mobile phone or other device. In other embodiments, the payment device 200 may be an electronic wallet where one account from a plurality of accounts previously stored in the wallet is selected and communicated to the system 100 to execute the transaction. As long as the payment device 200 is able to communicate securely with the system 100 and its components, the form of the payment device 200 may not be especially critical and may be a design choice. For example, many legacy payment devices may have to be read by a magnetic stripe reader and thus, the payment device 200 may have to be sized to fit through a magnetic card reader. In other examples, the payment device 200 may communicate through near field communication and the form of the payment device 200 may be virtually any form. Of course, other forms may be possible based on the use of the card, the type of reader being used, etc.

[0033] Physically, tile payment device 200 may be a card and the card may have a plurality of layers to contain the various elements that make up the payment device 200. In cme embodiment, the payment device 200 may have a substantially flat front surface 202 and a substantially flat back surface 204 opposite the front surface 202. Logically, in some embodiments, the surfaces 202, 204 may have some embossments 206 or other forms of legible writing including a personal account number (PAN) 206A and the card verification number (CVN) 206B. In some embodiments, tee payment device 200 may include data corresponding to the primary account holder, such as payment network account data 164 A for the account holder. A memory 254 generally and a module 254A in particular may be encrypted such that all data related to payment is secure from unwanted third parties. A communication interface 256 may include instructions to facilitate sending payment data 143B, 143A such as a payment payload, a payment token, or other data to identify payment information to one or more

components of tee system 100 via the network 102.

[0034] With reference to Fig. 3, a machine learning (ML) architecture 300 may be used with the transaction analysis module 112 of system 100 in accordance with the current disclosure. In some embodiments, an Al module 1 12D of the artificial intelligence system 110 may include instructions for execution on the processor 114 that implement the ML architecture 300. The ML architecture 300 may indude an input layer 302, a hidden layer 304, and an output layer 306. The input layer 302 may indude inputs 3Q8A, 3088, ete, coupled to the transaction analysis module 112 and represent those inputs that are observed from actual product, customer, and merchant data in the transaction data 142A, 166A. The hidden layer 304 may include weighted nodes 310 that have been trained for the transactions being observed. Each node 310 of the hidden layer 304 may receive tee sum of all inputs 308A, 308B, etc., multiplied by a corresponding weight. The output layer 306 may present various outcomes 312 based on the input values 308A, 308B, etc., and the weighting of the hidden layer 304. Just as a machine learning system for a self-driving car may be trained to determine hazard avoidance actions based on received visual input, tee machine learning architecture 300 may be trained to analyze a likely outcome for a given set of inputs based on thousands or evert millions of observations of previous customer/merchant transactions. For example, tee architecture 300 may be trained to determine transaction analysis data 144 to be associated with tee product data 143A. This provides an insight on the individual customer location pattern and may also be extended to estimate customer volume patterns at a specific merchant at a given time. [0035] During training of tile machine learning architecture 300, a dataset of inputs may be applied and the weights of the hidden layer 310 may be adjusted for the known outcome (e.g., a transaction analysis baseline) associated with that dataset. As more datasets are applied, the weighting accuracy may improve so that the outcome prediction is constantly refined to a more accurate result. In this case, the merchant transaction repository 142 and/or the payment network system repository 166

respectively including transaction data 142A and 166A may provide datasets for initial training and ongoing refining of the machine learning architecture 300.

[0036] Additional training of the machine learning architecture 300 may include an artificial intelligence engine (A) engine) 314 providing additional values to one or more controllable inputs 316 so that outcomes may be observed for particular changes to the transaction analysis data 142A and 166A. The values selected may represent different data types such as community responses, merchant ratings and other alternative data presented at various points in the transaction process with the product data and may be generated at random or by a pseudo-random process. By adding controlled variables to the transaction process, over time, the impact may be measured and fed back into the machine learning architecture 300 weighting to allow capture of an impact on a proposed change to the process in order to optimize the determination of the

transaction analysis data 144. Over time, the impact of various different data at different points in the transaction cycle may be used to predict an outcome for a given set of observed values at the inputs layer 302.

[003h After training of the machine learning architecture 300 is completed, data from tiie hidden layer may be fed to the artificial intelligence engine 314 to generate values tor controllable input(s) 316 to optimize the transaction analysis data 144.

Similarly, data from the output layer may be fed back into the artificial intelligence engine 314 so that the artificial intelligence engine 314 may, in some embodiments, iterate with different data to determine via the trained machine teaming architecture 300, whether the transaction analysis data 144 is accurate, and other determinations.

[0036] With reference to Fig. 4, in other embodiments, the machine learning architecture 300 and artificial intelligence engine 314 may include a second instance of a machine learning architecture 400 and/or an additional node layer may be used. In some embodiments, a transaction analysis data identification layer 402 may determine an optimum transaction analysis determination 404 from observed inputs 404A, 404B. A transaction analysis layer 406 with outputs 408A, 408B, etc., may be used to generate transaction analysis recommendations 410 to an artificial intelligence engine 412, which in turn, may modify one or more of telephone data generally and the transaction analysis data in particular when communicating this data via an appropriate SDK.

[0039] Referring to Fig. 5, a method for determining an extent of a natural disaster may be disclosed using the transaction analysis module 112 in the transaction analysis server 116. At block 505, a baseline transaction analysis in an area may be

determined. The baseline transaction analysis may be created in a variety of ways and may indude a variety of detail depending on Ore detail that is available.

[0040] In some embodiments, the method and system may attempt to separate people into groups for a variety of reasons. In some situations, foe method and system may simply be interested if any transactions are occurring in an area at a point in time to determine if an area have been devastated. In other situations, the level of sales may be low but foal low level of sale may be normal in that area or in that demographic group. In yet some additional embodiments, foe normal goods or service that are purchased may be compared to good and services that are being purchased at foe moment with foe idea being if people are purchasing emergency supplies, a disaster is likely.

[0041] In one embodiment, the baseline transaction analysis may include individual transactions of goods or services made by individual purchasers at specific points in time. The baseline transaction analysis may be created over a variety of periods of time. For example, foe period of time may be a year or a month or a week.

[0042] Further, the baseline transaction analysis may be broken down even further. For example, the baseline transaction analysis may be reviewed for all Tuesday mornings and transaction for foe past year may be averaged to determine an average amount of sales in an area during Tuesday mornings.

[0043] The artificial intelligence system 110 may also be used by the system in determining a variety of decisions in the system and method. For example, the baseline creations of Fig. 5 may use artificial intelligence to create a variety of baseline measurements. In addition, the threshold may be created by studying past transactions and determining using artificial intelligence 110 what transaction levels, transaction amounts, or transactions for certain goods or services would indicate that an emergency is present.

[0044] The area may also be defined in a variety of ways. In some embodiments, it may be a geographic area such as a zip code area, In other embodiments, the area may be smaller than a zip code such as a block or side of a street. In other

embodiments, the area may be larger such as a city of county or state.

$0045] In other embodiments, the area may be defined primarily through

demographic data with a geographic qualifier. For example, the area may be people with annual income over $50,000 in the state of Idaho, As another example, foe area may be defined as daily debit card users in foe dty of Seattle.

[0046] In some embodiments, the groups may be separated into groups where the groups have similar spending patterns. In other embodiments, foe groups may be created with people that have similar income. In other embodiments, foe groups may be separated by people that purchase goods or services in a defined area during a defined time with a defined frequency.

E004h Communities may be determined in a variety of ways and the Al engine 110 may be used to determine the communities. In some embodiments, foe communities may be formed using demographic data if such data is available. For example, if users are senior citizens, they may form a group. Similarly, a community may be users in the same zip code or with the same area. [0048] The Al engine 110 may create communities or may be used to refine communities. As an example, the Al engine 110 may analyze the senior citizen group and determine which have similar purchase habits. By learning which have similar purchase habits, it may be determined if one of the community has a purchase, other members may also have purchases.

[0049] As mentioned previously, the broader group may be broken into sub-groups. One subgroup may be used as the test group by the Al engine 110 and the other groups may be the training groups to train the Al engine 110. The training may continue until all groups have been used as a test group.

[0050] The transaction analysis also may take on a variety of forms. In some embodiments, the qualifying individuals during the defined time period may simply be totaled making the transaction analysis a gross amount of goods and services

purchased. In other embodiments, the transaction analysis may be further defined by taking an average of goods and services purchased during a defined period of time by a defined group in a defined area.

[0051] Ih yet another embodiment, the goods or services purchased by an individual may be reviewed to determine if die individual is a more experienced consumer of those goods or services than others. For example, a user that routinely shops for gardening tools may have more useful data cm gardening tools than someone that almost never buys gardening tools. This data would be useful to categorize customers based on spending habits purchasing specific kind of goods and the learning from this category of consumers can be applied to consumers of the same category.

[0052] Referring again to Fig. 5, at block 510 a notification of an emergency event may be received. The emergency event may take on a variety of forms. At a high level, the emergency event may be an event that would affect electronic transmission systems, electronic communication systems* electronic payment systems, water delivery systems, natural gas delivery systems or the like. Far example, an earthquake may qualify as an emergency event. Similarly, a cyber-attack which significantly affects communication systems may qualify as an emergency event. Logically, a tornado, hurricane, typhoon or other natural disaster may qualify as an emergency event.

[0053] The notification may occur in a variety of ways, in some embodiments, authorities may have an emergency system and the system and method may be notified just like the rest of the public. In other embodiments, an emergency network may exist for emergency personnel and the system and method may be part of the emergency network. In other embodiments, the system and method may be monitoring

transactions and a drop of transactions from an expected volume may be an indication that an emergency is occurring.

[0054] In yet other embodiments, the notification system may be more personal. The notification system may push notifications to authorized users that an emergency event may have occurred. In addition, the system and method may push notification to authorized users when a transaction has occurred related to the authorized user. For example, if a first spouse works downtown and an emergency event occurs downtown, the second spouse may receive a notification when foe first spouse makes a

transaction, indicating foe first spouse is ok.

[0055] At block 515, the system and method may determine a current transaction analysis in the area. Logically, the current transaction analysis may match baseline transaction analyses that have been previously calculated such that a change may be calculated. In addition, the current transaction analysis may be tailored according to requirements at foe current time. For example, the area of a power outage may not be known. For example, by creating a block by block current transaction analysis, the extent of a power outage may be estimated.

[0056] At bloc* 520, the system and method may determine a transaction analysis change. The tracked analysis change may compare the current transaction analysis to foe baseline transaction analysis. Logically, the change should focus on changes to foe same area to similar people during a comparable period of time.

[0057] In some embodiments, the comparison may compare a type of good or services purchase in the current time frame to good or service purchased in the past during the time frame. By studying the type of goods or services purchased, additional information may be obtained regarding the emergency. For example, if there are multiple purchases of generators, there may be an electricity outage. Similarly, if numerous cellular access points are purchased, there may be some local internet broadband issues. Likewise, if a user rarely buys plywood but buys plywood after an emergency, a logical conclusion may be that there was damage to the structure at the location of the user or a group of users.

[0068] In some other embodiments, the comparison may compare an amount of goods or services purchase in the current time frame to an amount of goods or services purchased on the past during the time frame. For example, if a user (or group of users) rarely buys bottled water and suddenly buys several gallons of water, a logical conclusion may be that there may be issues with the water delivery service at the home of the user. Other conclusions may be drawn from the amount of goods purchased.

[00S9] In additional embodiments, the comparison may compare a type of good ex’ services purchase in frie current time frame to good or service purchased in the past during the time frame for similar groups. Studying an individual may not catch larger patterns that may exist. By studying groups of users, larger patterns may be

determined. For example, if a geographic area has a spike in generators purchased when generators are rarely purchased in an area, it may be logical to conclude that electricity is exit to an area.

[0060] In yet another embodiment, the comparison may compare an amount of goods or services purchase in the current time frame to an amount of goods or services purchased on the past during the time frame for similar groups of users. Studying an individual may not catch larger patterns that may exist. By studying groups of users, larger patterns may be determined. For example, if a geographic area has a spike in the amount of water bottles purchased, it may be logical to conclude that water is out to an area.

[0061] In yet a further embodiment, the total number of transactions in an area may be reviewed. If an area usually has a large number of transactions and the area currently has very few, it may be logical to conclude that an emergency has occurred in an area. Similarly, if a nearby area has a spike in transactions, it may be logical to conclude teat surround areas may have emergency conditions.

[0062] At block 525, if the transaction analysis change from the current period compared to previous periods is over a change threshold, at block 530 an appropriate contact may be alerted. As mentioned previously, the transaction analysis change made be determined in a variety of ways. It may be determined in comparison to large groups or it may be in comparison to groups determined to be similar or may be in comparison to the individual transactions. The transaction change also may be oyer a large geographic area or a very small and defined area. In addition, the transaction change may be over the type of good purchased or the amount of goods purchased.

[0063] The calculation may take on a variety of forms. If the question is

mathematical such as when comparing past monetary amounts to current monetary amounts or when comparing a number of items purchased in the past to the number of items purchased currently, the threshold may be a percentage. For example, if there is a 50% increase or decrease in purchase amounts, an emergency may be in place.

[0064] In instances where the comparison has to do with the type of goods or services being purchased, words may be analyzed as vectors using an algorithm such as the doc2vec method and the distance between the words may be used to determine whether the differences in the words are over a threshold. Similarly, list of words of past purchases may be made and foe list of current purchases may be compared to past purchases and foe number of similar items may indicate how dose the purchase is to a normal purchase. Of course, other manners of comparing words to other words may be used and are contemplated.

[0065] The system and method may lend themselves to be implemented using Application Programming interfaces (APIs). For example, a police officer may enter a zip code and the police officer may receive a response that may be a rating of whether an emergency is taking place. The API may expect the input data in a known format and may respond with an output in a known format. The API may be as simple as (last name, first initial) or it may be more complex such as when more information is available. Further, the API may be flexible and may accept more or less information but still respond with a response. By using an API, errors in entering names and receiving false results may be limited which may be important under emergency conditions.

[0066] In other aspects, a person with authority may enter the name of an individual and the system 100 may respond with whether a transaction has been made and possibly where a user has made a most recent transaction, especially if the user is indicated as being missing.

[0067] In sorrie embodiments, the system 100 may identity individuals ih a given area and may make a list of individuals that have entered into a transaction after the emergency. Similarly, a list of individuals that have not made a transaction may be made and those individuals may be considered missing until a transaction occurs. The list may be provided to the public or to those with prior authorization or to muhidpal authorities trying to establish whether someone is missing such as whether a search should continue, whether relatives should be contacted, etc.

[0068] As mentioned previously, first responders may have emergency access to the system in time of an emergency. In other instances, authorized parties may have access to data on foe system. For example, a parent may have access to foe account data on a child. The access may be through a web site, through an app or users may be able to text a name ex- phone number to an authority and may receive a response. Security may be managed in a variety of ways such as passwords, two factor authentication, biometric verification, key fobs with crypto keys, keycards, secure direct links, etc.

[0069] Fig. 7 may be an illustration of a map 700 showing transactions analysis after an emergency event. Areas where transactions are occurring at a normal rate may be shown with a elide or other transaction indication 710 of the size of the transaction analysis that are occurring at the present time. Logically, other areas where there are no circle or other indications 720 may indicate that the transaction analysis has determined that the area is having fewer transactions under the baseline transaction analysis.

[0070] As may be noticed, some of the transaction indications 710 may indicate the number of transactions teat are occurring. In some embodiments, the number may be a percentage or an indication of the current number of transactions in comparison to tee baseline transaction analysis. In other embodiments, tee size or color of tee transaction indication 710 may indicate the level or amount of transactions in the area. For example, tee transaction indication 710a may be smaller and a different color to indicate that there are fewer transactions occurring in comparison to tee baseline analysis whereas tee indication 710b may be a different color and a larger size to indicate more transactions currently in occurring in comparison to the baseline transaction analysis. With a mere glance at tee graphical illustration, a user could determine if an area has been affected by an emergency as illustrated by fewer transactions whereas an area that has not been affected or is next to an area next to an affected area may show normal or higher level of transaction analysis in comparison to tee baseline transaction analysis tor an area. For example, in Fig. 7, tee Near South Side and Chinatown have no transaction indications 710 which may indicate an emergency has affected those areas.

[0071] A similar graphic may be created for individuals. The graphic may illustrate locations of transactions and the timing of the transaction. For an example, the graphic may illustrate the last transaction as a large transaction indication 710 and transactions further in the past may be smaller transaction indications 710.

[0072] As will be recognized by one skilled in the art, in light of the disclosure and teachings herein, other types of computing devices can be used that have different architectures. Processor systems similar or identical to tee example systems and methods described herein may be used to implement and execute the example systems and methods described herein. Although tee example system 100 is described below as including a plurality of peripherals, interfeces, chips, memories, etc., one or more of those elements may be omitted from other example processor systems used to implement and execute foe example systems and methods. Also, other components may be added.

[0073] Fig, 8 may be another illustration of a location of an emergency and the transactions that have been attempted since the emergency. The larger area 800 may indicate the general area where an emergency is believed to have occurred. The smaller areas which may be circles 805, 810, 815 and 820 may indicate areas of particular interest. For example, the circle 805 may indicate a group of people determined to be relevant to each other as described previously.

[0074] The areas 805, 810, 815, 820 may also have color or other visual indication to indicate the level of transactions in the area. For example, the area 805 may be red which may indicate a low number of transactions in comparison to an expected number of transactions. Similarly, the area 825 may be green to indicate a similar number of transactions after the emergency as before the emergency.

[0075] Some areas may indicate the data collected to provide detail to the color or visual indication assigned to the area. For example, area 825 may display the raw date 830 that was before the emergency, 14786 transactions occurred during a given period of time and 13789 transactions may have occurred after the emergency. The number after foe emergency may have been determined to be within a threshold of the number of transactions before foe emergency and the area may be noted as green. In some embodiments, selecting the area such as 825 may bring up the raw date 830 for the selected area. Referring to Fig. 9, foe area 810 may be selected and the raw data showing that there were 12493 transactions attempted before the emergency and 2 transactions attempted after the emergency which would likely mean the area tells below virtually all but foe lowest threshold and the area may be marked as being red to indicate problems exist in the area.

[0076] Fig. 10 may show an input display to inquire about a user to determine if any transaction have beat attempted by payment accounts registered to the user which may indicate the user is safe. In input fields 1005, 1010 and 1015, relevant information about a user may be entered and searched 1020. In some embodiments, the system may function with just some of the data but the data returned may be anonymized in case an incorrect match is found, if transactions are located, relevant details may be displayed such as the Merchant 1030 that handled the transaction, tee location 1035 of the Merchant where the transaction occurred and the time 1040 the transaction occurred. If no transaction attempts are found the found information 1025 may be blank.

[0077] Fig. 11 may illustrate the situation when all the information on an account holder is entered in the input fields 1005, 1010, 1015 and searched 1020 and

transactions are located based on the inputted information. The displayed information of the user transactions 1025 may include a merchant name 1030, a merchant location 1035 and tiie time 1040 of the attempted transaction. Thus, the data may provide useful information in determining whether an account holder is safe. More specifically, if the account holder attempted a transaction after the emergency occurred, it may be safe to assume the account holder is safe. Further, by reviewing the merchant and the location, additional assumptions may be made. For example, if the account holder follows their normal routine, it may be more probably that the emergency has not affected the account holder while if the account holder is buying plywood and fire extinguishers and the account holder has not purchased plywood and fire extinguishers previously, it may be more probably that the account user has been negatively affected by the emergency.

[0078] As shown in Fig. 6, the computing device 901 includes a processor 902 that is coupled to an interconnection bus. The processor 902 indudes a register set or register space 904, which is depicted in Fig. 6 as being entirely on-chip, but which could alternatively be located entirely or partially off-chip and directly coupled to the processor 902 via dedicated electrical connections and/or via the interconnection bus. The processor 902 may be any suitable processor, processing unit or microprocessor.

Although not shown in Fig. 6, the computing device 901 may be a multi-processor device and, thus, may indude one or more additional processors that are identical or similar to tee processor 902 and that are communicatively coupled to the

interconnection bus. [0079] The processor 902 of Fig. 6 is coupled to a chipset 906, which includes a memory controller 908 and a peripheral input/output (I/O) controller 910. As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 906. The memory controller 908 performs functions that enable the processor 902 (or processors if there are multiple processors) to access a system memory 912 and a mass storage memory 914, that may include either or both of an in-memory cache (e.g., a cache within the memory 912) or an on-disk cache (e.g., a cache within tine mass storage memory 914).

[0080] The system memory 912 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory 914 may include any desired type of mass storage device. For example, the computing device 901 may be used to implement a module 916 (e.g., the various modules as herein described). The mass storage memory 914 may indude a hard disk drive, an optical drive, a tape storage device, a solid-state memory (e.g,, a flash memory, a RAM memory, etc.), a magnetic memory (e.g,, a hard drive), or any other memory suitable for mass storage. As used herein, the terms module, block, function, operation, procedure, routine, step, and method refer to tangible computer program logic or tangible computer executable instructions that provide the specified functionality to the computing device 901 , the systems and methods described herein. Thus, a module, block, function, operation, procedure, routine, step, and method can be implemented in hardware, firmware, and/or software.

In one embodiment, program modules and routines are stored in mass storage memory 914, loaded into system memory 912, arid executed by a processor 902 or can be provided from computer program products that are stored in tangible computer-readable storage mediums (e.g. RAM, hard disk, optical/magnetic media, etc.).

[008] ] The peripheral I/O controller 910 performs functions that enable the processor 902 to communicate with a peripheral input/output (I/O) device 924, a network interface 926, a local network transceiver 928, (via the network interlace 926) via a peripheral I/O bus. The I/O device 924 may be any desired type of I/O device such as, for example, a keyboard, a display (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT) display, etc.), a navigation device (e.g., a mouse, a trackball, a capacitive touch pad, a joystick, etc.), etc. The I/O device 924 may be used with foe module 916, etc., to receive data from foe transceiver 928, send foe data to foe components of foe system 100, and perform any operations related to foe methods as described herein. The local network transceiver 928 may indude support for a Wi-Fi network, Bluetooth, Infrared, cellular, or other wireless data transmission protocols. In other embodiments, one element may simultaneously support each of foe various wireless protocols employed by the computing device 901. For example, a software- defined radio may be able to support multiple protocols via downloadable instructions.

In operation, the computing device 901 may be able to periodically poll for visible wireless network transmitters (both cellular and local network) on a periodic basis.

Such polling may be possible even while normal wireless traffic is being supported on the computing device 901. The network interface 926 may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 wireless interface device, a DSL modem, a cable modem, a cellular modem, etc., that enables the system 100 to communicate with another computer system having at least the elements described in relation to foe system 100.

[0082] While the memory controller 908 and the I/O controller 910 are depicted in Fig. 6 as separate functional blocks within the chipset 906, the functions performed by these blocks may be integrated within a single integrated circuit or may be implemented using two or more separate integrated circuits. The computing environment 900 may also implement foe module 916 cm a remote computing device 930. The remote computing device 930 may communicate with the computing device 901 oyer an

Ethernet link 932. In some embodiments, the module 916 may be retrieved by foe computing device 901 from a cloud computing server 934 via foe Internet 936. When using foe cloud computing server 934, the retrieved module 916 may be

programmatically linked with foe computing device 901. The module 916 may be a collection of various software platforms including artificial intelligence software and document creation software or may also be a Java® applet executing within a Java® Virtual Machine (JVM) environment resident in the computing device 901 or the remote computing device 930. The module 916 may also be a“plug-in” adapted to execute in a web-browser located on the computing devices 901 and 930. In some embodiments, the module 916 may communicate with back end components 938 via the Internet 936.

[0083] The system 900 may include but is not limited to any combination of a LAN, a MAN, a WAN, a mobile, a wired or wireless network, a private network, or a virtual private network. Moreover, while only one remote computing device 930 is illustrated in Fig. 6 to simplify and clarify the description, it is understood that any number of client computers are supported and can be in communication within the system 900.

[0084] Additionally, certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code or instructions embodied on a machine-readable medium or in a transmission signal, wherein the code is executed by a processor) or hardware modules. A hardware module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain Operations as described herein.

[0085] In various embodiments, a hardware module may be implemented

mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FRGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general- purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitiy, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. [0086] Accordingly, the term“hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed,

permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein,“hardware -implemented module" refers to a hardware module.

Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times.

Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. ΐoobh Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate cm a resource (e.g., a collection of information).

[0088] The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

[0089] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or processors or processor-implemented hardware modules. The performance of certain of die operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

[0090] The one or more processors may also operate to support performance of the relevant operations in a“cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application program interfaces (APIs).)

[0091] The performance of certain of the operations may be distributed among foe one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

[0092] Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). These algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an“algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as“data, * U

“bits,”“values,”“elements,”

“symbols,”“characters,"“terms,” "numbers,” "numerals," or toe like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities.

[0093] Unless specifically stated otherwise, discussions herein using words such as “processing,"“computing,”“calculating,”“determining,"“presenting,”“displaying, * or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physic»! (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, nonvolatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0094] As used herein any reference to‘some embodiments” or“an embodiment” or “teaching” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase“in some embodiments" or“teachings" in various places in the specification are not necessarily all referring to the same embodiment.

[0095] Some embodiments may be described using the expression“coupled” and "connected” along with their derivatives. For example, some embodiments may be described using the term“coupled" to indicate that two or more elements are in direct physical or electrical contact. The term "coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context. [0096] Further, the figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein

[009h Upon reading tilts disclosure, those of skit! in the art will appreciate still additional alternative structural and functional designs for the systems and methods described herein through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the systems and methods disclosed herein without departing from tiie spirit and scope defined in any appended claims.

Claims

1. A method for determining an extent of a natural disaster comprising:

establishing a baseline transaction analysis in an area wherein

tee baseline transaction analysis comprises individual transactions of goods or services made by individual purchasers at specific points in time;

receiving notification of an emergency event;

determining a current transaction analysis in the area;

determining a transaction analysis change wherein the transaction analysis change comprises a comparison of the current transaction analysis to tiie baseline transaction analysis;

if the transaction analysis change is over a change threshold, alerting a registered emergency contact; and

creating a user interface for the registered emergency contact to access a past transaction along with location details of the individual purchaser possibly affected by a natural disaster/emergency situation.

2. The method of claim 1 , wherein the area comprises a zip code.

3. The method of claim 1 , wherein the transaction analysis comprises an analysis of a dollar value of transactions in an area in gross.

4. The method of claim 1 , wherein transaction analysis comprises an analysis of goods and services purchased.

5. The method of claim i , wherein the baseline analysis and transaction analysis change comprises ah analysis of transactions for individuals determined to be similar.

6. The method of claim 1 , wherein foe baseline analysis and transaction analysis change reviews transactions for specific individuals.

7. The method of claim 1 , wherein a request for transaction data may be received in an API and a response may be provided.

8. The method of claim 7, wherein the response comprises location data and time of last transaction

9. The method of claim 7, wherein first responders or authorized parties have access to the transaction analysis change.

10. The method of claim 1 , wherein the emergency event comprises an event that would affect electronic transmission systems, electronic communication systems or electronic payment systems, water gas.

11. The method of claim 1 , wherein the comparison compares a type of good or services purchase in a current time frame to good or service purchased in a past time frame.

12. The method of claim 1 , wherein the comparison compares an amount of goods or services purchase in a current time frame to an amount of goods or services purchased in a past time frame.

13. The method of claim 1 , wherein the comparison compares a type of good or services purchase in a current time frame to a type of goods or services purchased in a past time frame for similar groups.

14. The method of claim 1 , wherein the comparison compares a type of good or services purchase in the in a current time frame to a type of goods or services purchased in a past time frame for individuals.

15. The method of claim 1 , wherein the comparison compares an amount of goods or services purchase in a current time frame to an amount of goods or services purchased in the past during a similar time frame for similar groups.

16. The method of claim 1 , wherein the comparison compares a type of good or services purchase in a current time frame to good or sendee purchased in the past during a similar time frame for individuals.

17. A user interface for displaying details of an extent of a natural disaster or emergency situation comprising:

establishing a baseline transaction analysis in an area wherein tiie baseline transaction analysis comprises individual transactions of goods or services made by individual purchasers at specific points in time;

receiving notification of an emergency event;

determining a current transaction analysis in the area;

determining a transaction analysis change wherein the transaction analysis change comprises a comparison of the current transaction analysis to tiie baseline transaction analysis;

if the transaction analysis change is over a change threshold, creating a display which indicates the estimated size of the natural disaster or emergency situation based on the transaction analysis change; and

creating a user interface for a registered emergency contact to access on a map a past transaction along with location details erf the individual purchaser possibly affected by a natural disaster/emergency situation.

18. The user interface of claim 17, wherein the transaction analysis comprises an analysts of a dollar value of transactions in an area in gross.

19. The user interface of claim 17, wherein the baseline analysis and transaction analysis change comprises an analysis of transactions for individuals determined to be similar.

20. The user interface of claim†7, wherein the comparison compares an amount of goods or services purchase in a current time frame to an amount of goods or services purchased in die past during a similar time frame for similar groups