Patent Publication Number: US-2023143436-A1

Title: Apparatus and methods for contact-minimized atm transaction processing using radar-based gesture recognition and authentication

Description:
FIELD OF TECHNOLOGY 
     This disclosure relates to apparatus and methods for contact-minimized automated teller machine use and transaction processing using Doppler-radar based gesture recognition and authentication. 
     BACKGROUND 
     Currently, in order to use an automated teller machine (“ATM”), or other interactive machines, customers must physically touch the ATM multiple times to complete a transaction. In a typical scenario, a customer must insert a bank card, enter a personal identification number (“PIN”) or password, and select from a multitude of options. These entries are typically performed with a keypad or other buttons, and/or a touchscreen. 
     Each of these, and other, actions may constitute another physical contact with the ATM. Each physical contact may be unhygienic, as multiple users interact with an ATM before it may be cleaned. Further, with less physical contact, a financial institution may not have to clean an ATM or other interactive machine as often. 
     Therefore, it is desirable to provide apparatus and methods for contact-minimized processing of automated teller machine (“ATM”) transactions utilizing doppler-radar based gesture recognition and authentication. 
     SUMMARY OF THE DISCLOSURE 
     It is an object of this disclosure to provide apparatus and methods for contact-minimized automated teller machine use and transaction processing using Doppler-radar based gesture recognition and authentication. 
     A contact-minimized automatic teller machine (“ATM”) is provided. The ATM may include, among other things, a housing. The housing may include a microprocessor, a card reader, a screen, a cash dispenser, a radar transmitter, a radar receiver, a signal converter, memory, a digital signal processor (“DSP”), a power supply, and a communication circuit. All of these components may be electronically coupled to one or more of each other. 
     The card reader may be configured to read an ATM card, or other bank card. The card may be read by inserting the card into the card reader and reading data contained in a magnetic stripe or a through a near-field communication (“NFC”) chip. Alternatively, the card reader may sense and read the card through any other appropriate methods, such as a wi-fi or Bluetooth signal. 
     In an embodiment, no ATM or bank card is necessary. A signal may be sent from a mobile device, e.g., from an application on a mobile device, including the necessary information so that the ATM may conduct a transaction without a physical ATM card. This information may include bank account information such as a routing number and account number, along with information on the owner(s) of the account. 
     In an embodiment, the screen may be configured to display various transaction options, such as withdraw $100, $120, $140 etc., check account balances, deposit a check or cash, transfer between accounts, and other transactions. The screen may also display any other information necessary to complete a transaction, such as instructions to a user. 
     The cash dispenser may dispense cash in any necessary amount. In an embodiment, the cash dispenser may be configured to receive cash and/or checks for deposit. 
     In an embodiment, the radar transmitter is a millimeter-wave radar transmitter. It may operate at a frequency between 3 gigahertz (“GHz”) and 300 GHz. The radar transmitter may be configured to provide a pulsed or continuous radar field at a location in front of the housing. This location may begin within a few millimeters of the housing and extend as far as fifteen feet. The radar field may have a height, depth, and width. It may be preferable to have a radar field that begins one inch form the housing and extends to a depth of two feet. This smaller radar field may be more secure and may prevent attenuation by environmental effects such as rain and moisture. 
     The radar receiver may be configured to receive reflections from any large-enough object within the radar field. Objects that are too small may not reflect the waves of the radar field. Generally, objects one-half the size of the radar frequency may be too small to reflect the radar waves. In an embodiment, the radar receiver may receive reflections from individual fingers belonging to a hand of a user of the ATM. 
     The signal converter may be an analog-to-digital signal converter configured to convert analog radar reflections to digital data. Digital data may be easier to store, communicate, and interpret. 
     In an embodiment, the memory may be non-transitory memory, including one or both of random-access memory (“RAM”) and read only memory (“ROM”). The memory may be configured to store an operating system to run the ATM and its components. The memory may also be configured to store the radar reflections and/or the digital data of the radar reflections. 
     In an embodiment, the ATM may include a digital signal processor (“DSP”). In an alternative embodiment, the DSP may be remote from the ATM, such as, e.g., on a remote server. 
     The DSP may be configured to analyze the radar reflections (in their digital data form) and identify any objects within the radar field that have caused radar reflections. In an embodiment, these objects may be individual fingers of a hand belonging to a customer using the ATM. Alternatively, the objects may be a stylus or other synthetic object. 
     Using a Doppler method and calculations, the DSP may analyze the movement of the objects, if any. The DSP may then convert the movement of each object into gestures and writing symbols. Gestures may include selecting one or more items on the screen, pinching in or out to zoom, swiping right or left, single or double-tapping, or any other appropriate gesture. Writing symbols may include letters, words, numerals, and symbols, and the DSP may translate the writing symbols into the appropriate letters, words, numerals and symbols. In an embodiment, these writing symbols may be used to authenticate a user and may be a passcode or PIN. 
     In an embodiment, the DSP may use machine-learning and deep-learning neural network algorithms to translate the writing symbols. Any appropriate machine-learning or deep-learning neural network algorithm may be used. 
     In an embodiment, the communication circuit may be configured to transmit and receive data including the digital data, gestures, words, numerals, and symbols. In alternative embodiments, the communication circuit may include a network interface card (“NIC”), a Bluetooth antenna, a cellular antenna, a wi-fi antenna, or any other appropriate antenna. A 5g-capable cellular antenna and communication circuit may be preferable to increase the speed of ATM transactions. 
     In an embodiment, the ATM may include an encryption controller. The encryption controller may allow for accurately authenticating the user/owner, as well as protecting the user/owner and financial institutions from users with malicious intent and/or fraud. In an embodiment, the non-transitory memory may include executable instructions and at least one datum configured to authenticate the user. These instructions and data may work in concert with, or separate from, any encryption controller. 
     Methods for contact-minimized interaction with an ATM are provided. The method may include the steps of sensing an ATM card and identifying, at the ATM, or through a remote server, a customer associated with the ATM card. By identifying the customer, the relevant bank account details may be ascertained. 
     Once an ATM card is sensed, the ATM may activate a Doppler millimeter-wave radar transmitter, which may then create a continuous-wave radar field in front of the ATM. 
     The ATM may then request the customer write an authentication passcode within the radar field. The customer may use his/her finger(s), a stylus, or another object. Finger(s) may be preferable. 
     The ATM may then receive, at a radar receiver, Doppler radar reflections from one or more objects within the radar field, such as the user’s fingers. 
     The ATM, through an analog-to-digital signal converter or other appropriate methods, may digitize the Doppler radar reflections. 
     Next, the Doppler radar reflections may be processed by a digital signal processor (“DSP”). The DSP may be located at the ATM or may be at a location remote from the ATM. If the DSP is at a location remote from the ATM, the digital data must be sent to the DSP. 
     The DSP may then identify one or more targets, which may be all or a portion of the objects. For example, the DSP may identify discrete scattering targets, which may be each of the five fingers on a customer’s hand. 
     The DSP may then resolve and analyze any movement of the target(s), using Doppler methods and data. 
     The DSP may then translate the movement of the target(s) into gestures, words, numerals, and symbols. In an embodiment, the ATM, through a communication circuit, may then send to an authentication server, the gestures, words, numerals, and symbols. The authentication server may then use the gestures, words, numerals, and symbols to authenticate the user, or not. The authentication server may authenticate the customer by matching the gestures, words, numerals, and symbols to a saved passcode belonging to the user. 
     In an alternative embodiment, the DSP, and not the ATM, may send the gestures, words, numerals, and symbols to an authentication server. 
     The authentication server may then inform the ATM if the customer has entered the correct passcode or note. 
     If the customer has entered the correct passcode, the ATM may then display various transaction options to the customer. The customer may then select and complete one or more transactions using one or more appropriate gestures within the radar field. The ATM may then process the transaction(s). 
     In an embodiment, the DSP may use one or more machine or deep-learning algorithms to identify the target(s), resolve the movement of the target(s), and translate the movement(s) into gestures, words, numerals, and symbols. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The objects and advantages of the invention will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: 
         FIG.  1    shows an illustrative process schematic in accordance with the principles of the disclosure; 
         FIG.  2    shows an illustrative process schematic in accordance with the principles of the disclosure; 
         FIG.  3    shows an illustrative process schematic in accordance with the principles of the disclosure; 
         FIG.  4    shows an illustrative image resulting from a radar field in accordance with the principles of the disclosure; 
         FIG.  5    shows an illustrative method in accordance with the principles of the disclosure; 
         FIG.  6    shows an illustrative process schematic in accordance with the principles of the disclosure; 
         FIG.  7    shows an illustrative method in accordance with the principles of the disclosure; 
         FIG.  8    shows an illustrative system in accordance with the principles of the disclosure; and 
         FIG.  9    shows an illustrative system in accordance with the principles of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Apparatus and methods for a contact-minimized (or contactless) ATM are provided. The contact-minimized ATM may utilize doppler-radar based gesture recognition and authentication. 
     The contact-minimized ATM may include a housing. In addition to typical ATM components (such as, e.g., a screen, keypad, microprocessor, non-transitory memory, encryption and authentication circuit, communication circuit, card reader, money-holding container, and money-dispenser), the housing may include a radar system with components including a radar transmitter, a radar receiver, analog-to-digital signal converter, and a DSP. The communication circuit may enable 5g cellular service. Each of these components may be electronically coupled to one or more of each other. 
     The contact-minimized ATM may also include apparatus to remotely sense and read an ATM card. The ATM card may be an EMV (Eurocard, Mastercard, VISA) chip card. The ATM card may include an NFC chip or other communication circuit such as Bluetooth, cellular connection, or wi-fi. 
     The card may be read by inserting the card into the card reader and reading data contained in a magnetic stripe or a through a near-field communication (“NFC”) chip. Alternatively, the card reader may sense and read the card through any other appropriate methods, such as a wi-fi or Bluetooth signal. 
     In an embodiment, no physical card may be necessary. For example, a customer may open a banking application on a mobile phone to initiate an ATM transaction. If the customer is within range of a contact-minimized ATM, the mobile phone application may take the place of a physical ATM card. The data sent from the mobile phone application may include the necessary information so that the ATM may conduct a transaction without a physical ATM card. This information may include bank account information such as a routing number and account number, along with information on the owner(s) of the account. 
     In an embodiment, the screen may be configured to display various transaction options, such as withdraw $100, $120, $140 etc., check account balances, deposit a check or cash, transfer between accounts, and other transactions. The screen may also display any other information necessary to complete a transaction, such as instructions. The screen may also display any error codes and may preferably include options for navigating the ATM via gestures or the standard keypad. 
     The cash dispenser may dispense cash in any necessary amount. In an embodiment, the cash dispenser may be configured to receive cash and/or checks for deposit. 
     In an embodiment, the radar transmitter and radar receiver may be monostatic, i.e., they may use the same antennae, or their respective antennae may be adjacent to one another. In another embodiment, the radar transmitter and radar receiver may be quasi-monostatic, wherein the antennae are within approximately three feet of each other. 
     In an embodiment, the radar transmitter is a millimeter-wave radar transmitter. It may operate at a frequency between 3 gigahertz (“GHz”) and 300 GHz. The radar transmitter may be configured to provide a pulsed or continuous radar field at a location in front of the housing. This location may begin within a few millimeters of the housing and extend as far as fifteen feet. The radar field may have a height, depth, and width. It may be preferable to have a radar field that begins one inch form the housing and extends to a depth of two feet. This smaller radar field may be more secure and may prevent attenuation/signal loss by environmental effects such as rain and moisture. 
     In an embodiment, the housing may include external walls and a roof surrounding and exceeding the radar field. These walls and roof may provide security to a customer as well as protect the ATM from environmental effects. For example, the ATM may be placed at one end of an enclosed room that is larger than the radar field. 
     In various embodiments, the radar transmitter may be a continuous-wave Doppler radar. The radar may operate on the “millimeter-band,” i.e., between 30-300 GHz. At 30 GHz the radar’s wavelength may be 10 mm, and at 300 GHz, the radar’s wavelength may be 1 mm. Alternatively, the radar may operate between 3 and 30 GHz, in the “microwave band.” The smaller the wavelength the greater the resolution the radar may have. However, at a smaller wavelength the radar may detect extra erroneous objects and overwhelm the signal processor. It may be preferable to operate at a wavelength between 1 and 10 mm. 
     The radar transmitter may operate at a power up to 1000W, although lower power outputs may be safer. The higher the power, the more range the radar may have. A preferred power level may depend on the preferred range. 
     In an embodiment the radar transmitter may have a range between 1 inch and 15 feet. 
     In an embodiment, the radar transmitter may be a frequency-modulated continuous wave Doppler radar. Alternatively, the radar transmitter may be a pulse Doppler with a medium to high pulse repetition frequency (“PRF”). Alternatively, the radar transmitter may be able to operate in multiple modes, and a particular mode may be chosen by the ATM depending on environmental conditions or other factors. 
     In an embodiment, the DSP may be configured to identify and track the movement of human fingers or fingertips in the air. These movements may form gestures or writing. Alternatively, the DSP may be configured to also sense and track the movement of an object such as a stylus or a metallic pin. 
     The radar receiver may be configured to receive any radio waves reflected by an object such as a finger, a fingertip, a hand, multiple fingers, and/or a different object, and process the reflections using the digital signal processor. The radar receiver may be configured to receive reflections from any large-enough object within the radar field. Objects that are too small may not reflect the waves of the radar field. Generally, objects one-half the size of the radar frequency may be too small to reflect the radar waves. In an embodiment, the radar receiver may receive reflections from individual fingers belonging to a hand of a user of the ATM. 
     In an embodiment, the analog-to-digital signal converter may convert the received signals to digital data, and the DSP may perform various calculations on the digital data. Such calculations may include Doppler-effect calculations to determine the movement performed by the object. Other calculations may include range and velocity. 
     In an embodiment, the DSP may include a target identification module, a gesture sensing module, a gesture translation module, a user identification module, a user authentication module, and/or a transaction processing module. In another embodiment, one or more of the target identification module, gesture sensing module, gesture translation module, user identification module, user authentication module, and transaction processing module may be separate from the DSP. 
     In an embodiment, one or more of the target identification module, gesture sensing module, gesture translation module, user identification module, user authentication module, and transaction processing module may be referred to as a part of a feature extraction and translation engine. In an embodiment, the feature extraction and translation engine is another term for the DSP. In an embodiment, the feature extraction and translation engine is a part of the DSP. 
     In an embodiment, the DSP or feature extraction and translation engine may disregard objects with a size outside of a predetermined range. For example, the DSP or feature extraction and translation engine may disregard any objects with an area that is smaller than 1 mm^2 or larger than 5 cm^2. 
     In an embodiment, the DSP or one or more of the target identification module, gesture translation module, user identification module, and user authentication module may be located on a remote server. The ATM and/or its components may communicate with the remote server using wi-fi, LAN, WAN, internet connectivity, cellular networks, and/or 5G networks. Using 5G networks and communication protocols may enable faster processing of transaction and authentication requests. 
     In an embodiment, the gesture translation module may translate various gestures performed by a user to manipulate and use the ATM. Such gestures may include a pinch to change the screen size, a swipe left or right to change screens, a tap to select an object on the ATM screen, or other gestures. The ATM may display instructions to a user on how to perform gestures and which gestures perform particular actions. In an embodiment, a user is taught these gestures before using the ATM for a first time. For example, a user may be given instructions when opening an account at a financial institution. 
     In an embodiment, the gesture translation module, or another module, may translate gestures performed by a user as letters, words, numbers and symbols. Such gestures may be used to authenticate the user and may act as a written password or passcode. 
     The gesture translation module may utilize machine learning and deep-learning algorithms, such as, e.g., convolutional neural networks and random forest, to translate gestures into actions, letters, numbers, and/or symbols. 
     Such machine learning algorithms may be utilized at the time a user is interacting with an ATM. In an embodiment, a gesture translation module may have been trained with machine learning algorithms before a user has interacted with an ATM. This training may utilize sample data sets or prior data from the user. 
     In an embodiment, the gesture translation module, or another module, may be able to recognize a user’s distinct handwriting and handwriting style. This recognition may be useful in authenticating the user. 
     In an embodiment, a user may perform a pre-determined gesture, or write a pre-determined phrase or word, to indicate authenticity of a proposed transaction, or indicate that the user requires assistance. For example, a user may write “help” or “call 911” if the user requires assistance. 
     In an embodiment, the communication circuit may be configured to transmit and receive data including the digital data, gestures, words, numerals, and symbols. In alternative embodiments, the communication circuit may include a network interface card (“NIC”), a Bluetooth antenna, a cellular antenna, a wi-fi antenna, or any other appropriate antenna. A 5g-capable cellular antenna and communication circuit may be preferable to increase the speed of ATM transactions. 
     In an embodiment, the ATM may include an encryption controller. The encryption controller may allow for accurately authenticating the user/owner, as well as protecting the user/owner and financial institutions from users with malicious intent and/or fraud. In an embodiment, the non-transitory memory may include executable instructions and at least one datum configured to authenticate the user. These instructions and data may work in concert with, or separate from, any encryption controller. 
     Methods for processing ATM transactions using radar-based gesture recognition and authentication are provided. 
     A customer may process transactions through an ATM without physically touching the ATM, or by minimizing the number of touches. The ATM may sense the customer’s ATM or bank card through radar sensing, visual sensing with a camera, a near-field communication (“NFC”) circuit, magnetic sensing or some other method. In an embodiment, a user may activate the ATM through a mobile phone application instead of an ATM card. 
     After the ATM senses the presence of a customer and the customer’s ATM or bank card (or mobile phone application), the ATM may enable a screen and display various options. 
     Before the customer may process any transaction through the ATM, the customer must be authenticated. The ATM may prompt the customer to enter a passcode or PIN. At this time, and in an embodiment, after the ATM senses the presence of the customer’s ATM or bank card, the ATM may begin generating a Doppler continuous-wave radar field using radar components such as a transmitter, receiver, signal converter, and DSP. 
     In an embodiment, the radar field extends only for a few inches away from the ATM. In another embodiment, the radar field may extend as far as 15 feet away from the ATM. These ranges may be achieved by modulating the power output from the transmitter (i.e., less power equals less range). 
     In an embodiment, the passcode or PIN may be a particular gesture instead of a combination of numbers and letters. For example, a user may draw a shape in a particular manner (e.g., clockwise or counterclockwise, or right-handed vs. left-handed) and/or in a particular size. Every unique aspect of the gesture may be useful in authenticating the user. 
     To enter a passcode or PIN, the customer may write the passcode or PIN in the air, within the radar field, and without touching the ATM. The customer may use one or more fingers, a whole hand, or an object such as a stylus or pen. 
     As the radar field is being generated by a continuous-wave radio-frequency (“RF”) signal (and in an embodiment, by a pulsed radio-frequency signal), any object within the field larger than one-half the wavelength of the RF signal should preferably reflect the RF signal back to a receiver. The signal converter may convert these reflections into digital data which may then be sent to the DSP and/or various modules such as, e.g., a gesture sensing module, gesture translation module, user identification module, user authentication module, and transaction processing module. These modules may be a part of or separate from the DSP. These modules may be referred to as a feature extraction and translation engine. In an embodiment, the DSP and the modules are a part of the ATM. In an alternative embodiment, the DSP and the modules are at a location remote from the ATM. 
     By analyzing the reflected data, the feature extraction and translation engine (in an embodiment, this may be referred to as the DSP) may identify discrete scattering centers, i.e., discrete objects reflecting RF waves within the radar field. In an embodiment, these discrete scattering centers may be separate fingertips (or whole fingers) on the user’s hand. Alternatively, these discrete scattering centers may be a combination of one or more objects (such as a stylus) and fingers. 
     As the radar field is being generated by a RF signal, the movement of any discrete scattering centers within the field should preferably create a Doppler effect in the reflected RF signal. By analyzing the Doppler effect, the DSP/feature extraction and translation engine may track and record the movements of the object(s). Multiple objects may be tracked at any time, although tracking more objects may require more processing power. The movements of the object over time may be converted into a digital image (such as a heatmap, line, curve, or combination thereof). The digital image of the movements may be analyzed to identify a gesture or writing pattern, if any. This analysis may be performed using machine or deep-learning algorithms. 
     In an embodiment, the writing pattern may be mapped, using a machine learning algorithm, to a language, such as English, to determine if the customer wrote letters, numbers, and/or symbols, and to determine what letters, numbers, and/or symbols the customer wrote within the radar field. For example, when prompted to enter a password, the customer may write Password123! Within the radar field. In an alternative embodiment, instead of writing letters, numbers, and symbols, the customer may draw a unique image or gesture in the air in lieu of a password or PIN. 
     In an embodiment, the converting of the movements, identification of a writing pattern, and mapping to a language may be performed using deep neural machine learning or other algorithms. One or more of these steps may be performed locally at the ATM or the data may be transferred to a remote server with additional computing power. 
     In an embodiment, the results are sent to an external authentication server to authenticate the user/customer. In another embodiment, the authentication server may be a part of the DSP. 
     The authentication results may be sent back to the ATM. If the customer/user is authenticated, the ATM may continue with various transactions. If the user/customer is not authenticated, the ATM may block any transactions and may alert the customer, the financial institution, and/or the police, as necessary. 
     The digital data transfer (in both directions) may be sent over any suitable communications network, including 5G cellular networks. 
     In an embodiment, the unique way a customer writes or draws may be used to authenticate the user in lieu of, or in addition to, the password or PIN itself. For example, when opening a bank account, the financial institution may require the customer to write a password within a radar field for future authentication purposes. This initial interaction may be recorded and saved by the financial institution’s authentication servers as part of the customer’s profile. 
     After authentication/validation of the customer, the customer may use various gestures to perform any typical transaction with the ATM (such as disbursing cash, making a deposit, checking balances etc.). A final gesture may terminate the transaction(s), or the transaction(s) may be terminated by the user withdrawing to a location that is beyond the range of the radar. 
     One of ordinary skill in the art will appreciate that the steps shown and described herein may be performed in other than the recited order and that one or more steps illustrated may be optional. The methods of the above-referenced embodiments may involve the use of any suitable elements, steps, computer-executable instructions, or computer-readable data structures. In this regard, other embodiments are disclosed herein as well that can be partially or wholly implemented on a computer-readable medium, for example, by storing computer-executable instructions or modules or by utilizing computer-readable data structures. 
       FIG.  1    shows an illustrative process schematic of a method for contact-minimized ATM use and transaction processing using Doppler-radar based gesture recognition and authentication, in accordance with the principles of the disclosure.  FIG.  1    contains both illustrative steps and numbered components. 
     Contact-minimized ATM use and transaction processing method  100  may include an ATM user/customer  101  with a bank card  103  belonging to the user/customer  101 . A contact-minimized ATM  105  may include a radar system  104 . Radar system  104  may include a radar transmitter configured to send out radar signals to create a radar field  106 , along with a radar receiver, signal converter, and a DSP. The ATM  105  may also include a screen  112 . The screen  112  may display multiple transaction and other options  113  to the user  101 . 
     After the ATM  105  senses the customer’s  101  ATM card  103 , it may activate the radar  104  and create a radar field  106 . The customer  101  may write in the air (radar field  106 )/gesture  102  using the customer’s  101  finger or some other object such as a stylus or pen. The radar system  104  may receive Doppler signals reflected from the gesture  102  and store the signals in memory (not shown). The signals may also be converted from analog to digital data through an analog-to-digital converter (not shown). The gesture  102  may be an authentication passcode or instruction gesture(s) directing a transaction at the ATM  105 . 
     In an embodiment, the digital data may be sent to a DSP  108  located remotely from the ATM  105  on a server at financial institution  110 . Alternatively, the DSP  108  may be located at the ATM  105  (location at ATM not shown). 
     The DSP  108  may include various modules. For example, the DSP  108  may include a target identification module  115 , a target movement analysis module  117 , a deep-learning gesture translation mapping module  109 , and a user identification, authentication, and transaction processing module  111 . In an embodiment, some of these modules may be separate from the DSP  108 . 
     The target identification module  115  may be configured to identify objects in the radar field  106 , such as a finger or fingers of the customer  101 . The target movement analysis module  117  may be configured to analyze the movement of one or more identified targets using Doppler methods and calculations. The DSP may then use the translation mapping module  109  to map the movement(s) of the targets to gestures, words, letters, numbers and symbols. These gestures, words, letters, numbers and symbols may be sent to the identification, authentication and transaction processing module  111 . At module  111 , the user  101  may be authenticated and gestures indicating actions desired (e.g., transactions) may be determined. 
     At the next step, results from the DSP may be sent to the ATM  105 . If the user  101  is not authenticated, further transactions may be blocked by the ATM  105 . If the user  101  is authenticated, the user may use various gestures  102  in the radar field  106  to manipulate and choose from among the transaction options  113  shown on screen  112 . These gestures  102  may be sent to the DSP  108  for translation and mapping, with the results shared with the ATM  105 . 
       FIG.  2    shows an illustrative process schematic of a method for contact-minimized ATM use and transaction processing using Doppler-radar based gesture recognition and authentication, in accordance with the principles of the disclosure.  FIG.  2    contains both illustrative steps and numbered components. 
     Contact-minimized ATM use and transaction processing method  200  may include an ATM user/customer  201  with a bank card  203  belonging to the user/customer  101 . At step  220 , the customer  201  may use the bank card  203  to initiate an ATM transaction. At step  230 , the contact-minimized ATM  205  may sense the ATM card  203  from a distance using a variety of methods. The ATM  205  may sense the card  203  via an NFC chip, Bluetooth signal, wi-fi signal, or other method. 
     In an embodiment, the ATM  205  may generate a radar field (not shown) from Doppler radar  204  and prompt the user  201  to enter an authentication passcode within the radar field. The user  201  may use one or more fingers or an object such as a stylus to write a passcode using gestures  202  within the radar field. (Dotted line  207  represents that there is a distance between the physical location of the user  201  and the physical location of the ATM  205 .) 
     At step  240 , the ATM  205  may send the received signals from gestures  202  to a DSP (not shown) located remote from the ATM  205  at bank  210 . The DSP may analyze the received signals to authenticate the user as well as determine what transaction(s) the user is attempting to complete. The DSP may perform this analysis using various modules (not shown) as described above. 
       FIG.  3    shows an illustrative process schematic of a method for contact-minimized ATM use and transaction processing using Doppler-radar based gesture recognition and authentication, in accordance with the principles of the disclosure.  FIG.  3    contains both illustrative steps and numbered components. 
     Contact-minimized ATM use and transaction processing method  300  may include generating a radar field  306 . A user may write  302  an illustrative passcode  315  within the radar field  306 . The radar reflections, such as illustrative passcode  315 , may be analyzed by a DSP (not shown) which includes a machine or deep-learning gesture translation and mapping module  309 . The gesture translation mapping module  309  may recognize  312  the writing  302  in the air. In addition, translation and mapping module  309  may recognize  313  a customer’s distinctive writing  302  style and quirks. Distinctive writing  302  style and quirks may be associated with a particular user as part of an authentication protocol. 
     After the gesture translation and mapping module  309  analyzes the writing  302 , the data generated (including distinctive style, gestures, numbers, letters, and/or symbols) may be sent to a user identification and transaction processing module  311 . If the data generated matches the data associated with the user, the user may be authenticated  317 , and may continue to use the ATM. If the user is not authenticated, any attempted transaction with the ATM may be rejected. 
       FIG.  4    shows an illustrative image resulting from an object within a radar field, in accordance with the principles of the disclosure. 
     Radar field  400  may produce an illustrative snapshot image  401  at a particular time. Illustrative hand  437  may be within the radar field  400 . Hand  437  may be an object within the radar field  400  and may also include a subset of further objects  440  as portions of the hand  437 . A DSP may analyze the snapshot image  401  and identify particular targets  439  from the set of objects  440  (and hand  437 ). In this illustrative image, the particular targets  439  may be individual fingers of hand  437 . Each target  439  may generate its own radar reflections  450 . 
     In an embodiment, a DSP may analyze multiple snapshot images  401  to identify targets  439  from objects  440  and track and analyze the movement of targets  439  over time by mapping the radar reflections  450  using a Doppler or other method. The DSP may then translate and map the movement into gestures, words, letters, numbers, and/or symbols. 
     In an embodiment, the DSP, a different module or server may associate the gestures, words, letters, numbers, and/or symbols with a particular customer to authenticate the customer. In addition, the DSP, a different module or server may associate the gestures, words, letters, numbers, and/or symbols with a particular transaction the customer may choose to perform at the ATM. 
       FIG.  5    shows an illustrative method in accordance with the principles of the disclosure. Methods may include some or all of the method steps  501 - 551 . Methods may include the steps illustrated in  FIG.  5    in an order different from the illustrated order. The illustrative method shown in  FIG.  5    may include one or more steps performed in  FIGS.  1 - 3   , or described herein. 
     At step  501 , a DSP or feature extraction and translation engine may identify one or more target(s) from a plurality of objects that have reflected radio waves within a radar field. At step  511 , the DSP may analyze any movement of the target(s)over time using Doppler methods and calculations. For example, the DSP may analyze the movement of each target by comparing snapshots of the radar field taken at specific intervals (e.g., every five milliseconds). 
     At step  521 , the DSP may resolve the movement of the target(s) and at step  531 , the DSP may track the movement. Steps  511 ,  521 , and  531  may allow the DSP to convert the movement into a pattern to identify writing at step  541 . The writing may include gestures, letters, numbers, and/or symbols. 
     At step  551 , the DSP may use machine and deep-learning algorithms to map the converted movement to a particular language to determine what was actually written. At this point, what was written within the radar field may be an authentication passcode or directions. In an embodiment, the mapped movement to language may be sent to an authentication server for authenticating the customer. The mapped movement to language may also be sent to the ATM or other server to direct one or more ATM transactions. In an embodiment, language includes gestures such as pinching to zoom in or out, pressing to select an option, swiping to move screens or other gestures. 
       FIG.  6    shows an illustrative process schematic of a method for contact-minimized ATM use and transaction processing using Doppler-radar based gesture recognition and authentication, in accordance with the principles of the disclosure.  FIG.  6    contains both illustrative steps and numbered components. 
     Contact-minimized ATM use and transaction processing method  600  may include an authorization hub  630  and an ATM radar and user  640 . Authorization hub  630  may include a transaction processing module  611 , a deep learning gesture translation mapping module  609 , other modules, and process steps. ATM radar and user  640  may include a user  601 , a radar field  606 , an ATM  605 , a target moving analysis/gesture sensing module  617 , a card reader  607 , a bank card  603 , and user gestures/movements  602 , among other components and process steps. Authorization hub  630  and ATM radar and user  640  may communicate with each other using a communication circuit (not shown), using any suitable communication method, including 5g cellular communications. 
     Method  600  may include a customer/user  601  inserting a bank card  603  into a card reader  607  in an ATM  605  to initiate a transaction. In alternative embodiments, the card reader may utilize an NFC chip instead of physically inserting the card. Alternatively, no card may be necessary, and a user may instead initiate a transaction though a different method, such as through a mobile phone application. 
     After initiating a transaction, the ATM  605  may activate a gesture radar  604  which may transmit a radar field  606 . The ATM  605  may prompt the customer  601  to enter an authentication passcode. The customer  601  may then perform one or movements  602  in the radar field  606 , such as writing a passcode. 
     A target movement analysis module/gesture sensing module  617  may analyze the movement(s)  602  to identify targets to track. The tracked movements  602  may then be communicated with a machine and deep learning gesture translation mapping module  609  to determine what, if anything, the customer  601  wrote with movement(s)  602 . In alternative embodiments, the deep learning gesture translation mapping module  609  may be a part of ATM  605  or it may be remote from the ATM  605 . The deep learning gesture translation mapping module  609  may be a part of a DSP and it may be a part of feature extraction and translation engine. 
     At step  610 , the translated movement(s)  602  may be used to validate and authenticate the customer  601 . For example, if the customer  601  wrote with movement(s)  602  a correct passcode, a transaction processing module  611  may be activated. If the customer  601  did not write a correct passcode, any attempted transaction may be blocked  613 . Passcodes may be gestures, numbers, letters, symbols, shapes, a specific style of movement(s)  602 , or a combination thereof. 
       FIG.  7    shows an illustrative method in accordance with the principles of the disclosure. Methods may include some or all of the method steps  701 - 799 . Methods may include the steps illustrated in  FIG.  7    in an order different from the illustrated order. The illustrative method shown in  FIG.  7    may include one or more steps performed in  FIGS.  1 - 3 , and  5 - 6    or described herein. 
     The method may begin at step  700 . At step  701 , an ATM may sense a customer’s ATM card. In alternative embodiments, the customer may insert the card into a card reader, tap the card and utilize an NFC chip, may use a pseudo card such as appears in various phone applications, or the customer may use a mobile phone application instead of an ATM card. 
     Next, at step  711 , the ATM may activate a Doppler radar transmitter. At step  721 , the Doppler radar transmitter may emit a radar field. Next, at step  731 , the customer may perform one or more gestures to ‘write’ in the air within the radar field. The customer may use one or more fingers or an object such as a stylus. 
     Next, at step  741 , the ATM may detect the gesture(s) performed by the customer in step  731 . Preferably, the ATM will receive radar reflections from the customer’s fingers or object(s) used to write in the air in step  731 . At step  751  the ATM may digitize the radar reflections received in step  741 . This may be accomplished with an analog-to-digital signal converter. The analog-to-digital signal converter may use a Fourier transform method to convert the signal to digital data. 
     Next, the digital data from step  751  may be sent to a feature extraction and translation engine at step  761 . The feature extraction and translation engine may be a DSP. In an embodiment, steps  501 - 541  from  FIG.  5    may be the process used by the feature extraction and translation engine. Alternative methods may be used to extract the features of the radar reflections and translate the features. 
     Once the features are extracted and translated to gestures, words, letters, numbers, or symbols, the translated radar signature may be sent to a bank or financial institution in step  771 . Next, at step  781 , machine/deep learning algorithms may be used to analyze the translated radar signature and match it to the customer’s passcode. If the radar signature matches the customer’s passcode, the customer may be validated at step  791 . If the customer is validated, the ATM may be authorized to process a transaction selected by the customer at step  798 . Once the transaction is complete the ATM may de-activate the radar and stop any further transactions, at step  799 . 
       FIG.  8    shows an illustrative block diagram of apparatus  841  that includes an ATM computing device  801 . ATM computing device  801  may alternatively be referred to herein as a “control circuit.” Elements of apparatus  841 , including computing device  801 , may be used to implement various aspects of the apparatus and methods disclosed herein. A “user” of apparatus  841  or control circuit  801  may include other computer apparatus or servers, such as an authentication server. 
     Computing device  801  may have a microprocessor  803  for controlling the operation of the device and its associated components, and may include RAM  805 , ROM  807 , input/output module  809 , and a non-transitory memory  815 . The microprocessor  803  may also execute all software running on the computing device 801—e.g., the operating apparatus. Other components commonly used for computers, such as EEPROM or Flash memory or any other suitable components, may also be part of the control circuit  801 . 
     The memory  815  may be comprised of any suitable permanent storage technology-e.g., a hard drive or other non-transitory memory. The ROM  807  and RAM  805  may be included as all or part of memory  815 . The memory  815  may store software including the operating system  817  and application(s)  819  along with any other data  811  needed for the operation of the apparatus  841 . Memory  815  may also store videos, text, and/or audio assistance files. The videos, text, and/or audio assistance files may also be stored in cache memory, or any other suitable memory. Alternatively, some or all of computer executable instructions (alternatively referred to as “code”) may be embodied in hardware or firmware (not shown). The microprocessor  803  may execute the instructions embodied by the software and code to perform various functions. 
     The term “non-transitory memory,” as used in this disclosure, is a limitation of the medium itself, i.e., it is a tangible medium and not a signal, as opposed to a limitation on data storage types (e.g., RAM vs. ROM). “Non-transitory memory” may include both RAM and ROM, as well as other types of memory. 
     In an embodiment of the computing device  801 , the microprocessor  803  may execute the instructions in all or some of the operating system  817 , any applications  819  in the memory  815 , and any other code embodied in hardware or firmware (not shown). 
     An input/output (“I/O”) module  809  may include connectivity to a keypad, a touchscreen, a radar transmitter and receiver, or network interface through which higher hierarchal server or a user of apparatus  841  may provide input. The input may include input relating to cursor movement. The input/output module  809  may also include one or more speakers for providing audio output and a video display device, such as an LED screen and/or touchscreen, for providing textual, audio, audiovisual, and/or graphical output (not shown). The input and output may be related to results using and interacting with an ATM. 
     Apparatus  841  may be connected to other apparatus, computers, servers, and/or the internet via a local area network (LAN) interface  813 . 
     Apparatus  841  may operate in a networked environment supporting connections to one or more remote computers and servers, such as terminals  845  and  851 , including, in general, the internet and “cloud”. References to the “cloud” in this disclosure generally refer to the internet. “Cloud-based applications” generally refer to applications located on a server remote from a user, wherein some or all of the application data, logic, and instructions are located on the internet and are not located on a user’s local device. Cloud-based applications may be accessed via any type of internet connection (e.g., cellular or wi-fi). 
     Terminals  845  and  851  may be personal computers or servers that include many or all of the elements described above relative to apparatus  841 . The network connections depicted in  FIG.  8    include a local area network (LAN)  825  and a wide area network (WAN)  829  but may also include other networks, such as a cellular network. Computing device  801  may include a NIC  826 , which may include a modem  827  and LAN interface or adapter  813 , as well as other components and adapters (not shown). When used in a LAN networking environment, computing device  801  is connected to LAN  825  through a LAN interface or adapter  813 . When used in a WAN networking environment, computing device  801  may include a modem  827  or other means for establishing communications over WAN  829 , such as Internet  831 . The modem  827  and/or LAN interface  813  may connect to a network via an antenna (not shown). The antenna may be configured to operate over Bluetooth, wi-fi, cellular networks (including 5G), or other suitable frequencies. 
     It will be appreciated that the network connections shown are illustrative and other means of establishing a communications link between computers may be used. The existence of various well-known protocols such as TCP/IP, Ethernet, FTP, HTTP and the like is presumed, and the apparatus can be operated in a client-server configuration to permit a user to retrieve web pages from a web-based server. The web-based server may transmit data to any other suitable computer apparatus. The web-based server may also send computer-readable instructions, together with the data, to any suitable computer apparatus. The computer-readable instructions may be to store the data in cache memory, the hard drive, secondary memory, or any other suitable memory. 
     Application program(s)  819  (which may be alternatively referred to herein as “plugins,” “applications,” or “apps”) may include computer executable instructions for invoking user functionality related to performing various tasks such as interacting with an ATM. In an embodiment, application program(s)  819  may be cloud-based applications. The various tasks may be related to authenticating a user and processing one or more ATM transactions. 
     Computing device  801  may also include various other components, such as a battery (not shown), power supply (not shown), radar components (not shown), screen (not shown), speaker (not shown), NIC  826 , and/or antennas (not shown). 
     Terminal  851  and/or terminal  845  may be portable devices such as a laptop, cell phone, Blackberry(TM), tablet, smartphone, or any other suitable device for receiving, storing, transmitting and/or displaying relevant information. Terminals  851  and/or terminal  845  may be other devices such as remote servers, including authentication and transaction servers. 
     Any information described above in connection with data  811 , and any other suitable information, may be stored in memory  815 . One or more of applications  819  may include one or more algorithms that may be used to implement features of the disclosure, and/or any other suitable tasks. 
     The invention may be operational with numerous other general purpose or special purpose computing apparatus environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, tablets, mobile phones, smart phones and/or other personal digital assistants (“PDAs”), multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. Secure systems and servers may be preferable. 
     Aspects of the invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network, e.g., cloud-based applications or remote authentication protocols. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. 
       FIG.  9    shows illustrative apparatus  941  that may be configured in accordance with the principles of the disclosure. Apparatus  941  may be a contact-minimized ATM. Apparatus  941  may include one or more features of the apparatus and methods shown in  FIGS.  1 - 8   . Apparatus  941  may include circuit board  920  and chip module  902 , which may include one or more integrated circuits, and which may include logic configured to perform any other suitable logical operations. 
     Apparatus  941  and/or circuit board  920  may include one or more of the following components: I/O circuitry  904 , which may include a transmitter device and a receiver device and may interface with fiber optic cable, coaxial cable, telephone lines, wireless devices, PHY layer hardware, a keypad/display control device, an LED screen, a touchscreen, a radar transmitter and receiver, or any other suitable media or devices; peripheral devices  906 , which may include batteries and chargers, counter timers, real-time timers, power-on reset generators or any other suitable peripheral devices; logical processing device  908 , which may compute data structural information and structural parameters of the data; and machine-readable memory  910 . 
     Machine-readable memory  910  may be configured to store in machine-readable data structures: machine executable instructions (which may be alternatively referred to herein as “computer instructions” or “computer code”), applications, signals, encryption algorithm(s), recorded data, and/or any other suitable information or data structures. 
     Components  902 ,  904 ,  906 ,  908  and  910  may be coupled together by a system bus or other interconnections  912  and may be present on one or more circuit boards such as  920 . In some embodiments, the components may be integrated into a single chip. The chip may be silicon-based. 
     Thus, apparatus and methods for contact-minimized ATM use and transaction processing using doppler-radar based gesture authentication and control have been provided. Persons skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments, which are presented for purposes of illustration rather than of limitation.