GB2548713A

GB2548713A - A method and system for the sending of communications between mobile communication devices

Info

Publication number: GB2548713A
Application number: GB1703259.0A
Authority: GB
Inventors: Sparks Trent
Original assignee: Individual
Current assignee: Individual
Priority date: 2016-02-29
Filing date: 2017-02-28
Publication date: 2017-09-27
Also published as: GB201703259D0

Abstract

A proximity interaction event 108 between two mobile devices 106 107 is used to create an association between the devices; the devices themselves and the associations being registered in a database of user accounts 101. A given device is then able to send a message 109 to all of its associated devices. The proximity interaction event may be determined based on location data such as from GPS, short-range communication such as Bluetooth or NFC, correlated accelerometer data, or by one device scanning a barcode displayed on the other device. Alternatively, these events may be determined using a machine learning system which receives time, location and demographic data as its input. The messages may be sent anonymously, and in an exemplary embodiment may be used to alert previous sexual partners to the possibility of STD infection. The system may determine a risk profile for a user based on the number of previous proximity interaction events.

Description

A METHOD AND SYSTEM FOR THE SENDING OF COMMUNICATIONS BETWEEN MOBILE COMMUNICATION DEVICES

The present invention relates to a method and system for the sending of communications between mobile communication devices and in particular, but not necessarily entirely, to sending of communications between mobile communication devices having been associated by the detection of proximity interaction events between the mobile communication devices.

Whereas electronic communication messages may be sent between mobile communication devices according to conventional arrangements, such communications identify the sender of such communications.

We have identified scenarios wherein it would be desirous to send anonymous communications between mobile communication devices.

As such, a need exists for the ability to send anonymous communications between mobile communication devices.

In the embodiments that follow, there is described the solution for sending anonymous communications which involves the pairing of mobile communication devices by detecting proximity interaction events between these mobile communication devices (such as by using location-based GPS Geo-proximity detection, short range communication, including optical scanning and short range radiofrequency communication, or other transmission of data between the mobile communication devices) so as to then be able to send electronic communications between these paired mobile communication devices.

It is to be understood that, if any prior art information is referred to herein, such reference does not constitute an admission that the information forms part of the common general knowledge in the art.

According to an aspect of the present invention there is provided a method for the sending of communications between mobile communication devices, the method comprising: maintaining a database of a plurality of mobile communication devices, each associated with a user account: detecting a proximity interaction event between a first mobile communication device and one or more other mobile communication devices of the plurality of mobile communication devices, creating an association within the database between the first mobile communication device and the one or more other mobile communication devices; receiving an electronic communication message associated with a first mobile communication device; identifying the one or more other mobile communication devices as being associated with the first mobile communication device; and sending the electronic communication message to the one or more other mobile communication devices.

In one embodiment, detecting the proximity interaction event comprises utilising an supervised machine learning optimised system having a supervised machine learning module having as input date time data and location data and user demographic data associated with each of the first mobile communication device and the one or more other mobile communication devices.

Detecting the proximity interaction event may comprises utilising an supervised machine learning optimised system having a supervised machine learning module configured to optimise the supervised machine learning optimised system, the supervised machine learning module having as input date time data and location data and user demographic data associated with each of the first mobile communication device and the one or more other mobile communication devices.

In one embodiment, the supervised machine learning optimised system utilises an artificial neural network configured using neural network weightings optimised by the supervised machine learning module.

In one embodiment, detecting the proximity interaction event comprises receiving location data respectively from each of the first mobile communication device and the one or more other mobile communication device and calculating the proximity of the first mobile communication device and the one or other mobile communication device in accordance with the location data.

In one embodiment, detecting the proximity interaction event comprises short-range communication between the first mobile communication device and the one or more other mobile communication devices.

In one embodiment, the short-range communication is radiofrequency short-range communication.

In one embodiment, the radiofrequency short-range communication comprises at least one of Bluetooth, NFC and Wi-Fi radiofrequency communication.

In one embodiment, the short-range communication comprises the receipt of data displayed by the first mobile communication device by the one or more other mobile communication devices.

In one embodiment, the first mobile communication device is configured for displaying a computer readable media and wherein the one or more other mobile communication devices may be configured for optically reading the computer readable media.

In one embodiment, the computer readable media comprises a 2D barcode.

In one embodiment, detecting the proximity interaction event comprises the input of user identification information associated with user accounts respectively into each of the first mobile communication device and the one or more other mobile communication devices.

In one embodiment, detecting a proximity direction event comprises recording accelerometer data utilising respective accelerometer sensors of each of the first mobile communication device and the one or more other mobile communication devices and correlating the accelerometer data received respectively from the accelerometer sensors.

In one embodiment, sending the electronic communication message to the one or more other mobile communication devices comprises sending the electronic communication message only to the one or more other mobile communication devices having associated proximity interaction events occurring within a previous time period.

In one embodiment, the electronic communication message does not identify a user or user account associated with the first mobile communication device.

In one embodiment, the method further comprise calculating a user profile associated with at least one of a first user account associated with the first mobile communication device and one or more other user accounts associated respectively with the one or more other mobile communication devices.

In one embodiment, the user profile represents a risk profile.

In one embodiment, the method further comprise calculating the risk profile in accordance with a number of proximity interaction events.

In one embodiment, the method further comprise calculating the risk profile further in accordance with a risk profile associated with each of the number of proximity interaction events.

In one embodiment, the method further comprises calculating the risk profile further in accordance with additional user-specified parameters.

In one embodiment, correlating the risk profile comprises utilising a rules-based risk profile calculation.

In one embodiment, calculating the risk profile comprises utilising a supervised machine learning optimised system having a supervised machine learning module having as input at least proximity interaction event data.

Calculating the risk profile may comprises utilising a supervised machine learning optimised system having a supervised machine learning module configured to optimise the supervised machine learning optimised system, the supervised machine learning module having as input at least proximity interaction event data.

In one embodiment, the supervised machine learning module further has as input at least one of demographic data and user specified additional data.

Other aspects of the invention are also disclosed.

Notwithstanding any other forms which may fall within the scope of the present invention, preferred embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

Figure 1 shows a system architectural overview of a system for the sending of communications between mobile communication devices in accordance with an embodiment of the present disclosure;

Figure 2 shows a computing device for utilisation in the system of Figure 1 in accordance with an embodiment of the present disclosure;

Figure 3 shows a further detailed view of the system specifically showing the configuration of the server and mobile communication device of the system in accordance with an embodiment of the present disclosure;

Figure 4 shows an exemplary method for the sending of communications between mobile communication devices in accordance with an embodiment of the present disclosure;

Figure 5 shows an exemplary supervised machine learning optimised artificial neural network configured for the automated detection of proximity interaction events in accordance with an embodiment of the present disclosure;

Figure 6 shows an exemplary supervised machine learning optimised artificial neural network configured for automating the calculation of user profiles in accordance with an embodiment of the present disclosure; and

Figures 7-11 show exemplary screenshots displayed by the mobile communication device of the system of Figure 1 in accordance with an exemplary embodiment.

For the purposes of promoting an understanding of the principles in accordance with the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Any alterations and further modifications of the inventive features illustrated herein, and any additional applications of the principles of the disclosure as illustrated herein, which would normally occur to one skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the disclosure.

Before the structures, systems and associated methods relating to the method for the sending of communications between mobile communication devices are disclosed and described, it is to be understood that this disclosure is not limited to the particular configurations, process steps, and materials disclosed herein as such may vary somewhat. It is also to be understood that the terminology employed herein is used for the purpose of describing particular embodiments only and is not intended to be limiting since the scope of the disclosure will be limited only by the claims and equivalents thereof.

In describing and claiming the subject matter of the disclosure, the following terminology will be used in accordance with the definitions set out below.

It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

As used herein, the terms "comprising," "including," "containing," "characterised by," and grammatical equivalents thereof are inclusive or open-ended terms that do not exclude additional, unrecited elements or method steps.

It should be noted in the following description that like or the same reference numerals in different embodiments denote the same or similar features.

System for the sending of communications between mobile communication devices

Turning now to figure 1, there is shown a system 100 for the sending of communications between mobile communication devices in accordance with an illustrative exemplary embodiment. Preferably, the system 100 is configured for sending of anonymous communications in the manner alluded to above, and as is described in further detail below.

As is shown, the system 100 comprises a server 102 in operable communication with a plurality of mobile communication devices 105, 106, 107 across a data network 104, such as the Internet.

For illustrative convenience, there is provided a first mobile communication device 106 and one or more other mobile communication devices 107 provided for illustrating the interaction between the various computing devices.

The server 102 may maintain a database of user accounts 101 and comprise a communications interface 103 for the sending of communications between various of the mobile communication devices in the manner described herein.

Now, as will be described in further detail below, the system 100 is characterised in that the system 100 is configured for detecting proximity interactions between mobile communication devices wherein, in the exemplary embodiment shown in figure 1, the server 102 is configured for detecting a proximity interaction event 108 between a first mobile communication device 106 and one or other mobile communication devices 107.

In general terms, the proximity interaction event represents the locational proximity of the mobile communication devices, thereby representing the proximity also of the associated users of the mobile communication devices 106, 107.

There are differing manners by which the proximity interaction events may be determined by the server 102 as will be described in further detail below but, generally, may include location-based (GPS) monitoring of the mobile communication devices, short range communication between the mobile communication devices (such as by near field radio communication, such as NFC, Bluetooth or the like or optical scanning), artificial intelligence technique, user initiated proximity interaction and the like.

As such, having detected the proximity interaction event between the mobile communication devices 106, 107, the server 102 is configured for creating an association within the database 101 between the first mobile communication device 106 and the one or more other mobile communication devices 107.

Thereafter, the system 100 may receive, via the communications interface 103, a request to send an electronic communication associated with the first mobile communication device 106. As such, on account of having previously detected the proximity interaction events between the first mobile communication device 106 and the one or more other mobile communication devices 107 and having created an association within the database 101 accordingly, the server 102, upon receipt of the electronic communication sending request, is configured for identifying the one or more other mobile communication devices 106 associated with the first mobile communication device so as to be able to subsequently send electronic mobile communication messages to the one or more other mobile communication device 107.

Computing device for the sending of communications between mobile communication devices

Turning now to figure 2, there is shown a computing device which may be utilised as one or more of the computing actors of the system 100 of figure 1. Specifically, the computing device 200 provided may be implemented as the server 102, mobile communication device 105 or other computing actor of system 100.

Specifically, wherein the computer 200 takes the form of the server 102, the server 102 may take the form of a physical rack mounted server or alternatively a virtualised server instance, such as that which may be implemented utilising Amazon web services (AWS) for example.

Furthermore, the mobile communication device 105 may take the form of a small form factor portable computing device such as a smart phone device, such as an Apple iPhone or the like. In this regard, the mobile communication device 105 may be configured utilising a software application “app” which is downloaded from a software application store for installation and execution on the mobile communication device 105.

As is shown in figure 2, the computing device 200 comprises a processor 204 for processing digital data. The processor 204 is in operable communication with a memory device 203 across a data bus 205. The memory device 203 is configured for storing digital data including computer code instructions. As such, during execution, the processor 204 is configured for fetching instructions from the memory device 203 for execution by the processor 204 and wherein data results may be stored again within the memory 203.

In embodiments, the memory 203 may be implemented in differing memory formats, including nonvolatile ROM, volatile RAM or hard disk storage memory.

The device 200 further comprises an I/O interface 206 for interfacing with various computer peripheral devices including computer interface devices 208, such as display devices, user input devices, such as pointer devices, touchscreen displays, keyboard devices and the like.

Furthermore, the I/O interface 206 may interface with a storage medium reader 209, such as a USB host interface which may read computer code instructions and other data from storage media 210, such as a USB memory device. As such, the computer code instructions provided herein may be provided as computer code instructions borne by a computer readable storage media 210.

The computing device 200 may further comprise an AV interface 202 for interfacing a display device 202 which may take the form of a digital display device and wherein, wherein the computing device 200 takes the form of the mobile communication device 105, the display device 201 may take the form of a touch sensitive display device.

Furthermore, the computing device 200 comprises a network interface 207 for sending and receiving data across a data network 104, such as the interface.

Wherein the computer device 200 takes the form of the mobile communication device 105, the network interface 207 may be a wireless or cellular network interface so as to be able to, for example, send and receive data across a cellular network, such as 3G - 5G data network.

Server and mobile communication device for the sending of communications between mobile communication devices

Having provided an exemplary system architecture 100 in figure 1 and described the constituent computer devices 200 in figure 2, there will now be described a further specific server and mobile communication device system diagram 100 further showing the specific configurations of the server and mobile communication device for the purposes of performing the computational tasks of present embodiments.

As can be seen, the system 100 provided in figure 3 comprises the server 102 in operable communication with a plurality of mobile communication devices 105. For illustrative convenience, only a single mobile communication device 105 as shown.

As is shown, each of the server 102 and mobile communication device 105 comprises a processor 204 for processing digital data, the processor 204 being in operable communication with a memory device 203 across a system bus 205.

Furthermore, the server 102 is in operable communication with a mobile communication device 105 across the data network 104 via the network interfaces 207.

Now, for illustrative convenience for illustrating the functionality of each of the server 102 and the mobile communication device 105, the memory device 203 is shown as comprising various modules, including control modules and data modules.

Specifically, considering initially the server 102, there is shown the memory device 203 comprising an operating system 307 such as the Linux operating system or the like. As such, during the bootstrap phase, the operating system 307 may be fetched for execution by the processor 204 whereafter the other functionality provided herein may be implemented.

There is further shown the memory device 203 of the server 102 comprising a plurality of control modules which, primarily for illustrative purposes, is shown in figure 3 as comprising a proximity interaction detector control module 306 and a communication control interface 305.

In general terms, the proximity interaction detector control module 306 is configured for detecting proximity interactions between the mobile communication devices 105 in the manner described herein so as to allow the pairing of the mobile communication devices.

Furthermore, the communication control interface 305 is configured for controlling the communication between associated or paired mobile communication devices.

There is shown the memory device 203 of the server 102 further comprising various data modules, in embodiment, may be stored within a relational database.

The data modules include a devices module 304 for storing data indicative of the mobile communication devices 105. The data modules may further comprise a user account/profiles data module 302 for storing user account and /or profile data associated with the mobile communication device 304.

Generally, users would be required to utilise the mobile communication devices 105 for the purposes of registering with the server 102 including the creation of user accounts which may be then subsequently utilised by the server 102 for detecting proximity interactions and sending communications.

The data modules may further comprise a proximity interactions data model 303 stored in relation to the devices/user profiles 302 for recording proximity interactions between these devices.

Furthermore, the data modules may comprise a communications data model 301 for storing electronic communications proposed to be sent or actually sent between mobile communication devices 105.

Furthermore, and considering specifically now the mobile communication device 105, the memory device 203 of the mobile communication device 105 may similarly comprise various software modules. Specifically, and as alluded to above, the various software modules and therefore functionality of the mobile communication device 105 may be configured via the download of a software application “app” which may, for example, be downloaded from a software application store for installation and execution on the mobile communication device 105.

In the exemplary embodiment shown, the memory device 203 of the mobile communication device 105 comprises a proximity interaction detector control module which may be utilised for detecting the proximity of the mobile communication device 105 with a proximate mobile communication device in embodiments.

Furthermore, the software modules of the mobile communication device 105 may further comprise a communications interface module for facilitating the communication between the mobile communication devices 105, including initiating the sending of a mobile communication and the display thereof.

As is also shown in figure 3, the I/O interface 206 may interface with various computer peripherals including the display device 201 as described above.

However, for the purposes of the detection of proximity interaction events, other computer componentry may be utilised as illustrated including a short range radiofrequency transceiver 310 which may be utilised for establishing short-range communications (such as by way of Wi-Fi, Bluetooth, near field communication (NFC) or the like) so as to detect the proximity of the mobile communication device 106 with proximate mobile communication devices.

Furthermore, the mobile communication device 105 may further comprise a sensor 311, such as an accelerometer, for the detecting of the “handshaking” of mobile communication devices 105. For example, in embodiments, as will be described in further detail below, proximity interaction between mobile communication devices 105 may be detected by holding the mobile communication devices 105 together and shaking them in a random manner wherein the accelerometer data received via the sensor 311 is compared/correlated at the server 102 for the purposes of detecting the proximity interaction of the particular mobile communication devices.

Furthermore, the mobile communication device 105 may comprise a camera 303 which, in embodiments, may be utilised for scanning computer readable data, such as a 2D barcode or the like for the purposes of detecting a proximity interaction between mobile communication devices 105.

Furthermore, the mobile communication device 105 may comprise a GPS receiver 312 which, in embodiments, may be utilised for monitoring the respective locations of the mobile communication devices 105 for the purposes of detecting proximity interaction events or increasing the accuracy of proximity interaction event detection.

Should be noted that the computational methodology described here and need not necessarily be limited by way of client/server architecture as a substantially provided in figures 1 and 3 and modifications may be made to the computational architecture within the spirit and scope of the embodiment provided herein.

Method for the sending of communications between mobile communication devices

Having provided the relevant technical architecture above, there is now provided an exemplary method 400 for the sending of communications between mobile communication devices in figure 4.

It should be noted that the methodology 400 of figure 4 is primarily given for illustrative purposes in accordance with one or more specific embodiments and therefore that no technical limitation should necessarily be imputed to all potential embodiments of the invention accordingly.

Now, the method 400 starts at step 407 wherein the mobile communication devices 106 are registered with the server 102. Specifically, the users of the relevant mobile communication devices 105 may download and install a software application “app” to each of the mobile communication devices 105 wherein, during the initial launch of the software application, the software application may perform a user registration request. During the user registration request, the user may provide various information including public and private information, such as private contact information, such as name, address, date of birth, communication settings (such as email, phone numbers and the like) and public information, such as a userid handle such as a nickname.

As alluded to above, when sending anonymous communications, certain information may be withheld such that the recipient of such may not or may not necessarily ascertain the identity of the sender of such communications.

Having registered at step 407, at step 408, the server 102 is configured for detecting a proximity interaction event 108 between the first mobile communication device 106 and one or more of the mobile communication device 107.

Now, as alluded to above, proximity interaction events may be detected by the server 102 in differing manners, including in automated and user initiated manners.

For example, in one manner, the server 102, at step 401, may monitor the mobile communication devices 105 such as by periodical receiving location data ascertained by the GPS receiver 312 of each mobile communication device 105. As such, using such location data, the server 102 is able to detect the proximity of mobile communication devices.

Alternatively, short range communications may be utilised between the mobile communication devices 105 at step 402 for the purposes of the detection of proximity interactions between the mobile communication devices 105.

Radiofrequency short range communications may be utilised wherein, in one embodiment, short range communications may comprise short range radiofrequency communications, such as Bluetooth, Wi-Fi, NFC communications wherein short range communication is established between mobile communication devices 105 (either in an automated manner, or user initiated) so as to detect the proximity of the mobile communication devices.

Alternatively, as opposed to utilising short range radiofrequency communication, a “handshaking” technique may be employed wherein both of the first mobile communication device 107 and one or more of the other mobile communication devices 107 are held together and shaken in a randomised manner wherein accelerometer data ascertained by the sensor 311 of each mobile communication device 105 is sent to the server 102 for correlation such that, should the sensor signature data correlate, the server 102 is able to detect the proximity interaction events of these mobile communication devices.

In alternative embodiments, proximity interaction events may be performed by scanning a computer readable media of one mobile communication device with another. For example, the first mobile communication device 106 may display, using the display device 201, a 2D barcode which may be read utilising the camera 303 of the one or more other mobile communication devices 107 so as to allow the server 102 to pair the mobile communication devices.

Other techniques for the pairing/proximity interaction detection between mobile communication devices may be utilised including wherein, for example, a unique code is displayed by the first mobile communication device 106 which is input into the one or more other mobile communication devices 107. Alternatively, a user nickname associated with the user profile of the first mobile communication device 106 may be input into the one or more other mobile communication devices 107.

In further embodiment, the system 100 may utilise a supervised machine learning optimised artificial neural network (or the like) at step 403 for detecting proximity interaction events in an automated manner as will be described in further detail below.

It should be noted that, in embodiments, a combination of the above provided techniques may be utilised for the purposes of proximity interaction event detection wherein, for example, wherein a short range communication may be detected between mobile communication devices 105, such may be qualified via the GPS locations received from each mobile communication devices.

At step 409, having detected the proximity interaction events 408 between the first mobile communication device 106 and one or more other mobile communication devices 107, the server 102 is configured for pairing or associating the mobile communication devices 105 by updating a data entry within the database 101. In a preferred embodiment, the date and/or time of the proximity interaction event is also recorded so as to allow the sending of communications for proximity interaction events having occurred within certain timeframes as will be described in further detail below.

In further embodiments, the server 102 is configured for calculating a user profile step 410 which may be utilised for various purposes, including sending of electronic Communications accordingly. In one embodiment as will be described in further detail below, the user profile calculated may represent a risk profile. Embodiments, electronic communications may be sent or tailored for users having a higher correlated risk profile. In embodiments, at step 404, a supervised machine learning optimised artificial neural network (or the like) may be utilised for calculating the user profile (such as user risk profile) in a substantially automated manner.

At step 411, the server 102 is configured for receiving a communication request associated with the first mobile communication device 106. Such request may be received via the communications interface 103 exposed by the server 102, which may take the form of a Web server interface, or alternatively be accessed utilising the first mobile communication device 106 (or other mobile communication device 105).

The communications request is associated with the first mobile communication device 106 such that the one or more other mobile communication devices 107 associated or paired with the first mobile communication device 106 may be ascertained by the server 106 inspecting the data entries of the database 101. As such, electronic communications may be sent to the one or more other mobile communication devices 107 associated with the first mobile communication device 106.

As alluded to above, at step 406, the communications request may receive a date range so as to send mobile communication messages only to the one or more associated mobile communication devices 107 for which proximity interaction events occurred during the provided date range.

In embodiment, at step 405, the communications request 411 may receive user ID handle “nickname” so as to identify the user account associated with the first mobile communication device 106. In embodiment, nicknames of user account associated with the one or more other manner communication devices 107 may be presented for selection so as to allow the sending of the electronic communication message only to a subset of the other mobile communication devices.

As such, at step 412, the server 102 is configured for sending the electronic communication messages to the one or more other mobile communication devices 107 associated with the first mobile communication device 106. The electronic communications may be centred differing electronic formats, such as by way of SMS, email, telephone call with associated computer based text-to-speech, push notification and the like.

As alluded to above, in one embodiment, the electronic communication messages are anonymous such that the recipient of the electronic communication message need not necessarily ascertain the identity of the sender or requester of the electronic communication message.

Supervised machine learning optimised artificial neural network for proximity interaction event detection

Turning now to figure 5, there is shown the utilisation of a supervised machine learning optimised artificial neural network system for proximity interaction event detection in accordance with one particular embodiment.

In the illustration provided, a supervised machine learning optimised artificial neural network 507 is utilised wherein the neural network 507 is optimised utilising optimised parameters 506 (such a neural network node weightings) which have been optimised utilising a training algorithm 505.

As such, for a particular user profile, various information including date time data 510, location data 511, demographic data 512 and other potentially relevant data may be input into the trained artificial neural network 507 so as to allow the trained artificial neural network 507 to automate the detection of the proximity interaction events 508.

For the training of the training algorithm 505, various historical data obtained in relation to other users may be utilised including the historical demographic data 501, historical date time data 502, historical location data 503 and historical proximity interaction event data 504. As such, the training algorithm 505 trains on such data so as to generate the optimised parameters 506.

In embodiments, the artificial intelligence system 505 may operate in feedback mode wherein the proximity interaction event detection 508 output via the trained artificial neural network 507 is fed again to the training algorithm 505 for further increasing the accuracy of the optimise parameters 506.

Supervised machine learning optimised artificial neural network for user profile calculation

Turning to figure 6, there is shown an artificial intelligence system 600 for user profile calculation.

Similarly, the artificial intelligence system 600 is shown as utilising a trained artificial neural network 604 which is optimised using optimised parameters 603 which have been optimised using a training algorithm 602.

Specifically, in use, the trained artificial neural network 604 is configured for taking as input demographic data 605 and proximity interaction event data 607 associated with a user account so as to be able to calculate a user profile 606. In embodiments as will be described in further detail below, the user profile 606 represents a user risk profile.

The training algorithm 602 may be trained utilising historical data from other users, including historical user profile data 601, historical demographic data 608 and historical proximity interaction event data 609 which are fed into the training algorithm 602 for the purposes of optimising the optimise parameters 603. Additional historical data 611 may be used also

Again, the training algorithm 602 may utilise feedback from the output of the trained neural network 604 in use wherein the calculate a user profile 606 fed back into the training algorithm 602 for the purposes of further optimising the optimise parameters 603.

Exemplary embodiment

Having provided the above exemplary architecture and methodology, there will now be provided an exemplary embodiments for further illustrative purposes with reference to the exemplary graphical user interfaces displayed by the mobile communication device 105 as is provided an figures 7 - 11.

In this exemplary embodiment, there will be described the system 100 for disease control and in particular for combating sexually transmitted diseases. In this example embodiment, the system 100 is utilised for sending of anonymous communications to previous sexual partners so as to alert such previous partners of an STD infection. In accordance with this exemplary embodiment, the system 100 may also perform other functions, such as by calculating risk profiles, such as STD infection risk profiles for users in accordance with the various data provided

The exemplary embodiment starts at figure 7, wherein a proximity interaction event is recorded between a first mobile communication device 106 and one or more other mobile communication devices 107. In this particular example, the first mobile communication device 106 is associated with a user account/profile having the user ID handle/nickname of Melissa. As such, Melissa is using her mobile communication device 160 create an association with another mobile communication device, so as to allow the sending of communications between these mobile communication devices if required later.

As alluded to above, the proximity interaction event may be initiated in differing manners including the user initiated manner as is provided by way of the user interface buttons shown in figure 7.

Specifically, in one embodiment, short range RF communication may be established between the mobile communication device 106 and the other mobile communication device 107 such as, for example, utilising near field communication technique wherein there is shown a first user interface button allowing the mobile phone devices 106, 107 to be tapped together (such as by using NFC) so as to allow data to be transmitted between the mobile communication device 106, 107 which may be subsequent conveyed to the server 102 for recording the interaction. As alluded to above, in embodiments, as opposed being user initiated, the short range communication may be initiated using a background process of the mobile communication device 105 such that the pairing of the devices may be performed in substantially an autonomous manner.

Alternatively, the barcode may be scanned wherein, for example, the first mobile communication device 106 may display a 2D barcode which may be read utilising the camera device 303 of the other mobile communication device 107.

Alternatively, a unique user ID, such as nickname or the like may be input into the devices 106, 107 wherein, for example, each user of each device 106, 107 respectively inputs the nickname of the other user into the relevant mobile communication device.

Furthermore, the phones may be “bumped” or “handshaked” together wherein both the first mobile communication device 106 and the other mobile communication device 107 may be held together and shaken such that accelerometer data ascertained by the respective sensors 311 are sent to the server 102 for correlation for the purposes of pairing the first and second mobile communication devices 106, 107.

As alluded to above, the server 102 may monitor the respective locations of the mobile communication devices 106, 107 by, for example, periodically receiving GPS data from the mobile communication devices. In this manner, the proximate interaction event need not necessarily be user initiated by the users and may be detected automatically by the system 100. However, in other in embodiments, the location data may be utilised to qualify proximity interaction events wherein, for example, for user initiated proximity interaction events, such may be qualified utilising the GPS location data so as to be able to only pair devices within the same location.

Furthermore, for the automated detection of proximity interaction events between the mobile communication devices 106, 107, the server 102 may utilise a rules-based approach wherein, for example, proximity interaction events are deemed as being events wherein the mobile communication devices 106, 107 at the same location for a predetermined amount of time at a particular time of day.

In this case, wherein the system 100 is utilised for utilising the proximity interaction events to represent sexual encounters, the rules-based approach made dictate that a proximity interaction (i.e. sexual encounter) is to be detected by the server 102 when the mobile communication devices 106, 107 are within proximity for more than one hour, between the hours of 7 PM at night and 5 AM in the morning. Additional information may be utilised for the purposes of such rules-based approach, such as demographic data (such as demographic data representing, for example, that the respective users of the mobile communication devices 106, 107 are male and female) and the like.

However, in one embodiment, the proximity interaction event detection may be utilised utilising the supervised machine learning optimised artificial neural network of figure 500 wherein the server 102 is configured for calculating (or calculating a probability) whether or not the relevant data represents a sexual encounter between the parties associated with the mobile communication devices 106, 107. For example, for each of the mobile communication devices 106, 107, the trained artificial neural network 507 may receive date time data 510, location data 511 and also demographic data 512 to calculate in an automated manner whether or not a sexual encounter has occurred or at least a probability thereof. Other potentially relevant data may be fed into the trained artificial neural network 507 for the purposes of making the decision.

Turning now to figure 8, there is shown an exemplary embodiment wherein the user profile is displayed to the users. In accordance with the present illustrative embodiment wherein the system 100 is utilised for controlling the transmission of sexual transmitted diseases, the user profile may represent a risk profile specifically, the risk profile of having contracted an STD.

Specifically, for the user of the other mobile communication device 107, having performed the proximity interaction event/pairing of the respective mobile communication devices 106, 107, the respective risk profile may be displayed to each user so as to allow each user to make an informed decision as to whether to engage in consensual sex or not. For example, for the respective male user (“Bob”) the risk profile of Bob and of the user of the first mobile communication device 106 “Melissa” may be displayed.

As can be seen, the risk profile for Bob is calculated in accordance with previous proximity interaction events (representing sexual encounters in this case) wherein, as is represented, Bob has a higher risk profile as opposed to Melissa.

In this case, such information being displayed to Melissa, Melissa may decline to engage in any form of sexual activity, or may decide whether to utilise protection as opposed to unprotected sex.

Again, the calculation of the user risk profile may be calculated by the server 102 in accordance with a rules-based approach wherein, for example, the rules-based approach may utilise the previous proximity interaction events (sexual encounters) for the respective user.

Specifically, for the exemplary embodiment provided in figure 8, the risk profile calculated for Bob is based on the number of previous proximity interaction events, in this case, being seven previous proximity interaction events (sexual encounters). Furthermore, an associated risk profile of each user account associated with each previous proximity interaction event may be further taken into account wherein for the previous seven sexual encounters, four were low risk profile partners and two were medium risk profile partners and one was a high risk profile partner. As such, when calculating the risk profile of Bob, the server 102 may utilise the rules-based approach to calculate an aggregate risk for Bob in accordance with the number of previous sexual encounters and, in embodiments, the risk profile for each user of each previous sexual encounter also.

Furthermore, various other information may be utilised also for calculating the risk based profile. Specifically, referring to figure 7, when recording the proximity interaction event, various additional information may be input wherein, for the present embodiment wherein the system 100 is utilised for recording sexual encounters, the additional information may represent whether or not the sexual encounter was protected or not.

As such, when calculating the associated risk profile as shown in figure 8, the server 102 may again utilise such information for the purposes of calculating the risk profile wherein, for unprotected sexual encounters, the risk profile may be calculated as being higher.

As alluded to above, the risk profile calculated may be calculated utilising the supervised machine learning optimised artificial neural network 600 as a substantially provided in figure 6.

For example, for each user, the trained artificial neural network 604 (or other trained artificial intelligence system) accepts input various information associated with the user account/profiles for the purposes of outputting the risk profile 606 of the user.

In this case, the trained artificial neural network 604 may take into account the proximity interaction event data 670 which, in this case, may represent the number of interactions, and, in embodiments, the associated risk profiles of these interactions associated with the user.

Furthermore, the additional data 610 may be utilised wherein, in this case, the additional data may represent whether the sexual encounter was protected or unprotected.

Furthermore, additional demographic data 605 may be utilised such as the gender, age and other demographic data associated with the user for further calculating the risk profile for the user 606.

The trained artificial neural network 604 may be trained utilising the supervised machine learning model 602 which may have as input various historical data obtained from other users including historical additional data 611, user profile data 601, demographic data 608 and proximity interaction event data 609.

Additionally, the supervised machine learning module 602 may be trained utilising historical medical data wherein, for other patients, STD diagnosis forms part of the historical medical data 612. As such, the supervised machine learning model 602 is able to correlate the various historical data with the actual STD prevalence rates indicated by the historical medical data 612 for the purposes of generating optimised parameters 603 (i.e. neural network node weightings).

In embodiments, and referring again to figure 7, certain system operational configurations settings may be set wherein, in the example provided, the ability to send non-anonymous communications may be provided wherein, for example, when establishing a proximity interaction event (in this case, sexual encounter), the user’s may specify whether the users wish to be able to chat later wherein, for example, a week later or the like, Melissa may be able to send a communication message to Bob and vice versa. Again, such communication may be devoid of any sensitive data, such as address data, email, phone communication data and the like so as to allow the user to communicate in a safe manner.

Turning now to figure 9, there is shown the initiation of the electronic communication sending request.

For example, and in the present embodiment wherein the system 100 is utilised for reducing the transmission of STDs, Bob may have subsequently discovered, such as via a routine medical examination, that Bob has contracted an STD. In this case, Bob may request the sending of anonymous electronic communication messages to previous sexual partners (as is recorded by the system 100 by the previous proximity interaction event detections) such that they may be able to take appropriate action.

As can be seen from the exemplary interface, Bob may input a date range, such as the previous six months, and a message. As such, upon receipt of the electronic communication message request, the message may be sent to all other mobile communication devices for which proximity interaction events were detected within the provided six-month previous date range.

In embodiments, the user ID handle/nickname of the relevant users of this timeframe may be displayed to Bob for selection so as to further qualify the recipients of the communication.

Specifically, turning now to figure 10, there is shown the receipt of an anonymous alert accordingly. For example, the mobile communication device 106 may be that belonging to Melissa such that the message input by Bob in figure 9 is displayed to Melissa informing Melissa of having been exposed to an STD and wherein Melissa may then have herself assessed by way of medical examination if needs be.

It should be noted that the identification of the sender of the message may be confidential such that, in this case, the message may simply inform Melissa that of her sexual encounters in the previous six months, at least one of those has contracted an STD.

Turning now to figure 11, there is shown a further exemplary embodiment wherein the proximate interaction events are shown in calendar format. For the example provided herein wherein the system 1 is utilised for recording sexual encounters, the relative size of the stars represents the number of sexual encounters on a particular day of the month. Furthermore, the colouring of the star may represent the risk profile of the sexual partner.

As alluded to above, in embodiment, the system 100 may allow for casual chatting between users in an anonymous manner. In this manner, users may consent to receiving anonymous communications without having to provide their personal contact information. In this manner, should a relationship develop, the users may decide to subsequently share their personal contact information if necessary. In this embodiment, the system 100, over and above being able to allow the anonymous social communications between users, may be adapted for disseminating information relating to events, such as local parties and the like between users 130 have consented to receive such event notifications.

In embodiment, the server 102 may be adapted to send health check reminders to users. In embodiment, the health check reminders may be spaced at regular intervals, such as every three months. However, in embodiment, the frequency of the health check reminders may be determined in accordance with the risk profile allocated with the user, wherein, for example, should a user have a higher risk profile, the server 102 may increment the frequency of health check reminders.

In further embodiment, the mobile communication device 105 may be adapted to display clinics within the proximity of the user, should the users 130 request information relating to their closest clinic for a health check.

In further embodiment, the mobile communication device 125 may be adapted to display information relating to the signs and symptoms of particular STDs.

In further embodiment, the server 102 may be adapted to identify areas of outbreaks of STDs, so as to allow for appropriate action by authorities. In this manner, anonymous statistics data may be retrieved from the server 102 by health authorities so as to take appropriate action.

In further embodiments, the system 100 may be utilised by authorities wherein, for example, for a particular user carrying measles, other users may have been in proximity of the infected user may be identified utilising the proximity interaction events detected by the server (such as a location based proximity interaction event identification technique) so as to be able to alert these potentially affected other users also.

In embodiment, and especially for female users, the server 102 may be adapted to track ovulation cycles.

Interpretation

Wireless:

The invention may be embodied using devices conforming to other network standards and for other applications, including, for example other WLAN standards and other wireless standards. Applications that can be accommodated include IEEE 802.11 wireless LANs and links, and wireless Ethernet.

In the context of this document, the term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. In the context of this document, the term "wired" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a solid medium. The term does not imply that the associated devices are coupled by electrically conductive wires.

Processes:

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "processing", "computing", "calculating", "determining", "analysing" or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

Processor:

In a similar manner, the term "processor" may refer to any device or portion of a device that processes electronic data, e.g., from registers and/or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and/or memory. A "computer" or a "computing device" or a "computing machine" or a "computing platform" may include one or more processors.

The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. The processing system further may include a memory subsystem including main RAM and/or a static RAM, and/or ROM.

Computer-Readable Medium:

Furthermore, a computer-readable carrier medium may form, or be included in a computer program product. A computer program product can be stored on a computer usable carrier medium, the computer program product comprising a computer readable program means for causing a processor to perform a method as described herein.

Networked or Multiple Processors:

In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. The one or more processors may form a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

Note that while some diagram(s) only show(s) a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

Additional Embodiments:

Thus, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g., a computer program that are for execution on one or more processors. Thus, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium. The computer-readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause a processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

Carrier Medium:

The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an example embodiment to be a single medium, the term "carrier medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "carrier medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media.

Implementation:

It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.

Means For Carrying out a Method or Function

Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a processor device, computer system, or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

Connected

Similarly, it is to be noticed that the term connected, when used in the claims, should not be interpreted as being limitative to direct connections only. Thus, the scope of the expression a device A connected to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. "Connected" may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

Embodiments:

Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments.

Similarly it should be appreciated that in the above description of example embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description of Specific Embodiments are hereby expressly incorporated into this Detailed Description of Specific Embodiments, with each claim standing on its own as a separate embodiment of this invention.

Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Different Instances of Objects

As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

Specific Details

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Terminology

In describing the preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. However, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar technical purpose. Terms such as "forward", "rearward", "radially", "peripherally", "upwardly", "downwardly", and the like are used as words of convenience to provide reference points and are not to be construed as limiting terms.

Comprising and Including

In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" are used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

Any one of the terms: including or which includes or that includes as used herein is also an open term that also means including at least the elements/features that follow the term, but not excluding others. Thus, including is synonymous with and means comprising.

Scope of Invention

Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as fall within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms.

Embodiments of the present invention have been described with particular reference to the examples illustrated. While specific examples are shown in the drawings and are herein described in detail, it should be understood, however, that the drawings and detailed description are not intended to limit the invention to the particular form disclosed. It will be appreciated that variations and modifications may be made to the examples described within the scope of the present invention.

Claims

1. A method for the sending of communications between mobile communication devices, the method comprising: maintaining a database of a plurality of mobile communication devices, each associated with a user account: detecting a proximity interaction event between a first mobile communication device and one or more other mobile communication devices of the plurality of mobile communication devices, creating an association within the database between the first mobile communication device and the one or more other mobile communication devices; receiving an electronic communication message associated with a first mobile communication device; identifying the one or more other mobile communication devices as being associated with the first mobile communication device; and sending the electronic communication message to the one or more other mobile communication devices.

2. A method as claimed in claim 1, wherein detecting the proximity interaction event comprises utilising an supervised machine learning optimised system having a supervised machine learning module having as input date time data and location data and user demographic data associated with each of the first mobile communication device and the one or more other mobile communication devices.

3. A method as claimed in claim 2, wherein the supervised machine learning optimised system utilises an artificial neural network configured using neural network weightings optimised by the supervised machine learning module.

4. A method as claimed in claim 1, wherein detecting the proximity interaction event comprises receiving location data respectively from each of the first mobile communication device and the one or more other mobile communication device and calculating the proximity of the first mobile communication device and the one or other mobile communication device in accordance with the location data.

5. A method as claimed in claim 1, wherein detecting the proximity interaction event comprises short-range communication between the first mobile communication device and the one or more other mobile communication devices.

6. A method as claimed in claim 5, wherein the short-range communication is radiofrequency short-range communication.

7. A method as claimed in claim 6, wherein the radiofrequency short-range communication comprises at least one of Bluetooth, NFC and Wi-Fi radiofrequency communication.

8. A method as claimed in claim 1, wherein the short-range communication comprises the receipt of data displayed by the first mobile communication device by the one or more other mobile communication devices.

9. A method as claimed in claim 8, wherein the first mobile communication device is configured for displaying a computer readable media and wherein the one or more other mobile communication devices is configured for optically reading the computer readable media.

10. A method as claimed in claim 9, wherein the computer readable media comprises a 2D barcode.

11. A method as claimed in claim 1, wherein detecting the proximity interaction event comprises the input of user identification information associated with user accounts respectively into each of the first mobile communication device and the one or more other mobile communication devices.

12. A method as claimed in claim 1, wherein detecting a proximity direction event comprises recording accelerometer data utilising respective accelerometer sensors of each of the first mobile communication device and the one or more other mobile communication devices and correlating the accelerometer data received respectively from the accelerometer sensors.

13. A method as claimed in claim 1, wherein sending the electronic communication message to the one or more other mobile communication devices comprises sending the electronic communication message only to the one or more other mobile communication devices having associated proximity interaction events occurring within a previous time period.

14. A method as claimed in claim 1, wherein the electronic communication message does not identify a user or user account associated with the first mobile communication device.

15. A method as claimed in claim 1, further comprising calculating a user profile associated with at least one of a first user account associated with the first mobile communication device and one or more other user accounts associated respectively with the one or more other mobile communication devices.

16. A method as claimed in claim 15, wherein the user profile represents a risk profile.

17. A method as claimed in claim 16, wherein the method further comprises calculating the risk profile in accordance with a number of proximity interaction events.

18. A method as claimed in claim 17, wherein the method further comprises calculating the risk profile further in accordance with a risk profile associated with each of the number of proximity interaction events.

19. A method as claimed in claim 17, wherein the method further comprises calculating the risk profile further in accordance with additional user-specified parameters.

20. A method as claimed in claim 17, wherein correlating the risk profile comprises utilising a rules-based risk profile calculation.

21. A method as claimed in claim 16, wherein calculating the risk profile comprises utilising a supervised machine learning optimised system having a supervised machine learning module having as input at least proximity interaction event data.

22. A method as claimed in claim 21, wherein the supervised machine learning optimised system utilises an artificial neural network configured using neural network weightings optimised by the supervised machine learning module.

23. A method as claimed in claim 21, wherein the supervised machine learning module further has as input at least one of demographic data and user specified additional data.