Frameworks and methodologies configured to enable generation and utilisation of three-dimensional body scan data
The present invention relates to frameworks and methodologies configured to enable generation and utilisation of three-dimensional body scan data. Various embodiments are described by reference to applications by which body scan data is collected, and/or subsequently utilised in the context of providing downstream functionalities, for example in the context of enabling users and/or business derive benefit from three-dimensional body scan data.
1. A computer implemented method for enabling tracking of body shape variations, the method comprising:
receiving a subscription request from a vendor, wherein the subscription request is indicative of a specific category of size change events and is defined by the vendor based on marketing desires;
authenticating a user U 1 , the user U 1 being associated with first user record data, wherein the first user record data includes a first set of user physical attribute data associated with a time n (PAD 1 T n ), wherein the first set of user physical attribute data is derived from a three-dimensional body scanning process;
receiving input representative of a further set of user physical attribute data, the further set being associated with a time n+x (PAD 1 T n+x );
performing an analysis of a relationship between PAD 1 T n and PAD 1 T n+x ;
identifying a size change event in a case that the relationship between PAD 1 T n and PAD 1 T n+x indicates that the user Ui has moved from a first garment size to a second garment size; and
in response to identifying that the size change event of the user U 1 belongs to the specific category of size change events defined in the subscription request from the vendor:
providing data indicative of the size change event to the vendor and allowing the vendor to provide marketing information to the user U 1 .
2. The method according to claim 1 wherein each set of three dimensional body scan data includes a three dimensional point cloud, and wherein analysis of relationships between PAD 1 T n , and PAD 1 T n+x is based on comparison of the respective point clouds.
3. The method according to claim 2 wherein comparison of the point clouds includes utilisation of vector mathematics to determine spatial variations between corresponding points.
4. The method according to claim 2 further comprising providing output configured to enable rendering, at a client device associated with the user U 1 , a graphical object representative of the relationship between PAD 1 T n , and PAD 1 T n+x , wherein the graphical object is defined based on an overlay of the point cloud for PAD 1 T n+x , with respect to the point cloud at PAD 1 T n .
5. The method according to claim 4 wherein the graphical object provides visual indicators representative of variations between PAD 1 T n , and PAD 1 T n +x .
6. The method according to claim 5 wherein the visual indicators include colours.
7. The method according to claim 5 wherein a first visual indicator is used to identify an increase in localised body size, and a second visual indicator is used to identify a decrease in localised body size.
8. The method according to claim 5 wherein a first visual indicator is used to identify an identified improvement towards a predefined body goal, and a second visual indicator is used to identify regression away from the predefined body goal.
9. The method according to claim 5 wherein the graphical object includes a stylised body shape generated based on the point cloud data.
10. The method according to claim 4 wherein the client device associated with the user is a mobile device.
11. The method according to claim 4 wherein the client device associated with the user is a scanning booth at which the user is located.
12. A non-transitory computer readable storage medium carrying computer-readable code including a set of instructions to perform, when executed by one or more processors, a method according to claim 1 .
13. A computer implemented method for providing graphical data representative of forecasted body shape variations, the method comprising:
receiving a subscription request from a vendor, wherein the subscription request is indicative of a specific category of size change events and is defined by the vendor based on marketing desires;
authenticating a user U 1 , the user U 1 being associated with first user record data, wherein the first user record data includes a first set of user physical attribute data associated with a time n (PAD 1 T n ), wherein the set of user physical attribute data is derived from a three-dimensional body scanning process;
processing the user physical attribute data associated with time n (PAD 1 T n ) based on a selected forecasting protocol thereby to define forecasted user physical attribute data for the user U 1 associated with a time n+x (PAD 1 T n+x );
identifying a size change event in a case that the relationship between PAD 1 T n and PAD 1 T n+x indicates that the user U 1 is forecasted to move from a first garment size to a second garment size; and
in response to identifying that the size change event of the user U 1 belongs to the specific category of size change events defined in the subscription request from the vendor:
providing data indicative of the size change event to the vendor and allowing the vendor to provide marketing information to the user U 1 .
14. The method according to claim 13 including providing output configured to enable rendering, at a client device associated with the user U 1 , a graphical object representative of the relationship between PAD 1 T n , and PAD 1 T n+x .
15. The method according to claim 14 wherein each set of three dimensional body scan data includes a three dimensional point cloud, and wherein analysis of relationships between PAD 1 T n , and PAD 1 T n+x is based on comparison of the respective point clouds, wherein comparison of the point clouds includes utilisation of vector mathematics to determine spatial variations between corresponding points and wherein the graphical object is defined based on an overlay of the point cloud for PAD 1 T n+x , with respect to the point cloud at PAD 1 T n .
16. The method according to claim 15 wherein the graphical object provides visual indicators representative of variations between PAD 1 T n , and PAD 1 T n+x and wherein the visual indicators include colours and the graphical object includes a stylised body shape generated based on the point cloud data.
17. The method according to claim 16 wherein a first visual indicator is used to identify an increase in localised body size, and a second visual indicator is used to identify a decrease in localised body size.
18. The method according to claim 16 wherein a first visual indicator is used to identify an identified improvement towards a predefined body goal, and a second visual indicator is used to identify regression away from the predefined body goal.
19. The method according to claim 13 wherein the selected forecasting protocol is selected from a set of available forecasting protocols.
20. The method according to claim 19 wherein each forecasting protocol is associated with either or both of: (i) a defined goal; or (ii) an activity program.