Estimating footfall for a location
AssigneePlacense LTD.
InventorsEyal Shlomo Lanxner, Yair Hochner
Life of this patent
Estimating the term…
Abstract
The presently disclosed subject matter includes a computerized method and system for estimating footfall for a location of one or more users. Visitation data including a plurality of visit data items is obtained. The obtained visitation data is grouped into one or more groups of visit data items. For each group, the data items in the group is assigned to one or more clusters of visits. For each visit data item, the data item is assigned to a given cluster of the clusters. Based on the one or more clusters of visits data, a footfall estimation for a location is determined, wherein the footfall estimation for the location is indicative of a number of visits of the one or more users in the location, within one or more time intervals.
Claims18
A computerized method for estimating footfall for a location of one or more users, by a processor and memory circuitry (PMC), the computerized method comprising:
- a) obtaining visitation data including a plurality of visit data items, wherein each visit data item includes at least data indicative of visit time and visit coordinates associated with a user of the one or more users;
- b) grouping the obtained visitation data into one or more groups of visit data items, wherein each group is associated with a respective user;
- c) for each group, assigning the data items in the group to one or more clusters of visits, including, for each visit data item:
- assigning the data item to a given cluster of the clusters, if at least the visit time data and the visit coordinates data of the data item comply with corresponding visit time and visit coordinates conditions that are associated with the given cluster, wherein assigning the data item to the given cluster of the clusters comprises determining that the difference between the visit time of the data item and at least one visit time that is associated with the given cluster, does not exceed a time threshold, and that the distance between the visit coordinates of the data item and at least one visit coordinates associated with the given cluster, does not exceed a distance threshold;
- and
- d) determining, based on the one or more clusters of visits data, a footfall estimation for a location, wherein the footfall estimation for the location is indicative of a number of visits of the one or more users in the location, within one or more time intervals.
A system for estimating footfall for a location of one or more users, by a processor and memory circuitry (PMC), the processor being configured to:
- a) obtain visitation data including a plurality of visit data items, wherein each visit data item includes at least data indicative of visit time and visit coordinates associated with a user of the one or more users;
- b) group the obtained visitation data into one or more groups of visit data items, wherein each group is associated with a respective user;
- c) for each group, assign the data items in the group to one or more clusters of visits, including, for each visit data item:
- assign the data item to a given cluster of the clusters if at least the visit time data and the visit coordinates data of the data item comply with a corresponding visit time and visit coordinates conditions that are associated with the given cluster, wherein assigning the data item to the given cluster of the clusters comprises determining that the difference between the visit time of the data item and at least one visit time that is associated with the given cluster, does not exceed a time threshold, and that the distance between the visit coordinates of the data item and at least one visit coordinates associated with the given cluster, does not exceed a distance threshold;
- and
- d) determine, based on the one or more clusters of visits data, a footfall estimation for a location, wherein the footfall estimation for the location is indicative of a number of visits of the one or more users in the location within one or more time intervals.
A non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method for estimating a footfall for a location of one or more users, by a processor and memory circuitry (PMC), the method comprising:
- a) obtaining visitation data including a plurality of visit data items, wherein each visit data item includes at least data indicative of visit time and visit coordinates associated with a user of the one or more users;
- b) grouping the obtained visitation data into one or more groups of visit data items, wherein each group is associated with a respective user;
- c) for each group, assigning the data items in the group to one or more clusters of visits, including, for each visit data item:
- assigning the data item to a given cluster of the clusters, if at least the visit time data and the visit coordinates data of the data item comply with corresponding visit time and visit coordinates conditions that are associated with the given cluster, wherein assigning the data item to the given cluster of the clusters comprises determining that the difference between the visit time of the data item and at least one visit time that is associated with the given cluster, does not exceed a time threshold, and that the distance between the visit coordinates of the data item and at least one visit coordinates associated with the given cluster, does not exceed a distance threshold;
- and
- d) determining, based on one or more clusters of visits data, a footfall estimation of a location, wherein the footfall estimation for the location is indicative of a number of visits of the one or more users in the one or more locations in one or more time intervals.
A computerized method for training a machine learning (ML) estimation model to estimate footfall for a location, by a processor and memory circuitry (PMC), the computerized method comprising:
- a) obtaining training visitation data including a plurality of visit data items, wherein each visit data item includes at least data indicative of visit time and visit coordinates associated with a user;
- b) grouping the obtained training visitation data into one or more groups of visit data items, wherein each group is associated with a respective user;
- c) for each group, assigning the data items in the group to one or more clusters of visits data, including, for each data item:
- assigning the data item to a given cluster of the clusters if at least the visit time data and the visit coordinates data of the data item comply with corresponding visit time and visit coordinates conditions that are associated with the given cluster;
- and
- d) training a machine learning (ML) estimation model having a specified set of features, based on one or more clusters of visits data, to estimate footfall for a given location, wherein the footfall estimation is indicative of a number of visits of one or more users in the location within one or more time intervals;
- e) determining a model weight, wherein a model weight is calculated based on accuracy level of the ML model based on known footfall count of the location;
- and
- f) associating the determined model weight with the ML estimation model.
A system for estimating footfall for training a machine learning (ML) estimation model to estimate footfall for a location, by a processor and memory circuitry (PMC), the processor configured to execute a method comprising:
- a) obtaining training visitation data including a plurality of visit data items, wherein each visit data item includes at least data indicative of visit time and visit coordinates associated with a user;
- b) grouping the obtained training visitation data into one or more groups of visit data items, wherein each group is associated with a respective user;
- c) for each group, assigning the data items in the group to one or more clusters of visits data, including, for each data item:
- assigning the data item to a given cluster of the clusters if at least the visit time data and the visit coordinates data of the data item comply with corresponding visit time and visit coordinates conditions that are associated with the given cluster, d) training a machine learning (ML) estimation model having a specified set of features, based on one or more clusters of visits data, to estimate footfall for a given location, wherein the footfall estimation is indicative of a number of visits of one or more users in the location within one or more time intervals;
- e) determining a model weight, wherein a model weight is calculated based on accuracy level of the ML model based on known footfall count of the location;
- and
- f) associating the determined model weight with the ML estimation model.
Dependencies are inferred from the claim text.