Device tagging using micro-location movement data

From a collection of movement data of a device, a pattern of movement of the device in a venue is computed. An evaluation is made to determine whether the pattern fits a profile from a set of profiles. Each profile in the set of profiles includes a set of characteristics of a different type of user. A label of the profile indicative of a type of a user associated with the device is added to a data record in the collection when the evaluation is affirmative.

TECHNICAL FIELD

The present invention relates generally to a method, system, and computer program product for characterizing a behavior of a mobile device user based on the device's movement through an area. More particularly, the present invention relates to a method, system, and computer program product for device tagging using micro-location movement data.

BACKGROUND

A wireless data processing system, a wireless data communication device, or a wireless computing platform is collectively and interchangeably referred to herein as “mobile device” or “device”. For example, many mobile devices not only allow the users to make voice calls, but also exchange messages and data, access remote data processing systems, determine a user's location or activity, communicate with other mobile devices or data processing systems, or perform network-based interactions and other transactions.

Wearable devices are a category of mobile devices. A wearable device is essentially a mobile device, but has a form-factor that is suitable for wearing the device on a user's person.

Micro-location technology includes components, protocols, and data communications designed to determine and collect precise location information from a mobile device as the device traverses through a geographical area or space. A geographical area or space through which is a device moves is collectively and interchangeably referred to as a venue.

As an example, a Bluetooth beacon operates on a Bluetooth network established between the beacon and a mobile device over a short distance—generally thirty feet or less. The Bluetooth beacon performs a low-energy exchange of small amounts of data with a mobile device in close proximity.

Wi-Fi beacons are also available to perform similar functions, but over greater distances, involving more data, and with a different degree of precision. Hybrid beacons are also available to operate over multiple protocols in an attempt to capture the best aspects of multiple solutions.

The data received at a beacon from a device in this manner is usable to identify the device or a user of the device, the location of the device, sending payload data such as advertising or coupons to the device, performing payment transactions, and the like. Thus, a micro-location technology uses the data collected from the device to track the device's movement in a venue where multiple beacons are deployed.

As a device moves away from one beacon and towards another beacon, the transition between the beacons allow the device's path to be mapped through the venue. When a device remains in communication with a beacon for a duration, the lack of transition to another beacon is interpreted as a pause at a location in the venue. The path and the pause are used to select payloads to be delivered to the device and other movement tracking purposes.

SUMMARY

The illustrative embodiments provide a method, system, and computer program product. An embodiment includes a method that computes, using a processor and a memory, from a collection of movement data of a device, a pattern of movement of the device in a venue. The embodiment evaluates whether the pattern fits a profile from a set of profiles, each profile in the set of profiles comprising a set of characteristics of a different type of user. The embodiment adds to a data record in the collection, responsive to the evaluating being affirmative, a label of the profile indicative of a type of a user associated with the device.

DETAILED DESCRIPTION

An enormous number of devices enter and move in a business venue every day. A business enterprise may have several venues where such devices are present and moving. As the devices are moving in a venue, they produce device movement data, which is collected through a suitable micro-location technology.

It is quite common for business systems to end up with a large collection of device movement data in which the devices and/or their users are identified merely by the alphanumeric identifiers transmitted by the devices. in many cases, the identifiers are sufficient, e.g., to deliver payload data to a device identified in this manner while the device is traversing the venue. However, the illustrative embodiments recognize that such a manner of identifying the device and/or the user of the device is insufficient for a variety of other purposes.

The illustrative embodiments recognize that the device movement data can remain useful long after the device has left the venue. For example, it is often desirable for a business entity to analyze the behavior of a particular customer or a group of customers. Note that it is not necessary to identify the customer by the customer's name, but knowing the customer by the characteristics of the customer can lead to better customer service, improved floor design, advantageous product placement, and many other purposes.

Presently, the available device movement data can be analyzed to determine customer flow through the aisles in a venue, linger times at various locations in the venue, entry-to-exit durations, and so on. However, the illustrative embodiments recognize that the device movement data of a device is not sufficiently identified, tagged, annotated, or otherwise marked to be able to characterize the user associated with the device. For example, while it can be presently determined that a particular device entering a venue at a present time has traversed through the venue in the past, the device movement data of the present and past traversals is not suitably marked for instantly learning the characteristics of the device's user.

To give some non-limiting examples, it cannot be presently ascertained by the presently available device movement data analytics whether the user is an employee or a customer, male or female, a teenager or a retired person, a buyer or a browser, a sportsperson or a foodie, and many other such characteristics that can be associated with users of mobile devices. The illustrative embodiments recognize that characterizing a user simply by analyzing the movement data produced by the user's device is a challenging task.

The illustrative embodiments further recognize that once such a characterization is accomplished with a sufficient degree of confidence, the device of the user can be tagged or marked with the computed characteristics of the user. Such a manner of determining a user's characteristics from the movement data of the user's device and associating the characteristics with the user's device is presently unavailable. Associating user characteristic data with the user's device is helpful in rapid recognition of the type of the user when the device is present in a venue, in adjusting the venue for various user characteristics, and many other purposes.

The illustrative embodiments used to describe the invention generally address and solve the above-described problems and other problems related to device tagging using micro-location movement data.

An embodiment can be implemented as a software application. The application implementing an embodiment can be configured as a modification of an existing device movement data collection system, as a separate application that operates in conjunction with an existing device movement data collection system, a standalone application, or some combination thereof.

In many cases, although not necessarily, a mobile device is already configured with an application to enable communication and data exchange with beacons in a venue. In some cases, an embodiment optionally configures such an application on the mobile device to collect certain additional types of data and provide that data to the movement data collection system for use by the embodiment, as described herein.

A collection of device movement data corresponds to a venue. The venue is mapped using a suitable manner of identifying sub-areas within the venue, e.g., zones. In other words, a venue comprises a set of zones, where a zone is an area that is usable for any particular purpose. As some non-limiting examples in a retailing venue, a zone can be an aisle, a checkout area, a storage area, an employee breakroom, a rest area, a floor area for merchandise of a certain type, and so on. Only as an example and not to imply any limitations on the illustrative embodiments, the various embodiments are described using a retailing venue and examples zones that may be found in such a venue.

An embodiment constructs a set of profiles. A profile includes a set of one or more characteristics of a type of a user, the user being a user of a mobile device that traverses a venue. For example, a user may be of type “employee” and may exhibit certain characteristics in their movements, i.e., in the movements of their device in a given venue. For example, one characteristic of an employee profile may be that the user dwells in one zone of the venue—to which the employee is assigned, e.g., footwear zone, cosmetics zone, appliances zone, and so on—for more than eighty percent of a typical work shift—e.g., between 10 AM and 6 PM. Another example characteristics of an employee profile may be that the user moves around in the venue during a typical work shift in a greater than a threshold percentage of user's visits to the venue.

A profile can describe a value or a range of values for a set of characteristics. Some non-limiting example characteristics include zone entry times, zone exit times, dwell times, total duration of visit, days of the visits, days of absence from the venue, times of absence from the venue or a zone, frequency of visits to the venue or a zone, number of pauses in a zone, zone or zones of interest, speed of movement through a zone or the venue, presence or absence of other users proximate to the user, and many others.

Depending on the type of user in a particular profile, all or some of these example characteristics, other or different similarly purposed characteristics, or some combination thereof may be present in the profile. Furthermore, even when the same characteristic is present in different profiles, the characteristic may have different value or range specified in such different profiles.

An example profile for an employee user is described above. Many other profiles for other types of users can similarly be defined. Some non-limiting examples of other profiles include profiles by roles such as employee, customer, browser or peruser, pass-through user, security worker, etc. Some other non-limiting examples of other profiles include profiles by history such as a customer who has previously bought something from the venue, a customer who has previously bought something from the venue, a customer who has previously gone to the customer service zone, etc.

Some other non-limiting examples of other profiles include profiles by age group or roles such as a student (appears at the venue for an hour after the school lets out), a retired person (appears at the venue during office hours and dwells for a threshold amount of time), an office worker (appears after office hours during weekdays and leaves within a threshold amount of time), and so on. Some other non-limiting examples of other profiles include profiles by gender or age such as a male (dwells more than a threshold percentage of visit time in zones for men's products), a female (dwells more than a threshold percentage of visit time in zones for women's products), a minor child (dwells more than a threshold percentage of visit time in zones for children's products), and so on.

Some other non-limiting examples of other profiles include profiles by preferences such as a sportsman (traverses through sporting goods zone in more than a threshold percentage of visits), a foodie (traverses directly to specialty foods zones and then to the checkout zone), and so on.

These simplified examples of profiles and the included characteristics with corresponding values or ranges are not intended to be limiting. From this disclosure, those of ordinary skill in the art will be able to conceive many other configurations of profiles and characteristics, and the same are contemplated within the scope of the illustrative embodiments. Furthermore, such configurations can be any complexity, e.g., from a simple configuration of one characteristics with one value or range, to a configuration of medium complexity using a combination of three or four characteristics with the corresponding values or ranges, to a very complex configuration which uses a combination of tens of characteristics with the corresponding values or ranges.

Furthermore, profiles can be grouped according to profile types. For example, one subset of profiles may apply only to employees where different profiles in the subset apply to employees at different positions in the organization. Similarly, another subset of profiles may apply only to patrons where different profiles in the subset apply to patrons of different types (customers, browsers, pass-through, etc.) Many other profiles types can be used to categorize profiles in a similar manner.

An embodiment analyzes a collection of device movement data pertaining to a device to identify a pattern of movement of the device during a period. For example, the pattern analysis uses a map or layout of the venue zones to construct a pattern of movement. A device can have more than one patterns of movements through a venue during a period.

An embodiment further analyzes a pattern of movement of the device according to one or more profiles. Some profiles are contextual. Accordingly, determining whether the pattern fits a contextual profile requires a contextual analysis. A contextual profile is a profile that uses some other data to provide a context or relationship information. For example, a profile of a customer who has previously purchased something from the venue is a contextual profile because it uses past data of the device's movement to provide the context for determining whether the profile fits the user or not. Similarly, as another example, a profile of a customer who has previously visited a zone in the venue is a contextual profile because it uses past data of the device's movement to provide the context for determining whether the profile fits the user or not.

Thus, an embodiment performs contextual analysis of the pattern to determine whether the pattern of the user's movement match a contextual profile. Matching a profile means matching or satisfying at least a threshold number of characteristics of the profile. Matching a characteristic of a profile means determining whether a given value—e.g. from a pattern—satisfies the value or range specified for that characteristic.

Similarly, some profiles are cognitive. Cognitive computing attempts to answer questions about a subject matter based on information available about the subject matter domain. Cognitive analytics is the process of analyzing available information or knowledge to create, infer, deduce, or derive new information. Cognitive analytics is frequently implemented in the form of a Question and Answer system (Q and A system).

Some profiles can be cognitive in that given what is generally known to be true about human behavior can be used in a cognitive analysis to determine whether a user is behaving in that manner. The distinction from contextual analysis is that contextual analysis uses the user's own data to provide context to an analysis of the user's other data, whereas cognitive analysis uses non-user specific behavioral information to compute a behavior parameter of the user.

For example, it is generally known that an upset human has elevated heartrate. If biometric data is available to an embodiment from a particular user's mobile device, the embodiment can perform a cognitive analysis using the knowledge about the relationship between heartrate and mood to establish whether the user fits an upset user's profile. Similarly, it is generally known that office workers are not available for shopping during office hours, and it is also generally known that office hours are generally Monday through Friday 9 Am through 5 PM. The embodiment can perform a cognitive analysis using the knowledge about the relationship between office times and roles of users to establish whether the user fits or matches an office worker's profile.

When the pattern matches a profile, an embodiment associates a label of that profile, e.g., a name of the profile, with the device identifier in the collection of device movement data. A device may have multiple profile labels associated therewith in this manner. The set of labels associated with a device forms the tag of the device.

Furthermore, some or all characteristics of a matching profile may be designated for specific tagging or annotation. For example, a profile of a customer type user may include a characteristic “zones visited”. The “zones visited” characteristics may be designated for specific tagging. Accordingly, when a device's pattern fits the customer profile, the label “customer” is added to the tag and the “zones visited” characteristic is added as an additional annotation with the device.

Additionally, an embodiment further determines whether at least a threshold number or fraction of the profiles whose labels are included in the tag belong to a common profile type. When at least a threshold number or fraction of the profiles whose labels are included in the tag belong to a common profile type, the embodiment further associates with the device the common profile type.

The tag, the additional annotation, the profile type, or some combination thereof can be used to tag or mark the device within the scope of the illustrative embodiments. Furthermore, the tag or marking can be constructed using the labels, the additional annotation, the profile type, or some combination thereof, in any manner suitable to a particular implementation without departing the scope of the illustrative embodiments.

The manner of device tagging using micro-location movement data described herein is unavailable in the presently available methods. A method of an embodiment described herein, when implemented to execute on a device or data processing system, comprises substantial advancement of the functionality of that device or data processing system in marking device movement data with the characteristics of the user associated with the device. Such markings are usable to rapidly provide a service, change an arrangement in a venue, or deliver a payload to the user during a visit to the venue.

The illustrative embodiments are described with respect to certain types of venues, zones, device movements, movement data, profiles, profile types, profile characteristics, values, value ranges, thresholds, matchings, labels, tags, annotations, markings, analyses, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the invention. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments.

Application105implements an embodiment described herein. Application105analyzes device movement data109A using profiles109A, and in part using cognitive analytics system107as needed according to the profile being used in the analysis. As a result of the analysis, application105marks device movement data109A in a manner described herein.

With reference toFIG. 3, this figure depicts a block diagram of an example configuration for device tagging using micro-location movement data in accordance with an illustrative embodiment. Application302is an example of application105inFIG. 1.

Beacon304is a component of a micro-location infrastructure as described herein. Beacon304communicates with mobile device306to collect device movement data308in repository310. Data308is collected over a period, and may include data from more than one visits or traversals of device306in a given venue. Optionally, in some cases, application312executing in device306facilitates the collection of device movement data308.

Component314creates and manages one or more profiles as described herein. Component314creates new profiles320, changes an existing profile320, removes a profile320that is no longer to be used, or some combination thereof.

Using data308, component316performs a pattern analysis to determine a pattern of movement of device306. When determining whether the pattern fits a contextual profile from profiles320, component318uses past data of device306from repository310to perform contextual analysis of the pattern identified by component314.

Similarly, component322determines whether the pattern fits a cognitive profile from profiles320. Component322provides additional data of the user in conjunction with the pattern and a cognitive profile to cognitive analytics system324. System324is trained in the subject-matter domain if human behavior analysis with a knowledgebase of human behavioral information. System324provides an answer with a corresponding degree of confidence whether the pattern and additional data of the user fit the cognitive profile as described herein.

The additional data of the user can take many forms. As an example, application312in mobile device306may collect and provide biometric data of a user of device306. The biometric data may include, but is not limited to skin temperature, perspiration, tone or pitch of voice, shifting of weight or restlessness, jitters, pulse, skin conductivity, and so on. Such biometric data is also stored in repository310.

As another example, the additional data may be other activities of device306. For example, application312may provide data that is indicative of stressful activities on device306, other visited locations prior to the visit at the venue, and many other types of data that can be used to determine a cognitive or behavioral state of the user. Device306can also provide information about a type of the device, a configuration of the device, and other similar information, which is usable to determine a level of education, tech-savvy, or other cognitive characteristics of the user of the device. Such information is another example of the additional data that is usable in the cognitive analysis of component322.

Based on the results of the matches found by components318and322, component326constructs a tag from the labels of the profiles, determines a profile type if applicable, composes one or more additional annotations, or some combination thereof, as may be applicable to the user of device306. Component326marks device movement data with the combination of tag, type, and annotation to form tagged/annotated/marked device movement data328.

With reference toFIG. 4, this figure depicts a simplified example of tagged device movement data in accordance with an illustrative embodiment. Data400is produced by an operation of application302ofFIG. 3as described herein.

Data400comprises movement data records, which are shown with some example data components and some example tags, profile types, and annotations. The actual device movement data is not shown, but could be populated in an additional column in the manner of the depicted data components.

For example, column402shows the tag associated with a record, the tag formed using one or more profile labels in a manner described herein. Column404depicts a profile type, if applicable to the record. Column406depicts a time the device movement data was recorded or updated. Column408depicts additional annotations associated with a record.

Row410shows an example structure adopted for the various components under the various columns. Rows412,414, and416show some non-limiting examples of tagged, annotated, or otherwise marked records for different example devices and their corresponding users, as described herein.

With reference toFIG. 5, this figure depicts a flowchart of an example process for device tagging using micro-location movement data in accordance with an illustrative embodiment. Process500can be implemented in application302inFIG. 3.

The application receives a set of profiles (block502). The application receives device movement data of a device (block504).

Using a zone map of a venue where the device is moving, or has moved, the application determines a pattern in the movements of the device (block506). The application determines whether the pattern or a portion thereof matches a profile (block508). The determination of block508is performed using the contextual and/or cognitive analysis described herein.

If the pattern or a portion thereof does not match the selected profile (“No” path of block508), the application proceeds to block516. If the pattern or a portion thereof matches the selected profile (“Yes” path of block508), the application adds the profile, or a label thereof, to a tag for the device (block510). Optionally, the application also adds one or more matching characteristics from the profile to an annotation for the device (block512). When a characteristic is added to the annotation, the application also adds to the annotation a value of the characteristic, as determined from the movement data, (block514).

The application determines whether more profiles remain to be processed for matching with the pattern in this manner (block516). If more profiles remain (“Yes” path of block516), the application returns to block508and selects another profile for the determination of block508.

If no more profiles remain to be matched (“No” path of block516), the application determines whether the all or at least a threshold fraction of the profiles used in the tag share a profile type (block518). If at least a threshold fraction of the profiles used in the tag share a profile type (“Yes” path of block518), the application identifies or marks the device with the profile type (block520). If at least a threshold fraction of the profiles used in the tag do not share a profile type (“No” path of block518), the application marks the device as belonging to a user of an uncertain profile type (block522).

The application updates the device movement data with the tag, type, annotation, or some combination thereof (block524). The application optionally adds a name for the user of the device, as depicted in one of the columns inFIG. 4(block526). The application ends process500thereafter.