PHYSICAL MARKETPLACE INTERACTION PLATFORM
Systems and techniques for a physical marketplace interaction platform are described herein. A user may be identified. An article may be identified based on a physical relationship with the user. A user activity with respect to the article may be determined based on an observed context of the user and the article, and the user activity may be reported.
Embodiments described herein generally relate to consumer interactions in a physical marketplace and more specifically to a physical marketplace interaction platform.
BACKGROUNDA user (e.g., shopper, consumer, etc.) may walk through a physical marketplace (such as a retailer location, store, shopping mall, grocery store, etc.) and interact (e.g., browse, carry, try on, sample, etc.) with articles (e.g., clothing, home goods, sporting goods, electronics, books, etc.) in the marketplace. In some examples, systems installed at the physical marketplace track when the user enters or leaves the physical market place, for example, using cameras. In some examples, the physical marketplace may track user purchasing decisions at the point of sale (POS).
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.
Current physical marketplace analysis of user article interactions is generally non-existent or limited to noting purchase decisions of users, for example at check-out, or user's entering or leaving the physical marketplace. Current physical marketplace observations, however, fail to provide insight into how users interact with the articles of the physical marketplace. In some examples, electronic marketplaces (e.g., web sites from which goods may be purchased) attempt to track user interactions with presented materials, such as viewing times, clicks, etc., however, these interactions are limited to user interactions with elements that may be displayed in an electronic form and the interactions are likewise limited to those that may be measured from electronic inputs to the electronic marketplace.
The lack of information describing user interaction with articles in a physical marketplace causes difficulties for operators to improve the user's shopping experience. To address this issue, a physical marketplace interaction platform may be used to collect information of user interactions with articles in the physical marketplace. In an example, the physical marketplace interaction platform may process the collected information into actionable data. In an example, the actionable data may be used to improve the user's experience in the physical marketplace. In an example, the actionable data may be used to promote sales in the physical marketplace, for example, by providing incentives to complete a sale, by featuring particular articles, etc. Thus, by observing user interactions with articles, the physical marketplace interaction platform may improve user experiences, and give operators greater options in productively running the physical marketplace.
For example, an article sensor (e.g., smart radio frequency identification (RFID) tag or other embedded devices) attached to clothing articles may connect with a shopper's smartphone when the shopper picks up the clothing article to look at it. Picking up the shirt may be the start of a “browsing event” between this shopper and this specific clothing article that may be logged. The browsing event may include information, such as, the duration of the contact, and the distance that the clothing article travels while connected with the shopper. The browsing event may be used to provide a framework for the retailer to offer real-time incentives to accelerate the shopper's purchasing decision. For example, the platform may learn that a specific shopper spent eight minutes picking up several different colors of polo jerseys, spending the most time with navy blue, black, and gray. The shopper tried on a medium navy blue polo but not a black or gray polo. The shopper carried both the navy blue and gray polo jerseys through the store but only purchased the gray polo. Knowing this behavior, an incentive for the shopper to purchase a second polo would have probably been successful.
As used herein, a “physical marketplace” is a physical location in which articles are made available for purchase by a user. Examples of physical marketplaces include stores, outlet centers, grocery stores, shopping malls, kiosks, service centers, physical markets (e.g., farmer's market, flee market, etc.), stalls, vehicles, such as a commercial airplane where a product purchase service is offered, etc. A physical marketplace is “entered” at a point when a user may interact with articles of the physical marketplace. Thus, in a traditional store, the physical marketplace is entered when a user steps inside the store. With respect to a kiosk or stall, the physical marketplace is entered when the user is close enough to observably interact with articles of the kiosk or stall. A physical marketplace is not a catalog, television/electronic service (e.g., such as an infomercial with telephone number), telephone based purchasing service, or electronic service (e.g., web site, mobile phone application, etc.). A retail area of the physical marketplace is an area in which articles are displayed. The retail area would not include, for example, a restroom, office, closet, etc. of a physical marketplace where those areas are not used for selling articles.
As used herein, an “article” is a product (e.g., clothes, toys, sporting equipment, office supplies, electronics, books, music, etc.) or a service that is available for purchase at a physical marketplace. A user is able to interact with an article or a proxy for the article at the physical marketplace. An article proxy may include such things as a demonstration model, a brochure (e.g., describing a vacation), a ticket (e.g., for an item too large to carry such as a large television, appliance, etc.), among other things. Either the article or the article proxy is physically available in the physical marketplace.
As used herein, a “user” is an entity (e.g., a person) that may enter the physical marketplace and effect a purchase of an article. In an example, the entity may be proxy for a person, such as a telepresence platform permitting a remote person to interact with the physical marketplace or articles over a distance. In an example, the user is a guest to the physical marketplace. A guest is not an employee, operator (e.g., owner), or servicer (e.g., contractor working on behalf of the operator), of the physical marketplace.
After entering the physical marketplace, the user may be identified. Identification may prevent tracking of uninteresting parties, such as employees. In an example, identifying the user permits the physical marketplace interaction platform to record observed activity of the user for future analytics. The user may be tracked to POI 110 V. A POI 110 may be any pre-defined area in which a context of the user and article 115 may be inferred. For example, POI 110 V may be a display of pants, POI 110 W may be a dressing room, POI 110 X may be a re-stocking area, POI 110 Y may be a checkout line, and POI 110 Z may be the checkout at the POS 130. In an example, the POI 110 may refer to an area, such as the room of the POI 110 V. In an example, a geometric area may be defined, such as geometric area 120 corresponding to the POI 110 W and the geometric area 125 corresponding to the POI 110 Y or the POI 110 Z. In the area around POI 110 V are four articles A, B, C, and D. Thus, it may be inferred that the user is interacting with these articles 115 based on the user's position at the POI 110 V. A further user interaction with the articles 115 A and B may be inferred by the position of the articles 115 and the user at the POI 110 W (a dressing room). The observed context of proximity of the user and the articles 115 A and B at the dressing room may denote a greater interest in the articles 115 A and B for the user than for articles 115 C and D, to which the user was exposed based on the user's proximity to the POI 110 V. Another example observable context may be determined when, at the POI 110 X (re-stocking) it is noted that article 115 B is no longer with the user while article 115 A remains with the user. Thus, an inference that an attribute of article 115 A is more important to the user than some attribute of article 115 B may be made. The POI 110 Y is the checkout line. The duration of the user's stay in the checkout line may be measured. In an example, the longer the duration, the greater weight may be given to the article 115 A's attributes to indicate desire on the part of the user. The position of the article 115 A and the user at the POI 110 Z (checkout) allows an inference that the user intends to purchase the article 115.
Any mechanism by which the user and articles 115 are tracked may be used to implement the POI 110 mechanism described above. In an example, a location based service (LBS) of the physical marketplace may be used to determine the user's position or the position of any article 115. Other example location technologies may include satellite positioning systems (e.g., the Global Positing system), ground based radio systems (e.g., cellular telephone trilateration), chemical sensors, etc.
The proximity of the user at POI 110 V to articles 115 A and B provides a type of observed context between the user and these articles 115 in which user activity may be measured. Other example observed contexts may include manipulation of the article 115 by the user. For example, a smart RFID may provide proximity information to, for example, a mobile device of the user via its RFID functionality while also including an accelerometer to determine if the article 115 is picked up. A smart RFID is an example of an article sensor. An article sensor is any sensor in observable contact with a specific article 115 and capable of providing information of an interaction context. Example observable sensors may include, a scale (upon which the article rests), an eye tracker (to determine whether the user is looking at the article), a touch sensor (such as a capacitive arrangement on a conductive article to sense human touch), among others.
The user module 205 may be used to identify a user. In an example, identifying the user includes using a mobile device of the user. For example, the user's mobile device may include an application to connect to the physical marketplace interaction platform 200 (e.g., the user module 205) and provide identification information, such as a user name, member ID, etc. In an example, identifying the user includes creating an anonymized identification of the user. Such an anonymized identification may include such information as an anonymous ID, demographic information (e.g., age, sex, job, etc.), time of day, day of month, etc. In an example, the anonymized identification is specific with respect to actions of the user and general with respect to a general identity of the user. For example, that the user picked up a particular plate pattern, carried a card of the set (e.g., an article proxy) to the register, and did not purchase the plates may be specifically attributed to the demographic information of the user, but not to the user himself.
In an example, identifying the user may include identifying a companion of the user. In this example, the observed context may include the companion. For example, it may be observed that a user is near another user. This proximity may be observed in different visits to the physical marketplace, or over an extended period during a single visit. Such companion identification may provide additional data to the observed context. For example, a pair shopping for flatware may allow the inference that they live together and suggest other items that may be of interest to them.
The activity module 215 may be used to identify an article based on a physical relationship with the user. In an example, the physical relationship may include a distance between the article and the user. For example, if the user is within RFID range of the article, the article may be identified. In another example, if the user is at a display, articles in the display may be identified. In an example, the physical relationship may include the presence of the user and the article within a predetermined geometric shape, such as geometric shape 120 or 125 described above. Use of the geometric shape may solve some interaction ambiguities. For example, a user may take several articles to a dressing room but only allowed one at a time in a stall. That the articles may not be next to the user (e.g., out of RFID range) is inconsequential until either the user or the article leaves the area. This may also be true for checkout lines if, for example, a belt, valet, or other mechanism exists to convey the article to the POS outside of the user's possession. In an example, the predetermined geometric shape may be one of a plurality of geometric shapes defined for a retail area including the article.
The activity module 215 may also be used to determine a user activity with respect to the user and the article based on an observed context of the user and the article. An observed context includes information obtainable by the activity module about the user and the article. For example, the observed context may include article position information. In an example, the position information may include a geospatial position relative to a retail area including the article. In an example, the user activity may include physical possession of the article by the user and at least one of duration of possession or location of possession.
In an example, the position information may include an article arrangement. In an example, the article arrangement may include an orientation with respect to the user. For example, a flat article may be picked up and tilted into a more vertical orientation (e.g., so that the user may see it better). In an example, article arrangement may include, such things as an unfolding of the article, repositioning the article on a stand, etc. As noted here and above, article sensors or positioning systems may be used, among other things, to provide observed context information. In an example, a mobile device operated by the user may be used to identify the article (e.g., via RFID, scanning a bar code, etc.). In an example, the mobile device may be used to determine the user activity. For example, a smart RFID tag on a tool may communicate, via the mobile phone, that the tool is moving (e.g., via an accelerometer). This information may be passed to the activity module 215 via the mobile device.
In an example, the activity module 215 may include, or may interface with, an article tracking module 225, a POI ID module 230, and a correlation module 235 to determine the user activity. The article tracking module 225 may track the article in a retail area. For example, the article may include a passive RFID tag. A series of RFID interrogators in a department may periodically interrogate RFID tags. By noting which interrogators may read a particular RFID tag, and knowing the positing of the interrogators within the retail area, as position of the article may be ascertained. In an example, an article sensor may employ a location service (e.g., GPS, LBS, etc.) and report its position to the article tracking module 225. In an example, a camera system may provide still or video images that may be processed to identify the article. Knowing the retail area positions any given view covers provides a location of the article.
The user tracking module 220 may track the user in the retail area. The user tracking module 220 is distinguishable from the user module 205 in that the first determines user position within the physical marketplace while the second is concerned with personal information about the user. The user tracking module 220 may employ any of the techniques described above with respect to article tracking. In an example, the mobile device of the user is employed to aid in the user tracking. In an example, the mobile device provides the user's position.
The POI ID module 230 may identify a POI in the retail area. For example, the POI IS module 230 may provide an interface in which a POI is defined. Such an interface may provide for the identification of an area (e.g., sporting goods) or a geometric shape.
The correlation module 235 may note a confluence of the article, the user, and the POI. For example, the user and the article may be tracked separately by the article tracking module 225 and the user tracking module 220 respectively. Observing that the article and user moved together into a POI may allow the inference that the user is in possession of the article. This may still, however, not be sufficient to determine other ways in which the user is interacting with the article. In this example, the POI may be used to provide additional interaction information. For example, if the user is observed with the article at a dressing room and also at checkout, it may be inferred that the user tried on the article and is willing to buy the article.
The report module 210 may report on the user activity. In an example, reporting the user activity may include adding an event to a browsing history for the user. In an example, the browsing history may be maintained by the user module 205. The browsing history may include user activity data indexed by visits of the user to the physical marketplace. In an example, the browsing history may include information about user visits to other physical marketplaces. In an example, the browsing history may include a correlation model to identify other users similar to the user. In an example, these other user's browsing history may be used to supplement the user's browsing history.
In an example, reporting the user activity may include communicating the activity to a market research platform 240. In an example, reporting to the market research platform 240 may include anonymizing the user. Such communications may provide an additional revenue stream for the physical market place. In an example, the market research platform 240 may aggregate user activity data for a particular article or class of articles. In an example, the market research platform 240 may communicate activity information to third parties, such as article manufacturers, other retailers, etc.
In an example, reporting the user activity may include communicating the activity to an incentives platform 245. In an example, a delivery module 250 may be used to deliver a purchase incentive to the user from the incentives platform. In an example, the purchase incentive may be delivered to a mobile device of the user.
Example purchase incentives may include a discount on the article, notification of a complimentary article (e.g., jewelry to match shirt, knife block to match knife, etc.), coupon on unrelated service (e.g., free car wash at another retailer for purchasing vacation), etc. In an example, the purchase incentive may include representations of other articles of interest to the user based on a browsing history of the user. For example, a user who has bought jeans in the past may be presented with an advertisement for a new brand of jeans being offered at the physical marketplace. In an example, the purchase incentive may include a map of the retail area indicating locations for the other articles, such as the jeans. In another example, a user observed interacting with a shirt may be presented other articles to complete an outfit. The map would indicate the locations of the other articles comprising the ensemble.
In an example, the purchase incentive may be selected from a plurality of purchase incentives based on an identified companion of the user. For example, a user with a partner identified as a companion near a holiday may be offered a romantic article in the purchase incentive. In another example, a user with a companion identified as a child may be offered a purchase incentive for a toy, for example, near the companion's birthday.
In an example, the purchase incentive may be selected based on a predictive analytic. A predictive analytic is a model designed to predict future behavior based on current behavior. Examples may include models based on age, socio-economic status, taste profiles, etc. In an example, the predictive analytic may be derived from a browsing history of the user.
The browsing event trigger module 305 may interface between article sensors and a user's mobile device to determine whether the user is interacting with the article. Once it is determined that an interaction is taking place, the browsing event trigger module 305 initiates a browsing event.
The browsing event tracking module 310 may track data of the browsing event. In an example, the tracked data may include a duration of the browsing event. In an example, the tracked data may include a distance that the user travels with the article. In an example, geospatial tracking information from an LBS of the physical marketplace may be part of the tracked data.
The browsing storage module 320 may store browsing information. In an example, the browsing information may include one or more of, the user, the article that was browsed, purchase tendencies of the user, relationship between browsing events and purchase decisions, etc. The browsing storage module 320 acts as a data repository from whence additional browsing analytics may be derived and stored for future use.
The browsing event comparison module 315 may compare an on-going (e.g., real-time) browsing event with historic characteristics (e.g., behaviors) of users (e.g., the current user of the on-going browsing event) to determine a predictive analytic for the browsing event. Thus, the browsing event comparison module 315 may bridge the gap between historical knowledge and current activities to identify a model for the current activity.
The browsing learning and incentive module 325 may use the determined predictive analytic from the browsing event comparison module 315 to enact an incentive designed to accelerate the user's decision to purchase an article. For example, the browsing learning and incentive module 325 may determine—e.g., based on the data mining of shopper's previous physical store browsing characteristics, and purchase results—whether the best timing for an incentive for this specific shopper is when they first pick up the clothing article or after they try it on in the changing room. In an example, the browsing learning and incentive module 325 may determine that the incentive worked in an unexpected manner (e.g., was more successful or less successful than predicted) and modify the predictive analytic to account for the variance. For example, an incentive that suggested an add-on item of a tie when a sport coat is being browsed, may be changed when suggesting the tie rarely results in a sale (e.g., when fashions change).
The following is an example of a scenario using the browsing event centric arrangement described above with respect to the physical marketplace interaction platform 300. Chris is shopping in the local clothing store, looking to buy a summer jersey. He is looking at the display of summer jerseys. He scans the various jerseys on the display table, and spots one that he likes. He picks it up off of the display table. The article sensor in the jersey detects that the jersey has been picked up off of the display table and connects with Chris' smartphone to start a browsing event. This is transmitted via the smartphone to the physical marketplace interaction platform 300 resident in the clothing store's private cloud where Chris's physical store browsing and purchase history are accessed.
The physical marketplace interaction platform 300 may measure the duration that Chris holds onto the jersey, as well as whether Chris moves the jersey from the table and carries it to another location in the store to a location, such as a dressing (e.g., changing) room. Using a predictive analytic, the physical marketplace interaction platform 300 determines Chris' affinity (e.g., buying desire) for the jersey by the duration of time that Chris holds the jersey and if Chris takes the jersey away from the display table. The physical marketplace interaction platform 300 sends (via text message, etc.) a real time incentive to Chris to buy the jersey in his hand now. Based on Chris' historical browsing and purchasing data at the clothing store, the physical marketplace interaction platform 300 determines that once Chris tries on a piece of clothing, or otherwise holds on to the clothing article for longer than two minutes and ten seconds, he will make a purchase with 87% probability. Therefore the physical marketplace interaction platform 300 offers him a 15% coupon incentive when he picks up the jersey and holds it for longer than 20 seconds.
In a further example of a scenario using the browsing event centric arrangement described above with respect to the physical marketplace interaction platform 300, Erin is shopping at the clothing store and is looking for a new pair of jeans. She takes a pair of jeans that she is interested in from the rack. As in the example with Chris, the physical marketplace interaction platform 300 logs a browsing event. The physical marketplace interaction platform 300 uses Erin's physical store browsing and purchasing records. The physical marketplace interaction platform 300 determines that she has previously looked at (e.g., triggered browsing events) several shirts and other tops. The physical marketplace interaction platform 300 texts Erin a real-time offer for a 30% discount if she buys the jeans that she is looking at, and a matching shirt and socks which she has looked at in a previous visit.
At operation 405, a user may be identified. In an example, identifying the user may include using a mobile device operated by the user. In an example, identifying the user may include creating an anonymized identification for the user. In an example the anonymized identification is specific with respect to actions of the user and general with respect to a general identify of the user. In an example, identifying the user may include identifying a companion of the user.
At operation 410, an article may be identified based on a physical relationship with the user. In an example, the physical relationship may include the distance between the article and the user. In an example, the physical relationship may include inclusion of both the user and the article within a predetermined geometric shape. In an example, the predetermined geometric shape may be one of a plurality of geometric shapes defined for a retail area including the article.
At operation 415, a user activity with respect to the user and the article may be determined based on an observed context of the user and the article. In an example, the observed context may include article position information. In an example, the article position information may include a geospatial position relative to a retail area including the article. In an example, the article position information may include an article arrangement. In an example, the article arrangement may include an orientation with respect to the user. In an example, the observed context may include the companion. In an example, the user activity may include physical possession of the article by the user. In an example, the user activity may include a duration of possession. In an example, the user activity may include a location of possession. In an example, the user activity may include a manipulation (e.g., holding, touching, wearing etc.) of the article by the user.
In an example, identifying the article and determining the user activity may be performed by a mobile device operated by the user. For example, the mobile device may identify the article via near field communications when the user touches the article with the mobile device. Further, the mobile device may report a picture of the article taken by the user and, for example, sent to a companion (e.g., a spouse).
In an example, determining the user activity may include tracking the article in a retail area, tracking the user in the retail area, identifying a POI in the retail area, and noting a confluence of the article, the user, and the POI.
At operation 420, the user activity may be reported. In an example, reporting the user activity may include adding an event to a browsing history for the user. In an example, reporting the user activity includes communicating the activity to a market research platform. In an example, reporting the user activity to the market research platform may include anonymizing the user.
In an example, reporting the user activity may include communicating the activity to an incentives platform.
In an example, the method 400 may comprise delivering a purchase incentive to the user from the incentives platform. In an example, the purchase incentive may be delivered to a mobile device of the user. In an example, the purchase incentive may include representations of other articles of interest to the user based on a browsing history of the user. In an example, the purchase incentive may include a map of a retail area. The map may indicate locations for the other articles.
In an example, the purchase incentive may be selected from a plurality of purchase incentives based on the companion. In an example, the purchase incentive may be selected based a predictive analytic. In an example, the predictive analytic may be derived from a browsing history of the user.
At point 505, is a starting condition of a shopper browsing at a physical retailer, such as a clothier.
At point 510, the shopper picks up a shirt.
At point 515, an article sensor, such as an embedded tag in the shirt, connects with the shopper's mobile device.
At point 520, the physical marketplace interaction platform (e.g., retailer platform) logs the start of the browsing event. In an example, the browsing event information is communicated to physical marketplace interaction platform from the mobile device. At this juncture, the physical marketplace interaction platform both processes browsing analytics for the browsing event (points 525-535 below) as well as continues to collect browsing event information (points 555-565 below).
At point 525, the physical marketplace interaction platform retrieves the shopper's historic shopping characteristics, such as browsing or purchasing characteristics. In an example, these characteristics may be specific to any of location, product, season, etc. In an example, these characteristics may be generalized. For example, the characteristic may be specific to the season but not specific to the location or product.
At point 530, the physical marketplace interaction platform may compare the historic shopping characteristics (e.g., historic data) to on-going browsing event data (e.g., real-time actuals of the browsing event). As illustrated by the dashed lines, this on-going browsing event data may be collected from a tracking processes running in parallel to this process. Comparing the on-going data with the historical data may permit a model of the current shopping experience to be identified. For example, different behavioral models may have been developed based on previous shopping trips that are recorded in the historic data. By identifying similarities between an on-going browsing event and a historic browsing event, a model corresponding to the historic browsing event may be relevant to the on-going browsing event. Thus, the historic data may be leveraged to provide greater insight into an on-going user interaction for the physical marketplace.
At decision point 535, a decision may be made—e.g., based on the model, historic data, or on-going browsing behavior—as to whether a targeted incentive will accelerate a purchasing decision. If it is determined that a targeted incentive will not move the shopper, the physical marketplace interaction platform proceeds back to the initial condition of point 505.
At point 540, if the determination from point 535 is that a targeted incentive is warranted, the physical marketplace interaction platform determines what that incentive is. In an example, the model, personal information about the user, or other factors may form the basis for the incentive.
At point 545, the physical marketplace interaction platform communicates the incentive to the user. In an example, the communication takes the form of a text message to the user's mobile device.
At decision point 550, it is determined whether the shopper accepts the targeted incentive. If the determination is no, the shopper did not accept the targeted incentive, the physical marketplace interaction platform proceeds back to point 530 to determine whether another incentive may work. If the determination is yes, the shopper did accept the targeted incentive, a browsing update may be recorded (e.g., at point 525) and the physical marketplace interaction platform proceeds back to the initial condition of point 505.
At point 555, an in-store LBS may track the location (e.g., within the store) of the combined shirt and mobile device. In an example, a physical relationship to a POI may also be tracked. For example, the location of the shirt may be tracked relative to the browsing event's origin (e.g., where the shirt was picked up) or the dressing room.
At decision point 560, a determination may be made as to whether the shopper is stationary with the shirt. If yes, the tracking of point 555 may continue. If no, the physical marketplace interaction platform may proceed to decision point 565. In either case, from decision point 560, 555, or, 565, tracking information may be communicated to point 530 continuously, continually, or otherwise, via the data feed.
At decision point 565, a determination may be made as to whether the shopper is in the dressing room. As a POI, the dressing room may signify a great interest in the product by the shopper. If it is determined that the shopper is not in the dressing room, the physical marketplace interaction platform may proceed to point 555 to continue tracking the browsing event. If it is determined that the shopper is in the dressing room, the physical marketplace interaction platform may proceed to point 540 (or 530) to determine whether it may accelerate the purchasing decision.
Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations when operating. A module includes hardware. In an example, the hardware may be specifically configured to carry out a specific operation (e.g., hardwired). In an example, the hardware may include configurable execution units (e.g., transistors, circuits, etc.) and a computer readable medium containing instructions, where the instructions configure the execution units to carry out a specific operation when in operation. The configuring may occur under the direction of the executions units or a loading mechanism. Accordingly, the execution units are communicatively coupled to the computer readable medium when the device is operating. In this example, the execution units may be a member of more than one module. For example, under operation, the execution units may be configured by a first set of instructions to implement a first module at one point in time and reconfigured by a second set of instructions to implement a second module.
Machine (e.g., computer system) 600 may include a hardware processor 602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 604 and a static memory 606, some or all of which may communicate with each other via an interlink (e.g., bus) 608. The machine 600 may further include a display unit 610, an alphanumeric input device 612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g., a mouse). In an example, the display unit 610, input device 612 and UI navigation device 614 may be a touch screen display. The machine 600 may additionally include a storage device (e.g., drive unit) 616, a signal generation device 618 (e.g., a speaker), a network interface device 620, and one or more sensors 621, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine 600 may include an output controller 628, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).
The storage device 616 may include a machine readable medium 622 on which is stored one or more sets of data structures or instructions 624 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 624 may also reside, completely or at least partially, within the main memory 604, within static memory 606, or within the hardware processor 602 during execution thereof by the machine 600. In an example, one or any combination of the hardware processor 602, the main memory 604, the static memory 606, or the storage device 616 may constitute machine readable media.
While the machine readable medium 622 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions 624.
The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 600 and that cause the machine 600 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine readable medium comprises a machine readable medium with a plurality of particles having resting mass. Specific examples of massed machine readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The instructions 624 may further be transmitted or received over a communications network 626 using a transmission medium via the network interface device 620 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 620 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 626. In an example, the network interface device 620 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 600, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Additional Notes & ExamplesExample 1 includes subject matter (such as a device, apparatus, or a system for monitoring physical interactions with merchandise) comprising: a user module to identify a user; an activity module to: identify an article based on a physical relationship with the user; and determine a user activity with respect to the user and the article based on an observed context of the user and the article; and a report module to report on the user activity.
In Example 2, the subject matter of Example 1 may optionally include, wherein to identify the user includes using a mobile device operated by the user.
In Example 3, the subject matter of any one of Examples 1-2 may optionally include. The system of claim C1, wherein to identify the user includes creating an anonymized identification for the user, the anonymized identification specific with respect to actions of the user and general with respect to a general identify of the user.
In Example 4, the subject matter of any one of Examples 1-3 may optionally include, wherein the physical relationship includes distance between the article and the user.
In Example 5, the subject matter of any one of Examples 1-4 may optionally include, wherein the physical relationship includes inclusion of both the user and the article within a predetermined geometric shape, the predetermined geometric shape being one of a plurality of geometric shapes defined for a retail area including the article.
In Example 6, the subject matter of any one of Examples 1-5 may optionally include, wherein the observed context includes article position information.
In Example 7, the subject matter of any one of Examples 1-6 may optionally include, wherein the position information includes a geospatial position relative to a retail area including the article.
In Example 8, the subject matter of any one of Examples 1-7 may optionally include, wherein the position information includes an article arrangement.
In Example 9, the subject matter of any one of Examples 1-8 may optionally include, wherein the article arrangement includes an orientation with respect to the user.
In Example 10, the subject matter of any one of Examples 1-9 may optionally include, wherein a mobile device operated by the user is used to both identify the article and determine the user activity.
In Example 11, the subject matter of any one of Examples 1-10 may optionally include, wherein to determine the user activity includes: an article tracking module to track the article in a retail area; a user tracking module to track the user in the retail area; point-of-interest identification module to identifying a point of interest in the retail area; and correlation module to note a confluence of the article, the user, and the point of interest.
In Example 12, the subject matter of any one of Examples 1-11 may optionally include, wherein to report the user activity includes adding an event to a browsing history for the user.
In Example 13, the subject matter of any one of Examples 1-12 may optionally include, wherein to report the user activity includes communicating the activity to a market research platform.
In Example 14, the subject matter of any one of Examples 1-13 may optionally include, wherein to report the user activity to the market research platform includes anonymizing the user.
In Example 15, the subject matter of any one of Examples 1-14 may optionally include, wherein to report the user activity includes communicating the activity to an incentives platform.
In Example 16, the subject matter of any one of Examples 1-15 may optionally include a delivery module to deliver a purchase incentive to the user from the incentives platform.
In Example 17, the subject matter of any one of Examples 1-16 may optionally include, wherein the purchase incentive is delivered to a mobile device of the user.
In Example 18, the subject matter of any one of Examples 1-17 may optionally include, wherein the purchase incentive includes representations of other articles of interest to the user based on a browsing history of the user.
In Example 19, the subject matter of any one of Examples 1-18 may optionally include, wherein the purchase incentive includes a map of a retail area, the map indicating locations for the other articles.
In Example 20, the subject matter of any one of Examples 1-19 may optionally include, wherein to identify the user includes identifying a companion of the user, wherein the observed context includes the companion, and wherein the purchase incentive is selected from a plurality of purchase incentives based on the companion.
In Example 21, the subject matter of any one of Examples 1-20 may optionally include, wherein the purchase incentive is selected based a predictive analytic, the predictive analytic derived from a browsing history of the user.
In Example 22, the subject matter of any one of Examples 1-21 may optionally include, wherein the user activity includes physical possession of the article by the user and at least one of duration of possession or location of possession.
Example 23 may include, or may optionally be combined with the subject matter of any one of Examples 1-22 to include, subject matter (such as a method, means for performing acts, or machine readable medium including instructions that, when performed by a machine cause the machine to performs acts) for monitoring physical interactions with merchandise, comprising: identifying a user; identifying an article based on a physical relationship with the user; determining a user activity with respect to the user and the article based on an observed context of the user and the article; and reporting the user activity.
In Example 24, the subject matter of Example 23 may optionally include, wherein identifying the user includes using a mobile device operated by the user.
In Example 25, the subject matter of any one of Examples 23-24 may optionally include, wherein identifying the user includes creating an anonymized identification for the user, the anonymized identification specific with respect to actions of the user and general with respect to a general identify of the user.
In Example 26, the subject matter of any one of Examples 23-25 may optionally include, wherein the physical relationship includes distance between the article and the user.
In Example 27, the subject matter of any one of Examples 23-26 may optionally include, wherein the physical relationship includes inclusion of both the user and the article within a predetermined geometric shape, the predetermined geometric shape being one of a plurality of geometric shapes defined for a retail area including the article.
In Example 28, the subject matter of any one of Examples 23-27 may optionally include, wherein the observed context includes article position information.
In Example 29, the subject matter of any one of Examples 23-28 may optionally include, wherein the position information includes a geospatial position relative to a retail area including the article.
In Example 30, the subject matter of any one of Examples 23-29 may optionally include, wherein the position information includes an article arrangement.
In Example 31, the subject matter of any one of Examples 23-30 may optionally include, wherein the article arrangement includes an orientation with respect to the user.
In Example 32, the subject matter of any one of Examples 23-31 may optionally include, wherein both identifying the article and determining the user activity are performed by a mobile device operated by the user.
In Example 33, the subject matter of any one of Examples 23-32 may optionally include, wherein determining the user activity includes: tracking the article in a retail area; tracking the user in the retail area; identifying a point of interest in the retail area; and noting a confluence of the article, the user, and the point of interest.
In Example 34, the subject matter of any one of Examples 23-33 may optionally include, wherein reporting the user activity includes adding an event to a browsing history for the user.
In Example 35, the subject matter of any one of Examples 23-34 may optionally include, wherein reporting the user activity includes communicating the activity to a market research platform.
In Example 36, the subject matter of any one of Examples 23-35 may optionally include, wherein reporting the user activity to the market research platform includes anonymizing the user.
In Example 37, the subject matter of any one of Examples 23-36 may optionally include, wherein reporting the user activity includes communicating the activity to an incentives platform.
In Example 38, the subject matter of any one of Examples 23-37 may optionally include, comprising delivering a purchase incentive to the user from the incentives platform.
In Example 39, the subject matter of any one of Examples 23-38 may optionally include, wherein the purchase incentive is delivered to a mobile device of the user.
In Example 40, the subject matter of any one of Examples 23-39 may optionally include, wherein the purchase incentive includes representations of other articles of interest to the user based on a browsing history of the user.
In Example 41, the subject matter of any one of Examples 23-40 may optionally include, wherein the purchase incentive includes a map of a retail area, the map indicating locations for the other articles.
In Example 42, the subject matter of any one of Examples 23-41 may optionally include, wherein identifying the user includes identifying a companion of the user, wherein the observed context includes the companion, and wherein the purchase incentive is selected from a plurality of purchase incentives based on the companion.
In Example 43, the subject matter of any one of Examples 23-42 may optionally include, wherein the purchase incentive is selected based a predictive analytic, the predictive analytic derived from a browsing history of the user.
In Example 44, the subject matter of any one of Examples 23-43 may optionally include, wherein the user activity includes physical possession of the article by the user and at least one of duration of possession or location of possession.
Example 45 includes subject matter, or may optionally be combined with any of Examples 1-44 to include subject matter, such as a system for monitoring physical interactions with merchandise, the system comprising: user identification means to identify a user; article identification means to identify an article based on a physical relationship with the user; activity determination means to determine a user activity with respect to the user and the article based on an observed context of the user and the article; and report means to report on the user activity.
In Example 46, the subject matter of Example 45 may optionally include, wherein to identify the user includes using a mobile device operated by the user.
In Example 47, the subject matter of any one of Examples 45-46 may optionally include, wherein to identify the user includes creating an anonymized identification for the user, the anonymized identification specific with respect to actions of the user and general with respect to a general identify of the user.
In Example 48, the subject matter of any one of Examples 45-47 may optionally include, wherein the physical relationship includes distance between the article and the user.
In Example 49, the subject matter of any one of Examples 45-48 may optionally include, wherein the physical relationship includes inclusion of both the user and the article within a predetermined geometric shape, the predetermined geometric shape being one of a plurality of geometric shapes defined for a retail area including the article.
In Example 50, the subject matter of any one of Examples 45-49 may optionally include, wherein the observed context includes article position information.
In Example 51, the subject matter of any one of Examples 45-50 may optionally include, wherein the position information includes a geospatial position relative to a retail area including the article.
In Example 52, the subject matter of any one of Examples 45-51 may optionally include, wherein the position information includes an article arrangement.
In Example 53, the subject matter of any one of Examples 45-52 may optionally include, wherein the article arrangement includes an orientation with respect to the user.
In Example 54, the subject matter of any one of Examples 45-53 may optionally include, wherein a mobile device operated by the user is used to both identify the article and determine the user activity.
In Example 55, the subject matter of any one of Examples 45-54 may optionally include, wherein to determine the user activity includes: article tracking means to track the article in a retail area; user tracking means to track the user in the retail area; point-of-interest identification means to identifying a point of interest in the retail area; and correlation means to note a confluence of the article, the user, and the point of interest.
In Example 56, the subject matter of any one of Examples 45-55 may optionally include, wherein to report the user activity includes adding an event to a browsing history for the user.
In Example 57, the subject matter of any one of Examples 45-56 may optionally include, wherein to report the user activity includes communicating the activity to a market research platform.
In Example 58, the subject matter of any one of Examples 45-57 may optionally include, wherein to report the user activity to the market research platform includes anonymizing the user.
In Example 59, the subject matter of any one of Examples 45-58 may optionally include, wherein to report the user activity includes communicating the activity to an incentives platform.
In Example 60, the subject matter of any one of Examples 45-59 may optionally include, delivery means to deliver a purchase incentive to the user from the incentives platform.
In Example 61, the subject matter of any one of Examples 45-60 may optionally include, wherein the purchase incentive is delivered to a mobile device of the user.
In Example 62, the subject matter of any one of Examples 45-61 may optionally include, wherein the purchase incentive includes representations of other articles of interest to the user based on a browsing history of the user.
In Example 63, the subject matter of any one of Examples 45-62 may optionally include, wherein the purchase incentive includes a map of a retail area, the map indicating locations for the other articles.
In Example 64, the subject matter of any one of Examples 45-63 may optionally include, wherein to identify the user includes identifying a companion of the user, wherein the observed context includes the companion, and wherein the purchase incentive is selected from a plurality of purchase incentives based on the companion.
In Example 65, the subject matter of any one of Examples 45-64 may optionally include, wherein the purchase incentive is selected based a predictive analytic, the predictive analytic derived from a browsing history of the user.
In Example 66, the subject matter of any one of Examples 45-65 may optionally include, wherein the user activity includes physical possession of the article by the user and at least one of duration of possession or location of possession.
The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments that may be practiced. These embodiments are also referred to herein as “examples.” Such examples may include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.
All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.
In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.
The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure, for example, to comply with 37 C.F.R. §1.72(b) in the United States of America. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the embodiments should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
Claims
1.-24. (canceled)
25. A machine-readable medium that is not a transitory propagating signal, the machine-readable medium include instructions that, when executed by a machine, cause the machine to performs operations for monitoring physical interactions with merchandise, the operations comprising:
- identifying a user;
- identifying an article based on a physical relationship with the user;
- determining a user activity with respect to the user and the article based on an observed context of the user and the article; and
- reporting the user activity.
26. The machine-readable medium of claim 25, wherein the physical relationship includes a distance between the article and the user.
27. The machine-readable medium of claim 25, wherein the physical relationship includes inclusion of both the user and the article within a predetermined geometric shape, the predetermined geometric shape being one of a plurality of geometric shapes defined for a retail area including the article.
28. The machine-readable medium of claim 25, wherein the observed context includes article position information.
29. The method of claim 28, wherein the position information includes a geospatial position relative to a retail area including the article.
30. The machine-readable medium of claim 28, wherein the position information includes an article arrangement.
31. The machine-readable medium of claim 30, wherein the article arrangement includes an orientation with respect to the user.
32. The machine-readable medium of claim 25, wherein determining the user activity includes:
- tracking the article in a retail area;
- tracking the user in the retail area;
- identifying a point of interest in the retail area; and
- noting a confluence of the article, the user, and the point of interest.
33. The machine-readable medium of claim 25, wherein reporting the user activity includes communicating the activity to an incentives platform.
34. The machine-readable medium of claim 33, comprising delivering a purchase incentive to the user from the incentives platform.
35. The machine-readable medium of claim 34, wherein the purchase incentive includes representations of other articles of interest to the user based on a browsing history of the user.
36. A system for monitoring physical interactions with merchandise, the system comprising:
- a user module to identify a user;
- an activity module to: identify an article based on a physical relationship with the user; and determine a user activity with respect to the user and the article based on an observed context of the user and the article; and
- a report module to report on the user activity.
37. The system of claim 36, wherein the physical relationship includes a distance between the article and the user.
38. The system of claim 36, wherein the physical relationship includes inclusion of both the user and the article within a predetermined geometric shape, the predetermined geometric shape being one of a plurality of geometric shapes defined for a retail area including the article.
39. The system of claim 36, wherein the observed context includes article position information.
40. The system of claim 39, wherein the position information includes a geospatial position relative to a retail area including the article.
41. The system of claim 39, wherein the position information includes an article arrangement.
42. The system of claim 41, wherein the article arrangement includes an orientation with respect to the user.
43. The system of claim 36, wherein to determine the user activity includes:
- an article tracking module to track the article in a retail area;
- a user tracking module to track the user in the retail area;
- a point-of-interest identification module to identify a point of interest in the retail area; and
- a correlation module to note a confluence of the article, the user, and the point of interest.
44. The system of claim 36, wherein to report the user activity includes communicating the activity to an incentives platform.
45. The system of claim 44, comprising a delivery module to deliver a purchase incentive to the user from the incentives platform.
46. The system of claim 45, wherein the purchase incentive includes representations of other articles of interest to the user based on a browsing history of the user.
47. A hardware implemented method for monitoring physical interactions with merchandise, the method comprising:
- identifying a user;
- identifying an article based on a physical relationship with the user;
- determining a user activity with respect to the user and the article based on an observed context of the user and the article; and
- reporting the user activity.
48. The method of claim 47, wherein the user activity includes physical possession of the article by the user and at least one of duration of possession or location of possession.
Type: Application
Filed: Dec 20, 2013
Publication Date: Aug 25, 2016
Inventors: David Stanasolovich (Beaverton, OR), Craig Owen (Folsom, CA), Catherine W. Spence (Merrimack, NH), Eddie Balthasar (Folsom, CA)
Application Number: 15/024,933
