Patent Application
20150032597
People move about in the physical world, sometimes without any purposeful destination, e.g., simply for exercise, and sometimes purposefully to reach a particular location. For example, throughout a given day people commute from their homes to their workplaces or schools, they visit retail locations such as malls and restaurants, and they walk their dogs in open spaces like parks. Their motivations to reach such destinations may change with the time of day, the day of the week, the season, the weather, holidays, and many other variables. In addition, people exhibit a variety of behaviors at the locations they do frequent, for example the length of time they stay, how much money they spend there, how often they return. They also exhibit other personal preferences as they make their choices as to which locations to visit, e.g., how far they are willing to travel to get there, which types of cuisine they prefer when they choose between restaurants, how loyal they are to particular establishments.
Further, people are exposed to multiple different venues other than their ultimate destinations as they move about. Driving through a city to a restaurant, for example, a person may pass by hundreds of other possible places to eat, relax, shop, or socialize. Simply taking a different route the next time, that person could pass by hundreds more. Every location in the real world can thus be thought of as having an “audience,” made up of the people who encounter it as they move about in their daily lives. And the value of each location is informed in part by how its audience interacts with it. For example, the success of most retailers' businesses depends on whether or not people stop in to shop or eat. And the value of out-of-home media advertising displays, (e.g., billboards, subway posters, advertisements in malls), is measured according to the number and demographics of the people who have an opportunity to see them as they pass by.
“Geographic location information,” describing where individuals or population groups go as they move about in the physical world, along with the inferences that can be made about those individuals or population groups from their geographic location information, is derived from a type of “personal data”: “geographic location data.”
In the past decade, a change in how and what personal data can be and is gathered, including geographic location data, has been the result of the pervasive adoption of mobile computing devices and the dependence many people place upon them, keeping their smartphones within arm's reach throughout the day. Each of these mobile computing devices contains sensors that can collect a variety of data about the environment in which the device exists. For example, sensors in smartphones can determine the device's location and orientation, what its user is doing with the device, what networks the device is connected to, and the presence of nearby radio frequency transmitters and receivers. Furthermore, such devices can be used, among other things, to capture photographs, to make purchases, to search websites, and to elicit direct responses from their users, e.g., queries about a given user's preferences, demographics, or opinions. All of the data collected by and from someone's mobile computing device can be thought of as “personal” to that user.
There are also types of personal data that may be obtained through means other than using mobile computing devices. For example, credit card holders generate paper trails of purchase behavior, businesses track the visits and purchases of the members of their loyalty programs, credit rating agencies keep track of loans people apply for and their history of making payments, and hospitals and health insurers keep medical records on each person who uses their services. The public sector maintains extensive databases that contain data personal to individuals, from licenses they have applied for and been granted to their border crossings, their marital status, criminal histories, et cetera. Research companies survey individuals about purchase, media viewing, and voting preferences, among many other topics. Additionally, individuals are increasingly curating datasets about themselves. For example, individuals using social networking platforms, e.g., Facebook, upload many personal details about their lives.
All of this personal data—whether it is derived from mobile computing devices or not—can be of consequential use to businesses, marketers, governments, individuals, and anyone interested either in understanding how people behave currently, or in predicting how they will behave in the future. For example, automobile dealers may wish to target advertising to people who (a) live within 50 miles of their dealerships, (b) already own a car that is at least three years old and comparable in price to those they sell, (c) are from a demographic category likely to purchase a car such as those they sell, and (d) have children coming of driving age or infants about to be born. Being able to identify individuals who meet all of these criteria, combined with other behavioral personal data, would allow such automobile dealers to select out-of-home media displays sited where such potential buyers are most likely to pass, and be able to target direct mail advertisements to their residences, place advertisements on radio and television stations they frequently tune to, or target online advertisements through the websites they visit.
At present, most personal data, if it is collected at all, exists in silos that are often inaccessible either to the individuals the data is personal to, or to third parties other than those who collect it and thereafter control further access to it. Because of the private nature of most of this data, people may wish to control access to it themselves, and to share in any value that is created through its use. However, under many if not most circumstances, people believe they are required to relinquish personal data in exchange for necessary services, e.g., their home zip codes when using a credit card to purchase gasoline. It is becoming common knowledge that Internet search engines, e.g., Google, and social networks, e.g., Facebook, mine the personal data that is a byproduct of individuals' use of their sites and services to generate income from advertising targeted to those individuals. The consequent threat to individual privacy that results from the widespread collection of personal data has become a controversial and pressing topic facing consumers, businesses, policymakers, and regulators today.
Technology is disclosed for collecting and employing personal data (“the disclosed technology”). Various embodiments are described herein that may operate independently or concurrently, as would be recognized by one skilled in the art.
The disclosed technology envisioned herein includes multiple inputs (variously “data”) that, when processed (e.g., subjected by the disclosed technology to aggregation, amalgamation among datasets, and algorithmic computation), produce multiple outputs (variously “information”). Inputs can include (1) data specific to individual people (“personal data”), e.g., geographic location data collected periodically or received from third party sources that describes the coordinates of the path a person takes as he/she moves about in the physical world, and (2) data that adds meaning, (e.g. contextual), to personal data (“informing dataset”), e.g., mapping data that combined with geographic location data produces geographic location information, which describes where individuals go as they move about in the physical world, along with the inferences, including demographic, that can be made about those individuals from their location information. Outputs of the disclosed technology, containing information derived from the processing of inputs from multiple individuals, third party personal data sources, and informing datasets, may be reports that may be of value to businesses, organizations, individuals, and the operator of the disclosed technology (“operator”).
Further, the disclosed technology envisioned herein may include an input of geographic location data from a third party source which may not include known or reported demographics, from which can be inferred demographic information relating to the individuals from whom the geographic location data is collected.
Further, the disclosed technology envisioned herein includes the collection of personal data from individuals resulting from the intentional participation of those individuals in exchange in part for (1) their retaining ownership of their personal data and control over third-party access to it, and (2) compensation for access that may include a bidding methodology and/or sharing of the revenue generated by the disclosed technology.
Further, the disclosed technology envisioned herein includes multiple application programming interfaces (“APIs”) designed to allow a variety of output scenarios.
Inputs to the disclosed technology are collected from multiple sources, including passively from mobile computing devices (also called “monitoring devices”) owned by and/or carried by participating individuals (“users”). For example, software installed on monitoring devices, which may include smartphones, can facilitate the periodic or constant collection of geographic location data. Although specific technologies or embodiments of location detection techniques are described herein, one skilled in the art would recognize that other technological means presently existing or developed in the future could be as easily integrated or used. Other data also may be collected directly and automatically without the user's active involvement, e.g., websites the user visits using a web browser installed on the monitoring device or other software the user may run on the monitoring device, and various parameters of the monitoring device itself, for example battery status, operating system versions, and the outputs of other sensors that may be built into a monitoring device.
Other personal data may be collected from the active inputs of users via monitoring devices or through other interfaces or mediums (e.g., a website), to supplement data types measurable through the monitoring device's sensors, e.g., queries to determine a user's demographics or consumer and/or brand preferences, queries to determine other behavioral traits and preferences. Users may create individual accounts, authenticated with account credentials, (e.g., email address and password). Further, users may log in and log out using their account credentials. If the user logs out, the monitoring device may suspend collection of geographic location data. Queries that require input from users may be triggered at any time by the software in the monitoring devices, including at times when the user is in a location or exhibiting a behavior relevant to the content of the query. Further, such queries may be triggered by the central server. New query content may be transmitted to monitoring devices remotely. Also, users may be prompted to upload other data they collect describing non-electronic content, e.g., photographs, QR or other visual “bar” codes.
Data collected via the monitoring device may be stored locally in the monitoring device's memory and uploaded to a central server at a later time, or uploaded in real time to a central server. Uploading of collected data may be initiated by the user, by the central server, or periodically automatically by the monitoring device's software.
Some behaviors of the monitoring device may be controlled remotely by a central server. For example, the monitoring device may be set to collect geographic location data during only some hours of each day and/or on some days, to collect geographic location data with variable regularity, to attempt collection of geographic location data for a variable duration of time each instance its collection is initiated, to upload collected geographic location data to a central server on a desired schedule or when desired conditions exist, (e.g., when the monitoring device is plugged into a power supply and/or is connected to a Wi-Fi network), and to change behavior when one or more settable levels of battery status are encountered. Changes to the settings for device behavior may be pushed to each device or may be synced at the time that the device uploads collected data to the server.
Behaviors of the monitoring device may be implemented in order to conserve the available battery power on the device. For example, the frequency at which the device collects geographic location data may be changed in accordance with the existence of determined conditions, (e.g., when the device is connected to a Wi-Fi network or other radio frequency system, the speed at which the device is traveling, the physical environment surrounding the device, and/or if previous attempts to collect geographic location data have succeeded or failed). Further, the periods of time during which the monitoring device collects geographic location data may be limited and varied in order to conserve available battery power and capture the location-dependent behaviors of users on a longitudinal (or extended) basis.
Behaviors of the monitoring device may be implemented in order to minimize data transmitted to the central server via radio frequency methods, (e.g., cellular data network). For example, the monitoring device may be set to upload collected data only when connected to a Wi-Fi network, or to delay the uploading of such data for a set duration of time waiting for a possible connection to a Wi-Fi network.
Techniques may be used by the monitoring device and/or by the central server to minimize and/or optimize the collected data. For example, data compression methods may be implemented, and algorithms may be employed to discard or archive redundant (or practically redundant) collected data.
Additional inputs to the disclosed technology may take the form of personal data that describes individual participants, but that will not necessarily be collected from monitoring devices. Such data may be obtained with or without the user's direct participation. For example, the disclosed technology may import datasets of users' shopping and other financial data (e.g., investment statements, credit card transactions), users' web browsing and computing activity and history, users' social interactions (e.g., social networking, gaming, e-mail, phone), users' membership in loyalty programs and/or other clubs, users' exposure to all forms of media and advertising (e.g., magazines, television, radio), and users' travel activity (e.g., vacations, business travel, air travel).
Personal data inputs collected and stored in the disclosed technology are analyzed in a variety of ways in order to produce the outputted reports. The disclosed technology may provide three primary levels of analysis: the individual level, the group level, and the population level. At the individual level, the disclosed technology may simply produce reports summarizing the behaviors or traits of a given individual user. Collected personal data may be partially anonymized by removing or abstracting tags that identify the user to whom the data pertains. At the group level, the disclosed technology may produce reports summarizing the behaviors or traits of groups of users through an aggregation of the individual data, thereby further anonymizing the personal data incorporated in the report. And at the population level, the disclosed technology may use additional inputs, (e.g., U. S. Census data), to project the observed behaviors and traits of users to the population at large or to specific sub-populations (e.g., all those who live in a given neighborhood, or all those who shop at a particular store chain). Such projection is the final step in transforming personal data inputs from “personally identifiable as” to “impersonally characteristic of.” Additionally, demographic and behavioral traits may be inferred from personal data. For example, an individual user's music preferences may be inferred from his/her attendance at concerts or other events, or an individual user's income may be inferred from his/her hobbies or membership in clubs.
Informing database inputs, (e.g., U.S. Census data, as above), are amalgamated with personal data inputs and with each other to add actionable meaning for report outputs. For example, individual geographic location data, combined with map-matching and purchase behavior databases, and projected to the local community population, could help predict the likelihood of success for a new, high-end, specialty retail store in that community. The value of a geographic location is informed in part by how its audience interacts with it. For example, the success of most retailers' businesses depends on whether or not people stop in to shop or eat. And the value of out-of-home media advertising displays, (e.g., billboards, subway posters, advertisements in malls), is measured according to the number and demographics of the people who have an opportunity to see them as they pass by. The disclosed technology enables prediction of such transit patterns and audience exposure.
A type of analysis that may be performed at all three levels quantifies traits or behaviors. For example, the disclosed technology may measure the frequency that individuals, groups, or populations drive past a particular billboard, or eat at a particular restaurant.
A further type of analysis that may be performed at all three levels may identify and quantify correlations between the behaviors and traits of individuals, groups, or populations. For example, the disclosed technology may quantify the relationship (or interdependence) between shopping at a particular store chain and owning a particular brand of car.
A further type of analysis that may be performed at all three levels may predict the likelihood of observing a given (observable or unobservable) behavior or trait depending on other known variables. For example, the disclosed technology may predict how a sub-population of people may react to an advertising campaign given their exposure to advertisements, their demographics, their previous purchasing behavior, their location information, and their proximity to the advertiser's stores.
The disclosed technology may also categorize users according to their behaviors and traits. For example, the disclosed technology may create an A, B, C, et cetera grading system to label individual users on a spectrum from heavy commuters to stay-at-home workers or parents. A further example may feature A, B, C, et cetera grades to categorize users on a spectrum of high-end shoppers to bargain hunters. Such a feature may be included in reports to simplify and summarize the behaviors and traits of individuals, groups, and sub-populations.
As an example of these several types of analyses, collected geographic location data for a given user can be processed to determine if the user is ever located inside or transits through a delineated geographic zone (an “impact zone”). Impact zones may be defined using electronic mapping systems, e.g., geographic information systems, or other publicly available solutions, e.g., Google Maps. As part of a disclosed technology report output, impact zones may take the form of a polygon of any size and shape, and may be defined to correspond to geographic areas of particular interest to the recipient of the report. For example, an advertiser may wish to determine how many users, between the ages of 25 and 45, pass through the visible area of an out-of-home media display. To provide the requested report output of transit counts, the geographic location data of multiple users with appropriate demographics are map-matched for transit accuracy and then correlated with the coordinates of the advertiser's designated impact zone, as drawn using the disclosed technology's web-based tools and format.
Further analysis that may be accomplished on identified user transits through a specified impact zone, incorporating multiple inputs, include determining, for example, the speed at and direction in which the user moves through the impact zone, the duration of time the user spends inside the impact zone, (“dwell time”), the time of day, day of week, season, et cetera when the user transits the impact zone, the mode of transit (or transportation) the user employs to pass through the impact zone, (e.g., walking, driving, cycling, et cetera), the frequency with which the user has transited the impact zone during a defined period of time, and the average distance the user covers on any given trip before and/or after transiting a given impact zone. Another analysis may include determining how many users transit through a given impact zone who have also displayed one or more observable characteristics, for example shopping at a particular store.
Further, besides counting and analyzing user transits through impact zones, and in combination with other known characteristics of the population at large, user transits through impact zones may be projected to infer the population's movements through such an impact zone. There may be many considerations involved in creating such an inference, for example the demographics and travel patterns of users may be adjusted (e.g., weighted) in order to improve the accuracy and generality of such an inference. The final results of this process may include reports on the reach, frequency, and demographics of the population's exposure to any given impact zone. Besides the general population, the impact zone transits of subsets of the population, defined by their characteristics or behaviors, may also be projected and quantified. For example, a subset of the population may consist of female shoppers who patronize a particular department store, and a projected and weighted impact zone report may provide an estimate of the subset's transits through the visible area of that department store's out-of-home media displays, as compared to other subsets of the population.
Multiple impact zones may also be considered and evaluated to yield a variety of analyses. For example, impact zones may be combined to determine aggregate user transits, (e.g., for audience measurement of an out-of-home media campaign), or the correlation of user transits through two or more given impact zones, (e.g., to determine if exposure to an out-of-home media display influences user choice as to where to shop, or which competing stores users may visit).
Further statistics may be calculated on collected user geographic location data, once it has been combined with informing dataset inputs. For example, tabulations may be made for a specified time period on the distance users travel, the distance covered and time spent using various modes of transportation, and the distance covered between destinations.
Among other options, the results of each analysis of impact zones and projections to a population may be conveyed in textual descriptions, graphical depictions (e.g., bar graphs), and matrices (e.g., spreadsheets) and in electronic or print format.
Aspects of technology for measuring the effectiveness of advertising or other media displays for various intended purposes are disclosed in U.S. Pat. Nos. 6,970,131; 7,038,619; 7,176,834; 7,215,280; 7,408,502; and 7,586,439; and in U.S. Patent Pub. No. 2009-0073035, the disclosures of which are incorporated herein in their entireties by reference.
Traditional personal data acquirers have included research companies, (e.g. Nielson Company), and Internet and mobile platforms, (e.g. Google, Facebook). These entities' methodology has been to pay individuals, (e.g., in cash, prizes, or services), to participate as research panel members or to accept advertising targeted at them, with the entity exercising ownership of the personal data once it is acquired and then using it itself or selling/providing it for a specific purpose and/or to a specific client base. In contrast, the disclosed technology envisioned herein may allow the ownership of personal data collected over users' mobile devices to reside with the users. Analyses may be performed on the collected data in accordance with the needs of the end consumers of reports generated by the disclosed technology (“clients”), as defined by the clients, and the clients themselves may use the processed information for a variety of purposes and in a variety of contexts. Users may make choices about the ways in which their individual data may be incorporated in reports and the clients who may access those reports in exchange for a form of compensation. Users may also delete their data from the operator's collected database, and/or terminate their accounts.
The disclosed technology may offer users relationships (“partnerships”) with clients, through which clients gain the right to include consenting users' processed information in the reports that the disclosed technology generates on their behalf. For such partnerships, clients may be identified to users individually, either by name or by function category, (e.g. car dealership, fast food restaurant), or included in groups that share some common trait. For example, users may be offered two “big box” retailers with which to form separate partnerships, or may be offered a single partnership with a group of such retailers. In another example, a user may choose to select one client or group of clients and not another, e.g., the user may agree to having his/her data used in a report processed for a local coffee shop but not for a chain coffee company. Different clients may or may not be aware of each other's status as clients, e.g., two “big box” retailers may be competitors and may not know that the other is a client.
Users indicate their preferences and consent for partnerships through software on their monitoring devices or through a website where they log in with their individual account credentials. Available partnerships are presented to each user, and may include information such as the types of reports to be generated for the client or group of clients, and the way such reports will be used.
Users may be compensated by clients for their participation in partnerships, and/or may be compensated by the operator of the disclosed technology for their initial registration and participation prior to joining partnerships. The operator of the disclosed technology may act as a client, and compensate users accordingly. The operator of the disclosed technology may earn revenue by taking a derivative share of compensation paid to users. Compensation to users may take the form, among others, of direct monetary payments or similar instruments (e.g., gift cards), contributions to nonprofit organizations, functionality created by the operator of the disclosed technology or third parties, (e.g. a travel diary app), event or entertainment opportunities, (e.g., a food cart lunch for users whose work is located nearby, tickets for a sports event where users sit together), games with prizes, (e.g. an impact zone game awarding a daily prize for the user transiting a predesignated but unidentified impact zone at a randomly picked time of day), or securities offered by the operator of the disclosed technology, (e.g., shares of stock or stock options), that may represent a pro rata portion of ownership in the entity operating of the disclosed technology, the value of which may change over time.
Different users may have different values for clients, depending on a variety of parameters. For example, a clothing retailer that targets consumers from young demographics may desire to have only the participation of those types of users in a partnership. Additionally, users with defined traits may be more plentiful than users with other traits. For example, younger users may ultimately register to participate in larger numbers than older users, who thereby gain in value due to scarcity. Accordingly, when users are offered choices of partnerships, they may see different levels of compensation “bid” for their participation.
Further, bids for users' participation in partnerships may also vary depending on where geographically such users live, work, or otherwise spend time. For example, users who live in dense urban areas may be more plentiful than users living in outlying suburban areas, and therefore the suburban users may be offered higher bids to form partnerships. An additional variation in bid price may be determined by one or more behavioral traits some users exhibit. For example, one “big box” retailer may wish to include information in its reports on users who shop at a competitor's stores. Therefore, those users may see higher bids from such a client for their participation in a partnership.
Another aspect is that a given client may in fact provide reports to multiple sub-clients. For example, Company X may sponsor a partnership on behalf of multiple independent real estate brokers, each of which would otherwise have to bid for partnerships separately.
Another aspect is that disclosed technology output information may be used to develop a trading strategy for securities or other assets. For example, before similar information is available in the public market, analyses performed by the disclosed technology may quantify how consumers respond to changes in gas prices, or if a fast food chain's store traffic is beating or missing expectations. Further, the operator of the disclosed technology may acquire valuable iintelligence by knowing what party is accessing information through the disclosed technology and what queries it is performing.
The disclosed technology may include a website that, for example, allows users to customize their experience with the disclosed technology, (e.g., define demographics, participate in a forum), and obtain information about the disclosed technology, (e.g. updated Privacy Policy or FAQ statements, the nonprofits receiving contributions).
The disclosed technology may include a series of APIs to enable clients to query collected inputs, (e.g. request an impact zone analysis), as permitted through their partnerships. The APIs may allow clients to query collected inputs from a subset of consenting users determined by their demographic or behavioral characteristics. For example, a client may initiate a call through an API to generate a report on how often male users over thirty years old visit the client's retail outlets. Multiple APIs may be incorporated into the disclosed technology to produce different types of report outputs. For example, another API may allow clients to request a report that is not specific to a given impact zone, but which graphically depicts concentrations of users (or the inferred population) depending on defined demographic or behavioral traits, (e.g., a report could be produced by an API that shows a graphical “heat map” of where 30-45 year old white males are concentrated in a city at a given time of day, or where concentrations of sports bar patrons are at a given time of day).
Another API may permit users to review processed information.
Another API may permit third parties to create products and services intended for use by the users themselves, that take advantage of the collected inputs. For example, a third party software developer may create an application to identify promotions and “daily deals” that are convenient for any given user based on his/her typical travel patterns. Further, such an API may be used by third parties to create products and services for the disclosed technology's operator's and their own clients.
Another API may permit the operator of the disclosed technology or third parties to target advertisements that are location and/or behaviorally relevant to users and populations and to clients.
The disclosed technology may include a website that allows clients to design, customize, (e.g., define impact zones, define populations of interest), and obtain reports, create partnerships, bid for users' information, and pay the operator of the disclosed technology.
Several embodiments of the disclosed technology are described in more detail in reference to the Figures. The computing devices on which the disclosed technology may be implemented may include one or more central processing units, memory, input devices (e.g., keyboard and pointing devices), output devices (e.g., display devices), storage devices (e.g., disk drives), and network devices (e.g., network interfaces). The memory and storage devices are computer-readable storage media that may store instructions that implement at least portions of the disclosed technology. In addition, the data structures and message structures may be stored or transmitted via a data transmission medium, such as a signal on a communications link. Various communications links may be used, such as the Internet, a local area network, a wide area network, or a point-to-point dial-up connection.
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Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. Accordingly, the invention is not limited except as by the appended claims.
This patent application is a divisional of U.S. patent application Ser. No. 13/507,565, filed Jul. 10, 2012, and entitled “Online Exchange for Personal Data,” and claims the benefit of U.S. Provisional Patent Application Ser. No. 61/571,979, filed on Jul. 8, 2011, and entitled “The Exchange,” and U.S. Provisional Patent Application Ser. No. 61/633,291 filed on Feb. 7, 2012, and entitled “Personal Information Exchange and Monetization Tool,” the disclosure of all applications are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 61633291 | Feb 2012 | US | |
| 61571979 | Jul 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13507565 | Jul 2012 | US |
| Child | 14451034 | US |
