SYSTEMS FOR REDUCING DATA ERRORS WITHIN A GIVEN DATASET TO PREVENT USER DISENGAGEMENT

A system for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other is described. The system comprises performing a process for receiving feedback on datasets from service users and transmitting alerts to a particular set of users whose scores fall below a determined threshold. A non-transitory computer-readable medium storing a set of instructions for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other is also described.

BACKGROUND

Technical Field

The present disclosure relates generally to systems for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other.

Background

In an Internet service wherein users interact and provide mutual social or economic benefits to each other, it is often desirable to collect the users' views on the quality of the interactions in the form of user feedback. The feedback from one user on an interaction with a second user may be shared with other users so the other users may decide, upon reviewing the first user's feedback, whether to interact with the second user. An Internet environment facilitates providing a very large amount of feedback to a user in a very short period of time due to remote connectivity, anonymity, and data transmission speeds. In such an environment, well-intentioned users who receive a substantial amount of negative feedback may become disengaged from using the service because their negative feedback would alienate other users from interacting with them. This is undesirable if such well-intentioned users could and are inclined to improve the way they interact with other users. While giving such a well-intentioned user the capability to partially or completely hide their feedback from other users would solve the problem, in doing so one may potentially also hide the feedback of users with no capability or inclination to improve the way they interact with other users. One or more embodiments of the disclosed systems provide a solution to this problem.

A user often provides feedback on an interaction that reflects the degree to which the user's expectations of the interaction were met. In order to increase the users' perception of the quality of an Internet service wherein users interact and then provide feedback on the interactions, it may be beneficial to incentivize users to interact with one another in a manner such that the expectations of all users in the interaction are incorporated into the interaction. Therefore, a system and method for encouraging uncooperative users (i.e., users not interested in participating in interactions in a manner such that other users' expectations are incorporated into the interaction) to stop using the Internet service is desirable.

A system that encourages uncooperative users to stop using the Internet service may confuse benevolent-but-unsuccessful users (i.e., users who genuinely desire to participate in interactions in a manner such that other users' expectations are incorporated into the interaction but who were previously unsuccessful) with those who are simply uncooperative. It is desirable for such a system to differentiate benevolent-but-unsuccessful users from uncooperative users. This is so because proprietors of Internet services generally desire to maximize the number of cooperative users of their service and benevolent-but unsuccessful users may, if given a chance, eventually become such cooperative users. Therefore, avoiding indiscriminately encouraging both uncooperative users and benevolent-but-unsuccessful users to stop using the Internet service is desirable.

Internet services allow for a very large number of users and often derive benefit from a very large number of users. Managing such a large number of users to a service was impractical if not impossible before the advent of Internet and computer technology. Therefore, avoiding indiscriminately encouraging both uncooperative users and benevolent-but-unsuccessful users to stop using the service creates a challenge unique to services that use the Internet. Additionally, because Internet technology facilitates users from distant locations interacting instantaneously through a service, solutions that may have worked for non-Internet services are not practical or effective for Internet services. For example, a non-Internet service may allow for scheduling interviews with each user to determine whether he or she is an uncooperative as opposed to a benevolent-but-unsuccessful user. Doing so for an Internet service, however, would require contacting users in distant locations, in different time zones, speaking different languages, and interviewing thousands or millions of such users. Therefore, a solution to the problem for non-Internet services does not work for Internet services, and another solution is required to determine whether a given user of an Internet service is an uncooperative as opposed to a benevolent-but-unsuccessful user.

SUMMARY

The present disclosure is directed to systems for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other.

Consistent with at least one disclosed embodiment, a system is disclosed for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other. In one embodiment this may be accomplished with one or more storage mediums storing datasets for users of the service, wherein at least one element of the datasets is subjective; scores generated based on the user datasets; and instructions for determining whether to issue alerts.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising receiving a request for feedback based on an interaction between a first user and a second user.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising receiving quantitative feedback on at least one of the datasets from the first user and the second user.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising updating the scores based on the received feedback after verifying the receipt of feedback from the first user and the second user.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising determining a score threshold based on the updated scores.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising determining a set of users by comparing the updated scores of the users to the determined score threshold.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising transmitting alerts to the determined set of users, the alerts comprising at least an option to improve an alerted user's score.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the alerts comprise messages transmitted over a wireless communication channel.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process comprising wherein receiving the feedback comprises receiving feedback on the at least one subjective element of the dataset.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the datasets include user profiles.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the dataset for at least one user comprises spam.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the feedback received from the first user comprises a survey on the accuracy of a user profile of the second user.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the service comprises a dating application.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the service comprises a social network.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the service comprises a marketplace.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the service comprises a marketed product or service from a third party.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the option presents a proposed modification to the user profile.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein updating the scores based on the received feedback comprises changing the scores for the at least one of the datasets associated with the received feedback, and normalizing the scores for each of the datasets after the scores for the at least one of the datasets associated with the received feedback are changed.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the score threshold comprises a score on a probability distribution curve comprising the normalized scores.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein the score threshold comprises a score at which there is an inflection below and closest to the median score in the probability distribution curve.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein execution of the instructions performs a process wherein the score threshold comprises a score that is the midpoint between (i) the score at which there is a local minimum and that is greater than and closest to the score with a local maximum with the highest probability and below the mean score, and (ii) the mean score.

Reducing data errors may also be accomplished with one or more processors configured to execute the instructions, wherein the score threshold comprises a score that is the midpoint between (i) the score at which there is a local minimum and that is greater than and closest to the score with a local maximum with the highest probability and below the median score, and (ii) the median score.

Other embodiments of this disclosure are disclosed in the accompanying drawings, description, and claims. Thus, this summary is exemplary only, and is not to be considered restrictive.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Reference will now be made to certain embodiments consistent with the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts.

The present disclosure describes systems for reducing data errors within a given dataset to prevent user disengagement from an Internet service wherein users provide mutual social or economic benefits to each other. Such a service may comprise, among other things, a dating application, a social network, marketed product or service from a third party, or a marketplace. The system may operate in an environment such as system environment100, illustrated inFIG. 1. The environment may comprise a service system110, a network120, user devices such as first user device130A and second user device140A, and users such as first user130and second user140.

Such a system210, illustrated inFIG. 2, may comprise one or more storage mediums or memory devices such as220, storing datasets for users of the service, wherein at least one element of the datasets is subjective. The storage mediums may also store scores generated based on the user datasets and instructions for determining whether to issue alerts.

The datasets may include user profiles, accounts, depictions, or portrayals of one's self or a marketed product, service, or advertisement. The dataset for at least one user may comprise spam.

The system210ofFIG. 2may also comprise one or more processors230configured to execute the instructions240, wherein execution of the instructions performs a process300illustrated inFIG. 3.

In an exemplary system environment100ofFIG. 1in which embodiments consistent with the present disclosure may be practiced and implemented includes a system that may include one or more server or service systems110, databases, and/or computing systems configured to receive information from entities in network120, process the information, and communicate the information with other entities in the network120, such as first user130and second user140. For example, the system110may be configured to receive data over an electronic network120(e.g., the Internet), process/analyze queries and data, and provide an application to users130and140. This may be done over devices130A and140A.

The various components of the system210, illustrated inFIG. 2, may include an assembly of hardware, software, and/or firmware, including a memory220, a central processing unit (“CPU”), and/or a user interface250. Memory220may include any type of RAM or ROM embodied in a physical storage medium, such as magnetic storage including floppy disk, hard disk, or magnetic tape; semiconductor storage such as solid state disk (SSD) or flash memory; optical disc storage; or magneto-optical disc storage. A CPU may include one or more processors, such as processor230, for processing data according to a set of programmable instructions240or software stored in the memory220. The functions of each processor230may be provided by a single dedicated processor230or by a plurality of processors. Moreover, processors may include, without limitation, digital signal processor (DSP) hardware, or any other hardware capable of executing software. An optional user interface may include any type or combination of input/output devices250, such as a display monitor, keyboard, touch screen, and/or mouse.

As described above, the system110ofFIG. 1may be configured to receive data over a network (such as an electronic network), process/analyze queries and data, and provide geographic locations to users. Examples of an electronic network120include a local area network (LAN), a wireless LAN (e.g., a “WiFi” network), a wireless Metropolitan Area Network (MAN) that connects multiple wireless LANs, a wide area network (WAN) (e.g., the Internet), and a dial-up connection (e.g., using a V.90 protocol or a V.92 protocol). In the embodiments described herein, the Internet may include any publicly-accessible network or networks interconnected via one or more communication protocols, including, but not limited to, hypertext transfer protocol (HTTP) and transmission control protocol/internet protocol (TCP/IP). Moreover, the electronic network may also include one or more mobile device networks, such as a GSM network or a PCS network, that allow mobile devices, such as a first or second user device130A and140A, to send and receive data via applicable communications protocols, including those described above. Further, the system may operate and/or interact with one or more host servers, one or more user devices for the purpose of implementing features described herein.

At step310of process300inFIG. 3, the instructions240executed by the one or more processors230cause the system to receive a request for feedback based on an interaction between a first user130and a second user140.

At step320, the instructions240executed by the one or more processors230cause the system to receive quantitative feedback on at least one of the datasets from the first user and the second user. This may include receiving feedback on the at least one subjective element of the dataset. The feedback received from the first user130may comprise a survey, questionnaire, poll, review, inquiry, or study on the accuracy of a user profile, account, depiction, or portrayal of one's self or a marketed product, service, or advertisement of the second user140. The subjective element may comprise connectivity, adaptability, perception of potential future interactions as a beneficial opportunity, possibility of future interaction, or level to which expectations were met.

At step330, the instructions240executed by the one or more processors230cause the system to verify the receipt of feedback from the first user130and the second user140.

At step340, the instructions240executed by the one or more processors230cause the system to update the scores based on the received feedback after verifying the receipt of feedback from the first user130and the second user140at step330. The scores may be changed for at least one of the datasets, and the scores for each dataset may be normalized after at least one of the scores is changed.

At step350, the instructions executed by the one or more processors cause the system to determine a score threshold based on the updated scores. The score threshold may be a threshold score on a probability distribution curve410ofFIG. 4comprising the normalized scores. While the threshold score may be calculated in any manner, in one embodiment, the score threshold may comprise the score420at which there is an inflection below and closest to the median score430in the probability distribution curve410. While this method of determining the score threshold may be used in the cases of both normal and non-normal probability distributions of the normalized scores, in the latter case of a non-normal curve, such as curve500ofFIG. 5, one may also use the mean score560instead of or in addition to the median score520. When using the median score520in the case of non-normal distribution, the score threshold indicated by vertical line510may comprise the midpoint between (i) the score540at which there is a local minimum and that is greater than and closest to the score550with a local maximum with the highest probability and below the median score520, and (ii) the median score520. When using the mean score560in case of non-normal distribution, the score threshold indicated by vertical line530may comprise the midpoint between (i) the score540at which there is a local minimum and that is greater than and closest to the score550with a local maximum with the highest probability and below the mean score560, and (ii) the mean score560. This latter method may yield better data error reduction when the distribution of scores is not normal. All local minima and maxima may be found by, for example, determining where the slope of the curves equals zero and whether the curves are concave or convex in the particular region.

All of the threshold determination methods described may be used instead of or in addition to all other determination methods described.

At step360of process300inFIG. 3, the instructions240executed by the one or more processors230cause the system to determine a set of users by comparing the updated scores of the users to the determined score threshold, such as score threshold470. Such comparison may include stack ranking, segmenting, grouping, sorting, arranging, dividing, assembling, classifying, or batching, the updated scores and all other scores.

At step370, the instructions240executed by the one or more processors230cause the system to transmit alerts to the determined set of users, the alerts may comprise, among other things, at least an option to improve an alerted user's score. This improvement may be performed in return for consideration from the user, such as payment or performance of one or more actions. The option may present a proposed modification to the user profile. This modification may be performed in return for consideration from the user, such as payment or performance of one or more actions. The option may allow a user to be eligible for the receipt of additional products or rendering of additional services. The option may allow redistribution, recirculation, or additional publishing of a user's profile, account, depiction, or portrayal of one's self or a marketed product, service, or advertisement.

The alerts may comprise messages transmitted over a wireless communication channel, which may include the Internet, emails, text messages, pop-ups, mobile push notifications, and messages or buttons in user's account within the application.

The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments.