A system for controlling and optimizing the distribution of information among users in information exchange.

Abstract

This application relates to a system for controlling and optimizing the distribution of information among users in information exchange. An automatic control system for regulating information exchange between information producers and information consumers. A control mechanism can dynamically refine decisions to include or exclude information items from the consumer's information flow to improve success metrics for similar participation. One or more system interfaces request the control mechanism to dynamically provide limits on audience objectives, priorities, preferences, and other data for stimuli and inputs. Administrators can set parameters and select success metrics to balance the objectives of information exchange participants and stakeholders. The system can also be used to resolve conflicts between consumer selection criteria and producer audience objectives.

Description

[0001] This application is a divisional application of the application filed on March 16, 2014, with application number 201480026012.X and invention title "System for Controlling and Optimizing Information Distribution Among Users in Information Exchange".

[0002] Cross-references to related applications

[0003] This application claims the benefit of provisional patent application number 61852280 filed by Brian D McFadden on March 15, 2013. Technical Field

[0004] This invention relates to a system for controlling and optimizing the distribution of information among users in information exchange. background

[0005] A major shortcoming in services where users or members exchange information, such as social networks, user groups, list servers, forums, and Q&A services, is the inability to accurately and optimally regulate the flow of information between producers and consumers. While some practices exist to make these exchanges manageable and relevant to participants, they lack the automated, dynamic refinement needed to potentially optimize or broadly improve stakeholder objectives. Other services where one group exchanges information with another, such as news aggregation services, newspapers, magazines, media outlets, advertising networks, blogs, and research services, face similar problems.

[0006] One partial solution used in many information exchanges is to add groups, tags, or topics that information consumers can subscribe to or use to filter the set of information available to them. This is an improvement, but not a complete solution, because increasing the number of topics to reduce the information rate per topic is still inefficient, as consumers must choose between a low rate of information flow and the possibility of missing some valuable information items from peripheral topics. Once they subscribe to peripheral topics, the information rate and value dilution increase. Even if the information consumer's interests are contained within a single topic, there is still a degree of variability in interests that can lead to particularly inefficiencies if there are many information items within a given topic.

[0007] Another problem with topic-only approaches is that they require information-consuming users to specify topic selections. This is particularly problematic given the constantly changing and evolving nature of topic ontologies. Techniques are often employed to obtain preferences or interests from revelations of a user's previous actions. A wide variety of methods are available (both public and proprietary) to identify items of interest based on past behavior and interactions (e.g., click and view history), collaborative filtering suggestions, machine learning, and others. These methods generate a set of preferences for information consumers that may be conflicting or have varying degrees of applicability and accuracy. The uncertainty of derived preferences will also vary. To accommodate these types of scenarios, preferences are often ranked and applied in rank. This approach has limitations in considering dynamic externalities, the state of information exchange, the creators of information items, and their target preferences for those items. These and other factors can influence the applicability of information consumer preferences (especially when there is anticipated uncertainty regarding the preferences being acquired).

[0008] Another contributing issue is the practice of reducing or eliminating any restrictions on the information items input by information producers in many information exchanges. This approach encourages quantity but also leads to a decrease in the quality of the variable contribution of potential consumers to the value of that interaction and underestimates the problems stated so far. This situation is unlikely to change significantly (even when there is a monetary assessment of contribution). While payment is a constraint that may be associated with quality, it does not guarantee a better level of quality.

[0009] While various methods can be used to control the flow of information to users and allow users to self-regulate the flow of information, in many cases they are suboptimal, unemotional, and ineffective. Summary of the Invention

[0010] An automated control system for regulating information exchange between information producers and consumers. A control mechanism can dynamically refine decisions to include or exclude information items from the consumer's information flow to improve success metrics for similar engagements. One or more system interfaces request the control mechanism to dynamically provide limits on audience objectives, priorities, preferences, and other data for stimuli and inputs. Administrators can set parameters and select success metrics to balance the goals of information exchange participants and stakeholders. The system can also be used to resolve conflicts between consumer selection criteria and producer audience objectives.

[0011] In one exemplary embodiment, a method is provided for using a computer system in information exchange to distribute information items from at least one information producer to at least one information consumer, the method comprising the steps of: obtaining an audience target, the audience target indicating the type of information consumer the producer wants to receive the information item; obtaining a selection criterion, the selection criterion indicating whether the information consumer wants to receive the information item; adjusting a decision matrix using a decision control loop to improve or maintain a success metric; and determining, based on the decision matrix, whether to deliver the information item to the information consumer.

[0012] Other variations:

[0013] In the above method, multiple audience targets are specified.

[0014] In the above method, priority is assigned to the target audience.

[0015] In the above method, multiple selection criteria for the information consumer are used.

[0016] In the above method, priority is assigned to the selection criteria.

[0017] In the above method, the audience target represents a continuous mapping from consumer user profile attributes to continuum or priority values.

[0018] In the above method, the selection criteria represent a continuous mapping from the creator's user profile attributes to a continuum or priority value.

[0019] In the above method, the selection criteria represent a continuous mapping from information item attributes to continuum or priority values.

[0020] In the above method, the decision matrix resolves the conflict between the information producer's audience goals and the information consumer's selection criteria.

[0021] In the above method, the success metric is the participation of the information consumer.

[0022] In the above method, the decision matrix is ​​a two-dimensional region, which represents the audience target priority in one dimension and the selection criterion priority in the other dimension.

[0023] In the above method, the audience target priority is between -1 and 1, and the selection criterion priority is between -1 and 1.

[0024] In the above method, the threshold limit divides the region into an included region and an excluded region.

[0025] In the above method, the threshold limit is dynamically derived from internal and external metrics.

[0026] In one exemplary embodiment, a computer system interface is provided for inputting audience targets, the computer system interface including an information producer limit control loop and performing the following: receiving at least one audience target from an information producer; calculating the audience size that the audience target can reach; using an audience size limit mapping to correlate audience size and priority; determining audience target priority from the audience size limit mapping; and thereby assigning priority to the audience target.

[0027] In the above system interface, the audience size limit is mapped to assign a lower priority to larger audience sizes.

[0028] In the above system interface, the target audience is sorted.

[0029] In the above system interface, the further accumulated size is calculated for each audience target having the included behavior, and the accumulated size is a union of audiences matching the higher-ranked audience targets having the included behavior, and the audience size is used to determine the audience target priority of a specified audience target, wherein the audience target behavior is included, which is the accumulated size.

[0030] In the aforementioned system interface, the cumulative size excludes the overlap between audiences that match the higher-ranked audience target, wherein the audience target behavior is excluded. Brief description of the attached diagram

[0031] Figure 1 Describe an example of information exchange.

[0032] Figure 2 Examples describing the interaction between the creator and the creator.

[0033] Figure 3 Examples describing typical user interactions.

[0034] Figure 4 Examples describing consumer interaction.

[0035] Figure 5 An example describing a basic decision matrix, where no behavioral priorities are shown for the producer's vertical downward and audience targets along the horizontal.

[0036] Figure 6 An exemplary embodiment of a decision matrix with behavioral priorities is shown.

[0037] Figure 7 An exemplary embodiment with sequential priority is shown.

[0038] Figure 8 For decision grids or decision matrices, this includes regions and threshold lines.

[0039] Figure 9Describe the audience size limit.

[0040] Figure 10 Describe the system interface used to input audience targets. Detailed description

[0041] Example of information exchange 29 in Figure 1 As shown in the diagram. The user 20 of information exchange 29 can be an information producer 22 or an information consumer 28, or both. Information exchange 29 transmits information item 24 from information producer 22 to information consumer 28. In the most general definition, information exchange consists of one or more producers, one or more consumers, and a distributor 26. Distributor 26 specifies how information items flow from producer to consumer.

[0042] Distributor 26 can take many forms, including simple publisher-to-consumer, sender-to-receiver, publish-subscribe information switches, or any other form in which information is transferred from producer to consumer. Distributor 26 will include, for example, cases where consumers are friends with or follow one or more producers or join groups, or cases where producers and consumers have agreed to follow or associate with each other or exchange information and allow other groups to do so as well. Distributor 26 may or may not support subscriptions. If subscriptions are supported, consumers 28 can subscribe to one, several, or all producers. If distributor 26 does not support subscriptions, consumers 28 will be able to receive from all producers. There may be one or more producers 22. There may be one or more consumers 28. Information exchange 29 can be a social network, a group within a social network, a list server, a news aggregation service, news delivery, a newsletter, a digest, a bidding system, an alert, an ad exchange, an ad network, an email client, a news reader, a web browser, a portal, or any service that facilitates the flow of information items from producers to consumers.

[0043] Creator 22 is one or more other users or users 20 who send, post, place, contribute, publish, write, create, instruct, respond to, or otherwise cause information to be distributed to information exchange. Figure 1 It's not about showing every detail of the information flow.

[0044] Information consumer 28 is a user who receives information items from the producer. Consumer 28 may or may not consume the information items available to them.

[0045] It should be noted that label makers and consumers are related to information production and consumption, but this does not imply a commercial relationship.

[0046] Information item 24 can be a message, email, notification, response, video clip, audio clip, news, article, story, inquiry, offer, advertisement, URL, or any other form of communication that the creator or consumer can send or make available.

[0047] Information flow is an aggregate or collection of information items that are delivered sequentially or together (directly or embeddedly) to a consumer via media including but not limited to print, email, web delivery, mobile text messaging, video, audio, broadcasting, or any other means of transmitting information.

[0048] exist Figure 3 The diagram illustrates an example of information exchange 29 in which user 20 can input user profile data 64 into a system interface for inputting user profile 61. The system interface for inputting user profile 61 stores user profile 60 in user profile storage 62. User profile storage can be an internal part of the information exchange, an external part of the information exchange, or a combination of both. A set of user profile data 63 acquired by the system can also be stored in user profile storage 62, and in some systems, the user may not need to input any user profile data.

[0049] User profile 60 includes available information about the user (not limited to any particular format). This includes, but is not limited to, behavior, biography, demographics, history, rank, feedback, tracking, or other general or specific information from internal and external sources of information exchange 29. User profiles may be stored in relational databases, name-value pairs, NoSQL, hierarchical data, objects, nested objects, nested hierarchical data, or a combination of databases in a single or multiple sources. If accessible via an API, user profile 60 may be represented in XML, JSON, CVS, or any other data representation.

[0050] Figure 4 Consumer 28 can input selection criteria data 66 into a system interface for inputting selection criteria 68, and selection criteria 65 are stored in selection criteria storage 67. Selection criteria 65 can indicate the type or set of information items that the consumer may be potentially interested in or not interested in receiving. The system interface for inputting selection criteria 68 stores the selection criteria in selection criteria storage 67. Selection criteria storage 67 can be internal to information exchange 29, external to information exchange 29, or a combination of internal and external. Selection criteria can also include system-acquired selection criteria 69, which can also be stored in selection criteria storage 67. In one embodiment, selection criteria can be stored together with user profile data, and the user profile storage and selection criteria storage can be the same.

[0051] In one embodiment, standard storage and user profile storage can be stored together on contiguous storage for fast access and processing.

[0052] exist Figure 2 In this context, audience target 50 defines a set of consumers or audiences that producer 22 is willing or unwilling to reach. The system interface for inputting audience target 44 interacts with producer constraint control loop 46 and audience target request control loop 48. Producer constraint control loop 46 and audience target request control loop 48 regulate the audience target 50 included in the information item 24 to be processed by distribution subsystem 52.

[0053] exist Figure 2 In this system interface for inputting information item 40, information item 24 is received from the creator 22. The metadata request control loop 42 interacts with the system interface for inputting information item 40 and adjusts the amount of additional descriptive data collected when information item 24 is input. Figure 2 In this context, the distribution subsystem 52 processes information items 24, audience targets 50, a set of metrics 54, user profiles from user profile storage 62, and selection criteria from selection criteria storage to determine what consumers should receive, receive, or view information items (as described below). Metrics 54 may be measures, statistics, and parameters obtained directly or computationally from one or more sources within or outside the information exchange.

[0054] In one embodiment, the distribution subsystem 52 and the allocator 26 can be the same. In another embodiment, they can be independent.

[0055] Operation Description

[0056] In one embodiment, the system described herein is an information exchange or a component of an information exchange. In another embodiment, the system exists independently of the information exchange while the subsystem interacts with it, as detailed below.

[0057] In one embodiment, the system is computer-coded software running on a computer system. The computer system can be any combination of one or more physical computer hardware systems, physical servers, devices, mobile devices, CPUs, auxiliary CPUs, embedded processors, workstations, desktop computers, virtual devices, virtual servers, virtual machines, or similar related hardware having a suitable operating system for the particular hardware, and in cases where there are more than one, they are interconnected via private or public networks.

[0058] In one embodiment, the system can operate as a self-regulating automatic control system.

[0059] Creator

[0060] In one embodiment, the creator can input information item 24 into a system interface for inputting information item 40. The information item consists of content and a meta-description. Content may include an overview, title, full story, images, videos, audio, multimedia, or other primary information delivery objects. The meta-description may include a summary, source, keywords, author, attribution, related links, subject, type, limitations, price, or any other field or object or hierarchical data used to classify, categorize, track, identify, or otherwise describe the content and information item. In one embodiment, the metadata description and the information item may be the same.

[0061] In one embodiment, the creator can input audience targets into a system interface for inputting audience targets 44. Audience targets describe consumers that the creator wants to reach or does not want to reach. The specifications of the audience targets can refer to any aspect of the user profiles that specify potential consumers. Audience targets will have behaviors that specify whether users matching the audience targets should receive information. In one embodiment, the system interface for inputting information items and the system interface for inputting audience targets can be the same.

[0062] In one embodiment, the creator can specify one or more additional audience targets they want. The primary audience target is the main set of users that the consumer wants to include or exclude. Each additional audience can have a lower priority than the previously selected audience.

[0063] In one embodiment, producer 22 can construct audience targets and priorities by selecting one or more parameters from available data in the consumer's user profile and assigning priorities to the value range of each discrete parameter and the value range of the continuous parameter. The maximum and minimum values ​​of the combination of all field values ​​can be used to determine the normalized priority range.

[0064] In one embodiment, the producer can initially target the largest audience they wish to reach. The system can set a limit smaller than the audience size specified by the first audience target. In one embodiment, the limit can be determined by the context of the message, past history of interactions with previous messages from the producer, and current system-wide metrics. In another embodiment, the system can adjust the limit in the payment transaction or some other concession from the producer. In one embodiment, the producer can specify additional audience targets to reach an audience that is closer in size to the limit. If the audience size is smaller than the limit, the audience target can be used as input and priority for allocation. In one embodiment, if the audience size is larger than the limit, the system will refine the target to meet the limit or adjust the priority of the audience target. In one embodiment, if the size exceeds the limit, the system can adjust the priority of the audience target. In one embodiment, the producer can scale the priority based on one or more discrete or continuous parameters used in the consumer profile.

[0065] In one embodiment, the creator may have a profile of predefined audience targets that can be selected to replace input and create new audience targets.

[0066] In one embodiment, information items and audience targets may be sent to a distribution subsystem. In one embodiment, the distribution subsystem may be integrated with the information exchange distributor. In another embodiment, the distribution subsystem may be external to the information exchange distributor.

[0067] In one embodiment, the creator's audience targeting may be required. In another embodiment, the creator's audience targeting may be optional.

[0068] In one embodiment, the creator can use a visual input slider to indicate the priority of audience targets and specific profile attributes. For example, audience targets with higher priority are defined based on consumers' years of experience. In another embodiment, the creator can use drag-and-drop visuals to rank audience targets and set audience target priorities.

[0069] In one embodiment, the audience target input by the creator can be applied to a single message item, multiple message items, or all message items from the creator.

[0070] In one embodiment, the maker can be an autonomous agent.

[0071] user

[0072] In one embodiment, users, producers, and consumers may input data into user profile 60. In another embodiment, user profile 60 may also include system data and information about the user, including but not limited to performance, behavior, history, tracking, or any other information that the system may record or calculate for the user. In another embodiment, user profile may also include external information obtained from external systems, including but not limited to performance, behavior, history, tracking, recording, or any other information that may be obtained from or calculated by external systems or combined with internal profile data. In yet another embodiment, user profile may have data from all data sources.

[0073] Information exchange user 20 can input user profile data 64 into a system interface for inputting user profile 61. The system interface for inputting user profile 61 stores user profile data 64 in user profile storage 62. In one embodiment, user profile storage may be part of information exchange 29. In another embodiment, user profile storage 62 may be external to information exchange 29. In yet another embodiment, user profile storage 62 may be distributed between information exchange 29 and external locations. In one embodiment, user profile data 63 obtained externally and from the system may be stored in user profile storage 62.

[0074] consumer

[0075] In one embodiment, a consumer can input selection criteria that define both the information item type and the producer type. In another embodiment, the selection criteria can specify only the information item type or the producer type. In one embodiment, a consumer can input an action related to the selection criteria to specify whether an information item matching the criteria is an item they want to receive or do not want to receive. In another embodiment, the action assigned to the selection criteria can be assigned by the system from the consumer's behavioral actions. For example, by a consumer expressing interest in a relevant item or metadata topic.

[0076] Consumers can input more than one selection criterion. In one embodiment, if more than one selection criterion is specified, the consumer can specify the priority that defines how important the criterion is. Priority can be represented by sorting the criteria or by selecting priority preference input. In another embodiment, the priority of the selection criteria can be assigned by the system from the context or behavior of inputting or obtaining the selection criteria, history, or actions that led to the creation of the selection criteria.

[0077] Selection criteria can also overlap and conflict. For example, a conflict may arise if two selection criteria match and one criterion, for instance, includes the specified item while the other criterion, for instance, excludes it. In one embodiment, a conflict can be resolved by selecting the criterion with the highest priority. In one embodiment, priorities can be combined in a mathematical function to determine priorities, where optionally multiplying by -1 excludes priorities. The function may take into account the higher weight of higher priorities or may simply average the priorities. If two priorities are the same in a conflict, they can be considered unresolved or open. In another embodiment, a conflict can be resolved by the system according to the optimization criteria discussed below.

[0078] In one embodiment, selection criteria and their priority can be determined from consumer performance, history, behavior, or tracking data. In another embodiment, selection criteria and priority can be determined from predictive statistical methods. In yet another embodiment, selection criteria input by the consumer can be combined with selection criteria determined from all other means.

[0079] In one embodiment, the priority can be set by the system for each selection criterion. In another embodiment, the system sets a default priority for the selection criteria that can be changed by the consumer.

[0080] In one embodiment, the processing of consumer selection criteria can be integrated with the information exchange distributor. In another embodiment, the processing can be external to the default distributor.

[0081] In one embodiment, the consumer's selection criteria can be entered by a person. In another embodiment, the selection criteria can be entered by an autonomous agent.

[0082] Consumers can input selection criteria into a system interface for inputting selection criteria. The system interface for inputting selection criteria 68 stores the selection criteria in a selection criteria storage 67. In one embodiment, selection criteria storage 67 may be part of the information exchange. In another embodiment, selection criteria storage 67 may be external to the information exchange. In yet another embodiment, selection criteria storage 67 may be distributed between the information exchange and the external environment of the information exchange 29. In one embodiment, selection criteria 69 obtained by the system may be stored in selection criteria storage 67.

[0083] In one embodiment, consumers use drag-and-drop visuals to rank selection criteria and set selection criteria priorities.

[0084] In one embodiment, the consumer can be an autonomous agent.

[0085] Decision matrix

[0086] In one embodiment, decision matrix 70 can be used to determine whether information item 24 should be included in the information flow of consumer 28.

[0087] Figure 5 The decision matrix 70a shows the basic case where there is no audience target priority or selection criterion priority.

[0088] exist Figure 5 In the diagram, the producer 22 actions of two audience targets 50 are shown horizontally. The two audience targets 50 are used for sending and not sending actions. The letter 'S' indicates the sending action and the letter 'DS' indicates the not sending action. The letter 'O' for open indicates the case where no audience target is applied to the information consumer.

[0089] exist Figure 5 In the image, consumer behaviors for the two selection criteria 65 are shown vertically. Two audience targets 50 are used for desired and unwanted behaviors. The letter 'W' indicates a desired behavior, and the letter 'DW' indicates an unwanted behavior. The letter 'O' indicates the case where no selection criterion 65 is applied to information item 24.

[0090] exist Figure 5 In the decision matrix 70a, information item 24 should be included in or excluded from the information flow of consumer 28. In decision matrix 70a, the letter 'T' indicates that information item 24 is included in the information flow, and the letter 'E' indicates that information item 24 is excluded from the information flow. The symbol '?' indicates whether the system can decide whether to include information item 24.

[0091] exist Figure 6 In the table, the extension is in Figure 5 The table shown illustrates the prioritization of audience target 50 and selection criteria 65. The producer's audience target 50 is again arranged vertically, and the consumer selection criteria 65 horizontally. The producer's audience target 50 is shown with combined behavioral and preference priorities. The letter 'H' indicates high priority. The letter 'M' indicates medium priority. The letter 'L' indicates low priority. Figure 6 In the example, six target audience behaviors and priority combinations are shown for producers (22). For consumers, six selection criteria behaviors and priority combinations are shown. (Example...) Figure 5 As shown in the table, it also shows cases where no behavioral objective 50 is applied to information item 24 and cases where no selection criterion 65 is applied to information item.

[0092] Decision matrix 70b has and Figure 5 The same meaning, but combined symbols have been added to indicate cases where the system might override the default. The symbol 'I?' indicates a case where, in one embodiment, the default would include items in the stream, but the system might decide to switch that decision. The symbol 'E?' indicates a case where, in one embodiment, the default would exclude items in the stream, but the system might decide to switch that decision. Figure 6 The other symbols shown in the text have and Figure 5 The same meaning as in Chinese.

[0093] There is no limit to the number of discrete priority levels that can be assigned to audience target 50 or selection criteria 65. Fewer priority levels are also allowed, making... Figure 5 The table and Figure 6 Combining tables within the system is possible. For a decision matrix 70 with discrete priority levels, the system can choose which cells to rewrite.

[0094] In one embodiment, priority can be determined from a continuous function of variables such as the producer's user profile 60, the consumer's user profile 60, the meta tag of information item 24, external factors, or any other data available to the system. The priority of the continuous function can have any scale, and the scale can be infinite, fixed, or normalized (e.g., normalized to zero or an interval).

[0095] For cases of consecutive priorities, decision matrix 70c may contain logical functions relating to each combination of the behaviors of producer 22 and consumer 28 in decision matrix 70c, such as... Figure 7 As shown in the diagram. The logic function can evaluate the prioritization of producer and consumer behaviors, along with other factors discussed below, to determine whether information item 24 is included in the stream or excluded from it.

[0096] exist Figure 5-7 Any combination of the decision matrix 70 shown can be possible. For example, a consumer can have several priority levels for desired behavior and one priority for unwanted behavior, and a producer can have consecutive priorities for sending behavior and three priorities for not sending behavior.

[0097] In one embodiment, the processing of decision matrix 70 may be integrated with the default allocator 26 of information exchange 29. In another embodiment, the processing of decision matrix 70 may be external to the default allocator 26. In one embodiment, the processing of decision matrix 70 may be distributed between the default allocator and external processing. In another embodiment, decision matrix 70 may be partially evaluated to identify desirable consumers, and the remaining processing of decision matrix 70 may be completed to refine consumers who will have items included in their streams.

[0098] In one embodiment, the maker sees descriptions similar to "Never send", "Preferably not send", and "Okay, if they get it, but not included in my count" for high, medium, and low priorities.

[0099] In one embodiment, the behavioral priority of a consumer’s desired behavior can be represented by descriptions such as “must have,” “having is good,” and “give me if it matters”, which are converted into high, medium, and low priorities.

[0100] In one embodiment, the decision grid 70d represents the decision matrix 70 for discrete, continuous, or mixed priority cases as a two-dimensional interval, where each dimension has a range of [1, -1]. Unwanted and unsent behaviors are multiplied by -1 in priority, and open cases are represented by 0. The two-dimensional interval is equivalent to any non-normalized two-dimensional interval. A threshold line 71 separates this interval from the included region 72 and the excluded region 73. The threshold line or boundary can be obtained from metric 54 and can be represented by a threshold function, mapping, or relation.

[0101] In one embodiment, exclusion zone 73 can be divided into an reachable exclusion zone and an unreachable exclusion zone. The reachable exclusion zone can be defined as the portion of the exclusion zone below threshold line 71. If the producer can increase the priority of matching the consumer's audience target, the reachable exclusion zone can also be defined as the portion of the exclusion zone that can be reached by the producer.

[0102] In one embodiment, the decision grid or decision matrix may contain priority boundaries in which threshold lines may not intersect.

[0103] In the discrete case, the threshold is a set of units that form the boundary between region 72 and the excluded region 73. For example, in Figure 6 In this context, the threshold setting will be the boundary along any row or column where a switch from inclusion to exclusion exists. The range or subset of the decision matrix 70 is a set of cells or regions in a two-dimensional interval.

[0104] Use of Measurement

[0105] In one embodiment, a consumer engagement metric can be used as a measure of information item consumption or interaction with information items. Consumer engagement metrics can be obtained or calculated from views, interactions, clicks, openness, or any other available indicators of consumer consumption of information items and their usefulness to information exchange. In one embodiment, the engagement metric can be precise. In another embodiment, the engagement metric is estimable. In one embodiment, the engagement metric can be the number of items participated in at a specified stage.

[0106] In one embodiment, consumer engagement metrics for a specific phase that are useful for information exchange may be stored in a database. In another embodiment, all historical data used to calculate or obtain consumer engagement metrics may be stored in a database.

[0107] In one embodiment, the participation rate of information consumer 28 can be measured as the number of information items participated in divided by the number of information items transmitted or sent to or available to the consumer during a specified period (e.g., a day). In one embodiment, the participation rate can be obtained from other sources, including surveys, monitoring, or other internal and external metrics.

[0108] In one embodiment, a historical engagement rate can be calculated for each consumer. The historical engagement rate can be calculated from the consumer's previous engagement in any of a number of ways. For example, using weighted history, rolling average, or other calculations. Various measures of historical engagement can be used. In one embodiment, the historical engagement rate for each consumer can be maintained in a database. In one embodiment, all historical data used to calculate or obtain consumer engagement rate metrics can be stored in the database.

[0109] In one embodiment, the consumer item value of a consumer's information item can be estimated using a priority established from the consumer's selection criteria. In one embodiment, the priority of the information item can be the highest priority matching the selection criteria. In another embodiment, the consumer item value can be calculated from the priority of overlapping selection criteria. In one embodiment, the item value can be calculated from priority and other metrics.

[0110] In one embodiment, a mapping from priority to consumer value can be used. In another embodiment, it can be assumed that consumer value and priority are equivalent.

[0111] In one embodiment, an average consumer item value over a period of time can be calculated. The average consumer item value can be calculated as the sum of the consumer item values ​​of participating items during that period divided by the number of participating items during that period. In one embodiment, a weighted average can be used to calculate the average consumer item value, wherein the weights depend on the information item metadata or other metrics. In one embodiment, a historical time series of the average consumer item value can be calculated. In one embodiment, the historical time series of the average consumer item value can be maintained in a database.

[0112] In one embodiment, historical time series of average consumer item values ​​can be used to estimate the consumer's expected item value for information items that the consumer has not yet received. Several formulas specific to information exchange can be used for this estimation. For example, weighted historical averages, rolling averages, or other calculations can be used. Various measures of expected item values ​​can be used. In one embodiment, expected item values ​​can be calculated from historical average consumer item values ​​and other metrics.

[0113] In one embodiment, the expected item value may be calculated or obtained from a survey, sentiment analysis, or other metric.

[0114] In one embodiment, a predicted participation rate can be calculated. In one embodiment, the predicted participation rate can be derived from statistical or predictive analysis using historical participation rates as well as internal and external metrics and signals. In one embodiment, the predicted participation rate can be the same as the historical participation rate.

[0115] In one embodiment, a participation prediction mapping can correlate expected item values ​​with predicted participation levels. Predicted participation levels can represent the number of information items per specified phase. The participation prediction mapping can be a discrete, continuous, or mixed logistic function or mapping. In one embodiment, statistical methods suitable for information exchange can use consumer expected item values ​​and additional external and internal metrics and signals to calculate and derive the prediction participation formula or mapping. In one embodiment, metrics from other consumers can be used to determine the participation prediction mapping.

[0116] In one embodiment, the inverse participation prediction mapping can be used to correlate participation levels with expected item values.

[0117] In one embodiment, the producer item value per consumer can be a value set by the producer for the information item received and consumed by the consumer. The producer item value can be calculated using a priority established from audience objectives for that information item. In one embodiment, the producer item value for a consumer can be calculated from priority and other metrics.

[0118] In one embodiment, a mapping from priority to producer item value per consumer can be used. In another embodiment, it can be assumed that producer value and priority are equivalent.

[0119] In one embodiment, the distribution of information items on a two-dimensional decision matrix 70 or decision grid can be calculated for each consumer. The distribution records the number of information items for each point in the decision matrix 70 or decision grid 70d over a period of time. Any number of techniques dedicated to information exchange can be used to record the distribution based on historical data. For example, weighted history, rolling average, or other calculations can be used. Multiple distributions are possible and can be used for different purposes in calculating other metrics. In one embodiment, distribution aggregation across information consumers can be used.

[0120] In one embodiment, the historical distribution of information items and optional additional metrics can be used to calculate the predicted distribution of information items for consumers in the current or future stages. In one embodiment, the distribution of information items in future stages can be pre-specified.

[0121] In one embodiment, the target consumer expectation value can be calculated from a metric to determine the expected value of each consumer's information exchange expectations.

[0122] In one embodiment, the threshold line 71 of the decision matrix 70 or decision grid 70 can be calculated using the predicted distribution of consumer information items, the mapping of priority to consumer values, the mapping of priority to producer values, the participation prediction mapping, consumer expected item values, target expected item values, or other metrics.

[0123] In one embodiment, a swap value function can be specified to indicate the combined values ​​to be swapped for each point on the grid. For example, the swap value function could be T(p, c) = ap + bc, where p = producer value, c = consumer value, a = 1 if p > 0 and a = 2 if p < 0, b = 1 if c > 0, and b = 2 if c < 0. This type of function encompasses tradeoffs when either the consumer or producer value is negative. Other functions can be used depending on the purpose of the information exchange, and the function can vary with consumers, temporary parameters, or other internal or external parameters specific to the exchange. In one embodiment, the swap value function can define priority bounds.

[0124] In one embodiment, the number of information items in the distribution area of ​​information items can be calculated as the sum of the items at each point in that area. For example, the distribution could indicate that the number of items is 5, 4, 7, 3, 11 (for 5 points defining a specific area). The sum of the information items in that area is 30.

[0125] In one embodiment, the average consumer value over the information item distribution area can be calculated as the sum of consumers multiplied by the distribution value at each point in the area divided by the number of information items in the area.

[0126] In one embodiment, the threshold line 71 can be calculated as a region within the decision grid 70d for the distribution of information items, wherein the participation level of the prediction from the participation prediction map of the specified expected item value is approximately equal to the number of information items in that region. In one embodiment, the specified expected item value can be the average consumer value over the distribution region. In another embodiment, the specified expected item value can be determined from internal and external metrics.

[0127] In one embodiment, first, by dividing the decision grid 70d into discrete points, including region 72, which can be selectively used for a specified distribution of information items. For example, to divide the consumer and producer priority axes of the decision grid into 10, 20x20 or 400 discrete points would be generated. For the decision matrix 70, cells are used as discrete points. Second, the exchange-value function at each discrete point on the decision grid is evaluated to determine which points are first included in the region. Third, these points are sorted in descending order of preference, and the number of information items added at each point in the region is calculated, and the expected item value is also calculated using the average consumer value or other metric in the region. Fourth, the predicted participation level from the participation prediction map of the specified expected item value is evaluated, and the process stops when the predicted participation level is less than the number of items. Fifth, the processed points are used to define region 72 and threshold line 71.

[0128] In one embodiment, the distribution of potentially consumer-related information items can be stored in a database. The distribution can be updated in real time. Threshold line 71 can be updated in real time as the distribution or other metrics change.

[0129] In one embodiment, consumer data collection may include selection criteria, distributions, decision matrices, threshold lines, and other consumer metrics. In another embodiment, consumer data collection may be stored on adjacent storage for fast access and processing.

[0130] In one embodiment, the consumer audience details query method can be used to evaluate information items, audience targets, consumer data collection, or other internal metrics to determine a set of consumer audience details, which may include, but are not limited to, consumer priority, producer priority for that consumer, producer priority at a threshold (if available), and range indicators (exclusion, inclusion, or reachable exclusion).

[0131] In one embodiment, a consumer audience detail query can use a column of meta tag groups, fields, and values ​​to evaluate consumer priority in response to meta tags. The consumer audience detail query method evaluates a list of meta tag groups, fields, and values ​​against selection criteria logic to determine the consumer priority to be assigned to each meta tag option in the list, and may also include related combinations. Consumer priority in response to meta tags may include the priority level of each item in the list and may also include a summary through combinations of fields, groups, and selections.

[0132] In one embodiment, the audience details query method can evaluate the consumer audience details for each consumer to calculate and aggregate a set of audience details. Audience details can be presented to a metadata control loop, an audience restriction control loop, an audience targeting control loop, a system interface for inputting audience targets, or a system interface for inputting information items.

[0133] In one embodiment, consumer selection criteria and a sufficiently large statistical sample of consumer data collection can be used instead of actual consumer data to provide an estimate of audience details.

[0134] In one embodiment, audience details for each audience target may include, but are not limited to, the original audience size, the increased audience size, the cumulative audience size, the priority of the audience size limit applied, the audience size within the included range, the audience size within the reachable exclusion range, or the average producer priority change required to move from the reachable exclusion area. Audience details for all specified audience targets may include, but are not limited to, the maximum audience size for all targets or the cumulative size for all targets. Audience details for information items may include, but are not limited to, the distribution of consumer priorities for information items, user profile summary statistics for specified priority ranges, or priorities in response to meta tags.

[0135] In one embodiment, responses to meta tags can be aggregated and generalized across all consumers based on consumer priority to obtain the meta tag value.

[0136] control loop

[0137] A set of control loops uses metrics 54 to control the flow of information items in information exchange. Metrics are measures and parameters that can be internal to or outside of information exchange. Sampled internal metrics include, but are not limited to, metrics related to producers, consumers, system information flow, or information exchange in general. Sampled external metrics include, but are not limited to, indicators of significant events occurring on a particular day, inclement weather, a particular day of the week, political or business events, news and information flow, or measures of behavior outside of information exchange, the flow behavior of external information exchange, historical projections, statistics, or any other relevant data.

[0138] One embodiment may have multiple control loops. Another embodiment may have a single control loop. Yet another embodiment may have no control loops.

[0139] Decision matrix control loop

[0140] In one embodiment, the decision matrix control loop adjusts the threshold lines 71 or limits in the decision matrix 70 or decision grid 70d that include region 72 and exclude region 73 to improve or maintain a set of success metrics.

[0141] In one embodiment, if a point on the decision matrix 70 or decision grid 70d, represented by selection criterion priority and audience target priority, is within the included region 72 defined by the consumer's threshold line 71, then the information item is included in the consumer's information flow.

[0142] In one embodiment, the decision matrix control loop may use a set of success metrics derived from consumers, producers, information items, audience targets, external sources, or from the general system. Consumer-related metrics include, but are not limited to, time taken to process the information stream, estimates of missed information items, engagement metrics, engagement rates, average selection criteria priority of the information stream in recent and historical periods, consumer expected item values, predicted engagement rates, predicted engagement levels, or other consumer metrics. Producer-related metrics include, but are not limited to, user profile data, which includes producer history, performance, or behavioral data. Metrics from external sources include, but are not limited to, indicators of significant events occurring on that day, inclement weather, a particular day of the week, political or business events, metrics of news and information from outside the information exchange, or any factors considered relevant to predictions that consumers pay attention to and value. In another embodiment, only some metrics may be used, or only one metric may be used.

[0143] In one embodiment, the decision matrix control loop may be part of the distributed subsystem 52.

[0144] In one embodiment, for each information item 24 processed by the decision control loop, consumer priority can be obtained from consumer selection criteria 65, and producer priority can be obtained from audience objectives 50 regarding that information item.

[0145] In one embodiment, multiple information items can be processed at once as a distribution of information items on decision grid 70d or decision matrix 70, and region 72 can be calculated to determine which information item can be included in the consumer's information flow. In one embodiment, information items can be delayed or queued to be evaluated together as a distribution of information items.

[0146] In one embodiment, an estimate of the probability that an information item will be omitted can be derived from a metric, whereby the omission of an information item means that the information item will be received by the consumer but not processed by the consumer. A systematic limit on the probability that an information item will be omitted can be derived from the metric. Within a specified range or subset of decision matrix 70, if the estimated probability that an information item will be omitted is greater than this estimated systematic limit, the information item is excluded. In one embodiment, within a specified range or subset of decision matrix 70, if the consumer item value is greater than the consumer's expected item value, the information item is included.

[0147] Creator Limit Control Ring

[0148] The producer limit control loop 46 determines the audience size limit set on the producer's audience target 50 at a specific priority level.

[0149] Figure 9 An embodiment is shown in which the audience size limit can be represented as an audience size limit mapping 75. The audience size limit mapping 75 can be used to obtain a priority for a given audience size or an audience size for a given priority. The audience size limit mapping 75 can be a function of priority and audience size limit, or a relationship between priority and audience size limit. The mapping can be continuous, discrete, or hybrid. Figure 9 Mapping the audience size limit to 75 is shown as a continuous mapping.

[0150] In one embodiment, the audience size limit mapping 75 may be determined first by metric 54. Metric 54 may include, but is not limited to, the current number of information items flowing through the system, the relevant number of consumers receiving too few or too many items, or the predicted number of information items flowing through the system in the future. The audience size limit mapping 75 may be adjusted using metadata and content of information item 24, and may be further adjusted using metrics from the creator's user profile, which includes, but is not limited to, expertise, background, reputation, number of items the creator has previously sent, interaction rate of items the creator has previously sent, or performance.

[0151] In one embodiment, the audience size limit mapping 75 can be determined dynamically in real time. In one embodiment, the basic audience size limit mapping can be set by the administrator, and the basic level set can be adjusted in the producer limit control loop 46 or not.

[0152] In one embodiment, the maximum audience size 76 can be determined from the maximum limit in an audience size limit map 75 with positive priority. In one embodiment, the total audience that the creator can reach may not exceed the maximum audience size 76.

[0153] Figure 10 An embodiment of the system interface for inputting audience targets 44 is shown. Option 90 allows the creator 22 to create audience targets 91, edit audience targets 92, reorder audience targets 93, manage audience target archives 94, view audience target details 95, view audience size limits 96, or complete the process when finished 97.

[0154] In one embodiment, producer 22 may interact with producer limit control loop 46 to initially create an audience target or retrieve an audience target from an archive. After entering a first audience target, producer 22 may evaluate the audience target and may view and process audience target details 95. Producer 22 may accept a priority level, reorder audience targets 93, or edit audience targets 92. If accepting a priority level for an audience target, the producer has the option to enter additional audience targets. If the producer enters additional audience targets, the process used for the first audience target can be repeated. If the combined audience from all audience targets exceeds the maximum audience size 76, the producer may reorder audience targets 93 or edit audience targets 92. In one embodiment, each additional audience target may have a lower or higher priority than the entered first audience target (depending on the order and audience target behavior).

[0155] In one embodiment, audience targets can be evaluated in an order specified by the producer to determine the incremental size of that audience target. The incremental audience size can be the size of the additional audience that each subsequent lower-ranked audience target can reach. Figure 9 The audience targets are categorized as A1-A5. For example, consider audience targets A1, A3, and A4 as those for which the producer wants to include or send informational items, and consider A2 and A5 as those for which the producer wants to exclude or not send information. The audiences for targets A1-A5 are evaluated to obtain an increment size. The increment sizes of audience targets that include behavior (in this example, A1, A3, and A4) can be accumulated sequentially downwards to obtain a cumulative size for the audience targets. An increment in the audience excludes any consumer that matches one of the higher-ranked audience targets. In one embodiment, the cumulative size of audience targets that include behavior is also added to the increment size of that audience target. The cumulative size can then be evaluated from the audience size limit map 75 to determine the priority of that audience target. Figure 9 In this context, audience targets A1, A3, and A4 have assigned priorities P1, P3, and P4, respectively. In one embodiment, the cumulative size can be used as a lookup size to obtain priorities from the audience size limit mapping 75. In one embodiment, the cumulative size can be adjusted by partially or fully increasing the size.

[0156] In one embodiment, the increments of audience targets with exclusionary behaviors (in this example, A2 and A5) can be accumulated sequentially downwards to obtain the cumulative size for each audience target with exclusionary behaviors. In another embodiment, the cumulative size of an audience target with exclusionary behaviors is not added to the increment of that audience target. The cumulative size can then be evaluated from the audience size limit map 75 to determine the priority of that audience target. Figure 9 In this context, audience targets A2 and A5 are assigned priorities P2 and P5, respectively.

[0157] In one embodiment, the creator can assign any priority to an audience target that has exclusionary behavior.

[0158] In one embodiment, using audience size limit mapping, audience targets can be automatically adjusted to improve audience size by: first, identifying audiences that will be included with low priority; second, generating audience targets for the identified audiences; third, directly assigning a priority to the audience target and placing the priority in the appropriate audience target order; and fourth, excluding increased audiences from the cumulative size and excluding such increased audiences from the increase count of subsequent targets.

[0159] In one embodiment, assuming priority > 0, audience targets including behaviors can be processed. In one embodiment, audience targets that may exceed the maximum audience size 76 but have a cumulative size less than the cumulative size of the audience target's increment can be system-limited to a size that will not exceed the maximum audience size 76. In another embodiment, the creator may have the option to refine audience targets that intersect with the limit.

[0160] In one embodiment, the audience size of the target audience can be an estimate, or in another embodiment, it can be a precise numerical value.

[0161] In one embodiment, the numerical value of the target audience can be finite.

[0162] In one embodiment, the creator inputs one or more audience targets. The system then automatically ranks the audience targets by audience size and assigns priorities using the method described above.

[0163] In one embodiment, the creator can specify a priority preference as F(X) within a user profile range, where X is a span of user profile characteristics. For example, specifying an age between 30 and 40, where 30 is the most preferred. Using F(X), a ranking is obtained for each potential target user profile. The priority mapping is in the [0,1] interval, and an audience size limit mapping 75 is used to assign priority to each increasing target from the highest to the lowest ranking, stopping when the maximum audience size 76 or the span of X is reached.

[0164] Methods for determining audience size limit mapping

[0165] In one embodiment, the audience size limit map 75 can be determined using the inverse cumulative distribution of consumer cluster density priority over the normalized interval [-1, 1] of the information item. The inverse cumulative distribution is the number of consumers whose selection criteria will register a given priority or higher priority for the information item. The consumer cluster density of the information item is the number of consumers at each consumer priority level, and this number can be obtained by accumulating the consumer counts at each priority level. Then, the inverse distribution is obtained by accumulating the cluster density starting from the top of the [-1, 1] interval. In one embodiment, only the cluster density and inverse cumulative distribution over the interval [0, 1] may be needed. In one embodiment, consumers with similar selection criteria can be clustered in a reduced representation of the map with representative selection criteria and consumer numbers. This is so that for a similar set of consumers, only a representative consumer needs to be evaluated. In one embodiment, the inverse cumulative distribution can be used directly as the audience limit map. In another embodiment, the inverse cumulative distribution can be scaled or adjusted before being used as the audience limit map. The advantage of using the inverse cumulative distribution is that it provides the producer 22 with a higher limit that will have a natural consumer priority for the information item 24. For example, a popular merchant offering a free giveaway or a popular news agency with exclusive breaking news might have a large number of consumers who prioritize such information when receiving it. In this case, the producer might not need to enter any audience targeting at all, as the default priority level is already high enough. On the other hand, a product supplier offering a marketing message that is targeted to only a small group of consumers might have a very strict audience limit mapping and might need to enter very specific audience targeting.

[0166] In one embodiment, audience limit mapping can be determined from the producer’s sending history, past behavior, and other mechanical analysis of keywords or information items.

[0167] In one embodiment, the parameters of the audience size limit mapping can be determined from a metric using discrete or continuous audience size limits. For example, in the continuous case, a linear relationship between size and priority can be used, and the metric will determine the slope and intercept of the line. More specifically, in this linear example, where the priority is in the interval [0, 1], the parameter would be the priority level of the maximum audience size 76 and at audience size zero. Other mathematical functions, mappings, and parameterized relationships can also be used, where the parameters of such functions, mappings, and relationships are determined in a similar manner by a metric.

[0168] The creator requested a control ring.

[0169] The producer request control loop can consider the impact of requesting additional metadata from the producer, the impact of audience targeting on the number of information items the producer can send or contribute over time, and the perceived impact on consumer-added information items. The producer request control loop may use metrics, including but not limited to: the opportunity cost of the producer's time, the time spent by the producer to complete sending, the time to obtain additional requested data, the availability of additional data, the value of additional meta tags or refinements, the time spent by the producer to input new audience targets or change them, and the time spent ranking or prioritizing audience targets. Consumer perception, engagement, or response metrics may also be used.

[0170] In one embodiment, the producer request control ring can be used via the metadata request control ring 42 to control the input of information item metadata. In one embodiment, the producer request control ring can be used to control the input of audience target 50 via the audience target request control ring 48. In one embodiment, the producer request control ring can supplement the producer limit control ring 46. In another embodiment, the producer request control ring can be an alternative to the producer limit control ring 46.

[0171] In one embodiment, the subject domain of an information item can be determined through automatic analysis of the information item as the creator inputs it. The subject domain can be used to obtain a list of meta-tag schemes and usage data for the determined subject domains. The list of meta-tag schemes and usage data can be used to generate a list of meta-tag groups, fields, and values ​​that can be provided to an audience detail query method. A response to a meta-tag with an audience size increment for a specified meta-tag, which can be provided by the audience detail query method, can be used to obtain the audience size increment, which can be used to select the order in which meta-tag questions are requested.

[0172] In one embodiment, the list of meta-tag schemes can be generic. In another embodiment, the meta-tag schemes can be subject-domain specific.

[0173] In one embodiment, the list of meta-tag schemes can be selected to complete a process that cannot be accomplished by automatically analyzing reliably provided metadata or confirming known fields or the creator's expertise. This can also be used to limit autonomous and non-autonomous creators.

[0174] In one embodiment, the creator may not be able to see the audience size associated with a specified meta tag.

[0175] In one embodiment, the desired audience distribution can be used to modulate the author request control loop. The audience distribution can be parameterized to quantify the desire using statistical measures or metrics. For example, it could be a distribution across a consumer priority interval where the density is reduced to zero or near zero.

[0176] In one embodiment, the metadata request control loop can use an audience distribution within a consumer priority zone. Using an audience distribution within a consumer priority zone has the advantage that well-known producers with a naturally large audience and high acceptance can avoid the additional requirements for meta tags or audience targeting, as well as the associated time burden.

[0177] In another embodiment, the metadata request control loop or audience request control loop can use an audience distribution on a two-dimensional decision grid or decision matrix, and the desired audience distribution can be based on both consumer priority and producer priority. Information producers can use the metadata request control loop or audience request control loop to achieve the desired audience distribution.

[0178] In one embodiment, any necessary metadata requests or audience targeting requests may be stopped when the desired audience distribution has been reached or the maximum number of requests from the producer has been made.

[0179] The topic field can be compared with the topic fields that can be inferred from the creator's user profile and previous information items generated by the creator. If the implied topic field of the information item does not align with the creator's topic fields and the creator's history, metadata questions can be queried to verify the validity of the post and the sender.

[0180] Control loop management

[0181] The administrator of the information exchange can select the control ring to use and configure its settings. Success metrics can be directed to balance values ​​across different exchange users with the objectives of information exchange stakeholders.

[0182] in conclusion

[0183] The computer system described in this paper is widely applicable to existing information exchange or serves as the basis for new information exchange to optimize and better engage participants.

[0184] The examples and variations given in this specification are not intended to be limiting, and other examples and variations will be apparent to those skilled in the art.

Claims

1. A metadata request control loop apparatus for selecting a metadata tag option in response, the apparatus being configured to: Obtain at least one consumer data collection, wherein the data collection includes selection criteria; Get multiple meta tag options; For each consumer data collection, consumer audience details for each meta tag option are calculated, wherein the consumer audience details include consumer priority in response to the meta tag, and an indicator of whether the consumer priority is in an included area, and wherein the selection criteria are used in determining the consumer priority in response to the meta tag; The consumer audience details for each meta tag option are aggregated across consumers to generate an aggregated response to a meta tag with an audience size increment, wherein the audience size in the included region is used at least in part to determine the aggregated response to the meta tag with the audience size increment. The order in which the meta tag options are requested is selected using the aggregated response to the meta tag with an audience size increment.

2. The apparatus of claim 1, wherein the information item is obtained from a system interface for inputting the information item, and wherein a list of meta-tag options for the information item is obtained.

3. A non-transitory computer-readable storage medium storing instructions that can be executed by at least one processor of a computer system to cause the computer system to at least: Obtain at least one consumer data collection; Get meta tag options; The meta tag options and the consumer data collection are used to calculate consumer audience details, wherein the consumer audience details include consumer priority in response to the meta tag and an indicator of whether the consumer priority is in an included region; The consumer audience details of the meta tag options are aggregated across multiple consumers to generate an aggregated response to the meta tag options; The meta tag for requesting the meta tag option; The aggregated response to the meta tags for each meta tag option is used to select the order in which multiple meta tag options are requested.

4. The non-transitory computer-readable storage medium of claim 3, wherein the instructions further cause the computer system to: obtain an information item, and wherein the meta-tag option of the information item is obtained.

5. A manufacturer-requested control ring device, comprising: Information items; One or more target audiences for the information item; A means for determining the audience distribution of the information item and the target audience; A device for quantifying the expected distribution of the audience; A means for requesting input modification, wherein the input modification includes additional meta tags or changes to the audience target; The means for determining to stop requesting the input modification, wherein the means for determining to stop depends on the desired audience distribution.

6. A method for manipulating a maker-requested control loop, comprising: Information items; One or more target audiences; Request input modification, wherein the input modification includes additional meta tags for the information item or changes to the audience target; Determine the audience distribution over a two-dimensional interval of consumer priority and producer priority for the information item and the one or more audience targets; Use one or more parameters or statistical measures to quantify the expected distribution of the audience; Stop requesting input modifications once the desired audience distribution has been reached.

7. A system interface for inputting information items and the target audience for the information items, comprising: The producer requests a control loop, which is configured to request additional meta tags for the information item or changes to the audience target using the method according to claim 6.

8. A non-transitory computer-readable medium storing instructions executable by at least one processor of a computer system to cause the computer system to at least: Obtain metadata or audience targets from the system interface; Generate an audience distribution on a two-dimensional interval of consumer priority and producer priority for the obtained information item metadata and audience targets; Calculate one or more statistical measures or metrics to quantify the expected distribution of the audience; Once the desired audience distribution is achieved, stop requesting additional information items (metadata) and making changes to the audience target from the system interface.

9. A non-transitory computer-readable medium storing instructions executable by at least one processor of a computer system to cause the computer system to at least: Obtain the distribution of information items, wherein the distribution is on a grid, and wherein the grid contains multiple points; Calculate the number of terms and the expected term value for the region of the distribution; For the expected item value, the predicted participation level is calculated from the predicted participation map; Calculate a threshold line that serves as the boundary of the region, at which the predicted participation level is approximately equal to the number of items.

10. A computer-implemented method for determining, in information exchange, the allocation of information items from at least one information producer to at least one information consumer, the method comprising: The step of obtaining at least one target audience that indicates the type of information consumer the producer wants to receive the information item; The step of obtaining at least one selection criterion indicating whether the information consumer wants to receive the information item; Steps to adjust the decision matrix using the decision control loop to improve or maintain success metrics; The step of determining whether to transmit the information item to the information consumer based on the decision matrix.

11. A computer-implemented method for prioritizing multiple audience targets used with information items in an information exchange system, the method comprising: The order in which the audience targets are obtained, wherein the audience targets are sorted; Determine the derived audience size associated with each audience target, wherein the derived audience size depends on the order in which the audience targets are sorted; Audience size limit mapping correlates audience size with priority; The priority of each audience target is determined using the derived audience size and the audience size limit mapping for the audience target; The target audience is thus prioritized.

12. The computer-implemented method according to claim 11, further comprising: Calculate the cumulative size of each audience target having a behavior, wherein the cumulative size is the combination of the audience matching the audience target with a higher-ranked audience target having the behavior, and wherein the cumulative size is used as the derived audience size to determine the audience target priority from the audience size limit mapping.

13. A non-transitory computer-readable medium storing instructions executable by at least one processor of a computer system to cause the computer system to at least: Obtain multiple instances of a data structure representing an audience target, wherein the data structure of the audience target indicates whether the audience target has behaviors; The order in which the target audience is obtained; Calculate the cumulative size of each audience target having the included behavior, wherein the cumulative size is the combination of the audience matching the higher-ranked audience target having the included behavior and the audience target. Use the cumulative size of each audience target to find the priority of the audience target from the audience size limit map; Assign producer priorities to information items for information consumers, wherein the producer priority is the priority that achieves the highest-ranked audience target of the information consumer.

14. An information exchange device for limiting the target priority of an audience, comprising: Audience size limit mapping; At least two audience targets with a specified order; Use information items from at least two of the target audiences; A means for calculating the audience size of the target audience, wherein the means for calculation takes into account the specified order of the target audience; A means for determining the priority of the audience target, wherein the means for determining the priority uses the audience size and the audience size limit mapping; The priority of the information item and the producer priority of the consumer are assigned to the highest audience target in the specified order.

15. A system interface for inputting audience targets, comprising: One or more target audiences; Audience details, wherein the audience details include the priority or audience size of each audience target; A device for interacting with the maker limit control loop to obtain the audience details; A device for interacting with an information creator, wherein the device for interaction includes options for the following operations: (a) Enter one or more of the aforementioned audience targets, (b) Review the audience details. (c) Edit one or more of the aforementioned audience targets, (d) Reorder the audience targets, (e) The instruction is completed, whereby the audience target and the information item are accepted and sent to the subsystem.

16. A method for assigning audience target priorities for use with information items, comprising: Audience size limit mapping; At least two audience targets with a specified order; The step for calculating the increment of one or more of the audience targets; Select an audience target that is lower than at least one other audience target in the said order; The step of determining the lookup size of the selected audience target using the increment size of the audience target with higher ranking and the increment size of the selected audience target; and Use the lookup size and the audience size limit mapping to assign priorities to the selected audience targets; The highest priority of the audience target assigned to the information consumer in the specified order is assigned as the producer priority of the information item and the consumer.

17. A method for determining an audience size limit mapping, comprising: Information items; Information consumers or a representative collection of information consumers; The step of determining the priority level of the information item for each of the information consumers in the set; The step for obtaining cluster density, wherein the step uses the priority level of each of the information consumers in the set; The step for obtaining the inverse cumulative distribution, wherein the cluster density is used in the step; The step of using the inverse cumulative distribution to determine the audience size limit mapping.

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