EP2973293A2 - Système servant à la commande et à l'optimisation de la distribution des informations entre utilisateurs dans un échange d'informations - Google Patents

Système servant à la commande et à l'optimisation de la distribution des informations entre utilisateurs dans un échange d'informations

Info

Publication number
EP2973293A2
EP2973293A2 EP14762314.4A EP14762314A EP2973293A2 EP 2973293 A2 EP2973293 A2 EP 2973293A2 EP 14762314 A EP14762314 A EP 14762314A EP 2973293 A2 EP2973293 A2 EP 2973293A2
Authority
EP
European Patent Office
Prior art keywords
audience
information
consumer
producer
priority
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP14762314.4A
Other languages
German (de)
English (en)
Other versions
EP2973293A4 (fr
Inventor
Brian Mcfadden
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Publication of EP2973293A2 publication Critical patent/EP2973293A2/fr
Publication of EP2973293A4 publication Critical patent/EP2973293A4/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • One of the partial solutions used by many information exchanges is to add group, tags, or topics that information consuming users can subscribe to or use to filter the set of information available to them. This is an improvement not a full solution as increasing the number of topics to decrease the rate of information produced per topic but still leaves an inefficiency as consuming users must choose between lower rate of information flow and potentially losing some valuable information items from peripheral topics. Once they subscribe to the peripheral topics the information rate and value dilution increases. Even if a information consumer's interests are contained in a single topic there will still be a degree of variability of interest that could lead to an inefficiency particularly if there are many information items in the given topic.
  • This approach has limits by not considering dynamic external factors, the state of the information exchange, the producer of the information item and their targeting preferences for the information item. These and other factors might have an influence on the applicability of the preference of the information consumer particularly when there is prediction uncertainty of the derived preference.
  • Another contributing problem is the practice of many information exchanges to reduce or eliminate any restrictions for information producers to enter information items. This approach encourages quantity but also leads to variable quality of contributions that ultimately lower the value to the potential consumers in the exchange and extenuates the problems stated heretofore. This situation may not be altered significantly even when there is a monetary assessment on the contribution. While payment is a restriction that may correlate with quality it does not assure that a more optimal quality level is achieved.
  • FIG 1. Describes an example of an information exchange
  • FIG 2. Describes an example of the producer interactions
  • FIG 3. Describes an example of the interactions of a general user
  • FIG 4. Describes an example of the interactions of the consumer
  • FIG 5. Describes an example of a basic decision matrix with not action priorities shows audience targets down the vertical for producer and along the horizontal.
  • FIG 6. An exemplary embodiment of decision matrix with action priorities
  • FIG 7. An exemplary embodiment with continuous priorities.
  • FIG 8. Describes include region and threshold line for a decision grid or decision matrix
  • FIG 9. Describes the audience size limits
  • FIG 10. Describes the system interface for inputing the audience targets
  • FIG. 1 An example of an information exchange 29 is shown in FIG. 1.
  • a user 20 of the information exchange 29 may be either a information producer 22 or an information consumer 28 or both.
  • the information exchange 29 delivers an information item 24 from the information producer 22 to the information consumer 28.
  • a information exchange consists of one or more producers, one or more consumers and a distributor 26.
  • the distributor 26 specifies how the information items flow from producer to consumer.
  • the distributor 26 can take multiple forms, for example an information switch including simple pass through, publisher to consumer, sender to receiver, publish-subscribe, or any other form where information is transferred from a producer to a consumer.
  • the distributor 26 would include for example cases where the consumer friends or follows one or more producers or joins a group or where a producer and consumer have agreed to follow or friend or exchange information with each other and allow the other party to do the same.
  • the distributor 26 may support subscriptions or not. If subscriptions are supported, the consumer 28 may be subscribed to one, several or all producers. If the distributor 26 does not support subscriptions the consumer 28 will be able to receive from all producers. There may be one or multiple producer 22. There may be one or multiple consumer 28.
  • the information exchange 29 could be a social network, a group within a social network, a list server, a news aggregation service, a news feed, a newsletter, a digest, offers, alerts, an ad exchange, an ad network, email client, news reader, web browser, portal or any service that facilitates a flow of information items from producers to consumers.
  • the producer 22 is the user 20 who will send, post, place, contribute, publish, author, create, direct, respond, or otherwise cause information to be distributed to, or made viewable by, one or more other users of the information exchange.
  • FIG. 1 is not intended to show every detail of the information flow.
  • the information consumer 28 is the user who will receive information items originating from the producers.
  • the consumer 28 may or may not consume the information items made available to them.
  • the information item 24 can be a message, email, notice, response, video clip, audio clip, news, article, story, solicitation, offer, advertisement, URL, or any other form of communication that can be sent or made available by a producer to a consumer.
  • An information stream is a collection or set of information items delivered sequentially or together to a consumer 28, either directly or embedded, via a medium including, but not limited to, print, email, web feeds, mobile messaging, video, audio, broadcast, or via any other means of delivering information.
  • FIG. 3 shows an example of an information exchange 29 where user 20 may enter user profile data 64 into a system interface for inputting the user profile 61.
  • the system interface for inputting the user profile 61 stores the user profile 60 in a user profile storage 62.
  • the user profile storage may be an internal part of the information exchange, external to the information exchange, or a combination of internal and external.
  • a set of system derived user profile data 63 can also be stored in the user profile storage 62, and in some system, the user may not input any user profile data.
  • a user profile 60 includes available information, not limited to form, about the user. This includes but is not limited to behavior, biographic, demographic, historical, ratings, feedback, tracking, or other general or specific information from sources internal and external to the information exchange 29.
  • the form for the user profile storage includes relational database, name value pair, no-sql, hierarchical data, objects, nested objects, nested hierarchical data, or combination of databases in a single source or in multiple sources. If accessible via an API the user profile 60 may be represented by XML, JSON, CVS, or any other data representation.
  • the consumer 28 in FIG. 4 may enter a selection criteria data 66 into a system interface for inputting the selection criteria 68, and a selection criteria 65 is stored in a selection criteria storage 67.
  • the selection criteria 65 can indicate the type or set of information items that the consumer is potentially interested in or not interested in receiving.
  • the system interface for inputting the selection criteria 68 stores the selection criteria in a selection criteria storage 67.
  • the selection criteria storage 67 can be internal to the information exchange 29, external to the information exchange 29, or a combination of internal and external.
  • Selection criteria can also include a system derived selection criteria 69 that can also be stored in the selection criteria storage 67.
  • the selection criteria may be stored with the user profile data and the user profile storage and selection criteria storage may be the same.
  • the selection criteria storage and user profile storage may be stored together on contiguous storage for fast access and processing.
  • audience targets 50 define a set of consumers or audiences that a producer 22 would like to reach or not reach.
  • a system interface for inputting the audience targets 44 interacts with a producer limits control loop 46 and an audience target request control loop 48.
  • the producer limits control loop 46 and the audience target request control loop 48 regulate the audience targets 50 included with an information item 24 to be processed by a distribution sub-system 52.
  • a system interface for inputting the information item 40 receives an information item 24 from the producer 22.
  • a meta data request control loop 42 interacts with the system interface for inputting the information item 40 and regulates the amount of additional descriptive data that is collected when an information item 24 is entered.
  • the distribution sub-system 52 processes the information item 24, audience targets 50, a set of metrics 54, user profiles from the user profile storage 62, and selections criteria from the selection criteria storage to determine what consumers should get, receive, or view the information item as described below.
  • the metrics 54 may be measures, statistics, and parameters obtained, in direct or computed form, from one or more sources internal or external to the information exchange.
  • the distribution sub-system 52 and the distributor 26 can be the same. In another embodiment, they may be separate.
  • system described here is the information exchange or an integral part of the information exchange.
  • system will exist separately from the information exchange as a sub-system interacting with the information exchange as detailed below.
  • the system is computer coded software operating 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 similarly related hardware with an applicable operating system appropriate for the specific hardware and, in the case of more than one, interconnected via a private or public network.
  • the system may operate as a self regulated automatic control system.
  • the Producer In one embodiment, the producer may enter the information item 24 into a system interface for inputting the information item 40.
  • the information item consists of contents and a meta description.
  • the contents can include summary, title, full story, image, video, audio, rich media, or other primary information delivery object.
  • the meta description can include abstract, source, keywords, authors, bylines, related links, topics, subjects, types, restrictions, pricing or any other fields or objects or hierarchical data used to classify, categorize, track, identify or otherwise describe the contents and the information item.
  • the meta data description and the information item may be the same.
  • the producer may enter the audience target into the system interface for inputting the audience targets 44.
  • the audience target describes the consumers that the producer would like to reach or not reach.
  • the specification of an audience target can reference any aspect of the user profile to specify the potential consumer.
  • the audience target will have an action to specify if the user matching the audience target should receive the information or not.
  • the system interface for inputting the information item and the system interface for inputting the audience targets may be the same.
  • the producer may specify one or more additional audience targets that they want.
  • the first audience target is the primary set of users the consumer wants to include or exclude. Each additional audience can have less priority than prior audiences selected.
  • the producer 22 may construct an audience target and priority by selecting one or more parameters from available data in the user profile of the consumer and assign a priority to values for each discrete parameters and range of values for continuous parameters.
  • the max and min values for all combination of field values may be used to determine a normalized priority scale.
  • the producer may first target the largest audience they want to reach.
  • the system may set a limit that is less than the size of the audience specified by the first audience target.
  • the limit may be determined by the context of the message, the past history of interactions with the prior messages from the producer, and current system-wide metrics.
  • the system may adjust the limit in exchange for payment or some other concession from the producer.
  • the producer may specify an additional audience target to reach an audience closer in size to the limit. If the size of the audience is less than the limit, the audience target may be used as entered and assigned a priority. In one embodiment, if the size of the audience is greater than limit the system will refine the target to make the audience target meet the limit or adjust the priority of the audience target. In another embodiment, the system may adjust the priority for the audience target if the size exceeds the limit.
  • the producer may scale the priority based on one or more discrete or continuous parameters used in the profile of the consumers.
  • the producer may have an archive of predefined audience targets that can be selected instead of entering and creating new audience targets.
  • the information items and audience targets may be sent to a distribution sub-system.
  • the distribution sub-system may be integral with the information exchange distributor. In another embodiment, the distribution sub-system can be external to the information exchange distributor.
  • the producer's audience targets may be required. In another embodiment, the producer's audience targets may be optional.
  • producers may use visual input sliders to indicate audience targets and priorities for specific profile attributes. For example, an audience target with higher priority targets based by years of experience of the consumer. In another embodiment, producers may use drag and drop visuals to rank audience targets and set audience target priorities.
  • the producer's entered audience target may be applied to single information item, multiple information items, or all information items from that producer.
  • the producer may be an autonomous agent.
  • the user, producer and consumer may enter data into the user profile 60.
  • the user profile 60 may also include system data and information about the user including, but not limited to, performance, behavioral, history, tracking, or any other information that the system can record or compute for a user.
  • the user profile may also include external information obtained from external systems including, but not limited to, performance, behavioral, history, tracking, records, or any other information that can be obtained or computed from external systems or combined with internal profile data.
  • the user profile may have data from all data sources.
  • the information exchange user 20 may enter user profile data 64 into a system interface for inputting the user profile 61.
  • the system interface for inputting the user profile 61 stores the user profile data 64 in a user profile storage 62.
  • the user profile storage may be part of the information exchange 29.
  • the user profile storage 62 may be external to the information exchange 29.
  • the user profile storage 62 may be distributed between the information exchange 29 and external to it.
  • external and system derived user profile data 63 may be stored in the user profile storage 62.
  • the consumer may enter the selection criteria that defines the type of information item and may also define a type of producer. In another embodiment, the selection criteria may only specify a type of information item or type of producer. In one embodiment, the consumer may enter an action for the selection criteria to specify if the information items matching the criteria are items they would want to receive or not receive. In another embodiment, the action assigned to the selection criteria may be assigned by the system from behavior actions of the consumer. For example, by the consumer expressing interest in an a related item or meta data topic.
  • the consumer can enter more than one selection criteria.
  • the consumer may specify a priority to define how important the criteria is. Priorities can be expressed by ordering the criteria or by selecting a priority preference input.
  • the priority of the selection criteria may be assigned by the system from the context of the inputed or derived selection criteria or the behavior, history, or actions leading to the creation of the selection criteria.
  • the selection criteria may also overlap and conflict. For example, a conflict between criteria may arise if two selection criteria match and one criterion says to include a specific item and another criterion says to exclude it.
  • the conflict may be resolved by preference to the highest priority selection criteria.
  • the priorities may be combined in a mathematical function to determine the priority with the exclude priority optionally multiplied by -1. The function may consider higher weighting for higher priorities or may simply average the priorities. If both priorities are the same in a conflict, it may be treated as unresolved or open.
  • the conflict may be resolved by the system depending on optimization criteria discussed below.
  • selection criteria and priority for the selection criteria may be determined from performance, historical, behavioral, or tracking data of the consumer. In another embodiment, selection criteria and priority may be determined from predictive statistical methods. In another embodiment, selection criteria entered by the consumer may be combined with selection criteria determined from all other means.
  • priorities may be set by the system for each selection criteria.
  • the system sets a default priority for the selection criteria that can be changed by the consumer.
  • processing of the consumers selection criteria may be integral with the information exchange distributor. In another embodiment, the processing may be external to the default distributor.
  • the consumer's selection criteria may be entered by a human. In another embodiment, the selection criteria may be entered by an autonomous agent.
  • the consumer may enter the selection criteria into a system interface for inputting the selection criteria.
  • the system interface for inputting the selection criteria 68 stores the selection criteria in a selection criteria storage 67.
  • the selection criteria storage 67 may part of the information exchange.
  • the selection criteria storage 67 may external to the information exchange.
  • the selection criteria storage 67 may distributed between the information exchange and external to it.
  • system derived selection criteria 69 may stored in the selection criteria storage 67.
  • consumers use drag and drop visuals to rank selection criteria and set selection criteria priorities.
  • the consumer may be an autonomous agent.
  • Decision Matrix
  • a decision matrix 70 may be used to determine if the information item 24 should be included in the information stream of the consumer 28.
  • FIG. 5 shows the decision matrix 70a for a basic case with no audience target priorities or selection criteria priorities.
  • the producer's 22 actions for the two audience targets 50 are shown along the horizontal.
  • the two audience targets 50 are for a send and a do-not-send action.
  • the letter 'S' indicates the send action and the letters 'DS' indicate the do-not-send action.
  • the letter ⁇ ' for open indicates the case where no audience target applies to the information item.
  • the consumer actions for two selection criteria 65 are shown along the vertical.
  • the two audience targets 50 are for a want and a do-not-want action.
  • the letter 'W indicates the want action and the letters 'DW indicate the do-not-want action.
  • the letter ⁇ ' for open indicates the case where no selection criteria 65 applies to the information item 24.
  • the decision matrix 70a used to indicate when the information item 24 should be included in the information stream of the consumer 28 or excluded from the information stream.
  • a letter T indicates inclusion of the information item 24 into the information stream
  • the letter ⁇ ' indicates exclusion of the information item 24 from the the information stream.
  • the symbol '?' indicates that the system may decide if the information item 24 is included or not.
  • the table extends the table shown in FIG. 5 to show priorities for the audience targets 50 and selection criteria 65.
  • the producer's audience targets 50 are again along the vertical and the consumers selection criteria 65 are along the horizontal.
  • the producer's audience targets 50 are shown with combined action and preference priority.
  • the letter ⁇ ' indicates a high priority.
  • the letter 'M' indicates medium priority.
  • the letter 'L' indicates low priority.
  • six audience target action and priority combinations are shown for the producer 22. For the consumer six selection criteria action and priority combinations are shown. As in FIG. 5, the case where no action targets 50 applies to the information item 24 and the case where no selection criteria 65 applies to the information item are also shown in the table.
  • the decision matrix 70b has the same meaning as in FIG. 5 but has added combined symbols to show cases where the system might override the default.
  • the symbols 'I?' indicate a case where In one embodiment, the default would be to include the item in the stream but the system may decide to switch the decision.
  • the symbols ⁇ ?' indicate a case where In one embodiment, the default would be to exclude the item from the stream, but the system may decide to switch the decision.
  • the other symbols shown in FIG. 6 have the same meaning as in FIG. 5.
  • the system can choose what cells to override.
  • the priorities may be determined from a continuous function of variables from the producer's user profile 60, the consumer's user profile 60, the meta tagging for the information item 24, external factors, or any other data available to the system.
  • the priority from the continuous function can be of any scale, and the scale can be infinite or fixed or normalized, for example normalized to the zero to one interval.
  • the decision matrix 70c may contain a logical function for each combination of actions for the producer 22 and the consumer 28 in the decision matrix 70c as shown in FIG. 7.
  • the logical function can evaluate the priorities for the producer and consumer actions along with other factors discussed below to determine if the information item 24 is included in or excluded from the stream.
  • any combination of the decision matrix 70 shown in FIG. 5 - 7 may be possible.
  • the consumer might have several priority levels for a want action one priority for the do-not-want action and the producer may have continuous priority for the send action and three priorities for the do-not-send actions.
  • the processing of the decision matrix 70 may be integral with the information exchange 29 default distributor 26. In another embodiment, the processing of the decision matrix 70 may external to the default distributor 26. In one embodiment, the processing of the decision matrix 70 may distributed between the default distributor and an external process. In another embodiment, the decision matrix 70 can be partially evaluated to identify eligible consumers and the remaining processing of the decision matrix 70 can be completed to refine the consumers that will have the item included in their stream.
  • the producer sees the descriptions for the do-not send priorities similar to "never send”, “prefer not to send”, “ok if they get it but dont include in my counts" translating to high, medium, and low priority.
  • the action priorities for a consumer want action may be represented with descriptions similar to “must have”, “ok to have”, “give to me if it might be important” translating to high, medium, and low priority.
  • a decision grid 70d represents the decision matrix 70 for the case of discrete, continuous, or mixed priorities as a two dimensional interval with a range of [1,-1] for each dimension.
  • the actions for do-not-want and do-not-send multiply their priorities by -1 and the open case is represented by 0.
  • the two dimensional interval is equivalent to any non-normalized two dimensional interval.
  • a threshold line 71 separates the interval from an include region 72 and an exclude region 73.
  • the threshold line or boundary can be derived from the metrics 54 and can be represented by a threshold function, mapping, or relation.
  • the exclude region 73 may be divided into a reachable exclude range and a non-reachable exclude range.
  • the reachable exclude range may be defined as the part of the exclude region below the threshold line 71.
  • the reachable exclude range may also be defined as the part of the exclude range that may be reached by the producer, if the producer can increase the priority of the audience target matching that consumer.
  • the threshold is a set of cells that form the boundary of the include region 72 and exclude region 73.
  • the threshold set would be the boundary along any row or column where there is a switch from include to exclude.
  • a range or subset of the decision matrix 70 is a set of cells or regions in the two dimensional interval.
  • a consumer participation metric may be used as a measure of information item consumption or interaction with the information item.
  • the consumer participation metric may be obtained or computed from views, interactions, clicks, opens or any other applicable indicator of information item consumption by the consumer and useful to the information exchange.
  • the participation metric may be exact.
  • the participation metric may be estimated.
  • the participation metric may be the number of items participated in for a specified period.
  • the consumer participation metric for specific periods useful to the information exchange may be stored in database. In one embodiment, all historical data used for computing or obtaining the consumer participation metric may be stored in a database.
  • a participation rate for the information consumer 28 may be measured as the number of information items participated in divided by the number of information items delivered or sent or made available to the consumer over a specified period (for example a day). In one embodiment, the participation rate may be obtained from other sources including surveys, monitoring, or other internal and external metrics.
  • an historical participation rate may be computed for each consumer.
  • the historical participation rate can be computed in any number of ways from prior participation of the consumer. For example using weighted history, rolling average or other computations. Multiple measures of historical participation can be used.
  • the historical participation rate for each consumer may be maintained in a database.
  • all historical data used for computing or obtaining the consumer participation rate metric may be stored in a database.
  • a consumer item value for the information item may be estimated for the consumer using the priority established from the selection criteria of the consumer.
  • the priority of the information item may be the highest priority of matching selection criteria.
  • the consumer item value may be computed from the priority of over lapping selection criteria.
  • the item value may be computed from the priority and other metrics.
  • a mapping of priority to value for the consumer may be used.
  • the consumer value and priority may be assumed to be equivalent.
  • an average consumer item value may be computed for a period of time.
  • the average consumer item value may be computed as the sum of the consumer item value for items participated in for the period divided by the number of items participated in for the period.
  • a weighted average may be used to compute the average consumer item value with weights depending on information item meta data or other metrics.
  • a historical time series of average consumer item value may be computed. In one embodiment, the historical time series of average consumer item value may be maintained in a database.
  • the historical time series of average consumer item value may be used to estimate a consumer expected item value for an information item the consumer has not yet received. Multiple formula specific to the information exchange can be used for this estimate. For example using weighted history, rolling average or other computations. Multiple measures of the expected item value can be used. In one embodiment, the expected item value may be computed from the historical average consumer item value and other metrics.
  • the expected item value may be computed or obtained from, surveys, sentiment analysis, or other metrics.
  • a predictive participation rate may be computed.
  • the predictive participation rate may be derived from statistical or predictive analytics using the historical participation rate and internal and external metrics and signals.
  • the predictive participation rate may be the same as the historical participation rate.
  • a participation prediction mapping may relate the expected item value to a predicted participation level.
  • the predicted participation level represents a number of information items per specified period.
  • the participation prediction mapping may be a discrete, continuous, or mixed logical function or mapping.
  • statistical methods appropriate to the information exchange may be used to compute and derive a predictive participation formula or mapping using the consumer expected item value and additional internal and external metrics and signals.
  • the participation prediction mapping may be determined using metrics from other consumers.
  • a inverse participation prediction mapping may be used to relate the participation level to an expected item value.
  • a producer item value per consumer may be the value to the producer for the consumer to receive and consume an information item.
  • the producer item value may be computed using the priority established from the audience targets for that information item.
  • the producer item value for a consumer may be computed from the priority and other metrics.
  • a mapping of priority to the producer item value per consumer may be used.
  • the producer value and priority maybe assumed equivalent.
  • a distribution of information items on the two dimensional decision matrix 70 or decision grid may be computed for each consumer.
  • the distribution records the number of information items for a time period for each point in the decision matrix 70 or decision grid 70d.
  • Any number of techniques specific to the information exchange can be used for recoding the distribution based on historical data. For example using weighted history, rolling average or other computations. Multiple distributions are possible and can be used for different purposes in computing other metrics.
  • aggregations of distributions across consumer may be used.
  • the historical distribution of information items and optional additional metrics may be used to compute a predicted distribution of information items for a consumer in a current period or a future period. In one embodiment, the distribution of information items for specified future period may be predetermined.
  • a targeted consumer expected item value may be computed from the metrics to determine the information exchange desired expected item value for each consumer.
  • the threshold line 71 of the decision matrix 70 or decision grid 70 may be computed using the predicted distribution of information items for a consumer, the mapping of priority to value for the consumer, the mapping of priority to value for the producer, the participation prediction mapping, the consumer expected item value, the targeted expected item value, or other metrics.
  • an exchange value function may be specified to indicate a combined value to the exchange for each point on the grid.
  • Functions of this type encapsulate the trade off when consumer item value or producer item value is negative.
  • Other functions could be used depending on the goals of the information exchange and the function could vary by consumer, temporal parameters, or other internal or external parameters particular to the exchange.
  • the exchange value function may define the priority bounds.
  • a number of information items over a region of the distribution of information items may be computed as the sum over every point in the region. For example the distribution might indicate that the number of items is 5, 4, 7, 3, 11 for 5 points defining the particular region. The sum of information items over this region is 30.
  • an average consumer value over a region of the distribution of information items may be computed as the sum of the consumer value times distribution value over every point in the region divided by number of information items in the region.
  • the threshold line 71 may be computed for the distribution of information items as the region inside the decision grid 70d where the predictive participation level from the predicted participation mapping for a specified expected item value is approximately equal to the number of information items in the region.
  • the specified expected item value may be the average consumer value over the region of the distribution. In another embodiment, the specified expected item value may be determined from internal and external metrics.
  • the include region 72 may be chosen for a specified distribution of information items by first dividing the decision grid 70d into discrete points. For example to divide the decision grid consumer and producer priority axis into lOths would yield 20 x 20 or 400 discrete points. For the decision matrix 70 use the cells as the discrete points. Second, evaluate the exchange value function at each discrete point on the decision grid to determine the points to include first in the region. Third, sequence through the points in descending preference order and compute the number of information items over the region incrementally at each point, and also compute the expected item value using the average consumer value over a region or other metrics to get the expected item value. Fourth, evaluate the predictive participation level from the predicted participation mapping for the specified expected item value, and when predictive participation level is less than the number of items stop. Fifth, use the processed points to define the include region 72 and the threshold line 71.
  • the distributions of information items that may be relevant for the consumer may be stored in a database.
  • the distributions may be updated in real-time.
  • the threshold line 71 may be updated in real time as the distributions or other metrics change.
  • a consumer data collection may include the selection criteria, distributions, decision matrix, threshold line, and other consumer metrics.
  • the consumer data collection may be stored on contiguous storage for fast access and processing.
  • a consumer audience details query method can be used to evaluate the information item, the audience targets, the consumer data collection, or other internal metrics to determine a set of consumer audience details that may include but not limited to the consumer priority, the producer priority for that consumer, the producer priority on the threshold line (if applicable), and an indicator of the range (exclude, include, or reachable exclude).
  • the consumer audience details query may use a list of meta tagging groups, fields, and values to evaluate a consumer priority responsiveness to meta tagging.
  • the consumer audience details query method may logically evaluate the list of meta tagging groups, fields, and values against the selection criteria to determine the consumer priority that would be assigned to each meta tag option in the list and may also include relevant combinations.
  • the consumer priority responsiveness to meta tagging may include priority levels the items in the list and may also include summaries by field, group, and selection combinations.
  • an audience details query method can evaluate the consumer audience details method for each consumer to compute and aggregate a set of audience details.
  • the audience details can be presented to the meta data control loop, the audience limits control loop, the audience target control loop, the system interface for inputting audience targets, or the system interface for inputing the information item.
  • a sufficiently sized statistical sample of the consumer's selection criteria and consumer data collections may be used instead of actual consumer data to provide an estimate of the audience details.
  • the audience details for individual audience targets may include, but not limited to, raw audience size, incremental audience size, accumulated audience size, audience size limits imposed priority, audience size in the include range, audience size in the reachable exclude range, or average producer priority change needed to move from reachable exclude region.
  • the audience details for all audience targets specified may include, but not limited to, max audience size for all targets, or accumulated size for all targets.
  • the audience details for an information item may include, but not limited to, distribution of consumer priority for the information item, user profile summary statistics for specific priority ranges, or priority responsiveness to meta tagging .
  • the consumer priority responsiveness to meta tagging can be aggregated and summarized across all consumers to get the responsiveness to meta tagging values.
  • a set of control loops use metrics 54 to control the flow of information items in the information exchange.
  • the metrics are measures and parameters that may be internal to the information exchange or external to it.
  • Sample internal metrics include, but are not limited to, metrics related to producer, consumer, system information flow, or the information exchange in general.
  • Sample external metrics include, but are not limited to, indications of important sporting events occurring that day, severe weather, day of week, political or business events occurring, measures of news and information flow or activity external to the information exchange, flow activity on external information exchanges, historical projections, statistics, or any other relevant data.
  • One embodiment may have multiple control loops. Another embodiment may have a single control loop. Another embodiment may have no control loops.
  • a decision matrix control loop adjusts the threshold line 71 or the boundary of the include region 72 and exclude region 73 in the decision matrix 70 or decision grid 70d to improve or maintain a set of success metrics.
  • the information item is included in the consumer's information stream.
  • the decision matrix control loop may use the set of success metrics derived from the consumer, the producer, the information item, audience targets, external sources, or from the system in general.
  • the metrics related to the consumer include, but are not limited to, the time processing the information stream, estimate of missed information items, participation metrics, participation rate, average selection criteria priority for the information stream over recent and historical periods, the consumer expected item value, predicted participation rate, predicted participation level, or other consumer metrics.
  • the metrics related to the producer include, but are not limited to, user profile data including producer historical, performance, or behavioral data.
  • the metrics from external sources could include, but are not limited to, indications of important sporting events occurring that day, severe weather, day of week, political or business events occurring, measures of news and information external to the information exchange, or any factor deemed relevant to the prediction of the attention and focus of the consumer. In another embodiment, only some of the metrics may be used or only one metric may be used.
  • the decision matrix control loop may be part of the distribution sub-system 52.
  • a consumer priority may be obtained from the consumer's selection criteria 65 and a producer priority may be obtained from the audience targets 50 for that information item.
  • multiple information items may be processed at one time as a distribution of information items on the decision grid 70d or decision matrix 70 and the include region 72 can be computed to determine which information items may be included in the consumer's information stream.
  • information items may be delayed or queued to be evaluated together as a distribution of information items.
  • an estimated value of the probability the information item will be missed meaning it will be received but is not be processed by the consumer, may be derived from the metrics.
  • a system limit for the probability the information item will be missed may be derived from the metrics.
  • the estimated value of the probability the information item will be missed is greater than the system limit for this estimate the information item is excluded.
  • the consumer item value is greater than the consumer expected item value, the information item is included.
  • the producer limits control loop 46 determines an audience size limit to be placed on the producer's audience targets 50 at a particular priority level.
  • FIG. 9 shows an embodiment where the audience size limit may be represented as an audience size limits mapping 75.
  • the audience size limits mapping 75 can be used to get a priority for a given audience size or to get an audience size for a given priority.
  • the audience size limits mapping 75 can be a function or relation between priority and the audience size limit.
  • the mapping can be continuous, discrete, or mixed.
  • Fig. 9 show the audience size limits mapping 75 as a continuous mapping.
  • the audience size limits mapping 75 may be first determined by the metrics 54.
  • the metrics 54 can include, but is not limited to, the current number of information items flowing through the system, the relative 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 limits mapping 75 may be adjusted using meta data and contents of the information item 24, and may be further adjusted using metrics from the producer's user profile that include, but are not limited to, expertise, background, reputation, number of prior sends by the producer, interaction rates or performance of past information items sent by the producer.
  • the audience size limits mapping 75 may be dynamically determined in real time.
  • a base audience size limits mapping may be set by an administer and the base set of levels may be adjusted or not adjusted in the producer limits control loop 46.
  • max audience size 76 may be determined from the largest limit in the audience size limits mapping 75 with positive priority. In one embodiment, the total audience the producer could reach may not exceed the max audience size 76.
  • Fig. 10 shows one embodiment of the system interface for inputting the audience targets 44.
  • Options 90 allow the producer 22 to create audience target 91, edit audience target 92, reorder audience targets 93, manage audience target archive 94, view audience target details 95, view audience size limits 96, or complete the process when done 97.
  • the producer 22 may interact with the producer limits control loop 46 to first create an audience target or retrieve an audience target from the archive. After the first audience target is entered, the audience target can be evaluated and the audience target details 95 can be viewed and processed by the producer 22.
  • the producer 22 can accept the priority level, reorder the audience targets 93, or edit the audience target 92. If the priority level for the audience target is accepted, the producer has the option to enter an additional audience target. If the producer enters the additional audience target, the process used for the first audience target can be repeated. If the combined audience from all audience targets exceeds the max audience size 76, the producer can reorder the audience targets 93 or edit the audience targets 92.
  • each additional audience target may have lower or higher priority than the first audience target entered depending on the order and audience target action.
  • the audience targets may be evaluated by the order specified by the producer to determine an incremental size for that audience target.
  • the incremental audience size can be the size of the additional audience that can be reached by each subsequent lower ordered audience target.
  • FIG. 9 shows the ordered audience targets as Al - A5.
  • the audiences for targets Al -A5 are evaluated to obtain the incremental size.
  • the incremental size for the audience targets with an include action (A1,A3, and A4 in this example) may be accumulated down the order to get an accumulated size for the audience target.
  • the incremental audience excludes any consumer that would match one of the audience targets of a higher order.
  • the accumulated size for the audience targets with the include action adds the incremental size for that audience target as well.
  • the accumulated size can then be evaluated from the audience size limits map 75 to determine the priority for that audience target.
  • the audience targets Al, A3, and A4 have priorities PI, P3, and P4 assigned respectively.
  • the accumulated size may be used as a lookup size to get the priority from the audience size limits map 75.
  • accumulated size may be adjusted by a fraction of, or all of, the incremental size.
  • the incremental size for the audience targets with an exclude action may be accumulated down the order to get an accumulated size for each audience target with the exclude action.
  • the accumulated size for the audience targets with the exclude action does not add the incremental size for that audience target.
  • the accumulated size can then be evaluated from the audience size limits map 75 to determine the priority for that audience target.
  • the audience targets A2 and A5 have priorities P2 and P5 assigned respectively.
  • the producer may assign any priority to audience targets with an exclude action.
  • the audience targets may be automatically adjusted to improve the audience size while using the audience size limits mapping, by first identifying an audience that would be included at a low priority; second, generating the audience target for the identified audience; third, assigning that audience target a priority directly and placing it in the appropriate audience target order; and fourth, exclude the incremental audience from the accumulated size, but exclude it from the incremental counts of the subsequent targets.
  • audience targets with an include action may be processed provided the priority is > 0.
  • the audience target that may have accumulated sizes that exceed the max audience size 76 by less than the incremental size for the audience target may be limited by the system to a size that will not exceed the max audience size 76.
  • the producer may have the option to refine the audience target that crossed the limit.
  • the audience size for an audience target could be an estimate in one embodiment, or an exact number in another embodiment.
  • the number of audience targets may be limited.
  • the producer enters one or more audience targets.
  • the system then automatically ranks the audience targets by audience size and assigns priorities using the methods above.
  • the producer may specify a priority preference over a range of user profiles as F(X) where X is user profile from a span of user profile characteristics. For example specifying age between 30 and 40 with 30 being the most preferred.
  • F(X) a rank of every potentially targeted user profile is obtained.
  • the mapping between priority in the [0,1] interval and audience size limits mapping 75 is used to assign a priority to each incremental target from highest rank to lowest and stopping when the max audience size 76 or the span of X is reached.
  • the audience size limit mapping 75 may be determined using the reverse cumulative distribution of the consumer population density over the priority on a normalized interval [-1,1] for the information item.
  • the reverse cumulative distribution is the the number of consumers whose selection criteria would register a given priority or higher for the information item.
  • the consumer population density for an information item is the number consumers at each consumer priority level, and this can be obtained by accumulating the counts of consumers at each priority level.
  • the reverse distribution is then obtained from cumulations of the population density starting at the top of the interval [-1,1]. In one embodiment, only the population density and reverse cumulative distribution on the interval [0.1] may be needed.
  • consumers with similar selection criteria may be aggregated in a map reduced representation with the representative selection criteria and the number of consumers.
  • the reverse cumulative distribution may be used directly as the audience limit mapping.
  • the reverse cumulative distribution may be scaled or adjusted before being used as the audience limit mapping.
  • the advantage of using reverse cumulative distribution is that it provides higher limits to the producer 22 that would have a natural consumer priority for the information item 24. For example, a popular merchant providing an information item about a free give away or a popular news organization with exclusive breaking news might have a large number of information consumers 28 who place a high priority on receiving such information items. In such a case the producer may not be required to enter any audience target at all because the default priority level is already sufficiently high.
  • a product vendor with an information item 24 that is a marketing message of value to only a small group of information consumers may have a very restrictive audience limit mapping and may need to input very specific audience targets.
  • the audience limit mappings may be determined from the producer's send history, past activity and keyword or other mechanical analysis of the information item.
  • the parameters of the audience size limits mapping may be determined from the metrics. For example in the continuous case a linear relationship between size and priority could be used and the metrics would determine the slope and intercept of the line. More specifically, in this linear example, with the priority on a [0.1] interval the parameters would be the priority level for the max audience size 76 and at audience size zero.
  • Other mathematical functions, mappings and parameterized relationships can be used also, with the parameters of such functions, mappings, and relationships determined by metrics in a similar manner.
  • the producer request control loops may consider the impact that requesting the producer to provide additional meta data and audience targets has on the number of information items the producer may send or contribute over time as well as the perceived impact of the additions to the information item with the consumer.
  • the producer request control loop may use metrics that include, but are not limited to, the opportunity cost of time for the producer, the time the producer is taking to complete a send, the time to get additional requested data, the availability of additional data, the value of additional meta tagging or refinements, time the producer takes to enter a new audience target, or the time taken to change, rank or prioritize audience targets. Consumer perception, participation, or response metrics may also be used.
  • the producer request control loop may be used for controlling the input of the information item meta data via the meta data request control loop 42. In one embodiment, the producer request control loop may be used for controlling the input of the audience targets 50 through the audience target request control loop 48. In one embodiment, the producer request control loops may supplement the producer limits control loop 46. In another embodiment, the producer request control loops may be an alternative to the producer limits control loop 46.
  • a subject domain for the information item may be determined by automated analysis of the information item as entered by the producer.
  • the subject domains may be used to get a list of meta tagging schema and usage data for the subject domain that was determined.
  • the list of meta tagging schema and usage data may be used to generate the list of meta tagging groups, fields, and values that could be provided to the audience details query method.
  • the responsiveness to meta tagging with audience size increments for the specified meta tagging that may be provided by the audience details query method can be used to get the audience size increments that can be used to select the order that the meta tagging questions are requested.
  • the list of meta tagging schema may be general. In another embodiment, the meta tagging schema may be subject domain specific.
  • the list of meta tagging schema may be selected to complete meta data that could not reliably be provided by the automated analysis, or to confirm known fields, or to confirm the expertise of the producer. This can also be used to limit autonomous and non-autonomous producers.
  • the producer may not see the audience sizes associated with the specified meta tagging.
  • a desired audience distribution may be used to regulate the producer request control loops.
  • the audience distribution can be parameterized by statistical measures or metrics to quantify desirability. For example, to have a distribution over consumer priorities interval that reduces the density at or near zero.
  • the meta data request control loop may use the audience distribution over the consumer priority interval.
  • Using the audience distribution over the consumer priority interval has advantages in that the well know producers with naturally high audiences and receptiveness can avoid extra requirements for meta tagging or audience targets and the associated time burden.
  • either the meta data request control loop or the audience request control loop may use the audience distribution over the two dimensional decision grid or decision matrix, and the desired audience distribution can be over both consumer and producer priorities.
  • the information producer can use either the meta data request control loop or the audience request control loop to reach a the desired audience distribution.
  • any required meta data requests or audience target requests may stop when either the desired audience distribution has been reached or a maximum number of requests to the producer has been made.
  • the subject domain may be compared to the subject domains that can be inferred from the user profile of the producer and the prior information items produced by the producer. If the implied subject domains of the information item do not align with the subject domains of the producer and the history of the producer the producer's history the meta data questions may be asked to confirm the validity of the post and the sender.
  • An administrator of the information exchange can select the control loops to use and set the configurations for the control loops.
  • the success metrics can be targeted to balance the value to the different users of the exchange with the goals of the information exchange stakeholders.
  • the computer system described here is broadly applicable to existing information exchanges or as basis for new information exchanges to optimize and better engage participants.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Accounting & Taxation (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Primary Health Care (AREA)
  • Human Resources & Organizations (AREA)
  • Health & Medical Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computing Systems (AREA)
  • Game Theory and Decision Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un système de commande automatique servant à la régulation de l'échange d'informations entre le producteur d'information et le consommateur d'information. Un mécanisme de commande peut affiner de façon dynamique la décision d'inclure ou d'exclure certains éléments d'information du flux d'informations du consommateur afin d'améliorer les métriques d'évaluation de la réussite, comme la participation. Au moins une interface système demande des mécanismes de commande qui peuvent fournir de façon dynamique une incitation et des limites destinées à l'entrée des cibles d'auditoire, des priorités, des préférences et autres données. Un administrateur peut définir les paramètres et sélectionner les métriques de succès pour établir un équilibre entre les objectifs des participants et des intervenants de l'échange d'informations. Le système peut également servir à résoudre les conflits entre les critères de sélection d'un consommateur et l'auditoire cible d'un producteur.
EP14762314.4A 2013-03-15 2014-03-16 Système servant à la commande et à l'optimisation de la distribution des informations entre utilisateurs dans un échange d'informations Ceased EP2973293A4 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201313726604A 2013-03-15 2013-03-15
US201361852280P 2013-03-15 2013-03-15
PCT/US2014/030113 WO2014145365A2 (fr) 2013-03-15 2014-03-16 Système servant à la commande et à l'optimisation de la distribution des informations entre utilisateurs dans un échange d'informations

Publications (2)

Publication Number Publication Date
EP2973293A2 true EP2973293A2 (fr) 2016-01-20
EP2973293A4 EP2973293A4 (fr) 2016-10-05

Family

ID=57348389

Family Applications (1)

Application Number Title Priority Date Filing Date
EP14762314.4A Ceased EP2973293A4 (fr) 2013-03-15 2014-03-16 Système servant à la commande et à l'optimisation de la distribution des informations entre utilisateurs dans un échange d'informations

Country Status (7)

Country Link
EP (1) EP2973293A4 (fr)
CN (2) CN105229684B (fr)
HK (1) HK1214389A1 (fr)
PH (1) PH12015502098A1 (fr)
SG (1) SG11201507452PA (fr)
WO (1) WO2014145365A2 (fr)
ZA (1) ZA201507465B (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230120633A1 (en) * 2021-10-18 2023-04-20 StageConnect Inc. Composition Generation and Presentation System
CN115085989B (zh) * 2022-06-07 2023-08-01 珠海高凌信息科技股份有限公司 一种基于矩阵的高扩展性拟态大数裁决优化方法

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050234765A1 (en) * 2004-04-15 2005-10-20 Blumberg Marc A Method for evaluating computer-network based marketing tactics
EP1841512A4 (fr) * 2004-12-23 2008-01-23 Massive Inc Mise en correspondance et marquage de publicites pour la distribution ciblee dans une pluralite de jeux video, tels que des jeux a joueur unique ou a plusieurs joueurs
US20060224447A1 (en) * 2005-03-31 2006-10-05 Ross Koningstein Automated offer management using audience segment information
US8571580B2 (en) * 2006-06-01 2013-10-29 Loopt Llc. Displaying the location of individuals on an interactive map display on a mobile communication device
US20080027738A1 (en) * 2006-07-31 2008-01-31 Microsoft Corporation Increasing business value through increased usage and adoption
US9705998B2 (en) * 2007-11-14 2017-07-11 Qualcomm Incorporated Method and system using keyword vectors and associated metrics for learning and prediction of user correlation of targeted content messages in a mobile environment
FR2932297A1 (fr) * 2008-06-09 2009-12-11 Alcatel Lucent Procede de selection d'un message publicitaire au sein d'une plateforme
US20080276270A1 (en) * 2008-06-16 2008-11-06 Chandra Shekar Kotaru System, method, and apparatus for implementing targeted advertising in communication networks
EP2159720A1 (fr) * 2008-08-28 2010-03-03 Bach Technology AS Appareil et procédé de génération d'un profil de collection et de communication basée sur le profil de collection
WO2010146608A1 (fr) * 2009-06-18 2010-12-23 Bellini, Marco Système et procédé de gestion de campagnes de publicité
US20110119278A1 (en) * 2009-08-28 2011-05-19 Resonate Networks, Inc. Method and apparatus for delivering targeted content to website visitors to promote products and brands
US20130024290A1 (en) * 2011-06-17 2013-01-24 Motiva79 Llc Method and apparatus for managing sales promotions and generating customer leads

Also Published As

Publication number Publication date
CN105229684B (zh) 2021-02-19
HK1214389A1 (zh) 2016-07-22
WO2014145365A2 (fr) 2014-09-18
PH12015502098A1 (en) 2016-02-15
WO2014145365A3 (fr) 2014-11-13
EP2973293A4 (fr) 2016-10-05
CN105229684A (zh) 2016-01-06
CN112967068A (zh) 2021-06-15
ZA201507465B (en) 2019-04-24
SG11201507452PA (en) 2015-10-29

Similar Documents

Publication Publication Date Title
US10747836B2 (en) Apparatus and methods for controlling and optimizing information distribution between users in an information exchange
US11288702B1 (en) Exploration in a real time messaging platform
US11409717B1 (en) Overspend control in a messaging platform
US9892431B1 (en) Temporal features in a messaging platform
JP4880962B2 (ja) 広告コンテンツ配信比率算出プログラム、広告コンテンツ配信比率算出方法、広告コンテンツ配信比率算出システム、コンテンツ配信制御システム、広告コンテンツ配信制御システム、広告コンテンツ配信制御方法および広告コンテンツ配信制御プログラム
US10133812B2 (en) System and method for finding and prioritizing content based on user specific interest profiles
Agarwal et al. Click shaping to optimize multiple objectives
US8386308B2 (en) Targeting content creation requests to content contributors
US20170251263A1 (en) Rating videos based on parental feedback
US10769677B1 (en) Temporal features in a messaging platform
CN105431834A (zh) 基于用户历史创建内容共享平台的个性化和连续播放列表
US20120030018A1 (en) Systems And Methods For Managing Electronic Content
US20140280554A1 (en) Method and system for dynamic discovery and adaptive crawling of content from the internet
US20230089961A1 (en) Optimizing content distribution using a model
US20110225239A1 (en) Generation of content creation requests for a content distribution system
CN105229684B (zh) 用于控制和优化信息交换中的用户之间信息分布的系统
CN114186050A (zh) 资源推荐方法、装置、电子设备及存储介质
RU2681698C2 (ru) Система для управления и оптимизации распространения информации между пользователями в системе обмена информацией
CN113886732A (zh) 一种信息推荐方法
Kille et al. News recommendation in real-time
US20180225719A1 (en) System and Methods for Operating an Information Exchange
WO2022147060A1 (fr) Fourniture d'entités sujets dans une plateforme de messagerie
Brown How many viewers does a cable network need? A survival analysis of cable networks
Jambor Goal-driven Collaborative Filtering

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20151012

AK Designated contracting states

Kind code of ref document: A2

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

DAX Request for extension of the european patent (deleted)
REG Reference to a national code

Ref country code: HK

Ref legal event code: DE

Ref document number: 1214389

Country of ref document: HK

A4 Supplementary search report drawn up and despatched

Effective date: 20160905

RIC1 Information provided on ipc code assigned before grant

Ipc: G06Q 30/02 20120101ALI20160830BHEP

Ipc: G06Q 50/00 20120101ALI20160830BHEP

Ipc: G06Q 30/00 20120101AFI20160830BHEP

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20190116

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

APBK Appeal reference recorded

Free format text: ORIGINAL CODE: EPIDOSNREFNE

APBN Date of receipt of notice of appeal recorded

Free format text: ORIGINAL CODE: EPIDOSNNOA2E

APBR Date of receipt of statement of grounds of appeal recorded

Free format text: ORIGINAL CODE: EPIDOSNNOA3E

APAF Appeal reference modified

Free format text: ORIGINAL CODE: EPIDOSCREFNE

APBT Appeal procedure closed

Free format text: ORIGINAL CODE: EPIDOSNNOA9E

REG Reference to a national code

Ref country code: DE

Ref legal event code: R003

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20231222

REG Reference to a national code

Ref country code: HK

Ref legal event code: WD

Ref document number: 1214389

Country of ref document: HK