WO2014035403A1 - Procédé et appareil pour annoter une sortie graphique - Google Patents

Procédé et appareil pour annoter une sortie graphique Download PDF

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Publication number
WO2014035403A1
WO2014035403A1 PCT/US2012/053128 US2012053128W WO2014035403A1 WO 2014035403 A1 WO2014035403 A1 WO 2014035403A1 US 2012053128 W US2012053128 W US 2012053128W WO 2014035403 A1 WO2014035403 A1 WO 2014035403A1
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WIPO (PCT)
Prior art keywords
patterns
data channel
key
data
significant
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PCT/US2012/053128
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English (en)
Inventor
Ehud B. REITER
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Data2Text Limited
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.)
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Publication date
Application filed by Data2Text Limited filed Critical Data2Text Limited
Priority to PCT/US2012/053128 priority Critical patent/WO2014035403A1/fr
Publication of WO2014035403A1 publication Critical patent/WO2014035403A1/fr
Priority to US14/634,035 priority patent/US9405448B2/en
Priority to US15/188,423 priority patent/US10282878B2/en
Priority to US16/367,095 priority patent/US10839580B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/169—Annotation, e.g. comment data or footnotes

Definitions

  • Embodiments of the present invention relate generally to natural language generation technologies and, more particularly, relate to a method, apparatus, and computer program product for textually annotating a graphical output.
  • a natural language generation (NLG) system is configured to transform raw input data that is expressed in a non-linguistic format into a format that can be expressed linguistically, such as through the use of natural language.
  • raw input data may take the form of a value of a stock market index over time and, as such, the raw input data may include data that is suggestive of a time, a duration, a value and/or the like. Therefore, an NLG system may be configured to input the raw input data and output text that linguistically describes the value of the stock market index. For example, "securities markets rose steadily through most of the morning, before sliding downhill late in the day.”
  • Data that is input into a NLG system may be provided in, for example, a recurrent formal structure.
  • the recurrent formal structure may comprise a plurality of individual fields and defined relationships between the plurality of individual fields.
  • the input data may be contained in a spreadsheet or database, presented in a tabulated log message or other defined structure, encoded in a 'knowledge representation' such as the resource description framework (RDF) triples that make up the Semantic Web and/or the like.
  • the data may include numerical content, symbolic content or the like. Symbolic content may include, but is not limited to, alphanumeric and other non- numeric character sequences in any character encoding, used to represent arbitrary elements of information.
  • the output of the NLG system is text in a natural language (e.g. English, Japanese or Swahili), but may also be in the form of synthesized speech.
  • Methods, apparatuses, and computer program products are described herein that are configured to generate a graph that is configured to display one or more key patterns that are detected in a data channel.
  • the graph may also include one or more significant patterns in one or more related channels and/or events.
  • a time period or duration of the data shown in the graph may be selected such that the displayed graph illustrates the portion of the data channel that contains the one or more key patterns.
  • the output graph is further configured to include textual annotations that provide a textual comment, phrase or otherwise is configured to explain, using text, the one or more key patterns, the one or more significant patterns and/or the events in a contextual channel in natural language.
  • the textual annotations are generated from the raw input data and further are designed, in some examples, to textually describe identified patterns, anomalies and/or the context of the graph.
  • a narrative may be included with the graph that provides situational awareness or an overview of the data/patterns displayed on and/or off of the graph.
  • the graph is configured to visually provide situational awareness to a viewer.
  • Figure 1 is a schematic representation of a graphical annotation system that may benefit from some example embodiments of the present invention
  • Figure 2 illustrates an example graphical output in accordance with some example embodiments of the present invention
  • Figure 3 illustrates a block diagram of an apparatus that embodies a graphical annotation system in accordance with some example embodiments of the present invention.
  • Figures 4-6 illustrate flowcharts that may be performed by a graphical annotation system in accordance with some example embodiments of the present invention.
  • graph or graphical output may be construed to comprise a graph that is configured to be displayable in a user interface, but may also describe an input into a graphing system such that a graph may be created for display in the user interface.
  • graph or graphical output may be used interchangeably herein.
  • the apparatus, method and computer program product is configured to generate a graph having a scale (e.g.
  • amplitude (y-axis) and/or time scale (x-axis)) that advantageously displays one or more data channels (e.g. a first or primary data channel, a secondary or related data channel and/or the like) that are derived from raw input data, one or more natural language text annotations and/or a narrative describing raw input data.
  • data channels e.g. a first or primary data channel, a secondary or related data channel and/or the like
  • a user viewing the graph in a user interface or using other viewing means, may be provided with situational awareness with regard to the patterns shown on the graph as well as the events and or patterns that may have influenced the patterns shown on the graph.
  • Situational awareness may be defined as the perception of environmental elements with respect to time and/or space, the comprehension of their meaning, and the projection of their status after some variable has changed, such as time, or based on the happening of an event such as an alarm or alert.
  • situational awareness is a state achieved when information that is qualitatively and quantitatively determined as suitable for particular purpose is made available to a user by engaging them in in an appropriate information exchange pattern or mental model.
  • Situational awareness involves being aware of what is happening in the vicinity of a person or event to understand how information, events, and/or one's own actions may impact goals and objectives, both immediately and in the near future.
  • Situational awareness may also be related to the perception of the environment critical to decision-makers in complex, dynamic areas from aviation, air traffic control, power plant operations, military command and control, engineering, machine monitoring, oil and gas, power plant monitoring, nuclear energy and emergency services such as firefighting and policing. Lacking or inadequate situational awareness has been identified as one of the primary factors in accidents attributed to human error. Accurate mental models are one of the prerequisites for achieving situational awareness. A mental model can be described as a set of well- defined, highly-organized yet dynamic knowledge structures developed over time from experience.
  • a first or primary data channel may be selected for inclusion in a graph based on a selection by a user, via a user interface, may be selected based on the happening of a condition such as, but not limited to, an alert, an alarm, an anomaly, a violation of a constraint, a warning, a predetermined condition and/or the like.
  • the selection of the primary data channel may be determined based on the occurrence and/or detection of a pattern in the primary data channel.
  • a secondary or related data channel may also be selected.
  • the secondary or related data channel may be selected for inclusion in a graph based on the detection of anomalous, unexpected or otherwise flagged behavior in the second or related channel.
  • the second or related channel is compared to one or more patterns in the primary data channel over a similar time period. For example, a first data channel may indicate a rise in heart rate, whereas a second data channel may indicate a stable or even a decline in respiration rate. Generally respiration rate rises with heart rate, and, as such, a stable respiration rate is generally unexpected. In some examples, unexpected behavior may lead to a life threatening condition, be indicative of a dangerous condition or the like.
  • Relationships between data channels may be defined as anomalous behavior by a qualitative model such as a domain model.
  • a domain model is a representation of information about the domain.
  • a domain model may contain an ontology that specifies the kinds of objects and concepts and the like that may exist in the domain in concrete or abstract form, properties that may be predicated of the objects and concepts and the like, relationships that may hold between the objects concepts and the like, and representations of any specific knowledge that is required to function in the domain.
  • multiple domain models may be provided for a single domain.
  • Example domains may include, but are not limited to, medical, oil and gas, industrial, weather, legal, financial and/or the like.
  • a plurality of related channels may be included, for example pulse rate, oxygen levels, blood pressure and/or the like.
  • patterns may be detected or otherwise identified in the primary data channel and/or in the one or more secondary data channels.
  • an importance level or importance is assigned to each of the patterns.
  • an importance level may be defined based on thresholds, constraints, predefined conditions or the like.
  • an importance level may also be assigned based on thresholds, constraints, predefined conditions or the like, however an importance level may also be assigned based on the relationship between the secondary data channels and the primary data channels and/or the relationships between the patterns detected in the primary data channels and the patterns detected in the secondary data channels.
  • a pattern in the primary channel may be defined as a key pattern in an instance in which the importance level of the pattern exceeds or otherwise satisfies a predefined importance level.
  • a significant pattern is a pattern in a secondary data channel that exceeds or otherwise satisfies a predefined importance level.
  • a pattern in the one or more secondary channels may also be classified as a significant pattern if it represents an anomaly or otherwise unexpected behavior when compared with the primary data channel.
  • a contextual channel may also be selected.
  • a contextual channel is a data channel that provides a background or circumstance information that may have caused or otherwise influenced the one or more key patterns and/or the one or more significant patterns (e.g. proximate cause).
  • a contextual channel may indicate an event, such as a medical treatment that was applied at the time of or just prior to the rise of the heartbeat and/or the fall or steady state of the respiration rate.
  • a plurality of data channels may also be selected for inclusion in a graph based on an anomaly or unexpected behavior.
  • one or more data channels may be selected for inclusion in a graph even though the one or more data channels are representative of expected behavior. For example, in the medical domain, a medical professional may expect to see both heart rate and respiration rate on a graph even if both are behaving in expected ways, since expected behavior may be indicative of an important result, namely a clean bill of health.
  • events may also be generated for display in the graph.
  • An event may be described in a contextual channel, may be entered into an event log that is input with the raw input data or may be inferred.
  • caffeine administration may be entered as an explicit event in a patient record (e.g. in an event log), the caffeine could be detected by a change in one or data channels which record what medication is being administered through an IV line and/or the caffeine
  • administration may be inferred based on a spike in heart rate.
  • the event may be displayed as a visual annotation.
  • events may be displayed as a vertical line (see e.g., Fig. 2).
  • events may be generated as a horizontal line with indicators showing the multiple occurrences of an event and/or the like.
  • events may be displayed via text, indicator or other visual outputs.
  • a scale may be selected for the graph based on the primary data channel, the secondary data channel or the like.
  • the scale may be determined based on a time period or duration in which a pattern that satisfies an importance threshold is identified, anomalous behavior occurs in a related data channel and/or the like.
  • the time period may be set by a user, may be a time period that is significant or specifically identified on the basis of properties of the domain, or the like.
  • textual annotations and/or a narrative may be included with the graph.
  • the textual annotations and/or the narrative may be provided by a natural language generation system that is configured to generate one or more textual annotations in the form of sentences or phrases that describe the patterns in the data channels, expected or unexpected behavior, an event, a contextual channel and/or the like.
  • the sentences or phrases may take the form of stand-alone text that provides situational awareness and/or situational analysis of the graph.
  • situation analysis text may be configured to include pattern descriptions that contribute to narrative coherence, background information or the like.
  • the textual annotations may be located on the graph, such as at the location where the anomalies and/or the patterns are represented in the graph.
  • the narrative or situational awareness text may be displayed on or near the graph in some examples.
  • the narrative or situational text may be contained in a separate file or may be generated before/after or otherwise separately from the generation of the graph.
  • the textual annotations and/or narrative may be provided via speech or other available modalities.
  • the systems and methods described herein are configured to generate a graph for display.
  • the graph is configured to display a time scale that contains those identified sections (e.g. key patterns and/or significant patterns) in the one or more data channels, the textual annotations, additional available visual annotations and/or the like.
  • user interaction with the narrative text may result in an annotation on the graphical output to be highlighted.
  • FIG. 1 is an example block diagram of example components of an example graphical annotation environment 100.
  • the graphical annotation environment 100 comprises a data analyzer 102, a data interpreter 104, a graphical annotation engine 106, a natural language generation system 108 and one or more data sources, such as but not limited to, raw input data 1 10, an event log 1 12 and a domain model 1 14.
  • historical data may also be accessed and/or otherwise analyzed.
  • the data analyzer 102, a data interpreter 104, graphical annotation engine 106, a natural language generation system 108 make take the form of, for example, a code module, a component, circuitry or the like.
  • the components of the graphical annotation environment 100 are configured to provide various logic (e.g. code, instructions, functions, routines and/or the like) and/or services related to the generation and/or annotation of a graphical output.
  • the data analyzer 102 is configured to input raw data, such as the raw data contained in the raw input data 1 10.
  • the receipt or input of the raw input data may occur in response to an alarm condition (e.g. an alarm received from a source such as, but not limited to, another system, another monitoring system or the like), a violation of a constraint (e.g. a data value over a threshold, within a threshold for a period of time and/or the like), a user input or the like.
  • an alarm condition e.g. an alarm received from a source such as, but not limited to, another system, another monitoring system or the like
  • a violation of a constraint e.g. a data value over a threshold, within a threshold for a period of time and/or the like
  • the data analyzer 102 may be configured to receive or input raw input data continuously or semi- continuously, such as via a data stream, and determine an importance of the raw input data (e.g., whether the data violates a constraint, satisfies a threshold and/or the like).
  • Raw input data may include data such as, but not limited to, time series data that captures variations across time (e.g. profits, rainfall amounts, temperature or the like), spatial data that indicates variation across location (e.g. rainfall in different regions), or spatial-temporal data that combines both time series data and spatial data (e.g. rainfall across time in different geographical output areas).
  • the raw input data contained or otherwise made accessible by the raw input data 1 10 may be provided in the form of numeric values for specific parameters across time and space, but the raw input data may also contain alphanumeric symbols, such as the RDF notation used in the semantic web, or as the content of database fields.
  • the raw input data 1 10 may be received from a plurality of sources and, as such, data received from each source, sub source or data that is otherwise related may be grouped into or otherwise to referred to as a data channel.
  • the data analyzer 102 is configured to detect patterns and trends in the one or more data channels that are derived from the raw input data to provide a set of abstractions from the raw input data in the data channels.
  • a time-series dataset may contain tens of thousands of individual records describing the temperature at various points on a component piece of machinery over the course of a day with a sample once every two or three seconds.
  • Trend analysis may then be used to identify that the temperature changes in a characteristic way throughout certain parts of the day.
  • trend analysis is configured to abstract those changes into an abstraction that is representative of the change over time.
  • the data analyzer 102 may be configured to fit a piecewise linear model to the data received in the primary data channel, related data channel or the like.
  • the data analyzer 102 is further configured to determine a first or primary data channel.
  • the primary data channel is generally related, for example, to the raw input data and/or the data channel having data values that caused or otherwise related to the alarm condition, a data channel identified by a user action or a data channel that has otherwise been provided to the data analyzer 102.
  • the data analyzer 102 may also be configured to identify data channels that are related to the primary data channel. Alternatively or additionally, relations between data channels may be defined by the domain model 1 14 and input into the data analyzer 102.
  • the data analyzer 102 may then identify trends, spikes, steps or other patterns in the data channels to generate abstractions that summarize the patterns determined in the primary data channel and/or the other related data channels. Alternatively or additionally, the data analyzer 102 may also be configured to perform pattern detection on the raw input data irrespective of data channels or the receipt of an alarm condition.
  • a data interpreter such as data interpreter 104, may then be configured to input the abstractions and determine an importance level and/or relationships between the abstractions identified in the one or more data channels.
  • the data interpreter 104 may access the domain model 1 14 directly or indirectly via the data analyzer 102 or the like.
  • the domain model 1 14 may contain information related to a particular domain or industry.
  • the domain model 1 14 may provide single data channel limits related to normal behaviors in a domain (e.g. normal ranges), information related to anomalous behaviors and/or the like.
  • the domain model 1 14 may describe relationships between various events and/or phenomena in multiple data channels.
  • a domain model may include wind speeds that are related to hurricane type events or temperatures that may cause harm to humans or other animals or may cause damage or interference to shipping. Extreme weather events may be labeled as important, whereas typical temperatures may not be marked as important.
  • the data interpreter 104 may be configured to determine the importance of the one or more detected patterns in the primary data channel, such as by using the domain model 1 14.
  • the data interpreter 104 may assign an importance level based on the pattern itself (e.g. magnitude, duration, rate of change or the like), defined constraints (e.g. defined thresholds or tolerances), temporal
  • a heart rate over 170 beats per minute, or 100 mile per hour winds may be assigned a high level of importance.
  • the patterns and/or the constraints may be defined by the domain model 1 14.
  • the data interpreter 104 may assign certain ones of the patterns as key patterns.
  • a key pattern may be selected based on a pre-determined importance level, such as a threshold defined by a user or a constraint defined by the domain model 1 14.
  • key patterns may be selected based on those patterns in the primary data channel with the highest level of importance, based on the alarm condition and/or the like. For example any wind readings over 50 miles per hour may be designated as key patterns, whereas in other examples only the highest wind reading over a time period may be a determined to be a key pattern.
  • the importance level determination may be performed over a plurality of time scales that may be user defined (e.g., one hour, one day, one week, one month and/or the like).
  • the data interpreter 104 may also be configured to determine the importance of patterns detected in one or more secondary or related data channels.
  • the data interpreter 104 may determine one or more patterns in the related data channels that overlap time-wise or occur within the same time period as the patterns in the primary data channel.
  • the data interpreter 104 may then mark the one or more patterns in the related channels as expected, unexpected or as having or not having some other property using the domain model 1 14.
  • the domain model may suggest that the one or more patterns in the related data channel were expected to rise as they did in the primary channel. By way of example, as winds are rising, a wave height may then be expected to rise. In other cases the behavior of the one or more related channels may be unexpected or may be anomalous when compared to the behavior of the primary data channel.
  • the data interpreter 104 may is configured to instantiate a plurality of messages based on the raw input data derived from the key events, the significant events, the primary data channel, the one or more related data channels, the historical data, the events (e.g. in the event log 1 12), the contextual channel and/or the like. In order to determine the one or more messages, the importance level of each of the messages and relationships between the messages, the data interpreter 104 may be configured to access the domain model 104 directly or indirectly via the data analyzer 102 or the like.
  • messages are language independent data structures that correspond to informational elements in a text and/or collect together underling data in such a way that the underlying data can be linguistically expressed.
  • messages are created based on a requirements analysis as to what is to be
  • a message typically corresponds to a fact about the underlying data (for example, the existence of some observed event) that could be expressed via a simple sentence (although it may ultimately be realized by some other linguistic means).
  • a user may want to know a speed, a direction, a time period or the like, but also the user wants to know changes in speed over time, warm or cold fronts, geographic areas and or the like. In some cases, users do not even want to know wind speed, they simply want an indication of a dangerous wind condition.
  • a message related to wind speed may include fields to be populated by data related to the speed, direction, time period or the like, and may have other fields related to different time points, front information or the like.
  • the mere fact that wind exists may be found in the data, but to linguistically describe "light wind” or “gusts” different data interpretation must be undertaken as is described herein.
  • the one or more patterns may be marked as significant patterns based on the domain model 1 14. For example, patterns in the related data channel that have an importance level above a predetermined threshold defined by the domain model 1 14 may be marked as significant patterns. In some example embodiments, unexpected patterns are also categorized as significant patterns as they are suggestive of a particular condition or fault. Other patterns may be determined to be significant patterns based on one or more constraints on channel value (e.g. expected range of values or the like), data anomalies, patterns marked as neither expected or unexpected that satisfy an importance level, and/or the like.
  • the data interpreter 104 may be configured to determine and/or infer one or more events from the one or more data channels. Events may include specific activities that may influence the one or more key patterns and/or may have caused the one or more significant patterns. In some examples, the one or more events may be inferred based in context with the one or more patterns in the primary data channel. Alternatively or additionally events may be provided as a separate channel, such as a contextual channel, in the raw input data 1 10 or may be provided directly, such as in an event log 1 12, to the data interpreter 104.
  • the one or more key patterns and the one or more significant patterns may be input into the graphical annotation engine 106 and the natural language generation system 108 to enable the generation of a graphical output and/or natural language annotations.
  • the graphical annotation engine 106 is configured to generate a graphical output having one or more textual annotations, such as the graphical output displayed with reference to Figure 2.
  • the graphical output and the one or more textual annotations are configured to be generated by one or more of a scale determination engine 120, an annotation location determiner 122 and a graphical output generator 124.
  • the scale determination engine 120 is configured to determine a time scale (e.g. x-axis) to be used in the graphical output.
  • the scale determination engine 120 may determine a time period that captures or otherwise includes one or more of the key patterns.
  • the time period may be chosen based on the highest number of key patterns, whereas in other embodiments the time scale chosen may include each of the one or more key patterns and/or each of the one or more significant patterns.
  • the scale determination engine 120 may also determine a scale for the amplitude or y-axis of the graph.
  • An annotation location determiner 122 is configured to place one or more annotations, such as textual or visual annotations, on a graphical output produced by the graphical output generator 124.
  • natural language annotations may be generated, such as by the natural language generation system 108, to explain or otherwise describe the one or more key patterns in the primary data channel.
  • natural language annotations may also be generated to explain the one or more significant patterns in the related data channels.
  • the annotation location determiner 122 is further configured to place an annotation on the graphical output in the proximity of the key pattern or the significant pattern.
  • the annotations may otherwise be linked to the graphical output by using reference lines, highlights or other visual indications on or around the graphical output.
  • a natural language generation system such as natural language generation system 108
  • Other linguistic constructs may be generated in some example embodiments.
  • the natural language generation system 108 comprises a document planner 130, a microplanner 132 and/or a realizer 134.
  • Other natural language generation systems may be used in some example embodiments, such as a natural language generation system as described in Building Natural Language Generation Systems by Ehud Reiter and Robert Dale, Cambridge University Press (2000), which is incorporated by reference in its entirety herein.
  • the document planner 130 is configured to input the one or more patterns from the data interpreter in the form of messages and determine how to use those messages to describe the patterns in the one or more data channels derived from the raw input data.
  • the document planner 130 may comprise a content determination process that is configured to select the messages, such as the messages that describe the key patterns and/or the significant patterns, that are be displayed in the graphical output by the graphical annotation engine 106.
  • the content determination process may be related to or otherwise limited by the scale determined by the scale determination engine 120.
  • the document planner 130 may also comprise a structuring process that determines the order of messages referring to the key patterns and/or significant patterns to be included in a narrative and/or the natural language annotations.
  • the document planner 130 may access one or more text schemas for the purposes of content determination and document structuring.
  • a text schema is a rule set that defines the order in which a number of messages are to be presented in a document. For example, an event (e.g. medication injection) may be described prior to a key pattern (e.g. rise in heart rate). In other examples, a significant pattern (e.g. falling or steady respiratory rate) may be described after, but in relation to, a description of the key pattern (e.g. rise in heart rate).
  • the output of the document planner 130 may be a tree-structured object or other data structure that is referred to as a document plan. In an instance in which a tree-structured object is chosen for the document plan, the leaf nodes of the tree may contain the messages, and the
  • intermediate nodes of the tree structure object may be configured to indicate how the subordinate nodes are related to each other.
  • the microplanner 132 is configured to modify the document plan from the document planner 130, such that the document plan may be expressed in natural language. In some example embodiments, the microplanner 132 may perform
  • aggregation includes, but is not limited to, determining whether two or more messages can be combined together linguistically to produce a more complex sentence. For example, one or more key patterns may be aggregated so that both of the key patterns can be described by a single sentence. Alternatively or additionally, aggregation may not be performed in some instances so as to enable stand-alone interpretation if a portion of the natural language text is shown as an annotation independently on a graphical output.
  • lexicalization includes, but is not limited to, choosing particular words for the expression of concepts and relations.
  • referring expression generation includes, but is not limited to, choosing how to refer to an entity so that it can be unambiguously identified by the reader.
  • the output of the microplanner 132 in some example embodiments, is a tree-structured realization specification whose leaf- nodes are sentence plans, and whose internal nodes express rhetorical relations between the leaf nodes.
  • the realizer 134 is configured to traverse the tree-structured realization specification to express the tree-structured realization specification in natural language.
  • the realization process that is applied to each sentence plan makes use of a grammar which specifies the valid syntactic structures in the language and further provides a way of mapping from sentence plans into the corresponding natural language sentences.
  • the output of the process is, in some example embodiments, a well-formed natural language text.
  • the natural language text may include embedded mark-up.
  • the output of the realizer 134 in some example embodiments, is the natural language annotations that are configured to be on or in proximity to a graphical output.
  • the realizer may also output situational analysis text or a narrative that is configured to describe or otherwise summarize the one or more key patterns, the one or more significant patterns, the one or more contextual channels, and/or the one or more events to be displayed in the graphical output.
  • the natural language annotations and/or the narrative may describe data that is not included on the graph to provide additional situational awareness.
  • the graphical output generator 124 is configured to generate a graphical output within the determined scale.
  • the graphical output includes the raw input data for the primary data channel and/or any related data channels within the determined scale.
  • the graphical output generator 124 is further configured to display the natural language annotations on the graphical output as well as a narrative that describes the data channels displayed in the graphical output.
  • the natural language annotations may be interactively linked to the graphical output. For example, phrases within the narrative may be underlined or otherwise highlighted such that in an instance in which the underlined or otherwise highlighted phrases are selected, a natural language annotation may be shown or otherwise emphasized on the graphical output.
  • the graphical output generator in conjunction with a user interface may underline or otherwise highlight a corresponding phrase in the narrative.
  • other visualizations may be provided by the graphical output generator 124 in conjunction with or in replacement of the graph or graphical output, such as, but not limited to, a visual image, a video, a chart and/or the like.
  • Figure 2 illustrates an example graphical output having multiple data channels in accordance with some example embodiments of the present invention.
  • Figure 2 provides a graphical output that visually represents the behavior of heart rate and respiration rate in response to an application of caffeine over a period of time.
  • the following example table (e.g. raw input data) illustrates a primary data channel (e.g. heart rate) and a related data channel (e.g. respiration rate):
  • a data analyzer 102, a data interpreter 104 and/or the like may receive an indication of an alarm condition, may determine that such a spike is representative of an alarm condition, may receive an indication by the user or the like.
  • the data analyzer 102, the data interpreter 104 or the like may cause the heart rate data channel to be selected as the primary data channel.
  • a user, domain knowledge or the like may indicate that the heart rate channel is selected to be the primary data channel.
  • a primary data channel in this case heart rate
  • the data analyzer 102, a data interpreter 104 and/or the like may determine whether one or more key patterns are present in the primary data channel 212.
  • a key pattern may be the rapid change of heart rate between time point 10 and time point 1 1 , but it may also include the rise and fall (e.g. spike) of the heart rate between time points 10 and 19, with the high point being at time point 13.
  • the data interpreter 104 may then determine whether there is a secondary or related data channel that contains a significant pattern, such as secondary channel 216 (e.g. respiration rate), that has a significant pattern (e.g. no change when a change is generally expected) in a corresponding time period.
  • a significant pattern such as secondary channel 216 (e.g. respiration rate)
  • secondary channel 216 e.g. respiration rate
  • a significant pattern e.g. no change when a change is generally expected
  • corresponding time period may be the same time period or may be a later time period when compared to the time period of the key patterns. Further, the corresponding time period may, in some examples, be defined by a domain model, such as domain model 1 14.
  • the data analyzer 102, a data interpreter 104 and/or the like may access data related to the respiration rate of the patient during the same or similar time period. Upon reviewing the corresponding data, the data analyzer 102, the data interpreter 104 and/or the like may determine that the secondary channel 216 was relatively flat and, based on the domain model, such a behavior was unexpected. As described herein, unexpected behavior in a related data channel is a significant pattern.
  • Figure 2 provides a key pattern, namely the heart rate change between time periods 10 and 19 and a significant pattern represented by the relatively steady respiration rate over the same time period.
  • Figure 2 further comprises an event 220 that was derived from a contextual channel, an event log or the like. The event 220 corresponds to the application of caffeine in this example.
  • the natural language generation system 108 may input the primary data channel, the secondary data channel, the contextual channel, events, and/or the like. As such, the natural language generation system 108 may be configured to generate a narrative, such as narrative 224, and one or more textual annotations, such as textual annotations 214, 218 and 222. In some examples, the textual annotations are configured to describe the one or more key patterns, the one or more significant patterns, the one or more events, the contextual channel or the like.
  • the graphical annotation engine 106 may input the key pattern and the significant pattern and may determine a time scale for the graph, such as by the scale determination engine 120.
  • the scale chosen is configured to highlight the key pattern, the significant pattern and the caffeine event.
  • the primary and secondary data channels are represented graphically by the graphical output generator 124.
  • the annotation location determiner 122 may locate the key pattern, significant pattern and/or event on the graph and then may assign a location of a textual annotation in a nearby area, such as is shown in Figure 2.
  • One or more text annotations, such as textual annotations 214, 218 and 222 may then be added to the graph in conjunction with a narrative 224.
  • Figure 3 is an example block diagram of an example computing device for practicing embodiments of an example graphical annotation environment.
  • Figure 3 shows a computing system 300 that may be utilized to implement a graphical annotation environment 100 having a data analyzer 102, a data interpreter 104, a graphical annotation engine 106, a natural language generation system 108 and/or a user interface 310.
  • One or more general purpose or special purpose computing may be utilized to implement a graphical annotation environment 100 having a data analyzer 102, a data interpreter 104, a graphical annotation engine 106, a natural language generation system 108 and/or a user interface 310.
  • One or more general purpose or special purpose computing may be utilized to implement a graphical annotation environment 100 having a data analyzer 102, a data interpreter 104, a graphical annotation engine 106, a natural language generation system 108 and/or a user interface 310.
  • One or more general purpose or special purpose computing may be utilized to implement a graphical annotation environment 100 having a data analyzer
  • the computing system 300 may comprise one or more distinct computing systems/devices and may span distributed locations.
  • the natural language generation system 108 may be accessible remotely via the network 350.
  • one or more of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be configured to operate remotely.
  • a pre-processing module or other module that requires heavy computational load may be configured to perform that computational load and thus may be on a remote device or server.
  • the data analyzer 102 and/or the data interpreter 104 may be accessed remotely.
  • each block shown may represent one or more such blocks as appropriate to a specific example embodiment. In some cases one or more of the blocks may be combined with other blocks.
  • the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be implemented in software, hardware, firmware, or in some combination to achieve the capabilities described herein.
  • computing system 300 comprises a computer memory (“memory”) 301 , a display 302, one or more processors 303, input/output devices 304 (e.g., keyboard, mouse, CRT or LCD display, touch screen, gesture sensing device and/or the like), other computer-readable media 305, and communications interface 306.
  • the processor 303 may, for example, be embodied as various means including one or more microprocessors with accompanying digital signal processor(s), one or more processor(s) without an accompanying digital signal processor, one or more coprocessors, one or more multi-core processors, one or more controllers, processing circuitry, one or more computers, various other processing elements including integrated circuits such as, for example, an application-specific integrated circuit (ASIC) or field- programmable gate array (FPGA), or some combination thereof. Accordingly, although illustrated in Figure 3 as a single processor, in some embodiments the processor 303 comprises a plurality of processors. The plurality of processors may be in operative communication with each other and may be collectively configured to perform one or more functionalities of the graphical annotation environment as described herein.
  • ASIC application-specific integrated circuit
  • FPGA field- programmable gate array
  • the data analyzer 102 the data interpreter 104, the graphical annotation engine
  • the memory 301 may comprise, for example, transitory and/or non-transitory memory, such as volatile memory, non-volatile memory, or some combination thereof. Although illustrated in Figure 3 as a single memory, the memory 301 may comprise a plurality of memories. The plurality of memories may be embodied on a single computing device or may be distributed across a plurality of computing devices collectively configured to function as the graphical annotation environment.
  • the memory 301 may comprise, for example, a hard disk, random access memory, cache memory, flash memory, a compact disc read only memory (CD- ROM), digital versatile disc read only memory (DVD-ROM), an optical disc, circuitry configured to store information, or some combination thereof.
  • a hard disk random access memory
  • cache memory flash memory
  • CD- ROM compact disc read only memory
  • DVD-ROM digital versatile disc read only memory
  • an optical disc circuitry configured to store information, or some combination thereof.
  • components of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be stored on and/or transmitted over the other computer-readable media 305.
  • the components of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 preferably execute on one or more processors 303 and are configured to generate graphical annotations, as described herein.
  • code or programs 330 e.g., an administrative interface, a Web server, and the like
  • data repositories such as data repository 340
  • code or programs 330 also reside in the memory 301 , and preferably execute on one or more processors 303.
  • code or programs 330 e.g., an administrative interface, a Web server, and the like
  • data repositories such as data repository 340
  • one or more of the components in Figure 3 may not be present in any specific implementation. For example, some embodiments may not provide other computer readable media 305 or a display 302.
  • the graphical annotation engine 106 may comprise a scale determination engine 120, an annotation location determiner 122, a graphical output generator 124 or the like.
  • the natural language generation system 108 may comprise a document planner 130, a microplanner 132, a realizer 134 and/or the like.
  • the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may also include or otherwise be in data communication with raw input data 1 10, event log 1 12 and/or the domain model 1 14.
  • the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 are further configured to provide functions such as those described with reference to Figure 1.
  • the data analyzer 102 the data interpreter 104, the graphical annotation engine
  • the natural language generation system 108 and/or the user interface 310 may interact with the network 350, via the communications interface 306, with remote data sources 356 (e.g. remote reference data, remote performance data, remote aggregation data and/or the like), third-party content providers 354 and/or client devices 358.
  • the network 350 may be any combination of media (e.g., twisted pair, coaxial, fiber optic, radio frequency), hardware (e.g., routers, switches, repeaters, transceivers), and protocols (e.g., TCP/IP, UDP, Ethernet, Wi-Fi, WiMAX, Bluetooth) that facilitate communication between remotely situated humans and/or devices.
  • the network 350 may take the form of the internet or may be embodied by a cellular network such as an LTE based network.
  • the communications interface 306 may be capable of operating with one or more air interface standards, communication protocols, modulation types, access types, and/or the like.
  • the client devices 358 include desktop computing systems, notebook computers, mobile phones, smart phones, personal digital assistants, tablets and/or the like.
  • components/modules of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 are implemented using standard programming techniques.
  • the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be implemented as a "native" executable running on the processor 303, along with one or more static or dynamic libraries.
  • the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be
  • a range of programming languages known in the art may be employed for implementing such example embodiments, including representative implementations of various programming language paradigms, including but not limited to, object-oriented (e.g., Java, C++, C#, Visual Basic.NET, Smalltalk, and the like), functional (e.g., ML, Lisp, Scheme, and the like), procedural (e.g., C, Pascal, Ada, Modula, and the like), scripting (e.g., Perl, Ruby, Python, JavaScript, VBScript, and the like), and declarative (e.g., SQL, Prolog, and the like).
  • object-oriented e.g., Java, C++, C#, Visual Basic.NET, Smalltalk, and the like
  • functional e.g., ML, Lisp, Scheme, and the like
  • procedural e.g., C, Pascal, Ada, Modula, and the like
  • scripting e.g., Perl, Ruby, Python, JavaScript, VBScript, and
  • the embodiments described above may also use synchronous or asynchronous client-server computing techniques.
  • the various components may be implemented using more monolithic programming techniques, for example, as an executable running on a single processor computer system, or alternatively decomposed using a variety of structuring techniques, including but not limited to, multiprogramming, multithreading, client-server, or peer-to-peer, running on one or more computer systems each having one or more processors.
  • Some embodiments may execute concurrently and asynchronously, and communicate using message passing techniques. Equivalent synchronous embodiments are also supported.
  • other functions could be implemented and/or performed by each component/module, and in different orders, and by different components/modules, yet still achieve the described functions.
  • programming interfaces to the data stored as part of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 can be made available by mechanisms such as through application programming interfaces (API) (e.g. C, C++, C#, and Java); libraries for accessing files, databases, or other data repositories; through scripting languages such as XML; or through Web servers, FTP servers, or other types of servers providing access to stored data.
  • API application programming interfaces
  • the raw input data 1 10, the event log 1 12 and the domain model 1 14 may be implemented as one or more database systems, file systems, or any other technique for storing such information, or any combination of the above, including implementations using distributed computing techniques.
  • the raw input data 1 10, the event log 1 12 and the domain model 1 14 may be local data stores but may also be configured to access data from the remote data sources 356.
  • some or all of the components of the data analyzer 102, the data interpreter 104, the graphical annotation engine 106, the natural language generation system 108 and/or the user interface 310 may be implemented or provided in other manners, such as at least partially in firmware and/or hardware, including, but not limited to one or more ASICs, standard integrated circuits, controllers executing appropriate instructions, and including microcontrollers and/or embedded controllers, FPGAs, complex programmable logic devices (“CPLDs”), and the like.
  • firmware and/or hardware including, but not limited to one or more ASICs, standard integrated circuits, controllers executing appropriate instructions, and including microcontrollers and/or embedded controllers, FPGAs, complex programmable logic devices (“CPLDs”), and the like.
  • system components and/or data structures may also be stored as contents (e.g., as executable or other machine-readable software instructions or structured data) on a computer-readable medium so as to enable or configure the computer-readable medium and/or one or more associated computing systems or devices to execute or otherwise use or provide the contents to perform at least some of the described techniques.
  • contents e.g., as executable or other machine-readable software instructions or structured data
  • system components and data structures may also be stored as data signals (e.g., by being encoded as part of a carrier wave or included as part of an analog or digital propagated signal) on a variety of computer-readable transmission mediums, which are then transmitted, including across wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames).
  • data signals e.g., by being encoded as part of a carrier wave or included as part of an analog or digital propagated signal
  • computer-readable transmission mediums which are then transmitted, including across wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames).
  • Such computer program products may also take other forms in other embodiments.
  • FIGS 4-6 illustrate example flowcharts of the operations performed by an apparatus, such as computing system 300 of Figure 3, in accordance with example embodiments of the present invention.
  • an apparatus such as computing system 300 of Figure 3
  • FIGS. 4-6 illustrate example flowcharts of the operations performed by an apparatus, such as computing system 300 of Figure 3, in accordance with example embodiments of the present invention.
  • each block of the flowcharts, and combinations of blocks in the flowcharts may be implemented by various means, such as hardware, firmware, one or more processors, circuitry and/or other devices associated with execution of software including one or more computer program instructions.
  • one or more of the procedures described above may be embodied by computer program instructions.
  • the computer program instructions which embody the procedures described above may be stored by a memory 301 of an apparatus employing an embodiment of the present invention and executed by a processor 303 in the apparatus.
  • any such computer program instructions may be loaded onto a computer or other programmable apparatus (e.g., hardware) to produce a machine, such that the resulting computer or other programmable apparatus provides for implementation of the functions specified in the flowcharts' block(s).
  • These computer program instructions may also be stored in a non-transitory computer-readable storage memory that may direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage memory produce an article of manufacture, the execution of which implements the function specified in the flowcharts' block(s).
  • the computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowcharts' block(s).
  • the operations of Figures 4-6 when executed, convert a computer or processing circuitry into a particular machine configured to perform an example embodiment of the present invention.
  • the operations of Figures 4-6 define an algorithm for configuring a computer or processor, to perform an example embodiment.
  • a general purpose computer may be provided with an instance of the processor which performs the algorithm of Figures 4-6 to transform the general purpose computer into a particular machine configured to perform an example embodiment.
  • blocks of the flowchart support combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will also be understood that one or more blocks of the flowcharts', and combinations of blocks in the flowchart, can be implemented by special purpose hardware-based computer systems which perform the specified functions, or
  • certain ones of the operations herein may be modified or further amplified as described below. Moreover, in some embodiments additional optional operations may also be included (some examples of which are shown in dashed lines in Figure 4). It should be appreciated that each of the modifications, optional additions or amplifications described herein may be included with the operations herein either alone or in combination with any others among the features described herein.
  • Figure 4 is a flow chart illustrating an example method for generating graphical annotations.
  • an apparatus may include means, such as the data analyzer 102, the graphical annotation engine 106, the processor 303, or the like, for receiving an indication of an alarm condition.
  • an alarm may cause the selection of a primary data channel and a determination of a time period in which the alarm was generated.
  • other means may be used to alert the apparatus to a primary data channel, such as, but not limited to, a user action, a detected pattern in the raw input data or a data channel, a determined value in the raw input data or a data channel, and/or the like.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for determining one or more key patterns in a primary data channel.
  • the key patterns may be determined based on the time period of the alarm condition, however in other examples a larger or smaller time period may be selected. The determination of the one or more key patterns is further described with reference to Figure 5.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for determining one or more significant patterns in one or more related data channels.
  • the apparatus such as via the data analyzer 102 may determine one or related channels based on one or more predefined relationships.
  • the predefined relationships may be defined by the domain model 1 14. The determination of the one or more significant patterns is further described with reference to Figure 6.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for determining one or more contextual channels to be included in the graphical output.
  • the one or more contextual channels may provide events or other context that may be indicative of the cause of the one or more key patterns and/or the one or more significant patterns.
  • an apparatus may include means, such as the graphical annotation engine 106, the scale determination engine 120, the processor 303, or the like, for determining a time period to be represented by the graphical output.
  • the time period chosen for the graph is the time period in which the one or more key patterns are displayed.
  • an apparatus may include means, such as the natural language generation system 108, the document planner 130, the microplanner 132, the realizer 134, the processor 303, or the like, for generating a natural language annotation of at least one of the one or more key patterns or the one or more significant patterns.
  • an apparatus may include means, such as the graphical annotation engine 106, the annotation location determiner 122, the graphical output generator 124, the processor 303, the user interface 310 or the like, for generating a graphical output that is configured to be displayed in a user interface.
  • the graph is configured to utilize the determined scale to display the primary data channel, one or more related channels having significant events, natural language annotations, a narrative, events and/or the like.
  • a user clicks on a text annotation in the graph a
  • corresponding phrase in the situation analysis text may be highlighted and/or in an instance in which a user clicks on underlined phrase in the narrative or situation analysis text, a corresponding annotation may be highlighted on the graph.
  • Figure 5 is a flow chart illustrating an example method determining one or more key patterns in a primary data channel.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for identifying one or more patterns wherein a pattern is at least one of a trend, spike or step in the data channel.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for assigning an importance level to the one or more patterns.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for identifying one or more key patterns of the one or more patterns, wherein a key pattern is a pattern that exceeds a predefined importance level.
  • Figure 6 is a flow chart illustrating an example method determining one or more significant patterns in a related data channel.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for identifying one or more unexpected patterns in a related data channel in response to detecting one or more patterns in the data channel.
  • the one or more patterns identified in an another data channel violate a predetermined constraint, threshold or the like may be considered as
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for assigning an importance level to the one or more unexpected patterns.
  • an apparatus may include means, such as the data analyzer 102, the data interpreter 104, the processor 303, or the like, for identifying one or more significant patterns of the one or more unexpected patterns, wherein a significant pattern is an unexpected pattern.

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Abstract

La présente invention concerne divers procédés pour générer et annoter un graphe. Un procédé donné à titre d'exemple peut comprendre la détermination d'un ou de plusieurs motifs clés dans un canal de données primaire, le canal de données primaire étant déduit à partir de données d'entrée brutes en réponse au fait qu'une contrainte a été satisfaite. Un procédé peut comprendre en outre la détermination d'un ou de plusieurs motifs significatifs dans un ou plusieurs canaux de données connexes. Un procédé peut comprendre en outre la génération d'une annotation en langage naturel pour le ou les motifs clés et/ou le ou les motifs significatifs. Un procédé peut comprendre en outre la génération d'un graphe qui est configuré pour être affiché sur une interface utilisateur, le graphe comprenant au moins une partie du ou des motifs clés, du ou des motifs significatifs et l'annotation en langage naturel.
PCT/US2012/053128 2012-08-30 2012-08-30 Procédé et appareil pour annoter une sortie graphique WO2014035403A1 (fr)

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US14/634,035 US9405448B2 (en) 2012-08-30 2015-02-27 Method and apparatus for annotating a graphical output
US15/188,423 US10282878B2 (en) 2012-08-30 2016-06-21 Method and apparatus for annotating a graphical output
US16/367,095 US10839580B2 (en) 2012-08-30 2019-03-27 Method and apparatus for annotating a graphical output

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Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9576009B1 (en) 2011-01-07 2017-02-21 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9697492B1 (en) 2011-01-07 2017-07-04 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9697197B1 (en) 2011-01-07 2017-07-04 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US10572606B1 (en) 2017-02-17 2020-02-25 Narrative Science Inc. Applied artificial intelligence technology for runtime computation of story outlines to support natural language generation (NLG)
US10699079B1 (en) 2017-02-17 2020-06-30 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on analysis communication goals
US10706236B1 (en) 2018-06-28 2020-07-07 Narrative Science Inc. Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system
US10747823B1 (en) 2014-10-22 2020-08-18 Narrative Science Inc. Interactive and conversational data exploration
US10755046B1 (en) 2018-02-19 2020-08-25 Narrative Science Inc. Applied artificial intelligence technology for conversational inferencing
US10853583B1 (en) 2016-08-31 2020-12-01 Narrative Science Inc. Applied artificial intelligence technology for selective control over narrative generation from visualizations of data
US10943069B1 (en) 2017-02-17 2021-03-09 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on a conditional outcome framework
US10963649B1 (en) 2018-01-17 2021-03-30 Narrative Science Inc. Applied artificial intelligence technology for narrative generation using an invocable analysis service and configuration-driven analytics
US10990767B1 (en) 2019-01-28 2021-04-27 Narrative Science Inc. Applied artificial intelligence technology for adaptive natural language understanding
US11042709B1 (en) 2018-01-02 2021-06-22 Narrative Science Inc. Context saliency-based deictic parser for natural language processing
US11068661B1 (en) 2017-02-17 2021-07-20 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on smart attributes
US11170038B1 (en) 2015-11-02 2021-11-09 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from multiple visualizations
US11222184B1 (en) 2015-11-02 2022-01-11 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from bar charts
US11232268B1 (en) 2015-11-02 2022-01-25 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from line charts
US11238090B1 (en) 2015-11-02 2022-02-01 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from visualization data
US11288328B2 (en) 2014-10-22 2022-03-29 Narrative Science Inc. Interactive and conversational data exploration
US11561684B1 (en) 2013-03-15 2023-01-24 Narrative Science Inc. Method and system for configuring automatic generation of narratives from data
US11568148B1 (en) 2017-02-17 2023-01-31 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on explanation communication goals
US11922344B2 (en) 2014-10-22 2024-03-05 Narrative Science Llc Automatic generation of narratives from data using communication goals and narrative analytics
US11954445B2 (en) 2017-02-17 2024-04-09 Narrative Science Llc Applied artificial intelligence technology for narrative generation based on explanation communication goals

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030131315A1 (en) * 2001-12-17 2003-07-10 Escher Richard E.A. Method for chart markup and annotation in technical analysis
US20050256703A1 (en) * 2004-05-14 2005-11-17 Markel Steven O System and method for analyzing a waveform to detect specified patterns
WO2006010044A2 (fr) * 2004-07-09 2006-01-26 Gapyx, Llc Systeme et procede pour finance comportementale
US20070129942A1 (en) * 2005-12-01 2007-06-07 Ban Oliver K Visualization and annotation of the content of a recorded business meeting via a computer display

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030131315A1 (en) * 2001-12-17 2003-07-10 Escher Richard E.A. Method for chart markup and annotation in technical analysis
US20050256703A1 (en) * 2004-05-14 2005-11-17 Markel Steven O System and method for analyzing a waveform to detect specified patterns
WO2006010044A2 (fr) * 2004-07-09 2006-01-26 Gapyx, Llc Systeme et procede pour finance comportementale
US20070129942A1 (en) * 2005-12-01 2007-06-07 Ban Oliver K Visualization and annotation of the content of a recorded business meeting via a computer display

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"Building Natural Language Generation Systems by Ehud Reiter and Robert Dale", 2000, CAMBRIDGE UNIVERSITY PRESS

Cited By (53)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10755042B2 (en) 2011-01-07 2020-08-25 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9697492B1 (en) 2011-01-07 2017-07-04 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9697197B1 (en) 2011-01-07 2017-07-04 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9720899B1 (en) 2011-01-07 2017-08-01 Narrative Science, Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9977773B1 (en) 2011-01-07 2018-05-22 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US9576009B1 (en) 2011-01-07 2017-02-21 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US11501220B2 (en) 2011-01-07 2022-11-15 Narrative Science Inc. Automatic generation of narratives from data using communication goals and narrative analytics
US11921985B2 (en) 2013-03-15 2024-03-05 Narrative Science Llc Method and system for configuring automatic generation of narratives from data
US11561684B1 (en) 2013-03-15 2023-01-24 Narrative Science Inc. Method and system for configuring automatic generation of narratives from data
US11922344B2 (en) 2014-10-22 2024-03-05 Narrative Science Llc Automatic generation of narratives from data using communication goals and narrative analytics
US11475076B2 (en) 2014-10-22 2022-10-18 Narrative Science Inc. Interactive and conversational data exploration
US10747823B1 (en) 2014-10-22 2020-08-18 Narrative Science Inc. Interactive and conversational data exploration
US11288328B2 (en) 2014-10-22 2022-03-29 Narrative Science Inc. Interactive and conversational data exploration
US11238090B1 (en) 2015-11-02 2022-02-01 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from visualization data
US11232268B1 (en) 2015-11-02 2022-01-25 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from line charts
US11222184B1 (en) 2015-11-02 2022-01-11 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from bar charts
US11188588B1 (en) 2015-11-02 2021-11-30 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to interactively generate narratives from visualization data
US11170038B1 (en) 2015-11-02 2021-11-09 Narrative Science Inc. Applied artificial intelligence technology for using narrative analytics to automatically generate narratives from multiple visualizations
US11341338B1 (en) 2016-08-31 2022-05-24 Narrative Science Inc. Applied artificial intelligence technology for interactively using narrative analytics to focus and control visualizations of data
US11144838B1 (en) 2016-08-31 2021-10-12 Narrative Science Inc. Applied artificial intelligence technology for evaluating drivers of data presented in visualizations
US10853583B1 (en) 2016-08-31 2020-12-01 Narrative Science Inc. Applied artificial intelligence technology for selective control over narrative generation from visualizations of data
US10755053B1 (en) 2017-02-17 2020-08-25 Narrative Science Inc. Applied artificial intelligence technology for story outline formation using composable communication goals to support natural language generation (NLG)
US11568148B1 (en) 2017-02-17 2023-01-31 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on explanation communication goals
US10713442B1 (en) 2017-02-17 2020-07-14 Narrative Science Inc. Applied artificial intelligence technology for interactive story editing to support natural language generation (NLG)
US11983503B2 (en) 2017-02-17 2024-05-14 Salesforce, Inc. Applied artificial intelligence technology for narrative generation based on a conditional outcome framework
US10762304B1 (en) 2017-02-17 2020-09-01 Narrative Science Applied artificial intelligence technology for performing natural language generation (NLG) using composable communication goals and ontologies to generate narrative stories
US11068661B1 (en) 2017-02-17 2021-07-20 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on smart attributes
US11954445B2 (en) 2017-02-17 2024-04-09 Narrative Science Llc Applied artificial intelligence technology for narrative generation based on explanation communication goals
US10572606B1 (en) 2017-02-17 2020-02-25 Narrative Science Inc. Applied artificial intelligence technology for runtime computation of story outlines to support natural language generation (NLG)
US10585983B1 (en) 2017-02-17 2020-03-10 Narrative Science Inc. Applied artificial intelligence technology for determining and mapping data requirements for narrative stories to support natural language generation (NLG) using composable communication goals
US10943069B1 (en) 2017-02-17 2021-03-09 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on a conditional outcome framework
US10719542B1 (en) 2017-02-17 2020-07-21 Narrative Science Inc. Applied artificial intelligence technology for ontology building to support natural language generation (NLG) using composable communication goals
US11562146B2 (en) 2017-02-17 2023-01-24 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on a conditional outcome framework
US10699079B1 (en) 2017-02-17 2020-06-30 Narrative Science Inc. Applied artificial intelligence technology for narrative generation based on analysis communication goals
US11816438B2 (en) 2018-01-02 2023-11-14 Narrative Science Inc. Context saliency-based deictic parser for natural language processing
US11042708B1 (en) 2018-01-02 2021-06-22 Narrative Science Inc. Context saliency-based deictic parser for natural language generation
US11042709B1 (en) 2018-01-02 2021-06-22 Narrative Science Inc. Context saliency-based deictic parser for natural language processing
US11003866B1 (en) 2018-01-17 2021-05-11 Narrative Science Inc. Applied artificial intelligence technology for narrative generation using an invocable analysis service and data re-organization
US11561986B1 (en) 2018-01-17 2023-01-24 Narrative Science Inc. Applied artificial intelligence technology for narrative generation using an invocable analysis service
US11023689B1 (en) 2018-01-17 2021-06-01 Narrative Science Inc. Applied artificial intelligence technology for narrative generation using an invocable analysis service with analysis libraries
US10963649B1 (en) 2018-01-17 2021-03-30 Narrative Science Inc. Applied artificial intelligence technology for narrative generation using an invocable analysis service and configuration-driven analytics
US11182556B1 (en) 2018-02-19 2021-11-23 Narrative Science Inc. Applied artificial intelligence technology for building a knowledge base using natural language processing
US10755046B1 (en) 2018-02-19 2020-08-25 Narrative Science Inc. Applied artificial intelligence technology for conversational inferencing
US11816435B1 (en) 2018-02-19 2023-11-14 Narrative Science Inc. Applied artificial intelligence technology for contextualizing words to a knowledge base using natural language processing
US11030408B1 (en) 2018-02-19 2021-06-08 Narrative Science Inc. Applied artificial intelligence technology for conversational inferencing using named entity reduction
US11126798B1 (en) 2018-02-19 2021-09-21 Narrative Science Inc. Applied artificial intelligence technology for conversational inferencing and interactive natural language generation
US10706236B1 (en) 2018-06-28 2020-07-07 Narrative Science Inc. Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system
US11232270B1 (en) 2018-06-28 2022-01-25 Narrative Science Inc. Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to numeric style features
US11334726B1 (en) 2018-06-28 2022-05-17 Narrative Science Inc. Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to date and number textual features
US11042713B1 (en) 2018-06-28 2021-06-22 Narrative Scienc Inc. Applied artificial intelligence technology for using natural language processing to train a natural language generation system
US11989519B2 (en) 2018-06-28 2024-05-21 Salesforce, Inc. Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system
US10990767B1 (en) 2019-01-28 2021-04-27 Narrative Science Inc. Applied artificial intelligence technology for adaptive natural language understanding
US11341330B1 (en) 2019-01-28 2022-05-24 Narrative Science Inc. Applied artificial intelligence technology for adaptive natural language understanding with term discovery

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