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公开(公告)号：US20190089659A1
公开(公告)日：2019-03-21
申请号：US15710684
申请日：2017-09-20
Applicant: FUJITSU LIMITED
Inventor： Jun WANG , Kanji UCHINO
IPC: H04L12/58
Abstract: A method may include receiving one or more electronic message streams. The method may also include selecting a time range for analysis of the one or more electronic message streams. The method may include analyzing a set of historic data of the one or more electronic message streams. The method may also include identifying one or more bursty phrases in the set of historic data of the one or more electronic message streams in the selected time range. The method may further include clustering the bursty phrases in one or more bursty topics. The method may include generating a rank for each of the one or more bursty topics. The method may also include displaying the one or more bursty topics in a graphical user interface (GUI) in view of the rank. The method may include storing the one or more bursty topics.
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公开(公告)号：US20180046628A1
公开(公告)日：2018-02-15
申请号：US15236183
申请日：2016-08-12
Applicant: FUJITSU LIMITED
Inventor： Jun WANG , Kanji UCHINO
CPC classification number: G06Q50/01
Abstract: A system for ranking social media content may be provided. The system may include one or more processors. The one or more processors may be configured to extract author profile data from one or more authors of domain-specific content and identify social media content based on the author profile data. The one or more processors may further be configured to rank the social media content based on at least one of user interest data, user preference data, statistics for the social media content, author data, statistics for a social media account including the social media content, and content age data.
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公开(公告)号：US20170316519A1
公开(公告)日：2017-11-02
申请号：US15653356
申请日：2017-07-18
Applicant: FUJITSU LIMITED
Inventor： Jun WANG , Kanji UCHINO
CPC classification number: G06Q50/01 , G06F16/24578 , G06F16/9535 , G06F16/9574 , G06N20/00 , H04L67/306
Abstract: A general type weight and an individual weight are determined for each of multiple social media accounts. The general type weight may be based on a social media account type corresponding to the social media account. The method may include encoding a mutually reinforcing relationship between the social media accounts and contents promoted by each of the social media accounts. The mutually reinforcing relationship may be encoded as a promotional link between the social media accounts and a content item of the contents promoted by each of the social media accounts. The method may include calculating a basic link strength for each promotional link, where the basic link strength is based on the general type weight and the individual weight for each social media account of multiple social media accounts, and calculating a mutually reinforcing ranking of the multiple social media accounts and contents based on the basic link strengths.
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公开(公告)号：US20170039873A1
公开(公告)日：2017-02-09
申请号：US14818672
申请日：2015-08-05
Applicant: FUJITSU LIMITED
Inventor： Takuro WATANABE , Jun WANG , Kanji UCHINO
IPC: G09B17/00
CPC classification number: G09B5/06
Abstract: A method may include estimating a current level of vocabulary knowledge of a learner. The method may also include determining a first vocabulary level of a first chunk in an electronic document read by the learner. Additionally, the method may include comparing the current level of vocabulary knowledge of the learner with the first vocabulary level of the first chunk. The method may further include determining whether to replace, in the electronic document, the first chunk with a second chunk of a vocabulary database based on the comparison of the current level with the first vocabulary level. The second chunk may have a second vocabulary level.
Abstract translation: 一种方法可以包括估计学习者的词汇知识的当前水平。 该方法还可以包括确定由学习者读取的电子文档中的第一块的第一词汇级别。 另外，该方法可以包括将学习者的词汇知识的当前水平与第一块的第一词汇水平进行比较。 该方法还可以包括基于当前级别与第一词汇级别的比较来确定在电子文档中是否用词汇数据库的第二块来替换第一块。 第二块可能具有第二个词汇级别。
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公开(公告)号：US20170011643A1
公开(公告)日：2017-01-12
申请号：US14796872
申请日：2015-07-10
Applicant: FUJITSU LIMITED
Inventor： Jun WANG , Kanji UCHINO
IPC: G09B5/02
Abstract: A method of automated ranking of segments of learning materials includes calculating a window similarity between first-window content of a first window in a learning material and second-window content of a second window in the learning material. The method may also include in response to the window similarity between the first-window content of the first window and the second-window content of the second window meeting a similarity threshold, generating a first segment that includes at least the first-window content and the second-window content. The method may include calculating a first-segment consistency measurement for the first segment based on a first-segment similarity between the first-segment content in the first segment and a knowledge point. The method may also include ranking the first segment with respect to one or more of the following: a second segment in the learning material and a third segment in a different learning material, wherein the ranking of the first segment is based on one or more of the following: a quality measurement, a learning material type of the learning material, a length of the first segment, and the first-segment consistency measurement of the first segment.
Abstract translation: 学习材料段的自动排序的方法包括计算学习材料中的第一窗口的第一窗口内容与学习材料中的第二窗口的第二窗口内容之间的窗口相似度。 该方法还可以包括响应于第一窗口的第一窗口内容与满足相似性阈值的第二窗口的第二窗口内容之间的窗口相似度，生成包括至少第一窗口内容的第一片段和 第二窗口内容。 该方法可以包括基于第一片段中的第一片段内容和知识点之间的第一片段相似度来计算第一片段的第一片段一致性度量。 该方法还可以包括相对于以下中的一个或多个对第一段进行排名：学习材料中的第二段和不同学习材料中的第三段，其中第一段的排序基于以下中的一个或多个 以下：质量测量，学习材料的学习材料类型，第一段的长度和第一段的第一段一致性测量。
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Patent Agency Ranking
公开(公告)号：US20150286718A1
公开(公告)日：2015-10-08
申请号：US14245725
申请日：2014-04-04
Applicant: FUJITSU LIMITED
Inventor： Jun WANG , Kanji UCHINO
IPC: G06F17/30
CPC classification number: G06F17/30796 , G06F17/30011 , G06F17/30038 , G06F17/3082 , G06F17/30864 , G06F17/30867
Abstract: A method of identifying topics in lecture videos may include receiving lecture video metadata, learning courses metadata, and a lecture video transcript (transcript). The transcript may include transcribed text of a lecture video (video). The method may include discovering candidate learning courses related to the video based on a measured similarity between the video metadata and the learning courses metadata. The method may include extracting key phrases from learning materials of the candidate learning courses. The method may also include assigning weights to the extracted key phrases based on a position of the extracted key phrases and a frequency with which the extracted key phrases appear, and the discovered candidate learning course in which the key phrases appear. The method may include apportioning the video into topic-specific portions based on topic segments generated in the transcript, the presence of the extracted key phrases therein, and the assigned weights.
Abstract translation: 识别讲座视频中的主题的方法可以包括接收演讲视频元数据，学习课程元数据和演讲录像抄本（抄本）。 抄本可以包括演讲视频（视频）的转录文本。 该方法可以包括基于视频元数据和学习课程元数据之间的测量相似度发现与视频相关的候选学习课程。 该方法可以包括从候选学习课程的学习材料中提取关键短语。 该方法还可以包括基于所提取的关键短语的位置和所提取的关键短语出现的频率以及发现的关键短语出现的所发现的候选学习课程，为所提取的关键短语分配权重。 该方法可以包括基于在抄本中生成的主题段，其中提取的关键短语的存在以及所分配的权重来将视频分配到主题特定部分。
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7.发明公开公开(公告)号：US20240330758A1
公开(公告)日：2024-10-03
申请号：US18194613
申请日：2023-03-31
Applicant: Fujitsu Limited
Inventor： Ramya MALUR SRINIVASAN , Kanji UCHINO
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: In an embodiment, a dataset associated with a domain is received. Further, domain knowledge information associated with the received dataset is encoded. Next, a structural causal model (SCM) associated with the dataset is constructed for a computing algorithm related to the domain, based on the encoded domain knowledge information. Further, a mediator variable and a confounder variable associated with the computing algorithm is identified based on the constructed SCM. Next, a causal effect associated with the computing algorithm is estimated based on the identified mediator variable and the identified confounder variable. Additionally, whether the computing algorithm suffers from an algorithmic monoculture is determined based on the estimated causal effect, to detect bias in the computing algorithm. Thereafter, information indicative of whether the computing algorithm suffers from the algorithmic monoculture is rendered.
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公开(公告)号：US20230376826A1
公开(公告)日：2023-11-23
申请号：US17663856
申请日：2022-05-18
Applicant: FUJITSU LIMITED
Inventor： Wing Yee AU , Kanji UCHINO
IPC: G06N20/00 , G06F16/901 , G06F16/21
CPC classification number: G06N20/00 , G06F16/9024 , G06F16/211
Abstract: According to an aspect of an embodiment, operations may include retrieving a first graph. The operations may further include identifying a set of node-types, determining a first count of each of the identified set of node-types, and determining first statistical information. The operations may further include identifying a set of edge-types, determining a second count of each of the identified set of edge-types and determining a two-dimensional (2D) distribution of each of the identified set of edge-types. The operations may further include determining second statistical information, identifying a set of combinations of edge-types connecting three node-types and determining a third count of each of a set of three node-type groups. The operations may further include determining a three-dimensional (3D) distribution of each of the set of three node-type groups, determining third statistical information, and transmitting first graph statistics associated with the retrieved first graph for generation a second graph.
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公开(公告)号：US20230259756A1
公开(公告)日：2023-08-17
申请号：US17669712
申请日：2022-02-11
Applicant: FUJITSU LIMITED
Inventor： Michael MCTHROW , Kanji UCHINO
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: A method may include obtaining a first result of a graph explainable artificial intelligence (GXAI) classification analysis of a dataset of graph-structured data and a second result of a graph analysis algorithm that represents relationships between elements of the dataset. The method may include determining a correlation between the first result and the second result and generating a display within a graphical user interface (GUI) that visualizes similarities between the first result and the second result based on the correlation. Determining the correlation between the first result and the second result may include generating a first vector of the first result of the classification analysis using GXAI techniques and a second vector of the second result of the graph analysis algorithm. A Pearson correlation coefficient or a cosine similarity coefficients may be computed based on the first vector and the second vector in which the computed coefficients are indicative of the correlation.
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公开(公告)号：US20220300736A1
公开(公告)日：2022-09-22
申请号：US17249868
申请日：2021-03-17
Applicant: FUJITSU LIMITED
Inventor： Ramya MALUR SRINIVASAN , Kanji UCHINO
Abstract: In an embodiment, operations include extracting first information about a first set of features of a first candidate, from a document or profile information of the first candidate. Second information about a second set of features, corresponding to the first set of features, is extracted from one or more databases. The second set of features is associated with a population of candidates with at least one demographic parameter same as that of the first candidate. A third set of features is determined based on difference of corresponding features from the first set of features and the second set of features. A pre-trained neural network model is applied on the third set of features to determine a set of weights associated with the third set of features. An empathy score of the first candidate is determined based on the set of weights. The empathy score of the first candidate is rendered.
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