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公开(公告)号：US11927448B2
公开(公告)日：2024-03-12
申请号：US17477701
申请日：2021-09-17
CPC分类号： G01C21/206 , G01V3/08 , G06F18/2148 , G06F18/24 , G06N3/02
摘要： A computer-implemented method of determining a position of a portable electronic device in an indoor environment includes: at a first rate, updating an absolute position of a portable electronic device within the indoor environment based on at least one of radio signal data and magnetic field data captured using the portable electronic device; at a second rate that is different than the first rate, selectively updating an estimated displacement of the portable electronic device within the indoor environment, the updating the estimated displacement comprising generating an estimated displacement, by a neural network module, based on inertial sensor data of the portable electronic device; and determining a present position of the portable electronic device within the indoor environment by updating a previous position based on at least one of (a) the estimated displacement and (b) the absolute position.
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2.发明授权公开(公告)号：US11880417B2
公开(公告)日：2024-01-23
申请号：US14709130
申请日：2015-05-11
申请人： NAVER CORPORATION
发明人： Hyun Jung Lee
摘要： Provided is a method, apparatus, system, and/or computer readable medium for providing a map service. The map providing method may include extracting region information attached to a posting in response to an execution of a storage capability or a sharing capability by a user to the posting displayed on a page accessible to a computer, storing address information of the posting and the extracted region information associated with the user, and displaying a point of interest corresponding to the region information on a map service screen associated with the user.
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公开(公告)号：US20230418848A1
公开(公告)日：2023-12-28
申请号：US18312703
申请日：2023-05-05
申请人： NAVER CORPORATION
IPC分类号： G06F16/332 , G06F40/40 , G06F40/284
CPC分类号： G06F16/332 , G06F40/40 , G06F40/284
摘要： A ranker for a neural information retrieval model comprises a document encoder having a pretrained language model layer and configured to receive one or more documents and generate a sparse representation for each of the documents predicting term importance of the document over a vocabulary. A separate query encoder is configured to receive a query and generate a representation of the query over the vocabulary. Generated representations are compared to generate a set of respective document scores and rank the one or more documents.
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公开(公告)号：US11837262B2
公开(公告)日：2023-12-05
申请号：US17104397
申请日：2020-11-25
申请人： NAVER CORPORATION
发明人： Jun Bong Baek , Hoonmok Moon , Ha Kyoung Sung , DongHo Lee , YoungNam Kang , HyunJoong Park , Seoyeon Hur
IPC分类号： G11B27/34 , G11B27/30 , H04N21/845 , G11B27/10 , H04N21/435 , H04N21/44 , H04N21/472
CPC分类号： G11B27/34 , G11B27/30 , H04N21/8455
摘要： An electronic device for tagging an event in a sports play video, and an operating method thereof. Various embodiments may include mapping text broadcasting data and a sports play video based on an event occurring during sports play, detecting a tagging location of the event in the sports play video, and displaying an indicator for the tagging location of the event in the progress bar of the sports play video.
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公开(公告)号：US20230353487A1
公开(公告)日：2023-11-02
申请号：US18053041
申请日：2022-11-07
申请人： NAVER CORPORATION
发明人： Young-Bum KIM , Gun Su LEE , Dongchan KIM , Sun KIM , Jinmyung WON , Hongyeon YU , Chanhee LEE
摘要： A method for providing an optimal path through a computer network includes predicting a subsequent action of a target user through an optimal path prediction model that is trained in a form of a graph representing a user action trajectory of a session unit and recommending a path of the predicted action as an optimal path. The recommendation includes identifying a user intent based on a previous action trajectory of the target user in a current session, and guiding a path corresponding to the user intent as one of the optimal paths.
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公开(公告)号：US11797591B2
公开(公告)日：2023-10-24
申请号：US17193748
申请日：2021-03-05
申请人： NAVER CORPORATION
发明人： Matthias Galle , Maximin Coavoux , Hady Elsahar
CPC分类号： G06F16/345 , G06F16/35 , G06N3/088
摘要： A method for generating enriched training data for a multi-source transformer neural network for generation of a summary of one or more passages of input text comprises creating, from a plurality of input text sets, training points each comprising an input text subset of the input text set and a corresponding reference input text from the input text set, wherein the size of the input text subset is a predetermined number. Control codes are selected based on reference features corresponding to categorical labels of reference texts in the created training points. The input text is enriched with the selected control codes to generate enriched training data.
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公开(公告)号：US11768078B2
公开(公告)日：2023-09-26
申请号：US16853914
申请日：2020-04-21
申请人： Naver Corporation
IPC分类号： G01C21/34 , G06Q10/047
CPC分类号： G01C21/3446 , G01C21/343 , G01C21/3423 , G06Q10/047
摘要： A method creates a reduced set of feasible transfers T(t, L′) for a trip t of line L, for each target line L′ from a set of all transfers from line L to all other lines, by computing, for each origin line L, feasible transfers between stations of the origin line L and a destination line L′; sorting the computed feasible transfers to create a transfer set T(L); determining, for each trip t of origin line L, and for each transfer in the transfer set T(L), an earliest trip t′ of L′ wherein the transfer is feasible; and adding, for each trip t of origin line L, the determined transfer from t to t′ to the reduced set of feasible transfers T(t, L′) when trip t′ is the only destination trip of the transfers in the reduced set of feasible transfers T(t, L′) passing at the destination station and when it is earlier than all the previous destination trips of the transfers in the reduced set of feasible transfers T(t, L′) passing by the destination station.
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公开(公告)号：USD996232S1
公开(公告)日：2023-08-22
申请号：US29758771
申请日：2020-11-18
设计人： Young In Koh , Youngdo Choi
摘要： FIG. 1 is a top front perspective view of a smart clock, showing our new design.
FIG. 2 is a front elevation view of the smart clock shown in FIG. 1.
FIG. 3 is a rear elevation view of the smart clock shown in FIG. 1.
FIG. 4 is a left elevation view of the smart clock shown in FIG. 1.
FIG. 5 is a right elevation view of the smart clock shown in FIG. 1.
FIG. 6 is a top plan view of the smart clock shown in FIG. 1; and,
FIG. 7 is a bottom plan view of the smart clock shown in FIG. 1.
The broken lines shown in the drawings illustrate the portions of the article that form no part of the claimed design.-
公开(公告)号：US11734352B2
公开(公告)日：2023-08-22
申请号：US16791368
申请日：2020-02-14
申请人： NAVER CORPORATION
IPC分类号： G06F16/9032 , G06F16/9038 , G06F17/16 , G06F18/22 , G06F18/214 , G06V10/74 , G06V10/764 , G06V10/82
CPC分类号： G06F16/9032 , G06F16/9038 , G06F16/90332 , G06F17/16 , G06F18/2148 , G06F18/22 , G06V10/761 , G06V10/764 , G06V10/82
摘要： A training system includes: a training dataset including first objects of a first modality and second objects of a second modality that are associated with the first objects, respectively; a first matrix including first relevance values indicative of relevance between the first objects and the second objects, respectively; a second matrix including second relevance values indicative of relevance between the second objects and the first objects, respectively; and a training module configured to: based on similarities between ones of the second objects, generate a third matrix by selectively adding first additional relevance values to the first matrix; based on the similarities between the ones of the second objects, generate a fourth matrix by selectively adding second additional relevance values to the second matrix; and store the third and fourth matrices in memory of a search module for cross-modal retrieval in response to receipt of search queries.
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公开(公告)号：US20230214650A1
公开(公告)日：2023-07-06
申请号：US18055613
申请日：2022-11-15
申请人： NAVER CORPORATION
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： Methods and systems for training a neural combinatorial optimization (NCO) model having a processor and memory for performing a task having a target distribution. The NCO model is meta-trained to learn an efficient heuristic on a set of distributions. The meta-trained NCO model is then fine-tuned to specialize a learned heuristic for the target distribution.
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