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1
US2021029255A1
MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND MACHINE LEARNING PROGRAM
Publication/Patent Number: US2021029255A1 Publication Date: 2021-01-28 Application Number: 16/923,509 Filing Date: 2020-07-08 Inventor: Kiriyama, Tomohiro   Saito, Koichi   Sugai, Shun   Kowase, Kazuhiko   Kashiwagura, Naoshi   Assignee: KONICA MINOLTA, INC.   IPC: H04N1/00 Abstract: A machine learning device learns an action of a driving source in a transport device continuously transporting at least two transported objects along a transport path, and includes: a hardware processor that: acquires position information of the at least two transported objects on the transport path on the basis of a result of detection by a sensor provided in the transport path; calculates a reward on the basis of the position information acquired, according to a predetermined rule; learns an action by calculating an action value in reinforcement learning on the basis of the position information acquired and the reward calculated; and generates and outputs control information that causes the driving source to perform an action determined on the basis of a learning result.
2
US2021088985A1
MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND MACHINE LEARNING PROGRAM
Publication/Patent Number: US2021088985A1 Publication Date: 2021-03-25 Application Number: 16/991,088 Filing Date: 2020-08-12 Inventor: Sugai, Shun   Saito, Koichi   Assignee: KONICA MINOLTA, INC.   IPC: G05B13/02 Abstract: A machine learning device generates a control parameter of image formation in an image forming device including an image forming part that forms an image on a paper sheet and an image reading part that reads the image formed on the paper sheet, and the machine learning device includes: a first hardware processor that generates the control parameter on the basis of machine learning; a second hardware processor that receives input of an image including a read image that is formed by the image forming part according to the control parameter and read by the image reading part, the second hardware processor making a determination relating to the read image on the basis of machine learning; and a third hardware processor that causes the first hardware processor and/or the second hardware processor to learn oil the basis of a determination result by the second hardware processor.
3
US2021012193A1
MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE
Publication/Patent Number: US2021012193A1 Publication Date: 2021-01-14 Application Number: 16/921,944 Filing Date: 2020-07-07 Inventor: Yasutomi, Suguru   Katoh, Takashi   Uemura, Kento   Assignee: FUJITSU LIMITED   IPC: G06N3/08 Abstract: A machine learning method includes: calculating, by a computer, a first loss function based on a first distribution and a previously set second distribution, the first distribution being a distribution of a feature amount output from an intermediate layer when first data is input to an input layer of a model that has the input layer, the intermediate layer, and an output layer; calculating a second loss function based on second data and correct data corresponding to the first data, the second data being output from the output layer when the first data is input to the input layer of the model; and training the model based on both the first loss function and the second loss function.
4
US2021089926A1
MACHINE LEARNING METHOD AND MACHINE LEARNING APPARATUS
Publication/Patent Number: US2021089926A1 Publication Date: 2021-03-25 Application Number: 17/112,135 Filing Date: 2020-12-04 Inventor: Nakajima, Hiroaki   Takahashi, Yu   Assignee: YAMAHA CORPORATION   IPC: G06N3/08 Abstract: A machine learning apparatus includes a memory storing instructions and a processor that implements the stored instructions to execute a plurality of tasks. The tasks include an obtaining task that obtains a mixture signal containing a first component and a second component, a first generating task that generates a first signal that emphasize the first component inputting a mixture signal to a neural network, a second generating task that generates a second signal by modifying the first signal, a calculating task that calculates an evaluation index from the second signal, and a training task that trains the neural network with the evaluation index.
5
EP3767552A1
MACHINE LEARNING METHOD, PROGRAM, AND MACHINE LEARNING DEVICE
Publication/Patent Number: EP3767552A1 Publication Date: 2021-01-20 Application Number: 20184191.3 Filing Date: 2020-07-06 Inventor: Yasutomi, Suguru   Katoh, Takashi   Uemura, Kento   Assignee: FUJITSU LIMITED   IPC: G06N3/08 Abstract: A machine learning method includes: calculating, by a computer, a first loss function based on a first distribution and a previously set second distribution, the first distribution being a distribution of a feature amount output from an intermediate layer when first data is input to an input layer of a model that has the input layer, the intermediate layer, and an output layer; calculating a second loss function based on second data and correct data corresponding to the first data, the second data being output from the output layer when the first data is input to the input layer of the model; and training the model based on both the first loss function and the second loss function.
6
US2021064994A1
MACHINE LEARNING DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021064994A1 Publication Date: 2021-03-04 Application Number: 16/922,395 Filing Date: 2020-07-07 Inventor: Tomaru, Tatsuya   Assignee: HITACHI, LTD.   IPC: G06N3/08 Abstract: A machine learning device includes a general arithmetic device that calculates data, and a reservoir arithmetic device including an input unit, an output unit, and one or more nodes. The reservoir arithmetic device performs a certain calculation on data and performs calculation in response to an input value input through an input unit using the dynamics of the nodes. Each node i outputs a measurement outcome zi(tk) at a time point tk. The general arithmetic device calculates y(tk)=Σizi(tk)wi. In the calculation of y(tk), in addition to zi(tk) at the time point zi(tk), the term zi(tk′) at a time point tk′ (tk′≠tk) is included. Thus, the calculation of y(tk) is performed by redundantly using zi(tk) at different time points, with the range of sum with respect to the subscript i being i=1, . . . qn, where q is redundancy.
7
US2021073376A1
LEARNING INPUT PREPROCESSING TO HARDEN MACHINE LEARNING MODELS
Publication/Patent Number: US2021073376A1 Publication Date: 2021-03-11 Application Number: 16/566,862 Filing Date: 2019-09-10 Inventor: Tran, Ngoc Minh   Sinn, Mathieu   Nicolae, Maria-irina   Wistuba, Martin   Rawat, Ambrish   Buesser, Beat   Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION   IPC: G06F21/55 Abstract: Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.
8
US2021055240A1
MACHINE LEARNING DEVICE, CONTROL SYSTEM, AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021055240A1 Publication Date: 2021-02-25 Application Number: 16/939,430 Filing Date: 2020-07-27 Inventor: Konishi, Kouki   Assignee: FANUC CORPORATION   IPC: G01N25/20 Abstract: A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.
9
US10901374B2
Machine learning device, control device, and machine learning method
Publication/Patent Number: US10901374B2 Publication Date: 2021-01-26 Application Number: 16/435,840 Filing Date: 2019-06-10 Inventor: Tsuneki, Ryoutarou   Ikai, Satoshi   Sonoda, Naoto   Assignee: FANUC CORPORATION   IPC: G05B13/02 Abstract: The settling time of machine learning is shortened. A machine learning device is configured to perform machine learning related to optimization of coefficients of a transfer function of an IIR filter of a feedforward calculation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine using feedforward control by a feedforward calculation unit having the IIR filter. The machine learning device represents a zero-point at which the transfer function of the IIR filter is zero and a pole at which the transfer function diverges infinitely in polar coordinates using a radius r and an angle θ, respectively, and searches for and learns, within a predetermined search range, the radius r and the angle θ to thereby perform the optimization of the coefficients of the transfer function of the IIR filter.
10
US2021065025A1
MACHINE LEARNING DEVICE, RECEIVING DEVICE AND MACHINE LEARNING METHOD
Publication/Patent Number: US2021065025A1 Publication Date: 2021-03-04 Application Number: 16/991,285 Filing Date: 2020-08-12 Inventor: Kurihara, Kenichiro   Akimoto, Shinji   Miyachi, Motoyoshi   Assignee: FANUC CORPORATION   IPC: G06N5/04 Abstract: To enable adjustment of digital filters suited to disturbances occurring in the surroundings. A receiving device includes: a digital filter that eliminates or attenuates a disturbance included in a signal received through a communication line; a coefficient adjusting unit that adjusts a coefficient of the digital filter based on operation schedule information of a device causing the disturbance in the communication line; and an information table that records a combination of operation information included in the operation schedule information and a coefficient of the digital filter corresponding to the operation information or correction information of the coefficient, in which the coefficient adjusting unit calculates the coefficient of the digital filter or the correction information of the coefficient from the information table based on the operation information included in the operation schedule information, and adjusts the coefficient of the digital filter.
11
US10891478B2
Method for correction of the eyes image using machine learning and method for machine learning
Publication/Patent Number: US10891478B2 Publication Date: 2021-01-12 Application Number: 15/567,365 Filing Date: 2016-03-03 Inventor: Kononenko, Daniil Sergeyevich   Lempitsky, Victor Sergeyevich   Assignee: Skolkovo Institute Of Science And Technology   IPC: G06K9/00 Abstract: The present invention refers to automatics and computing technology, namely to the field of processing images and video data, namely to correction the eyes image of interlocutors in course of video chats, video conferences with the purpose of gaze redirection. A method of correction of the image of eyes wherein the method obtains, at least, one frame with a face of a person, whereupon determines positions of eyes of the person in the image and forms two rectangular areas closely circumscribing the eyes, and finally replaces color components of each pixel in the eye areas for color components of a pixel shifted according to prediction of the predictor of machine learning. Technical effect of the present invention is rising of correction accuracy of the image of eyes with the purpose of gaze redirection, with decrease of resources required for the process of handling a video image.
12
US10891520B2
Machine learning device, inspection device and machine learning method
Publication/Patent Number: US10891520B2 Publication Date: 2021-01-12 Application Number: 16/029,827 Filing Date: 2018-07-09 Inventor: Namiki, Yuta   Assignee: FANUC CORPORATION   IPC: G06K9/62 Abstract: A machine learning device that creates training data to be used in machine learning includes: an image input unit that inputs an image which was obtained by capturing an inspection target on which a symbol indicating a defect is marked; and a creation unit that creates the training data based on the inputted image, in which the creation unit: creates training data consisting of a training image which is the image as inputted, and a label that retains a value of OK which signifies not having a defect, in a case of there not being the symbol in the image inputted; and creates training data consisting of a training image generated based on the image inputted, and a label that retains a value of NG signifying having a defect, in a case of there being the symbol in the image inputted.
13
US2021087033A1
MACHINE LEARNING METHOD, FORKLIFT CONTROL METHOD, AND MACHINE LEARNING APPARATUS
Publication/Patent Number: US2021087033A1 Publication Date: 2021-03-25 Application Number: 17/007,504 Filing Date: 2020-08-31 Inventor: Kimura, Nobutaka   Seto, Kouichi   Iwamoto, Yutaka   Ito, Hidekazu   Ara, Koji   Assignee: HITACHI, LTD.   HITACHI SOLUTIONS, LTD.   IPC: B66F9/075 Abstract: A forklift which carries a cargo is includes a machine learning apparatus which executes a procedure of: accepting input of learning data of a first category group and evaluation data of a second category group; extracting the learning data of at least one category from the first category group and calculating parameters of the estimation models for controlling the forklift by using the extracted learning data; extracting the evaluation data of at least one category from the second category group and evaluating the estimation models, for which the parameters are calculated, by using the extracted evaluation data; and outputting an estimation model M whose data evaluation result is equal to or larger than a specified threshold value, from among the estimation models for which the parameters are calculated, and a category of the evaluation data used for the evaluation of the estimation model M.
14
US10901396B2
Machine learning device, control device, and machine learning method
Publication/Patent Number: US10901396B2 Publication Date: 2021-01-26 Application Number: 16/376,025 Filing Date: 2019-04-05 Inventor: Liang, Yao   Tsuneki, Ryoutarou   Assignee: FANUC CORPORATION   IPC: G05B19/00 Abstract: A machine learning device performs machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device that includes a compensation generation unit configured to generate a compensation value to be added to a control command for controlling a servo motor and a limiting unit configured to limit the compensation value or the control command to which the compensation value is added so as to fall within a setting range. During a machine learning operation, when the compensation value or the control command is outside the setting range and the limiting unit limits the compensation value or the control command so as to fall within the setting range, the machine learning device applies the compensation value to the learning and continues with a new search to optimize the compensation value generated by the compensation generation unit.
15
US10937331B2
Learning systems and automatic transitioning between learning systems
Publication/Patent Number: US10937331B2 Publication Date: 2021-03-02 Application Number: 15/817,496 Filing Date: 2017-11-20 Inventor: Britto, Mattos Lima Andrea   Cardonha, Carlos H.   Laiola, Guimaraes Rodrigo   Santana, Vagner F. D.   Assignee: International Business Machines Corporation   IPC: G09B7/07 Abstract: An online learning system selects content for a learning session and opens a user interface to start the learning session on a plurality of devices. A mode of instruction is selected for the learning session. An activity to perform associated with the content is presented. Performance of the activity is monitored and a performance metric and/or a heterogeneity metric associated with a key performance indicator for the activity performed is generated. Responsive to determining that the performance metric and/or a heterogeneity metric is outside of the target range, the mode of instruction may be switched automatically.
16
US2021001482A1
MACHINE LEARNING DEVICE, ROBOT CONTROLLER, ROBOT SYSTEM, AND MACHINE LEARNING METHOD FOR LEARNING ACTION PATTERN OF HUMAN
Publication/Patent Number: US2021001482A1 Publication Date: 2021-01-07 Application Number: 17/023,376 Filing Date: 2020-09-17 Inventor: Tsuda, Taketsugu   Okanohara, Daisuke   Okuta, Ryosuke   Matsumoto, Eiichi   Kawaai, Keigo   Assignee: FANUC CORPORATION   Preferred Networks, Inc.   IPC: B25J9/16 Abstract: A machine learning device for a robot that allows a human and the robot to work cooperatively, the machine learning device including a state observation unit that observes a state variable representing a state of the robot during a period in that the human and the robot work cooperatively; a determination data obtaining unit that obtains determination data for at least one of a level of burden on the human and a working efficiency; and a learning unit that learns a training data set for setting an action of the robot, based on the state variable and the determination data.
17
US2021056488A1
MACHINE LEARNING METHOD AND MACHINE LEARNING APPARATUS PERFORMING LEARNING RELATING TO WORK PROCESS
Publication/Patent Number: US2021056488A1 Publication Date: 2021-02-25 Application Number: 16/929,107 Filing Date: 2020-07-15 Inventor: Sugiyama, Yuusuke   Assignee: FANUC CORPORATION   IPC: G06Q10/06 Abstract: A machine learning apparatus totals up all of the unit work operations included in the plurality of different work processes and judges if the plurality of unit work operations of the same type are similar to each other. The machine learning apparatus defines a similar first unit work operation and second unit work operation as a set of similar work operations, uses a common machine learning algorithm so as to generate a similar work learning model, and performs learning relating to a first work process including the first unit work operation and a second work process including the second unit work operation based on the similar work learning model.
18
US2021073644A1
COMPRESSION OF MACHINE LEARNING MODELS
Publication/Patent Number: US2021073644A1 Publication Date: 2021-03-11 Application Number: 16/563,226 Filing Date: 2019-09-06 Inventor: Lin, Zhe   Wang, Yilin   Qiao, Siyuan   Zhang, Jianming   Assignee: Adobe Inc.   IPC: G06N3/08 Abstract: A machine learning model compression system and related techniques are described herein. The machine learning model compression system can intelligently remove certain parameters of a machine learning model, without introducing a loss in performance of the machine learning model. Various parameters of a machine learning model can be removed during compression of the machine learning model, such as one or more channels of a single-branch or multi-branch neural network, one or more branches of a multi-branch neural network, certain weights of a channel of a single-branch or multi-branch neural network, and/or other parameters. In some cases, compression is performed only on certain selected layers or branches of the machine learning model. Candidate filters from the selected layers or branches can be removed from the machine learning model in a way that preserves local features of the machine learning model.
19
US2021012231A1
MACHINE LEARNING SYSTEM
Publication/Patent Number: US2021012231A1 Publication Date: 2021-01-14 Application Number: 16/913,082 Filing Date: 2020-06-26 Inventor: Uchigaito, Hiroshi   Assignee: Hitachi, Ltd.   IPC: G06N7/00 Abstract: A machine learning system includes a learning section and an operating section including a memory. The operating section holds a required accuracy, and an internal state and a weight value of a learner in the memory and executes calculation processing by using data input to the machine learning system and the weight value held in the memory to update the internal state. An accuracy of the internal state is calculated from a result of the calculation processing and an evaluation value is calculated using the data input to the machine learning system, the weight value, and the updated internal state held in the memory when the calculated accuracy is higher than the required accuracy. The evaluation value is transmitted to the learning section, which updates the weight value by using the evaluation value and notifies the number of times of updating the weight value to the operating section.
20
US2021019639A1
SELECTION OF MACHINE LEARNING ALGORITHMS
Publication/Patent Number: US2021019639A1 Publication Date: 2021-01-21 Application Number: 16/915,551 Filing Date: 2020-06-29 Inventor: Parker, Charles   Assignee: BigML, Inc.   IPC: G06N5/04 Abstract: Systems and methods of selecting machine learning models/algorithms for a candidate dataset are disclosed. A computer system may access historical data of a set of algorithms applied to a set of benchmark datasets; select a first algorithm of the set of algorithms; apply the first algorithm to an input dataset to create a model of the input dataset; evaluate and store results of the applying; and add the first algorithm to a set of tried algorithms. The computer system may select a next algorithm of the algorithm set via submodular optimization based on the historical data and the set of tried algorithms; apply the next algorithm to the input dataset; capture a next result based on the applying; add the next result to update the set of tried algorithms; and repeat the submodular optimization. The procedure may continue until a termination condition is reached.
Total 500 pages