Patent Publication Number: US-2023146504-A1

Title: Pen state detection circuit, system, and method

Description:
BACKGROUND 
     Technical Field 
     The present disclosure relates to a pen state detection circuit, a pen state detection system, and a pen state detection method. 
     Description of the Related Art 
     An electronic device is disclosed in Patent Document 1. The electronic device detects a first position where a hand of a user comes into contact with a detection surface of a touch sensor and a second position indicated by an electronic pen, uses coordinate values of the first position and the second position to estimate an inclination direction of the electronic pen, and corrects an instruction position of the electronic pen according to the inclination direction. 
     PRIOR ART DOCUMENT 
     Patent Document 
     Patent Document 1: Japanese Patent Laid-Open No. 2015-087785 
     BRIEF SUMMARY 
     Technical Problem 
     Incidentally, an electronic pen including two electrodes can be used to estimate the position and the posture of the electronic pen even when the hand of the user is not touching the detection surface. However, the two electrodes are physically separated, and thus, at least one electrode always does not come into contact with the detection surface when the electronic pen is being used. In this case, the relation between the inclination angle and the detection position of the electronic pen may change according to the three-dimensional shapes of the electrodes, and the estimation accuracy may vary depending on the position and the posture of the electronic pen. 
     An object of the present disclosure is to provide a pen state detection circuit, a pen state detection system, and a pen state detection method that can improve estimation accuracy for a pen state in an electronic pen including at least one electrode. 
     Technical Solution 
     A first present disclosure provides a pen state detection circuit that detects a state of an electronic pen including a first electrode, on the basis of a signal distribution detected by a capacitance touch sensor including a plurality of sensor electrodes arranged in a plane shape, the pen state detection circuit executing an acquisition step of acquiring, from the touch sensor, a first signal distribution indicating a change in capacitance associated with approach of the first electrode; and an estimation step of using a machine learning estimator to estimate an instruction position or an inclination angle of the electronic pen from first feature values related to the first signal distribution, in which the first feature values include first local feature values related to a first local distribution corresponding to sensor electrodes in a number fewer than the number of arranged sensor electrodes exhibiting the first signal distribution. 
     A second present disclosure provides a pen state detection system including an electronic device including the pen state detection circuit; an electronic pen used along with the electronic device; and a server apparatus that is configured to be capable of performing two-way communication with the electronic device and that storing learning parameter groups of an estimator constructed on the pen state detection circuit, in which the electronic device requests the server apparatus to transmit a learning parameter group corresponding to the electronic pen when the electronic pen is detected. 
     A third present disclosure provides a pen state detection method of detecting a state of an electronic pen including an electrode, on the basis of a signal distribution detected by a capacitance touch sensor including a plurality of sensor electrodes arranged in a plane shape, in which one or a plurality of processors execute an acquisition step of acquiring, from the touch sensor, a signal distribution indicating a change in capacitance associated with approach of the electrode; and an estimation step of using a machine learning estimator to estimate an instruction position or an inclination angle of the electronic pen from feature values related to the signal distribution, and the feature values include local feature values related to a local distribution corresponding to sensor electrodes in a number fewer than the number of arranged sensor electrodes exhibiting the signal distribution. 
     A fourth present disclosure provides a pen state detection circuit that detects a state of an electronic pen including an electrode, on the basis of a signal distribution detected by a capacitance touch sensor including a plurality of sensor electrodes arranged in a plane shape, the pen state detection circuit executing an acquisition step of acquiring, from the touch sensor, a signal distribution indicating a change in capacitance associated with approach of the electrode; and an estimation step of estimating an instruction position or an inclination angle of the electronic pen from feature values related to the signal distribution by following different computation rules according to a projection position of the electrode on a detection surface of the touch sensor. 
     A fifth present disclosure provides a pen state detection system including an electronic device including the pen state detection circuit; an electronic pen used along with the electronic device; and a server apparatus that is configured to be capable of performing two-way communication with the electronic device and storing learning parameter groups of an estimator constructed on the pen state detection circuit, in which the electronic device requests the server apparatus to transmit a learning parameter group corresponding to the electronic pen when the electronic pen is detected. 
     A sixth present disclosure provides a pen state detection method of detecting a state of an electronic pen including an electrode, on the basis of a signal distribution detected by a capacitance touch sensor including a plurality of sensor electrodes arranged in a plane shape, in which one or a plurality of processors execute an acquisition step of acquiring, from the touch sensor, a signal distribution indicating a change in capacitance associated with approach of the electrode; and an estimation step of estimating an instruction position or an inclination angle of the electronic pen from feature values related to the signal distribution by following different computation rules according to a projection position of the electrode on a detection surface of the touch sensor. 
     Advantageous Effects 
     According to the first to third present disclosures, the machine learning estimator can be used to extract potential detection patterns through machine learning, and this facilitates appropriate reflection of the tendency of the detection patterns in estimating the instruction position or the inclination angle. Thus, the pen state of the electronic pen including at least one electrode can be estimated with high accuracy. In addition, the local feature values related to the local distribution corresponding to the sensor electrodes in a number fewer than the number of arranged sensor electrodes exhibiting the signal distribution can be used to reduce the processing load of the estimator to which the local feature values are input. 
     According to the fourth to sixth present disclosures, an estimate suitable for the projection position can be made by application of different computation rules according to the projection position of the electrode included in the electrode pen, and this suppresses the reduction in the estimation accuracy for the pen state caused by the relative positional relation between the electronic pen and the touch sensor. Therefore, the pen state of the electronic pen including at least one electrode can be estimated with high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG.  1    is an overall configuration diagram of an input system common to embodiments of the present disclosure. 
         FIG.  2    is a schematic diagram partially illustrating an electronic pen of  FIG.  1   . 
         FIGS.  3 A and  3 B  are diagrams illustrating an example of signal distributions detected by a touch sensor in a contact state of the electronic pen. 
         FIG.  4    is a diagram illustrating a tendency of an estimation error related to an instruction position. 
         FIG.  5    is a block diagram illustrating a pen detection function according to a first embodiment. 
         FIG.  6    is a flow chart executed by the pen detection function illustrated in  FIG.  5   . 
         FIG.  7    is a diagram illustrating an example of signal distributions acquired from the touch sensor. 
         FIGS.  8 A- 8 C  are diagrams illustrating an example of a calculation method of local feature values. 
         FIG.  9    is a diagram illustrating a configuration of an estimator included in the pen detection function of  FIG.  5   . 
         FIG.  10    is a diagram illustrating an implementation example of the estimator in  FIG.  9   . 
         FIG.  11 A  is a diagram illustrating estimation accuracy of the instruction position according to a conventional example.  FIG.  11 B  is a diagram illustrating estimation accuracy of the instruction position according to the embodiments. 
         FIG.  12 A  is a block diagram illustrating a pen detection function according to a first modification of the first embodiment.  FIG.  12 B  is a diagram illustrating a configuration of an estimator included in the pen detection function of  FIG.  12 A . 
         FIG.  13 A  is a block diagram illustrating a pen detection function according to a second modification of the first embodiment.  FIG.  13 B  is a diagram illustrating a configuration of an estimator included in the pen detection function of  FIG.  13 A . 
         FIG.  14 A  is a block diagram illustrating a pen detection function according to a third modification of the first embodiment.  FIG.  14 B  is a diagram illustrating a configuration of an estimator included in the pen detection function of  FIG.  14 A . 
         FIG.  15    is a diagram illustrating a configuration of an estimator included in a pen detection function according to a fourth modification of the first embodiment. 
         FIG.  16    is a block diagram illustrating a pen detection function according to a second embodiment. 
         FIG.  17    is a flow chart executed by the pen detection function illustrated in  FIG.  16   . 
         FIG.  18    is a diagram illustrating an example of a definition of a sensor area included in the touch sensor. 
         FIG.  19 A  is a diagram illustrating local feature values when a projection position of a tip electrode ( FIG.  2   ) included in the electronic pen is in a general area.  FIG.  19 B  is a diagram illustrating local feature values when the projection position of the tip electrode is in a peripheral area. 
         FIG.  20    is a block diagram illustrating a pen detection function according to a modification of the second embodiment. 
         FIG.  21 A  is a diagram illustrating local feature values before a shift process. 
         FIG.  21 B  is a diagram illustrating local feature values after the shift process. 
         FIG.  22 A  is a block diagram illustrating a pen detection function according to a third embodiment.  FIG.  22 B  is a block diagram illustrating an example different from that of  FIG.  22 A . 
         FIG.  23    is a flow chart executed by the pen detection function illustrated in  FIG.  22   . 
         FIG.  24    is a diagram illustrating a configuration of an estimator included in the pen detection function of  FIG.  22   . 
         FIGS.  25 A and  25 B  are diagrams illustrating variations of local feature values before execution of an autoencoding process. 
         FIGS.  26 A and  26 B  are diagrams illustrating variations of local feature values after the execution of the autoencoding process. 
         FIG.  27 A  is a diagram illustrating estimation accuracy for the instruction position according to a reference example.  FIG.  27 B  is a diagram illustrating estimation accuracy for the instruction position according to the embodiments. 
         FIG.  28    is an overall configuration diagram of an input system as a pen state detection system according to a fourth embodiment. 
         FIG.  29    is a functional block diagram related to a learning process of a control unit illustrated in  FIG.  28   . 
         FIG.  30    is a diagram illustrating a first example of a setting method of a learning parameter group. 
         FIG.  31    is a diagram illustrating a second example of the setting method of the learning parameter group. 
     
    
    
     DETAILED DESCRIPTION 
     A pen state detection circuit, a pen state detection system, and a pen state detection method according to the present disclosure will be described with reference to the attached drawings. To facilitate the understanding of the description, the same reference signs are provided as much as possible to the same constituent elements and steps in the drawings, and the description may not be repeated. Note that the present disclosure is not limited to the following embodiments and modifications, and it is obvious that the present disclosure can freely be changed without departing from the scope of the disclosure. Alternatively, the configurations may be combined optionally as long as there is no technical contradiction. 
     Configuration Common to Embodiments 
     Overall Configuration of Input System  10   
       FIG.  1    is an overall configuration diagram of an input system  10  common to the embodiments of the present disclosure. The input system  10  basically includes an electronic device  12  including a touch panel display; and an electronic pen  14  (or, also referred to as a “stylus”) that is a pen-type pointing device. 
     The electronic device  12  includes, for example, a tablet terminal, a smartphone, and a personal computer. The user can hold the electronic pen  14  with one hand and move the electronic pen  14  while pressing the pen tip against the touch surface of the electronic device  12  to thereby depict pictures and write letters on the electronic device  12 . In addition, the user can touch the touch surface with a finger  16  of the user to perform a desired operation through a user controller being displayed. 
     The electronic device  12  includes a touch sensor  18 , a touch IC (Integrated Circuit)  20 , and a host processor  22 . An x-direction and a y-direction illustrated in  FIG.  1    correspond to an X-axis and a Y-axis of a Cartesian coordinate system (hereinafter, sensor coordinate system) defined on the detection surface of the touch sensor  18 . 
     The touch sensor  18  is a planar sensor including a plurality of electrodes arranged on a display panel not illustrated. The touch sensor  18  includes a plurality of line electrodes  18   x  for detecting an X-coordinate (position in the x-direction) and a plurality of line electrodes  18   y  for detecting a Y-coordinate (position in the y-direction). The plurality of line electrodes  18   x  are extended in the y-direction and arranged at equal intervals in the x-direction. The plurality of line electrodes  18   y  are extended in the x-direction and arranged at equal intervals in the y-direction. Hereinafter, the arrangement interval of the line electrodes  18   x  (or line electrodes  18   y ) will be referred to as a “pitch” in some cases. Note that the touch sensor  18  may be a self-capacitance sensor including block-like electrodes arranged in a two-dimensional grid, instead of the mutual capacitance sensor. 
     The touch IC  20  is an integrated circuit that can execute firmware  24  and is connected to each of the plurality of line electrodes  18   x  and  18   y  included in the touch sensor  18 . The firmware  24  can realize a touch detection function  26  of detecting a touch of the finger  16  of the user or the like and a pen detection function  28  of detecting the state of the electronic pen  14 . 
     The touch detection function  26  includes, for example, a scan function of the touch sensor  18 , a creation function of a heat map (two-dimensional distribution of a detection level) on the touch sensor  18  and an area classification function (for example, classification of the finger  16  and palm) on the heat map. The pen detection function  28  includes, for example, a scan function (global scan or local scan) of the touch sensor  18 , a reception and analysis function of a downlink signal, an estimation function of the state (for example, position, posture, and pen pressure) of the electronic pen  14 , and a generation and transmission function of an uplink signal including a command for the electronic pen  14 . 
     The host processor  22  is a processor including a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The host processor  22  reads programs from a memory not illustrated and executes the programs to thereby perform, for example, a process of using data from the touch IC  20  to generate digital ink, a visualization process for displaying drawing content indicated by the digital ink, and the like. 
     Estimation Method for Pen State 
       FIG.  2    is a schematic diagram partially illustrating the electronic pen  14  of  FIG.  1   . A tip electrode  30  in a substantially conical shape and an upper electrode  32  in a bottomless truncated conical shape are coaxially provided at the tip of the electronic pen  14 . Each of the tip electrode  30  and the upper electrode  32  is an electrode for outputting a signal (what is generally called a downlink signal) generated by an oscillation circuit  34 . The oscillation circuit  34  changes the oscillation frequency or switches the destination in time series, and this allows the electronic pen  14  to output two types of downlink signals through the tip electrode  30  and the upper electrode  32 . 
     The touch IC  20  ( FIG.  1   ) of the electronic device  12  acquires, from the touch sensor  18 , a signal distribution (hereinafter, referred to as a “first signal distribution”) indicating a change in capacitance (more specifically, mutual capacitance or self-capacitance) associated with approach of the tip electrode  30 . The first signal distribution typically has a shape including one peak at a position Q 1 . Here, the position Q 1  corresponds to a position of projection of the top (position P 1 ) of the tip electrode  30  onto the sensor plane. 
     Similarly, the touch IC  20  ( FIG.  1   ) of the electronic device  12  acquires, from the touch sensor  18 , a signal distribution (hereinafter, referred to as a “second signal distribution”) indicating a change in capacitance associated with approach of the upper electrode  32 . The second signal distribution typically has a shape including one or two peaks at a position Q 2 . Here, the position Q 2  corresponds to a position of projection of the shoulder (position P 2 ) of the upper electrode  32  onto the sensor plane. In addition, a position Q 3  described later corresponds to a position of projection of the center (position P 3 ) of the upper bottom surface of the upper electrode  32  onto the sensor plane. 
       FIG.  3    depicts diagrams illustrating an example of the signal distributions detected by the touch sensor  18  in the contact state of the electronic pen  14 . More specifically,  FIG.  3 A  illustrates first signal distributions, and  FIG.  3 B  illustrates second signal distributions. The horizontal axis of the graph represents relative positions (unit: mm) with respect to the instruction position of the electronic pen  14 , and the vertical axis of the graph represents signal values (unit: none) normalized to [0, 1]. The plus and minus signs are defined such that the signal value is “positive” when the electronic pen  14  approaches. The shapes of the first and second signal distributions change according to the inclination angle of the electronic pen  14 . In  FIGS.  3 A and  3 B , three curves obtained by changing the inclination angle are displayed on top of each other. 
     As illustrated in  FIG.  3 A , the first signal distributions have substantially similar shapes regardless of the size of the inclination angle. This is because the top of the tip electrode  30  is usually at a position closest to the sensor plane, when the electronic pen  14  is being used, and the position Q 1  substantially coincides with the position P 1 . On the other hand, as illustrated in  FIG.  3 B , the position or the number of peaks in the second signal distributions significantly varies according to the change in inclination angle. This is because part of the shoulder of the upper electrode  32  is usually at a position closest to the sensor plane, when the electronic pen  14  is being used, and the distance between the positions Q 1  and Q 2  varies according to the inclination angle. 
     The coordinates of the positions Q 1  and Q 2  can be used to estimate the position and the posture (hereinafter, also referred to as a pen state) of the electronic pen  14 . For example, the instruction position corresponds to the position Q 1  illustrated in  FIG.  2   . In addition, the inclination angle corresponds to an angle  0  formed by the sensor plane and the axis of the electronic pen  14 . More specifically, the angle θ is equal to 0° when the electronic pen  14  is parallel to the sensor plane, and the angle θ is equal to 90° when the electronic pen  14  is perpendicular to the sensor plane. Note that, other than the angle, the azimuth may be used as the physical quantity indicating the tilt state of the electronic pen  14 , for example. 
       FIG.  4    is a diagram illustrating a tendency of an estimation error related to the instruction position. The horizontal axis of the graph represents actual values (unit: mm) of the instruction position, and the vertical axis of the graph represents estimated values (unit: mm) of the instruction position. Here, the midpoint of the line electrode  18   x  in the width direction is defined as X=0 (mm). Note that, when the estimation error is 0, a straight line with a tilt of 1 passing through an origin O is obtained. 
     The signal distribution is, for example, a set of signal values sampled at equal intervals (pitch ΔX), and an interpolation operation is performed to more accurately estimate the peak of the signal distribution (that is, an instruction position). However, a fitting error occurs depending on the type of interpolation function, and periodical “interpolation approximation errors” occur in pitches. 
     In addition, when the inclination angle is estimated on the basis of the position P 3  (see  FIG.  2   ) of the upper electrode  32 , the position Q 2  coincides with the position Q 3  where θ=0°, and there is no estimation error caused by the inclination angle. However, in a case where θ&gt;0°, the estimated inclination angle is small due to the deviation of the positions Q 2  and Q 3 . As a result, the obtained estimated value is shifted in the positive direction (that is, an inclination direction of the electronic pen  14 ), and what is generally called an “offset error” occurs. 
     In this way, when two electrodes at different positions and shapes are used to estimate the pen state, the estimation accuracy of the instruction position or the inclination angle may vary due to the interpolation approximation error and the offset error. Thus, a method that reduces these two types of errors at the same time to improve the estimation accuracy of the pen state is proposed. 
     First Embodiment 
     Hereinafter, a pen detection function  28 A of the touch IC  20  according to a first embodiment will be described with reference to  FIGS.  5  to  11   . 
     Configuration and Operation 
       FIG.  5    is a block diagram illustrating the pen detection function  28 A according to the first embodiment. The pen detection function  28 A includes a signal acquisition unit  40 , a feature value calculation unit  42 , an angle estimation unit  44 , and a position estimation unit  46 . Next, an operation of the touch IC  20  associated with execution of the pen detection function  28 A will be described with reference to a flow chart of  FIG.  6   . 
     In step S 1  of  FIG.  6   , the signal acquisition unit  40  acquires, from the touch sensor  18 , the first signal distribution and the second signal distribution through the scan operation of the line electrodes  18   x  and  18   y.  The signal distributions may be one-dimensional signal distributions along the X-axis or the Y-axis or may be two-dimensional signal distributions on the XY-axis plane. Here, an example of one-dimensional signal distributions along the X-axis will be described. 
       FIG.  7    is a diagram illustrating an example of signal distributions acquired from the touch sensor  18 . The horizontal axis of the graph represents line numbers (that is, identification numbers of line electrodes  18   x ), and the vertical axis of the graph represents signal values. In the situation illustrated here, two electronic pens  14  are detected at the same time. In this case, two peaks with narrow widths are generated in the signal distributions, around the instruction positions of the electronic pens  14 . On the other hand, the signal values are 0 or small values at remaining positions excluding the two peaks. Hereinafter, the entire signal distribution may be referred to as an “entire distribution,” and a local signal distribution with a relatively large change in capacitance may be referred to as a “local distribution.” Here, “relatively large” may be that the amount of change is larger than that at positions other than the local distribution or may be that the amount of change is larger than a predetermined threshold. 
     From another point of view, the “entire distribution” is a signal distribution corresponding to all of the arranged line electrodes  18   x,  and the “local distribution” is a signal distribution corresponding to part of the arranged line electrodes  18   x.  The ratio (n/N) of the number of electrodes n exhibiting the local distribution to the number of electrodes N exhibiting the entire distribution is preferably, for example, equal to or smaller than ½, more preferably, equal to or smaller than ¼, and yet more preferably, equal to or smaller than ⅛. 
     In other words, the numbers of line electrodes  18   x  and  18   y  exhibiting the local distribution are smaller than the numbers of arranged line electrodes  18   x  and  18   y  exhibiting the entire distribution. Here, “small” denotes that, when, for example, the sensor electrodes include N rows vertically×M columns horizontally (for example, 50 rows×70 columns),
         [1] level values of current or voltage of less than N electrodes, preferably, less than N/2 electrodes, more ideally, less than 10 electrodes, are used to determine the coordinate in the vertical direction, and       

     [2] level values of current or voltage of less than M electrodes, preferably, less than M/2 electrodes, more ideally, less than 10 electrodes, are used to determine the coordinate in the horizontal direction. 
     It is desirable that the numbers be the same in the vertical direction and the horizontal direction. In this way, for example, in the case of the 50×70 sensor electrodes in the example described above, the two-dimensional coordinates can be obtained by learning of, for example, 10+10, as compared to learning of a neural network corresponding to the number of states of cross points (the number of inputs of 3,500). The order of the number of calculations, such as the number of multiplications, computed in the neural network can be reduced from exponential (square) to linear (10+10). 
     Note that, when the sensor electrodes include N block electrodes vertically and M block electrodes horizontally, level values of current or voltage of less than N electrodes in the vertical direction, preferably, less than N/2 electrodes in the vertical direction, and more ideally, less than 10 electrodes in the vertical direction, are used. 
     In step S 2 , the feature value calculation unit  42  uses the first signal distribution acquired in step S 1 , to calculate feature values (hereinafter, referred to as “first feature values”) indicating the shape feature of the first signal distribution. Similarly, the feature value calculation unit  42  uses the second signal distribution acquired in step S 1 , to calculate feature values (hereinafter, referred to as “second feature values”) indicating the shape feature of the second signal distribution. 
     As illustrated in  FIG.  8 A , it is assumed that the obtained signal distribution includes S n−2 =0.15/ Sn n−1 =0.40/S n =0.80/Sn +1 =0.30/Sn n+2 =0.10 in ascending order of line number. Note that the signal values in other line numbers are 0 or small values that can be ignored. {G i } and {F i } are calculated according to, for example, the following Equations (1) and (2). 
         G   i =( S   i   −S   i−2 )+( S   i−1   +S   i−3 )  (1)
 
         F   i   =|G   i |/max{|G i |}  (2)
 
     As a result, a “tilt with sign” {G i } illustrated in  FIG.  8 B  and a feature value {F i } illustrated in  FIG.  8 B  are calculated. As can be understood from Equation (2), the feature value {F i } corresponds to the “tilt without sign” normalized in the range of [0, 1]. 
     Note that the feature value calculation unit  42  may calculate various feature values characterizing the shape of the signal distribution instead of the tilts of the signal distribution or the absolute values of the tilts. In addition, the feature value calculation unit  42  may use the same calculation method as in the case of the first feature values to calculate the second feature values or may use a calculation method different from the case of the first feature values to calculate the second feature values. In addition, the feature values may be the signal distribution itself. Although the feature value calculation unit  42  calculates one feature value for each of the line electrodes  18   x  and  18   y,  the relation between the number of line electrodes  18   x  and  18   y  and the number of feature values is not limited to the example. That is, instead of the one-to-one relation, the relation may be a one-to-many, many-to-one, or many-to-many relation. 
     Here, the feature value calculation unit  42  uses only the local distributions to calculate the feature values (hereinafter, referred to as “local feature values”) and reduce the number of feature values used for estimation described later. Specifically, the feature value calculation unit  42  may extract the local distributions from the entire distribution and then use the local distributions to calculate the local feature values or may calculate the feature values across the entire distribution and then extract the local feature values corresponding to the local distributions. The local feature values may include a certain number of pieces of data (for example, N pieces) regardless of the number of arranged line electrodes  18   x  and  18   y.  The constant number of data used for estimation can make a uniform estimate independent of the configuration of the touch sensor  18 . 
     When the local feature values are used, the first feature values include first local feature values and a reference position, and the second feature values include second local feature values. The “first local feature values” denote local feature values related to only the local distribution (that is, the first local distribution) included in the first signal distribution. The “second local feature values” denote local feature values related to only the local distribution (that is, the second local distribution) included in the second signal distribution. The “reference position” denotes a position of a reference point of the first local distribution in the sensor coordinate system, and the “reference position” may be, for example, one of a rising position, a falling position, and a peak position of the first local distribution or may be a neighborhood position of these. 
     In step S 3  of  FIG.  6   , the angle estimation unit  44  estimates the inclination angle of the electronic pen  14  from the second feature values calculated in step S 2 . Further, the feature value calculation unit  42  estimates the instruction position of the electronic pen  14  from the first feature values and the inclination angle. A machine learning estimator  50  is used to estimate the pen state. The machine learning may be, for example, “learning with training” in which training data obtained by actual measurement or calculation simulation is used. 
       FIG.  9    is a diagram illustrating a configuration of the estimator  50  included in the pen detection function  28 A of  FIG.  5   . The estimator  50  includes a former computation element  52 , a latter computation element  54 , and an adder  56  sequentially connected in series. The former computation element  52  corresponds to the angle estimation unit  44  illustrated in  FIG.  5   , and the latter computation element  54  and the adder  56  correspond to the position estimation unit  46  illustrated in  FIG.  5   . 
     Note that circles in  FIG.  9    represent computation units corresponding to neurons of the neural network. The values of the “first local feature values” corresponding to the tip electrode  30  are stored in the computation units with “T.” The values of the “second local feature values” corresponding to the upper electrode  32  are stored in the computation units with “U.” The “inclination angle” is stored in the computation unit with “A.” The “relative position” is stored in the computation unit with “P.” 
     The former computation element  52  is, for example, a hierarchical neural net computation element including an input layer  52   i,  a middle layer  52   m,  and an output layer  52   o.  The input layer  52   i  includes N computation units for inputting the values of the second local feature values. The middle layer  52   m  includes M (here, M=N) computation units. The output layer  52   o  includes one computation unit for outputting the inclination angle. 
     The latter computation element  54  is, for example, a hierarchical neural net computation element including an input layer  54   i,  a middle layer  54   m,  and an output layer  54   o.  The input layer  54   i  includes (N+1) computation units for inputting the values of the first local feature values and the inclination angle. The middle layer  54   m  includes, for example, M (here, M=N) computation units. The output layer  54   o  includes one computation unit for outputting the relative position between the reference position and the instruction position. 
     The adder  56  adds the relative position from the latter computation element  54  to the reference position included in the first feature values, to output the instruction position of the electronic pen  14 . The instruction position is a position corresponding to the peak center of the first local distribution, and the resolution is higher than the pitch of the line electrodes  18   x  and  18   y.    
       FIG.  10    is a diagram illustrating an implementation example of the estimator  50  in  FIG.  9   . The estimator  50  includes a common computation element  60 , four switches  61 ,  62 ,  63 , and  64  that can be synchronously switched, and a holding circuit  65 . The common computation element  60  is a neural net computation element that inputs (N+1) variables and that outputs one variable, and the common computation element  60  can be used in common as the former computation element  52  or the latter computation element  54  of  FIG.  9   . 
     The switch  61  switches and outputs one of a first learning parameter group (that is, a learning parameter group for position computation) and a second learning parameter group (that is, a learning parameter group for angle computation) in response to input of a switch signal. Here, the output side of the switch  61  is connected to the common computation element  60 , and the learning parameter group is selectively supplied to the common computation element  60 . 
     The computation rule of the common computation element  60  is determined by values of learning parameters included in the learning parameter group. The learning parameter group includes, for example, coefficients describing activation functions of computation units, “variable parameters” including the coupling strength between computation units, and “fixed parameters” (what is generally called hyperparameters) for specifying the architecture of learning model. Examples of the hyperparameters include the number of computation units included in each layer and the number of middle layers. The architecture is fixed in the implementation example, and thus, the learning parameter group includes only the variable parameters. 
     The switch  62  outputs one of the first local feature values (that is, the input values for position computation) and the second local feature values (that is, the input values for angle computation) in response to input of a switch signal. The output side of the switch  62  is connected to the input side of the common computation element  60 , and the local feature values are selectively supplied to the common computation element  60 . 
     The switch  63  switches and outputs one of a held value (here, an estimated value of an inclination angle) in the holding circuit  65  and dummy information (for example, a zero value) in response to input of a switch signal. The output side of the switch  63  is connected to the input side of the common computation element  60 , and the inclination angle is supplied to the common computation element  60  only at the time of execution of the position computation. 
     The switch  64  switches and outputs one of an output value (here, an estimated value of an instruction position) of the common computation element  60  and dummy information (for example, a zero value) in response to input of a switch signal. Therefore, the instruction position is output from the switch  64  only at the time of execution of the position computation. 
     The holding circuit  65  temporarily holds the output value of the common computation element  60 . The inclination angle and the instruction position are alternately held in the holding circuit  65 , and in practice, the held value is read only at the time of execution of the position computation. 
     In this way, the estimator  50  of  FIGS.  9  and  10    is used to estimate the instruction position of the electronic pen  14  (step S 3 ). Although the neural network is used to construct the estimator  50  in the example, the method of machine learning is not limited to this. For example, various methods including a logistic regression model, a support vector machine (SVM), a decision tree, a random forest, and a boosting method may be adopted. 
     In step S 4  of  FIG.  6   , the pen detection function  28 A supplies data including the instruction position and the inclination angle estimated in step S 3  to the host processor  22 . For example, the pen detection function  28 A may repeat steps S 1  to S 3  twice to estimate the X-axis coordinate value and the Y-axis coordinate value and supply the coordinate values (X, Y) of the instruction position to the host processor  22 . Alternatively, the pen detection function  28 A may estimate the coordinate values (X, Y) of the instruction position at the same time through steps S 1  to S 3  and supply the coordinate values (X, Y) to the host processor  22 . 
     In this way, the flow chart of  FIG.  6    is finished. The touch IC  20  sequentially executes the flow chart at predetermined time intervals to detect the instruction positions according to the movement of the electronic pen  14 . 
     Comparison of Estimation Accuracy 
     Next, an improvement effect for the estimation accuracy of the machine learning estimator  50  will be described with reference to  FIG.  11   .  FIG.  11 A  is a diagram illustrating estimation accuracy of the instruction position in the “conventional example,” and  FIG.  11 B  is a diagram illustrating estimation accuracy of the instruction position in the “embodiments.” Here, five inclination angles are set, and the sizes of interpolation approximation errors (upper bars) and offset errors (lower bars) are calculated. Note that a method of using a predetermined interpolation function for the signal distribution to calculate the positions Q 1  and Q 2  is used for comparison (conventional example). 
     As illustrated in  FIG.  11 A , substantially constant interpolation approximation errors occur regardless of the inclination angle in the conventional example, and the offset errors increase with an increase in the inclination angle. On the other hand, as illustrated in  FIG.  11 B , the interpolation approximation errors in the embodiments are reduced to half or less than half the conventional example, and the offset errors are small regardless of the inclination angle. 
     Conclusion of First Embodiment 
     In this way, the touch IC  20  is a pen state detection circuit that detects the state of the electronic pen  14  including a first electrode, on the basis of the signal distribution detected by the capacitance touch sensor  18  including a plurality of sensor electrodes (line electrodes  18   x  and  18   y ) arranged in a plane shape. Further, the touch IC  20  (one or a plurality of processors) acquires, from the touch sensor  18 , the first signal distribution indicating the change in capacitance associated with the approach of the first electrode (S 1  of  FIG.  6   ) and uses the machine learning estimator  50  to estimate the instruction position or the inclination angle of the electronic pen  14  from the first feature values related to the first signal distribution (S 3 ). Further, the first feature values include the first local feature values related to the first local distribution corresponding to the line electrodes  18   x  and  18   y  in a number fewer than the number of arranged line electrodes  18   x  and  18   y  exhibiting the first signal distribution. 
     Alternatively, when the electronic pen  14  includes the first electrode and a second electrode, the touch IC  20  (one or a plurality of processors) acquires, from the touch sensor  18 , the first signal distribution indicating the change in capacitance associated with the approach of the first electrode and the second signal distribution indicating the change in capacitance associated with the approach of the second electrode (S 1  in  FIG.  6   ) and uses the machine learning estimator  50  to estimate the instruction position or the inclination angle of the electronic pen  14  from the first feature values related to the first signal distribution and the second feature values related to the second signal distribution (S 3 ). Further, the first feature values include the first local feature values corresponding to the line electrodes  18   x  and  18   y  in a number fewer than the number of arranged line electrodes  18   x  and  18   y  exhibiting the first signal distribution, and the second feature values include the second local feature values related to the second local distribution corresponding to the line electrodes  18   x  and  18   y  in a number fewer than the number of arranged line electrodes  18   x  and  18   y  exhibiting the second signal distribution. 
     In this way, the machine learning estimator  50  can be used to extract potential detection patterns through machine learning, and this facilitates appropriate reflection of the tendency of the detection patterns in estimating the instruction position or the inclination angle. This improves the estimation accuracy of the pen state in the electronic pen  14  including at least one electrode. In addition, the local feature values related to the local distribution corresponding to the line electrodes  18   x  and  18   y  in a number fewer than the number of arranged line electrodes  18   x  and  18   y  exhibiting the signal distribution can be used to reduce the processing load of the estimator  50  to which the local feature values are to be input. 
     In addition, the first electrode may be the tip electrode  30  that has a shape symmetrical with respect to the axis of the electronic pen  14  and that is provided at the tip of the electronic pen  14 , and the second electrode may be the upper electrode  32  that has a shape symmetrical with respect to the axis of the electronic pen  14  and that is provided on the base end side of the tip electrode  30 . The relation between the inclination angle and the detection position of the electronic pen  14  tends to vary according to the three-dimensional shape of the upper electrode  32 , making the improvement effect for the estimation accuracy more noticeable. 
     In addition, the first local feature values and/or the second local feature values may include a certain number of pieces of data regardless of the number of arranged line electrodes  18   x  and  18   y.  The constant number of data used for estimation can make a uniform estimate independent of the configuration of the touch sensor  18  (that is, the number of arranged line electrodes  18   x  and  18   y ). 
     In addition, the first (or second) local distribution may be a distribution with a relatively large change in capacitance in the first (or second) signal distribution. The first (or second) local feature values excluding the signal distribution with a relatively small change in capacitance as compared to the first (or second) local distribution are used, making the improvement effect for the estimation accuracy more noticeable. 
     In addition, the first feature values may further include the reference position of the first local distribution in the sensor coordinate system defined on the detection surface of the touch sensor  18 . The estimator  50  may be able to execute position computation with the relative position between the reference position and the instruction position as an output value. The touch IC  20  may add the relative position to the reference position to estimate the instruction position. 
     In addition, the estimator  50  may be able to sequentially execute angle computation with the second local feature values as input values and with the inclination angle as an output value; and position computation with the first local feature values and the inclination angle as input values and with the relative position as an output value. The inclination angle highly correlated with the instruction position is explicitly used to perform the position computation, and this further increases the estimation accuracy of the instruction position. 
     Further, the estimator  50  may include the switch  61  that can switch and output one of the learning parameter group for angle computation and the learning parameter group for position computation; the switch  62  that can switch and output one of the input value for angle computation and the input value for position computation; and the common computation element  60  that can selectively execute the angle computation or the position computation according to the switch of the switches  61  and  62 . As a result, the configuration of the computation element is simpler than that in the case where the computation elements used for two purposes are separately provided. 
     In addition, the first local feature values may include feature values indicating the tilts of the first local distribution or the absolute values of the tilts, and the second local feature values may include feature values indicating the tilts of the second local distribution or the absolute values of the tilts. The local feature values tend to strongly characterize the detection pattern, making it easier to improve the accuracy. 
     Modifications of First Embodiment 
     Next, first to fifth modifications of the first embodiment will be described with reference to  FIGS.  12  to  15   . Note that the same reference signs are provided to constituent elements similar to those of the case of the first embodiment, and the description may not be repeated. 
     First Modification 
       FIG.  12 A  is a block diagram illustrating a pen detection function  28 B according to the first modification of the first embodiment. The pen detection function  28 B includes the signal acquisition unit  40 , the feature value calculation unit  42 , and a position estimation unit  80  configured differently from that in the first embodiment. That is, the pen detection function  28 B is different from the configuration of the pen detection function  28 A of  FIG.  5    in that the angle estimation unit  44  is not provided. 
       FIG.  12 B  is a diagram illustrating a configuration of an estimator  82  included in the pen detection function  28 B of  FIG.  12 A . The estimator  82  corresponds to the position estimation unit  80  illustrated in  FIG.  12 A . The estimator  82  is, for example, a hierarchical neural net computation element including an input layer  82   i,  a middle layer  82   m,  and an output layer  82   o.  The input layer  82   i  includes 2N computation units for inputting the values of the first local feature values and the second local feature values. The middle layer  82   m  includes M (here, M=2N) computation units. The output layer  82   o  includes one computation unit for outputting the relative position between the reference position and the instruction position. 
     In this way, the estimator  82  of the pen detection function  28 B may execute position computation with the first local feature values and the second local feature values as input values and with the relative position as an output value. When this configuration is adopted, the instruction position of the electronic pen  14  can be estimated with high accuracy as in the estimator  50  ( FIG.  9   ) of the first embodiment. 
     Second Modification 
       FIG.  13 A  is a block diagram illustrating a pen detection function  28 C according to the second modification of the first embodiment. The pen detection function  28 C includes the signal acquisition unit  40 , the feature value calculation unit  42 , a feature value combining unit  90 , and a position estimation unit  92  with a function different from that in the first modification. That is, the pen detection function  28 C is different from the pen detection function  28 B of the first modification in that the feature value combining unit  90  is provided. 
       FIG.  13 B  is a diagram illustrating a configuration of an estimator  94  included in the pen detection function  28 C of  FIG.  13 A . The estimator  94  includes a combiner  96  and a computation element  98 . The combiner  96  corresponds to the feature value combining unit  90  illustrated in  FIG.  13 A , and the computation element  98  corresponds to the position estimation unit  92  illustrated in  FIG.  13 A . 
     The combiner  96  includes a computation element that outputs third feature values (for example, a difference or ratio of local feature values, an average of reference positions, and the like) indicating relative values between the first feature values and the second feature values. Note that the values of the “third feature values” obtained by combining are stored in computation units with “C.” 
     The computation element  98  is, for example, a hierarchical neural net computation element including an input layer  98   i,  a middle layer  98   m,  and an output layer  980 . The input layer  98   i  includes N computation units for inputting the values of the third feature values. The middle layer  98   m  includes M (here, M=N) computation units. The output layer  98   o  includes one computation unit for outputting the relative position between the reference position and the instruction position. Note that the computation element  98  may be able to output the inclination angle in addition to or instead of the relative position. 
     In this way, the estimator  94  of the pen detection function  28 C may include the combiner  96  that combines the first feature values and the second feature values to output the third feature values; and the computation element  98  that sets the third feature values as input values and sets the instruction position or the inclination angle as an output value. When this configuration is adopted, the instruction position of the electronic pen  14  can also be estimated with high accuracy as in the estimator  50  ( FIG.  9   ) of the first embodiment. 
     Third Modification 
       FIG.  14 A  is a block diagram illustrating a pen detection function  28 D according to the third modification of the first embodiment. The pen detection function  28 D includes the signal acquisition unit  40 , the feature value calculation unit  42 , the feature value combining unit  90 , and a position estimation unit  100  with a function different from that in the second modification. 
       FIG.  14 B  is a diagram illustrating a configuration of an estimator  102  included in the pen detection function  28 D of  FIG.  14 A . The estimator  102  includes a common computation element  104  and a switch  106  and corresponds to the position estimation unit  100  illustrated in  FIG.  14 A . The common computation element  104  is a neural net computation element that inputs third local feature values (N variables) from the feature value combining unit  90  illustrated in  FIG.  14 A  and that outputs the relative position (one variable). Note that the common computation element  104  may be able to output the inclination angle in addition to or instead of the relative position. 
     The switch  106  switches and outputs one of the first learning parameter group (that is, a learning parameter group suitable for the contact state) and the second learning parameter group (that is, a learning parameter group suitable for the hover state) in response to input of a switch signal. Here, the output side of the switch  106  is connected to the common computation element  104 , and the learning parameter group is selectively supplied to the common computation element  104 . 
     Note that the “contact state” denotes a state in which the tip portion of the electronic pen  14  is in touch with the detection surface of the electronic device  12 . On the other hand, the “hover state” denotes a state in which the tip portion of the electronic pen  14  is not in touch with the detection surface of the electronic device  12 . For example, when the electronic pen  14  includes a sensor that detects a press of the tip portion, the touch IC  20  can analyze the downlink signal transmitted from the electronic pen  14  and identify the two states. 
     In this way, the instruction position or the inclination angle of the electronic pen  14  may be estimated by using the estimator  102  in which different learning parameter groups are set according to whether the electronic pen  14  is in the contact state or the hover state. In this way, the tendency of the change in shape of the signal distribution according to the clearance between the electronic pen  14  and the touch sensor  18  can be reflected in the computation, and the estimation accuracy is increased in both states. 
     Fourth Modification 
     The line electrodes  18   x  and  18   y  are connected to one touch IC  20  through extension lines not illustrated. That is, the length of wiring varies according to the positions of the line electrodes  18   x  and  18   y,  and the degree of change in capacitance, that is, the sensitivity, varies in the detection surface of the touch sensor  18 . As a result, a phenomenon, such as distortion of local distribution, may occur, and this may impair the estimation accuracy of the pen state. Therefore, the non-uniformity of sensitivity may be taken into account to estimate the pen state. 
       FIG.  15    is a diagram illustrating a configuration of an estimator  110  according to the fourth modification of the first embodiment. The estimator includes a former computation element  112  and a latter computation element  114  sequentially connected in series. The former computation element  112  corresponds to the angle estimation unit  44  illustrated in  FIG.  5   , and the latter computation element  114  corresponds to the position estimation unit  46  illustrated in  FIG.  5   . 
     Note that circles in  FIG.  15    represent computation units corresponding to neurons of the neural network. The values of the “first local feature values” corresponding to the tip electrode  30  are stored in the computation units with “T.” The values of the “second local feature values” corresponding to the upper electrode  32  are stored in the computation units with “U.” The “inclination angle” is stored in the computation unit with “A.” The “position” (relative position or instruction position) is stored in the computation unit with “P.” 
     The former computation element  112  is, for example, a hierarchical neural net computation element including an input layer  112   i,  a middle layer  112   m,  and an output layer  112   o.  The input layer  112   i  includes (N+1) computation units for inputting the reference position of the second local distribution and the values of the second local feature values. The middle layer  112   m  includes M (here, M=N) computation units. The output layer  112   o  includes one computation unit for outputting the inclination angle. 
     The latter computation element  114  is, for example, a hierarchical neural net computation element including an input layer  114   i,  a middle layer  114   m,  and an output layer  114   o.  The input layer  114   i  includes (N+2) computation units for inputting the reference position of the first local distribution, the values of the first local feature values, and the inclination angle. The middle layer  114   m  includes M (here, M=N) computation units. The output layer  114   o  includes one computation unit for outputting the relative position (or the instruction position). 
     In this way, the estimator  110  may execute the position computation with the first local feature values and the reference position as input values and with the relative position or the instruction position as an output value. This can reflect the tendency of the change in shape of the first local distribution according to the reference position, and the estimation accuracy is higher than that in the case where the reference position is not input. 
     Fifth Modification 
     Although the holding circuit  65  illustrated in  FIG.  10    is connected to a first input side (upper side of  FIG.  10   ) of the switch  63  in the first embodiment, the holding circuit  65  may conversely be connected to a second input side (lower side of  FIG.  10   ) of the switch  63 . In this way, the estimator  50  can use the first local feature values and the instruction position of last time to estimate the inclination angle of this time. Alternatively, a delay circuit can be provided between the common computation element  60  and the holding circuit  65  in place of the switch  63  to make both [1] an estimate of the instruction position of this time by further using the inclination angle of this time and [2] an estimate of the inclination angle of this time by further using the instruction position of last time. 
     Second Embodiment 
     Next, a pen detection function  28 E of a touch IC  140  according to a second embodiment will be described with reference to  FIGS.  16  to  19   . 
     Configuration and Operation 
     The basic configuration in the second embodiment is similar to that in the first embodiment ( FIGS.  1  to  4   ), and the description will thus not be repeated. However, a case in which the electronic pen  14  ( FIG.  2   ) includes only the tip electrode  30  will be illustrated. 
       FIG.  16    is a block diagram illustrating the pen detection function  28 E according to the second embodiment. The pen detection function  28 E includes a signal acquisition unit  142 , a feature value calculation unit  144 , a computation selection unit  146 , and a position estimation unit  148 . Next, an operation of the touch IC  140  associated with execution of the pen detection function  28 E will be described with reference to a flow chart of  FIG.  17   . 
     In step S 11  of  FIG.  17   , the signal acquisition unit  142  acquires the signal distributions from the touch sensor  18  through the scan operation of each of the line electrodes  18   x  and  18   y.  This operation is similar to that in the first embodiment (step S 1  of  FIG.  6   ), and the details will not be described. 
     In step S 12 , the feature value calculation unit  144  uses the signal distributions acquired in step S 11  and calculates the feature values related to the signal distributions. The feature value calculation unit  144  may calculate the same feature values as those in the case of the first embodiment (step S 2  of  FIG.  6   ) or may calculate feature values different from those in the case of the first embodiment. For example, the feature value calculation unit  144  may calculate feature values related to the entire signal distribution instead of the local feature values. 
     In step S 13 , the computation selection unit  146  selects one of a plurality of learning parameter groups on the basis of the feature values calculated in step S 12 . Prior to the selection, the computation selection unit  146  determines whether or not the projection position of the tip electrode  30  interferes with a periphery of the touch sensor  18 . 
       FIG.  18    is a diagram illustrating an example of a definition of a sensor area  150  included in the touch sensor  18 . The sensor coordinate system is a two-dimensional 
     Cartesian coordinate system including two axes (X-axis and Y-axis) passing through an origin O. The origin O is a feature point (for example, an upper left vertex) on the detection surface of the touch sensor  18 . The X-Y plane coincides with the plane direction of the detection surface. A frame-shaped peripheral area  152  corresponding to the periphery of the touch sensor  18  is set in part of the sensor area  150 . The shape of the peripheral area  152  (for example, a width, position, size, and the like) can be set in various ways according to the electronic device  12  or the electronic pen  14 . Note that a remaining area of the sensor area  150  excluding the peripheral area  152  will be referred to as a general area  154 . 
       FIG.  19    depicts diagrams illustrating a tendency of local feature values calculated from various signal distributions. More specifically,  FIG.  19 A  illustrates local feature values of a case in which the projection position of the tip electrode  30  ( FIG.  2   ) included in the electronic pen  14  is in the general area  154 . In addition,  FIG.  19 B  illustrates local feature values of a case in which the projection position of the tip electrode  30  is in the peripheral area  152 . In  FIG.  19   , a plurality of polygonal lines or plots obtained by changing the inclination angles are displayed on top of each other. 
     For example, it is assumed that the feature value calculation unit  144  extracts six pieces of data with consecutive addresses from the feature values calculated across the entire signal distribution and thereby calculates the local feature values corresponding to unit numbers 0 to 5. As can be understood from  FIG.  19 B , part of the signal distribution cannot be detected outside of the sensor area  150 , and there may be a case where part of the local feature values is missing. That is, when the instruction position is estimated by applying a uniform computation rule to two types of local feature values with significantly different tendencies of shape, the estimation accuracy may vary. 
     Thus, the computation selection unit  146  selects a learning parameter group for general area computation and supplies the learning parameter group to the position estimation unit  148  when the reference position included in the feature values is in the general area  154 . On the other hand, the computation selection unit  146  selects a learning parameter group for peripheral area computation and supplies the learning parameter group to the position estimation unit  148  when the reference position is in the peripheral area  152 . 
     In step S 14  of  FIG.  17   , the position estimation unit  148  estimates the instruction position of the electronic pen  14  from the feature values calculated in step S 12 . Specifically, the position estimation unit  148  estimates the instruction position suitable for the projection position of the tip electrode  30  by using the estimator in which the learning parameter group is selectively set. Note that the position estimation unit  148  may be able to estimate the inclination angle in addition to or instead of the instruction position. 
     In step S 15 , the pen detection function  28 E supplies, to the host processor  22 , data including the instruction position estimated in step S 14 . In this way, the flow chart of  FIG.  17    is finished. The touch IC  140  sequentially executes the flow chart at predetermined time intervals to detect the instruction positions according to the movement of the electronic pen  14 . 
     Conclusion of Second Embodiment 
     As described above, the touch IC  140  is a pen state detection circuit that detects the state of the electronic pen  14  including the tip electrode  30 , on the basis of the signal distribution detected by the capacitance touch sensor  18  including the plurality of line electrodes  18   x  and  18   y  arranged in a plane shape. Further, the touch IC  140  (one or a plurality of processors) acquires, from the touch sensor  18 , the signal distribution indicating the change in capacitance associated with the approach of the tip electrode  30  (S 11  of  FIG.  17   ) and follows different computation rules according to the projection position of the tip electrode  30  on the detection surface of the touch sensor  18 , to estimate the instruction position or the inclination angle of the electronic pen  14  from the feature values related to the signal distribution (S 13  and S 14 ). 
     In this way, an estimate suitable for the projection position can be made by application of different computation rules according to the projection position of the tip electrode  30  included in the electronic pen  14 , and this can suppress the reduction in the estimation accuracy of the pen state caused by the relative positional relation between the electronic pen  14  and the touch sensor  18 . 
     For example, the computation rules may be rules for estimating the instruction position or the inclination angle of the electronic pen  14 , and the touch IC  140  may estimate the instruction position or the inclination angle by using an estimator in which different learning parameter groups are set according to whether or not the projection position of the tip electrode  30  interferes with the periphery of the touch sensor  18 . 
     In addition, the local feature values related to the local distribution corresponding to the line electrodes  18   x  and  18   y  in a number fewer than the number of arranged line electrodes  18   x  and  18   y  exhibiting the signal distribution can be used to reduce the processing load of the estimator  50  to which the local feature values are to be input. Alternatively, the local feature values excluding the signal distribution with a smaller change in capacitance than in the local distribution are used, making the improvement effect for the estimation accuracy more noticeable. 
     Modification of Second Embodiment 
     Although the computation rule for estimating the instruction position or the inclination angle of the electronic pen  14  is changed in the second embodiment, other computation rules may be changed. 
       FIG.  20    is a block diagram illustrating a pen detection function  28 F according to a modification of the second embodiment. The pen detection function  28 F includes the signal acquisition unit  142 , the feature value calculation unit  144 , a shift processing unit  160 , and the position estimation unit  148 . That is, the pen detection function  28 F is different from the configuration of the pen detection function  28 E of  FIG.  16    in that the shift processing unit  160  is provided in place of the computation selection unit  146 . 
     The shift processing unit  160  shifts the positions of the local feature values calculated by the feature value calculation unit  144 , as necessary. In terms of function, the shift processing unit  160  does not execute the shift process when there is no missing of local distribution, but the shift processing unit  160  executes the shift process when there is missing of local distribution. Specifically, the shift processing unit  160  specifies a rising position or a falling position of the local distribution from adjacent differences between the local feature values and determines the direction and amount of shift so that both positions fall within a predetermined range. In this way, when part of the local distribution is missing, the addresses of the local feature values are relatively shifted such that the peak center of the local distribution comes closer to the center. 
       FIG.  21    depicts diagrams illustrating an advantageous effect of the shift process of the local feature values in the peripheral area  152  of  FIG.  18   . More specifically,  FIG.  21 A  illustrates the local feature values before the shift process, and  FIG.  21 B  illustrates the local feature values after the shift process. In  FIG.  21   , two polygonal lines (solid line and dashed line) obtained by changing the inclination angles are displayed on top of each other. 
     The local feature values of  FIG.  21 A  are calculated by using the local distributions with the peak centers at the position of unit number 5. On the other hand, the addresses of the local feature values illustrated in  FIG.  21 A  are shifted by “2” to the negative side to obtain the local feature values of  FIG.  21 B . Through the shift process, the local feature values are adjusted such that the peak centers of the local distributions come to the position of unit number 3. As a result, the addresses of the local feature values in the peripheral area  152  where there may be missing of local distribution can be brought into line with the addresses of the local feature values in the general area  154  where there is no missing of local distribution. This can easily suppress the reduction in the estimation accuracy for the pen state caused by the relative positional relation between the electronic pen  14  and the touch sensor  18 . 
     In this way, the computation rules may be rules for calculating the local feature values, and the touch IC  140  may estimate the instruction position or the inclination angle from the local feature values calculated by following different rules according to whether or not the projection position of the tip electrode  30  interferes with the periphery of the touch sensor  18 . According to the configuration, an effect (that is, an advantageous effect of suppressing the reduction of estimation accuracy) similar to that of the second embodiment can also be obtained. 
     Third Embodiment 
     Next, a pen detection function  28 G of a touch IC  200  according to a third embodiment will be described with reference to  FIGS.  22  to  27   . 
     Configuration and Operation 
     The basic configuration in the third embodiment is similar to that in the first embodiment ( FIGS.  1  to  4   ), and the description will not be repeated. However, a case in which the electronic pen  14  ( FIG.  2   ) includes only the tip electrode  30  will be illustrated. 
       FIG.  22 A  is a block diagram illustrating the pen detection function  28 G according to the third embodiment. The pen detection function  28 G includes a signal acquisition unit  202 , a feature value calculation unit  204 , an autoencoding processing unit (hereinafter, AE processing unit  206 ), and a position estimation unit  208 . Alternatively, as illustrated in  FIG.  22 B , a pen detection function  28 H may include the signal acquisition unit  202 , the AE processing unit  206 , and the position estimation unit  208 . Next, an operation of the touch IC  200  associated with execution of the pen detection functions  28 G and  28 H will be described with reference to a flow chart of  FIG.  23   . 
     In step S 21  of  FIG.  23   , the signal acquisition unit  202  acquires the signal distributions from the touch sensor  18  through the scan operation of each of the line electrodes  18   x  and  18   y.  The operation is similar to that in the first embodiment (step S 1  of  FIG.  6   ), and the details will not be described. 
     In step S 22 , the feature value calculation unit  204  uses the signal distributions acquired in step S 21  and calculates the feature values related to the signal distributions. In the case of the configuration illustrated in  FIG.  22 A , the feature value calculation unit  204  may calculate feature values that are the same as or different from those of the case of the first embodiment (step S 2  of  FIG.  6   ). On the other hand, in the case of the configuration illustrated in  FIG.  22 B , the feature values are the signal distribution itself. For example, in the former case, the feature values related to the entire signal distribution may be used instead of the local feature values. 
     In step S 23 , the AE processing unit  206  applies an autoencoding process described later to the feature values calculated in step S 22 . In step S 24 , the position estimation unit  208  estimates the instruction position from the feature values to which the autoencoding process is applied in step S 23 . The autoencoding process and the estimation of the pen state are performed by a machine learning estimator  210 . 
       FIG.  24    is a diagram illustrating a configuration of the estimator  210  included in the pen detection functions  28 G and  28 H of  FIG.  22   . The estimator  210  includes a former computation element  212  and a latter computation element  214  connected in series. The former computation element  212  corresponds to the AE processing unit  206  illustrated in  FIGS.  22 A and  22 B , and the latter computation element  214  corresponds to the position estimation unit  208  illustrated in  FIGS.  22 A and  22 B . Note that the values of the “feature values” corresponding to the tip electrode  30  are stored in computation units labeled 0 to 5. 
     The estimator  210  is, for example, a five-layered neural net computation element including a first layer  221 , a second layer  222 , a third layer  223 , a fourth layer  224 , and a fifth layer  225 . The first layer  221  includes N computation units for inputting the values of the feature values. The second layer  222  includes M (here, M&lt;N) computation units. The third layer  223  includes the same number of (that is, N) computation units as in the configuration of the first layer  221 . The fourth layer  224  includes, for example, L (here, L=N) computation units. The fifth layer  225  includes one computation unit for outputting the instruction position. 
     The former computation element  212  is a hierarchical neural network computation element including the first layer  221  as an input layer, the second layer  222  as a middle layer, and the third layer  223  as an output layer. In the case of this configuration, the first layer  221  and the second layer  222  perform a dimension compression function, and the second layer  222  and the third layer  223  perform a dimension restoration function. A learning parameter group optimized by learning without training is used for the computation process of the former computation element  212 . 
     The latter computation element  214  is a hierarchical neural network computation element including the third layer  223  as an input layer, the fourth layer  224  as a middle layer, and the fifth layer  225  as an output layer. A learning parameter group optimized by learning with training is used for the computation process of the latter computation element  214 . 
     In step S 25  of  FIG.  23   , the pen detection functions  28 G and  28 H supply data including the instruction position estimated in step S 24  to the host processor  22 . In this way, the flow chart of  FIG.  23    is finished. The touch IC  200  sequentially executes the flow chart at predetermined time intervals to detect the instruction positions according to the movement of the electronic pen  14 . 
     Comparison of Estimation Accuracy 
     Next, an improvement effect for the estimation accuracy of the machine learning estimator  210  will be described with reference to  FIGS.  25  to  27   . 
       FIG.  25    depicts diagrams illustrating variations of the feature values before the execution of the autoencoding process. More specifically,  FIG.  25 A  is a diagram illustrating a tendency of feature values calculated from various signal distributions. In addition,  FIG.  25 B  illustrates a deviation calculated from populations of the feature values in  FIG.  25 A . In  FIGS.  25 A and  25 B , a plurality of polygonal lines or plots obtained by changing the inclination angles are displayed on top of each other. 
       FIG.  26    depicts diagrams illustrating variations of the feature values after the execution of the autoencoding process. More specifically,  FIG.  26 A  is a diagram illustrating results of applying the autoencoding process to the feature values in  FIG.  25 A . In addition,  FIG.  26 B  illustrates a deviation calculated from populations of the feature values in  FIG.  26 A . In  FIGS.  26 A and  26 B , a plurality of obtained polygonal lines or plots are displayed on top of each other. 
     As can be understood from  FIGS.  25 B and  26 B , the deviation (that is, variation) of the feature values is reduced to half or less than half before and after the autoencoding process. That is, an advantageous effect of removing noise components mixed in the feature values is obtained by applying the autoencoding process. 
       FIG.  27 A  is a diagram illustrating estimation accuracy for the instruction position in a “reference example.”  FIG.  27 B  is a diagram illustrating estimation accuracy for the instruction position in the “embodiments.” Here, each instruction position is estimated while the combination of the inclination angle and the amount of added noise is changed, and the relation between the actual value (unit: mm) of the instruction position and the estimation error (unit: μm) is expressed in a scatter diagram. Note that, in this comparison (reference example), only the latter computation element  214  of  FIG.  24    is used to estimate the instruction position. It can be understood by comparing the scatter diagrams that the estimation accuracy for the instruction position is improved by applying the autoencoding process to the feature values. 
     Conclusion of Third Embodiment 
     As described above, the touch IC  200  is a pen state detection circuit that detects the state of the electronic pen  14  including at least one electrode, on the basis of the signal distribution detected by the capacitance touch sensor  18  including the plurality of sensor electrodes (line electrodes  18   x  and  18   y ) arranged in a plane shape. Further, the touch IC  200  (one or a plurality of processors) acquires, from the touch sensor  18 , the signal distribution indicating the change in capacitance associated with the approach of the electrode (S 21  of  FIG.  23   ) and sequentially applies the dimension compression process and the dimension restoration process to the feature values related to the signal distribution, to thereby execute the autoencoding process of outputting the feature values equal to the number of dimensions of the input (S 23 ). The touch IC  200  estimates the instruction position or the inclination angle of the electronic pen  14  by using the feature values to which the autoencoding process is applied (S 24 ). 
     In this way, the autoencoding process can be applied to the feature values related to the signal distribution, to remove the noise components included in the feature values, and the estimation accuracy of the instruction position is improved. Particularly, the estimation accuracy of the instruction position is further increased by using the machine learning estimator  210  (more specifically, the latter computation element  214 ). Note that the feature values may be one of or both the first feature values and the second feature values in the first embodiment. 
     In addition, the touch IC  200  may use the machine learning estimator  210  to estimate the instruction position or the instruction angle from the feature values to which the autoencoding process is applied. For example, in the first embodiment and this modification, the AE processing unit  206  may be added to at least one section of [1] the input side of the position estimation unit  46  ( FIG.  5   ), [2] the input side of the angle estimation unit  68  ( FIG.  5   ), [3] the input side of the position estimation unit  80  ( FIG.  12   ), [4] the input side of the feature value combining unit  90  ( FIGS.  13  and  14   ), and [5] the input side of the position estimation unit  100  ( FIG.  14   ). 
     Fourth Embodiment 
     Next, an input system  250  as a pen state detection system according to a fourth embodiment will be described with reference to  FIGS.  28  to  31   . 
     Overall Configuration 
       FIG.  28    is an overall configuration diagram of the input system  250  as a pen state detection system according to the fourth embodiment. The input system  250  includes one or a plurality of electronic devices  12 , one or a plurality of electronic pens  14 , and a learning computer  252 . Each electronic device  12  can perform two-way communication with the learning computer  252  through a network NW. 
     The learning computer  252  is a server apparatus that performs a management function of a learning parameter group LP suitable for the electronic pen  14 . Specifically, the learning computer  252  includes a communication unit  254 , a control unit  256 , and a storage unit  258 . 
     The communication unit  254  includes a communication interface that can transmit and receive electrical signals to and from external apparatuses. Thus, the learning computer  252  can transmit, to the electronic device  12 , the learning parameter group LP corresponding to the electronic pen  14  according to a request from the electronic device  12 . 
     The control unit  256  may be a general-purpose processor including a CPU or may be a special-purpose processor including a GPU or an FPGA (Field Programmable Gate Array). The control unit  256  reads and executes programs stored in a memory including the storage unit  258 , to function as a data processing unit  260 , a learning processing unit  262 , and a learner  264 . 
     The storage unit  258  includes, for example, a non-transitory storage medium including a hard disk drive (HDD: Hard Disk Drive) and a solid state drive (SSD: Solid State Drive). In the example of  FIG.  28   , a training data group  266  including a set of training data TD and a database (hereinafter, parameter DB  268 ) related to learning parameters are stored in the storage unit  258 . 
     Functional Block Diagram 
       FIG.  29    is a functional block diagram related to a learning process of the control unit  256  illustrated in  FIG.  28   . The control unit  256  uses the prepared training data TD to execute a learning process for the learner  264  and thereby create one or more types of learning parameter groups LP to be applied to the electronic pen  14 .  FIG.  29    schematically illustrates the learning processing unit  262  and the learner  264  among the functional units that can be executed by the control unit  256 . 
     The learning processing unit  262  uses a plurality of sets of training data TD to execute the learning process for the learner  264  (in other words, optimization process of learning parameter groups LP). Specifically, the learning processing unit  262  includes a data acquisition unit  270 , a learning error calculation unit  272 , a parameter update unit  274 , and a convergence determination unit  276 . 
     The data acquisition unit  270  acquires one or a plurality of sets of training data TD from the prepared training data group  266 . The training data TD includes data sets of input vectors and output values and is obtained by actual measurement or calculation simulation. For example, in the case of “actual measurement,” a plurality of positions on the sensor plane may be randomly selected, and the signal distributions at the positions may be measured to create the training data TD. Furthermore, in the case of “calculation simulation,” one of a physical simulation including electromagnetic field analysis or electric circuit analysis and a mathematical simulation including a sampling process, an interpolation process, or noise addition may be used to create the training data TD. 
     The learning error calculation unit  272  calculates an error (hereinafter, referred to as a learning error) between an output value from the learner  284  with respect to the input vector of the training data TD and an output value of the training data TD. The learning error may be an L1-norm function for returning an absolute value of the difference or may be an L2-norm function for returning a square value of the difference. In addition, the learning error may be an error in one set of training data TD (in a case of online learning) or may be an error related to a plurality of sets of training data TD (in a case of batch learning or mini-batch learning). 
     The parameter update unit  274  updates variable parameters of the learning parameter group LP in order to reduce the learning error calculated by the learning error calculation unit  272 . Examples of an update algorithm that can be used include various methods including gradient descent, stochastic gradient descent, momentum method, and RMSprop. 
     The convergence determination unit  276  determines whether or not a predetermined convergence condition is satisfied at the time of current learning. Examples of the convergence condition include that [1] the learning error is sufficiently reduced, [2] the amount of update of the learning error is sufficiently reduced, and [3] the number of repetitions of learning has reached an upper limit. 
     Setting Method for Learning Parameter Group LP 
       FIG.  30    is a diagram illustrating a first example of a setting method for the learning parameter group LP. First, the learning computer  252  uses the training data TD related to various types of electronic pens  14  and performs machine learning. Consequently, a typical learning parameter group LP of the electronic pens  14  is generated. Further, a manufacturing worker of the touch IC  20 ,  140 , or  200  performs an operation of writing, to a memory  280 , the learning parameter group LP stored in the parameter DB  288 . In this way, the touch IC  20 ,  140 , or  200  provided with the memory  280  can fulfill the estimation function of the pen state while the touch IC  20 ,  140 , or  200  is incorporated into the electronic device  12 . 
       FIG.  31    is a diagram illustrating a second example of the setting method for the learning parameter group LP. [1] First, the electronic device  12  attempts to pair with an electronic pen  14  near the electronic device  12 . [2] When the pairing is successful and the electronic pen  14  is detected, the electronic device  12  transmits, to the learning computer  252 , a request signal including the identification information (that is, pen ID) acquired from the electronic pen  14 . [3] The data processing unit  260  of the learning computer  252  searches the parameter DB  268  to acquire the learning parameter group LP corresponding to the pen ID. [4] The learning computer  252  transmits the acquired learning parameter group LP to the electronic device  12  as a transmission source of the request signal. [5] The electronic device  12  sets the learning parameter group LP so that the touch IC  20 ,  140 , or  200  can use the learning parameter group LP. In this way, the touch IC  20 ,  140 , or  200  can fulfill the pen state estimation function. 
     Conclusion of Fourth Embodiment 
     In this way, the input system  250  includes the electronic device  12  including the touch IC  20 ,  140 , or  200 ; the electronic pen  14  used along with the electronic device  12 ; and the learning computer  252  that can perform two-way communication with the electronic device  12  and that can store the learning parameter group LP of the estimator constructed on the touch IC  20 ,  140 , or  200 , the estimator estimating the instruction position or the inclination angle of the electronic pen  14 . 
     Furthermore, when the electronic pen  14  is detected, the electronic device  12  requests the learning computer  252  to transmit the learning parameter group LP corresponding to the electronic pen  14  and holds the learning parameter group LP from the learning computer  252  so that the touch IC  20 ,  140 , or  200  can use the learning parameter group LP. In this way, an estimate suitable for the electronic pen  14  can be made even when the combination of the electronic device  12  and the electronic pen  14  is changed. 
     DESCRIPTION OF REFERENCE SYMBOLS 
       10 ,  250 : Input system (pen state detection system) 
       12 : Electronic device 
       14 : Electronic pen 
       16 : Finger 
       18 : Touch sensor 
       18   x,    18   y : Line electrode 
       20 ,  140 ,  200 : Touch IC (pen state detection circuit) 
       22 : Host processor 
       28  (A, B, C, D, E, F, G, H) : Pen detection function 
       30 : Tip electrode (first electrode) 
       32 : Upper electrode (second electrode) 
       34 : Oscillation circuit 
       50 ,  82 ,  94 ,  102 ,  100 ,  210 : Estimator 
       52 ,  112 ,  212 : Former computation element 
       54 ,  114 ,  214 : Latter computation element 
       60 ,  104 : Common computation element 
       61 : Switch (first switch) 
       62 : Switch (second switch) 
       250 : Learning computer (server apparatus) 
     LP: Learning parameter group 
     TD: Training data 
     The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments. 
     These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.