Author: claimsbot

What is claimed is:

1. A visual axis detecting apparatus comprising:
a visual axis detection circuit for detecting a visual axis of an observer;
an arithmetic circuit for performing an arithmetic operation of calculating a distributed state of variations of visual axis positions obtained in a plurality of visual axis detections executed by the visual axis detection circuit in a state in which the observer is observing a predetermined point, with respect to said predetermined point;
a determination circuit for determining a condition of the visual axis according to the result of detection by the visual axis detection circuit; and
a change circuit for changing a determination process of determining the condition of the visual axis by the determination circuit, according to the distributed state of variations.
2. The apparatus according to claim 1, further comprising:
a storage circuit for storing the distributed state calculated by said arithmetic circuit, wherein said change circuit changes the number of detections by said visual axis detection circuit to a smaller number when the distributed state of variations is small, but the change circuit changes the number of detections by said visual axis detection circuit to a larger number when the distributed state of variations is large, and wherein said determination circuit determines the condition of the visual axis on the basis of the number of detections changed by the change circuit.
3. The apparatus according to claim 2, wherein said determination circuit determines that a position of the visual axis is an average position of a plurality of visual axis positions detected by said visual axis detection circuit.
4. The apparatus according to claim 1, wherein said arithmetic circuit calculates a standard deviation of a plurality of visual axis positions.
5. The apparatus according to claim 1, wherein said predetermined point is displayed on a screen.
6. The apparatus according to claim 5, wherein a plurality of zones are displayed on said screen and a zone is selected out of said plurality of zones according to the condition of the visual axis determined by said determination circuit.
7. The apparatus according to claim 6, wherein said screen is a focusing screen of a camera.
8. The apparatus according to claim 1, further comprising:
a mode setting member for setting a mode of performing the arithmetic operation by said arithmetic circuit and a mode of performing the determination by said determination circuit.
9. A visual axis detecting apparatus comprising:
a screen in which a plurality of zones are formed;
a visual axis detection circuit for detecting a visual axis of an observer;
an arithmetic circuit for performing an arithmetic operation of calculating a distributed state of variations of visual axis positions obtained in a plurality of visual axis detections executed by the visual axis detection circuit in a state in which the observer is observing a predetermined point on said screen, with respect to said predetermined point;
a selection circuit for selecting one of said plurality of zones according to the result of detection by said visual axis detection circuit; and
a change circuit for changing a selection process by said selection circuit according to the distributed state of variations.
10. The apparatus according to claim 9, further comprising:
a storage circuit for storing the distributed state calculated by said arithmetic circuit, wherein said change circuit changes the number of detections by said visual axis detection circuit to a smaller number when the distributed state of variations is small, but the change circuit changes the number of detections by said visual axis detection circuit to a larger number when the distributed state of variations is large, and wherein said selection circuit selects one of said plurality of zones on the basis of the number of detections changed by the change circuit.
11. The apparatus according to claim 10, wherein said selection circuit selects a zone located at an average position of a plurality of visual axis positions detected by said visual axis detection circuit.
12. The apparatus according to claim 9, wherein said arithmetic circuit calculates a standard deviation of a plurality of visual axis positions.
13. The apparatus according to claim 9, wherein said screen is a focusing screen of a camera.
14. The apparatus according to claim 9, further comprising:
a mode setting member for setting a mode of Performing the arithmetic operation by said arithmetic circuit and a mode of performing the selection by said selection circuit.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A method of forming a semiconductor structure, the method comprising:
providing a semiconductor substrate; and
forming a memory cell at a surface of the semiconductor substrate comprising:
forming a gate dielectric on the semiconductor substrate;
forming a control gate on the gate dielectric;
forming a first and a second tunneling layer on a source side and a drain side of memory cell, respectively, wherein the first and the second tunneling layers each comprise a vertical portion on sidewalls of the gate dielectric and the control gate, and a horizontal portion on a top surface of the semiconductor substrate;
tilt implanting a lightly doped source region underlying the first tunneling layer, wherein the tilt implanting tilts only from the source side to the drain side, and wherein a portion of the semiconductor substrate under the second tunneling layer is free from the tilt implanting;
forming a storage on the horizontal portion of the second tunneling layer;
forming a source region in the semiconductor substrate and adjacent to the first tunneling layer; and
forming a drain region in the semiconductor substrate and adjacent to the second tunneling layer;
wherein the completed formed drain region is spaced apart from a respective edge of the control gate and at least a portion of the second tunneling layer overlies a portion of the semiconductor substrate free from impurities from the tilt implanting and the forming of the drain region.
2. The method of claim 1, wherein the step of tilt implanting is performed after the step of forming the storage.
3. The method of claim 1, wherein the storage comprises silicon nitride.
4. The method of claim 1, wherein the lightly doped source region comprises a portion underlying the control gate.
5. The method of claim 1, wherein the lightly doped source region is spaced apart from a respective edge of the control gate.
6. The method of claim 1, wherein the storage is L-shaped, and wherein the memory cell further comprises a dielectric material on a horizontal portion of the storage, and wherein the dielectric material does not extend over the drain region.
7. A method of forming a semiconductor structure, the method comprising:
providing a semiconductor substrate;
forming a gate stack comprising:
forming a gate dielectric on the semiconductor substrate; and
forming a control gate on the gate dielectric;

forming a dielectric layer on the gate stack and the semiconductor substrate;
patterning the dielectric layer to form two tunneling layers on opposite sides of the gate stack, wherein the tunneling layers each comprise a vertical portion on a sidewall of the gate stack and a horizontal portion on a top surface of the semiconductor substrate;
tilt implanting an impurity into the semiconductor substrate to form a lightly doped source region and a lightly doped drain region, wherein the tilt implanting only tilts from a source side to a drain side;
forming a storage layer after the step of forming the dielectric layer;
patterning the storage layer to form storages, wherein each of the storages is on a horizontal portion of one of the tunneling layers; and
implanting a source region and a drain region using the storages as masks;
wherein the completed formed drain region including the lightly doped drain region is spaced apart from a respective edge of the control gate and at least a portion of the second tunneling layer overlies a portion of the semiconductor substrate free from impurities implanted during the tilt implanting and the implanting of the drain region.
8. The method of claim 7, wherein the lightly doped drain region is spaced apart from a respective edge of the control gate by between about 100 \u212b and about 200 \u212b.
9. The method of claim 7, wherein the step of tilt implanting is performed before the step of forming the dielectric layer.
10. The method of claim 7, wherein the step of tilt implanting is performed after the step of forming the dielectric layer and before the step of forming the storage layer.
11. The method of claim 7, wherein the step of implanting is performed after the step of patterning the storage layer.
12. The method of claim 7, wherein the step of implanting the source and the drain regions is performed before the step of patterning the dielectric layer.
13. The method of claim 7, wherein the dielectric layer has a thickness of between about 30 \u212b and about 100 \u212b.
14. The method of claim 7, wherein the storages comprise silicon nitride.
15. The method of claim 7, wherein the lightly doped drain region comprises a portion not overlapped by the drain region.
16. he method of claim 7, wherein the lightly doped drain region is overlapped by the drain region.
17. A method of forming a memory cell, the method comprising:
providing a semiconductor substrate;
forming a gate stack comprising:
forming a gate dielectric on the semiconductor substrate; and
forming a control gate on the gate dielectric;

forming tunneling layers on opposite side of the gate stack, wherein each of the tunneling layer comprises a vertical portion on a sidewall of the gate stack, and a horizontal portion on a top surface of the semiconductor substrate;
forming storages, wherein each of the storages is on a horizontal portion of one of the tunneling layers;
tilt implanting an impurity into the semiconductor substrate after the step of forming the storages to form a lightly doped source region and a lightly doped drain region, wherein the tilt implanting only tilts from a source side to a drain side, and wherein the lightly doped source region and the lightly doped drain region are both spaced apart from respective edges of the control gate; and
implanting a source region and a drain region using the storages as masks.
18. The method of claim 17, wherein the lightly doped source region and a respective edge of the control gate have a first distance, and wherein the lightly doped drain region and a respective edge of the control gate have a second distance, and wherein the first distance is less than the second distance by greater than about 100 \u212b.
19. The method of claim 17, wherein the lightly doped drain region is overlapped by the drain region.

1. A combination lock, comprising:
a housing comprising a cover, a leg, and a resilient member;
a shackle pivotably connected to the housing and comprising a terminating end mated with an open end of the leg, an inner intermediate projection, and a spring-loaded member at the other end;
a tumbler wheel assembly comprising a plurality of tumbler wheels rotatably fastened within the housing, each tumbler wheel including a locking member having a notch, and a moveable member including a plurality of first openings for receiving the locking members, a plurality of protrusions each formed at an inner wall of the first opening, an inward projecting member including a bore and two opposite inclined grooves on an inner wall of the bore, the projecting member being urged by the resilient member;
a mechanism comprising a cylinder, a keyhole in the cylinder, a shaft projected inward, the shaft having a lengthwise flat on its surface, and a cam at a joining portion of the shaft and the cylinder; and
a spring-loaded shuttle assembly biased against the projecting member and comprising a cylinder, a head, a channel in the cylinder, the channel having a lengthwise flat on its inner wall, two opposite tabs on an outer surface of the cylinder proximate an end of the channel, a spring-loaded L-shaped member having a second opening engaged with both the cam and the spring-loaded member;
wherein:
the shaft is inserted through the second opening and the bore into the channel, a distance between one end of the shaft and a blind end of the channel is formed, and the head is engaged with the projection in an unlocked position of the lock; and
in a first lock opening operation a key is inserted into the keyhole and the key is turned to rotate the shaft, the cam, and the cylinder of the shuttle assembly for aligning the tabs with the grooves, causing the cam to contact and push the second opening laterally, causing the L-shaped member to compress the spring-loaded member for opening a gap between the leg and the shackle with the distance between one end of the shaft and the blind end of the channel being decreased to a minimum as the channel slides relative to the shaft, and moving the tabs along the grooves to project the cylinder of the shuttle assembly from one end of the bore to the other end thereof by compressing the resilient member.
2. The combination lock of claim 1, wherein in a second lock opening operation the tumbler wheels are turned to a set series of numbers for aligning the notches of the locking members with the protrusions such that pivotably pushing the shackle inward will cause the projection to press the head, move the projecting member to compress the resilient member, engage the notches of the locking members with the protrusions, and create a gap between the leg and the shackle.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A method of database management, comprising the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model base upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list.
2. The method of database management according to claim 1 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
3. The method of database management according to claim 1 wherein said modifying the condition items is accomplished by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
4. The method of database management according to claim 1 wherein said generating the speculation model further including additional steps of:
dividing the speculation data list into candidate model data and verification data;
generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information;
verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data;
evaluating the candidate speculation models based upon said verifying to generate evaluation values; and
selecting the speculation model from the candidate speculation models based upon the evaluation values.
5. The method of database management according to claim 4 wherein the evaluation values are mean square errors.
6. A method of database management, comprising the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized bated upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model based upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.
7. A system for data mining a database comprising:
a data storage unit for storing data definition information and data;
a characteristic rule generation unit connected to said data storage unit for generating characteristic rules based upon the data definition information and the data, the data definition information including items specifying analysis and conditions, the characteristic rules being stored in said data storage unit;
a segment selection unit connected to said data storage unit for generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension, the multidimensional database being stored in said data storage unit;
a user interface unit connected to said data storage unit for providing a predetermined user-interface for selecting one of the characteristic rules and for modifying the condition items;
a processing unit connected to said storage unit and said user interface unit for outputting to said storage unit a first portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the first portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension, said processing unit also outputting a second portion of the multidimensional database that is corresponding to the modified condition items;
a displaying unit connected to said user-interface unit, said processing unit and said storage unit for displaying the first portion of the multidimensional database and the second portion of the multidimensional database through the predetermined user-interface; and
a speculation processing unit connected to said storage unit and said processing unit for extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list, said speculation processing unit generating a speculation model based upon the data, the selected segment and the speculation data list, said speculation processing unit outputting speculation results based upon the speculation model and the speculation data list.
8. The system for data mining a database according to claim 7 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
9. The system for data mining a database according to claim 7 wherein said user interface unit modifying the condition items by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
10. The system for data mining a database according to claim 7 wherein said speculation processing unit further comprises:
a data dividing unit for dividing the speculation data list into candidate model data and verification data;
a candidate model generation unit for generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information; and
a verification unit for connected to said candidate model generation unit for verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data, said verification unit evaluating the candidate speculation models based upon said verifying to generate evaluation values and selecting the speculation model from the candidate speculation models based upon the evaluation values.
11. The system for data mining a database according to claim 10 wherein the evaluation values are mean square errors.
12. A system for data mining a database comprising:
a data storage unit for storing data definition information and data;
a characteristic rule generation unit connected to said data storage unit for generating characteristic rules based upon the data definition information and the data, the data definition information including items specifying analysis and conditions, the characteristic rules being stored in said data storage unit;
a segment selection unit connected to said data storage unit for generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension, the multidimensional database being stored in said data storage unit;
a user interface unit connected to said data storage unit for providing a predetermined user-interface for selecting one of the characteristic rules and for modifying the condition items;
a processing unit connected to said storage unit and said user interface unit for outputting to said storage unit a first portion of the multidimensional database that is corresponding to the selected one of the characteristic rules, the first portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension, said processing unit also outputting a second portion of the multidimensional database that is corresponding to the modified condition items;
a displaying unit connected to said user interface unit, said processing unit and said storage unit for displaying the first portion of the multidimensional database and the second portion of the multidimensional database through the predetermined user-interface; and
a speculation processing unit connected to said storage unit and said processing unit for extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list, said speculation processing unit generating a speculation model based upon the data, the selected segment and the speculation data list, said speculation processing unit outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.
13. A storage medium for storing computer executable instructions for managing a database, the computer executable instructions performing the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized based upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model base upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list.
14. The storage medium for storing computer executable instructions according to claim 13 wherein the data is customer data for maintaining a predetermined subscribed service while the analysis dimension is probability of cancellation of the predetermined subscribed service.
15. The storage medium for storing computer executable instructions according to claim 13 wherein said modifying the condition items is accomplished by displaying a predetermined set of the condition items in a pull-down menu and selecting one of the condition items by a pointing device.
16. The storage medium for storing computer executable instructions according to claim 13 wherein said generating the speculation model further including additional steps of:
dividing the speculation data list into candidate model data and verification data;
generating candidate speculation models based upon inputs as specified by combinations of the conditions in the data definition information and outputs as specified by the analysis in the data definition information;
verifying each of the candidate speculation models by extracting information from the candidate model data according to the candidate speculation model and speculating based upon the verification data;
evaluating the candidate speculation models based upon said verifying to generate evaluation values; and
selecting the speculation model from the candidate speculation models based upon the evaluation values.
17. The storage medium for storing computer executable instructions according to claim 16 wherein the evaluation values are mean square errors.
18. A storage medium for storing computer executable instructions for managing a database, the computer executable instructions performing the steps of:
generating characteristic rules based upon data definition information and data, the data definition information including items specifying analysis and conditions;
generating a multidimensional database based upon the characteristic rules, the data and the data definition information, the multidimensional database being organized bated upon conclusion items and condition items of the characteristic rules, the conclusion items specifying an analysis dimension, the condition items specifying a key dimension;
selecting one of the characteristic rules via a predetermined user-interface;
displaying a portion of the multidimensional database that is corresponding to the selected one of the characteristic rules through the predetermined user-interface, the displayed portion being organized in rows and columns to define cells based upon the condition items of the selected one of the characteristic rules, the cells each having a value for the analysis dimension;
modifying the condition items through the predetermined user-interface;
displaying another portion of the multidimensional database that is corresponding to the modified condition items through the predetermined user-interface;
extracting a selected segment and a speculation data list from the data based upon the modified condition items and the selected one of the characteristic rules, the selected segment specifying conditions for selecting the speculation data list;
generating a speculation model based upon the data, the selected segment and the speculation data list; and
outputting speculation results based upon the speculation model and the speculation data list, wherein the speculation results include the selected segment.