Author: claimsbot

1. A text mining device, including:
a text set storage unit for storing a plurality of texts obtained by forming a plurality of non-text data into texts;
a reliability storage unit for storing, for each of a plurality of texts stored in said text set storage unit, a reliability of the text which is derived from formation of said non-text data from which the text originates into texts;
a text mining unit for executing, with respect to a plurality of texts stored in said text set storage unit, text mining of searching a plurality of texts of the same class having the same characteristic from the plurality of texts; and
a text selection unit for reading, for each text of the same class searched by said text mining unit, a reliability of the text from said reliability storage unit to select a part of texts of the same class from the plurality of texts of the same class based on the reliability.
2. The text mining device according to claim 1, wherein said text selection unit generates, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class to select text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
3. The text mining device according to claim 2, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
said text mining unit uses a word as said characteristic, and
said text selection unit reads, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class from said reliability storage unit and generates, for each said text of the same class, said degree of assurance based on the reliability of the word as said characteristic in the text of the same class to select text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
4. The text mining device according to claim 2, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
said text mining unit uses a word as said characteristic, and
said text selection unit reads, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class and a reliability of a word in proximity to the word in question from said reliability storage unit and generates, for each said text of the same class, said degree of assurance based on the reliabilities of the word as said characteristic in the text of the same class and of the word in proximity to the word in question to select text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
5. The text mining device according to claim 2, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
said text mining unit uses a word as said characteristic, and
said text selection unit reads, for each said text of the same class, reliabilities of all the words in the text of the same class from said reliability storage unit and generates, for each said text of the same class, said degree of assurance based on the reliabilities of all the words in the text of the same class to select text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
6. The text mining device according claim 1, wherein said text selection unit includes
a degree of assurance calculating unit for generating, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class, and
a degree of assurance output unit for outputting said degree of assurance together with said part of texts of the same class.
7. A text mining method to be executed by a text mining device having a text set storage unit for storing a plurality of texts obtained by forming a plurality of non-text data into texts and a reliability storage unit for storing, for each of a plurality of texts stored in said text set storage unit, a reliability of the text which is derived from formation of said non-text data from which the text originates into texts, including:
a text mining step of executing, with respect to a plurality of texts stored in said text set storage unit, text mining of searching a plurality of texts of the same class having the same characteristic from the plurality of texts; and
a text selection step of reading, when said plurality of texts of the same class are searched, a reliability of each text of the same class from said reliability storage unit to select a part of texts of the same class from the plurality of texts of the same class based on the reliability.
8. The text mining method according to claim 7, wherein said text selection step including:
a degree of assurance generation step of generating, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class; and
a selection step of selecting text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
9. The text mining method according to claim 8, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
at said text mining step, a word is used as said characteristic, and
at said degree of assurance generation step, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class is read from said reliability storage unit and for each said text of the same class, said degree of assurance is generated based on the reliability of the word as said characteristic in the text of the same class.
10. The text mining method according to claim 8, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
at said text mining step, a word is used as said characteristic, and
at said degree of assurance generation step, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class and a reliability of a word in proximity to the word in question are read from said reliability storage unit and for each said text of the same class, said degree of assurance is generated based on the reliabilities of the word as said characteristic in the text of the same class and of the word in proximity to the word in question.
11. The text mining method according to claim 8, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
at said text mining step, a word is used as said characteristic, and
at said degree of assurance generation step, for each said text of the same class, reliabilities of all the words in the text of the same class are read from said reliability storage unit to generate, for each said text of the same class, said degree of assurance based on the reliabilities of all the words in the text of the same class.
12. The text mining method according to claim 7, wherein said text selection step including:
a degree of assurance generation step of generating, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class; and
a degree of assurance output step of outputting said degree of assurance together with said part of texts of the same class.
13. A computer readable medium for storing a text mining program causing a computer to execute text mining processing which computer is connected to a text set storage unit for storing a plurality of texts obtained by forming a plurality of non-text data into texts and a reliability storage unit for storing, for each of a plurality of texts stored in said text set storage unit, a reliability of the text which is derived from formation of said non-text data from which the text originates into texts,
said text mining processing including:
a text mining processing of executing, with respect to a plurality of texts stored in said text set storage unit, text mining of searching a plurality of texts of the same class having the same characteristic from the plurality of texts; and
a text selection processing of reading, when said plurality of texts of the same class are searched, a reliability of each text of the same class from said reliability storage unit to select a part of texts of the same class from the plurality of texts of the same class based on the reliability.
14. The computer readable medium for storing the text mining program according to claim 13, wherein said text selection processing including:
a degree of assurance generation processing of generating, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class; and
a selection processing of selecting text of the same class having the highest degree of assurance as said part of texts of the same class from among said plurality of texts of the same class.
15. The computer readable medium for storing the text mining program according to claim 14, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
in said text mining processing, a word is used as said characteristic, and
in said degree of assurance generation processing, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class is read from said reliability storage unit to generate, for each said text of the same class, said degree of assurance based on the reliability of the word as said characteristic in the text of the same class.
16. The computer readable medium for storing the text mining program according to claim 14, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
in said text mining processing, a word is used as said characteristic, and
in said degree of assurance generation processing, for each said text of the same class, a reliability of a word used as said characteristic in the text of the same class and a reliability of a word in proximity to the word in question are read from said reliability storage unit to generate, for each said text of the same class, said degree of assurance based on the reliabilities of the word as said characteristic in the text of the same class and of the word in proximity to the word in question.
17. The computer readable medium for storing the text mining program according to claim 14, wherein
said reliability storage unit stores, for each word included in said text, a reliability derived from formation of said non-text data from which the text originates into texts,
in said text mining processing, a word is used as said characteristic, and
in said degree of assurance generation processing, for each said text of the same class, reliabilities of all the words in the text of the same class are read from said reliability storage unit to generate, for each said text of the same class, said degree of assurance based on the reliabilities of all the words in the text of the same class.
18. The computer readable medium for storing the text mining program according to claim 13, wherein said text selection processing including:
a degree of assurance generation processing of generating, for each said text of the same class, a degree of assurance indicative of a degree of correlation between the text of the same class and non-text data from which the text of the same class originates based on a reliability of the text of the same class; and
a degree of assurance output processing of outputting said degree of assurance together with said part of texts of the same class.

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 cable connector, comprising:
an insulative housing defining a plurality of passageways therethrough;
a plurality of contacts assembled to the insulative housing and respectively received in the passageways;
a cable comprising a plurality of conductors respectively electrically connecting with the contacts to form a plurality of junctions;
a cover enclosing the rear end of the housing and junctions between the contacts and the cable, the cover having a mating face having at least a longer edge and a projection formed on an outer periphery of the cover with a front surface coplanar with the mating face of the cover the projection and defining a fastening hole therethrough; and
a fastening member fixed to the cover and coupling with the fastening hole for fastening the cable connector to a coupling device.
2. The cable connector as claimed in claim 1, wherein the cover further comprises a step formed in the inner periphery of the fastening hole.
3. The cable connector as claimed in claim 2, wherein the diameter of the rear portion of the fastening hole is different from that of the front portion of the fastening hole, and the length of the wider portion of the fastening hole is different from that of the narrower portion.
4. The cable connector as claimed in claim 2, wherein the fastening member comprises an operating portion, a fastening portion, and a flange protruding outwardly from the end of the base portion and adjoining the fastening portion to couple with the step in the fastening hole in the cover.
5. The cable connector as claimed in claim 1, wherein the cover of the cable connector comprises an inner mold enclosing the junctions of the contacts and the conductors of the cable and an outer mold enclosing the inner mold and the cable.
6. The cable connector as claimed in claim 5, wherein the projection is formed on the out periphery of the outer mold.
7. The cable connector as claimed in claim 5, further comprising a conductive shell enclosing the insulative housing, and wherein the rear portion of the conductive shell is enclosed by the inner mold.
8. The cable connector as claimed in claim 6, wherein the cable connector further comprises a spacer sealing the rear portion of the insulative housing.
9. The cable connector as claimed in claim 7, wherein the spacer has at least a protrusion on upper and lower surfaces and wherein the conductive shell defines an engaging hole coupling with the projection.
10. The cable connector as claimed in claim 1, wherein the cable connector is in accordance with HDMI (High-Definition Digital Multimedia Interface) standard.
11. The cable connector as claimed in claim 1, wherein the insulative housing comprises an upper wall, a lower wall and a receiving space formed therebetween, the upper wall parallels the lower wall and shorter than the lower wall.
12. The cable connector as claimed in claim 11, wherein the passageways are formed in the inner surfaces of the upper wall and lower wall and wherein the numbers of the passageways on the upper wall and lower wall are not equal to each other.
13. A cable connector assembly comprising:
an insulative housing defining a mating port in a front portion thereof and defining a mating direction along a front-to-back direction;
a plurality of contacts disposed in the housing and exposed to the mating port;
a cable having a plurality of conductors electrically connected to the corresponding contacts, respectively;
an insulative cover integrally molded over a rear portion of the housing and a front portion of the cable; and
said cover defines a protrusion with a locking screw attached thereto and back and forth moveable along said front-to-back direction; wherein
said mating port defines a long side and a short side, and the locking screw is located by the long side and accessibly operable from two sides of said locking screw along said long side.
14. The connector assembly as claimed in claim 13, wherein said cover and said housing are made from different materials.
15. The connector assembly as claimed in claim 13, wherein a surface of said cover, which the locking screw is located on, is raised for protecting a tail of the locking screw.
16. The connector assembly as claimed in claim 13, wherein said protrusion defines a peak configuration and the locking screw is close to said peak.

1. A method for controlling a memory device, comprising:
storing device class dependent information and a command in one or more of host system memory and host controller memory, wherein the device class dependent information and the command are associated with a memory device external to a host system that includes one or more of host system memory and host controller memory;
setting a pointer to the command in a register in a host controller;
directing access of the memory device to the command in the one or more of host system memory and host controller memory using the pointer via the host controller; and
executing the command with the memory device.
2. The method of claim 1, wherein directing access to the host system memory includes managing direct memory access (DMA) of data and providing flow control of data between a host and the memory device with the host controller.
3. The method of claim 2, wherein directing access to the host system memory includes managing DMA of data and providing flow control without host processor intervention.
4. The method of claim 1, wherein the method includes storing device class independent information in one or more registers in the host controller.
5. The method of claim 4, wherein storing device class independent information includes storing one or more types of information selected from the group including:
device control and interrupt information;
device enabledisable information;
device power state controlstatus information;
one or more pointers to device-class dependent information in the host system memory;
one or more pointers to commands in the host system memory;
a particular device class associated with a particular transaction layer register entry; and
a reset command.
6. The method of claim 1, wherein the command is written with small computer system interface (SCSI) protocol.
7. The method of claim 6, wherein the command is modified with the device class dependent information and the device class independent information.
8. The method of claim 1, wherein directing access to the one or more of host system memory and host controller memory with the memory device via the host controller includes providing access to a read command and a write command and executing the read command and write command with the memory device.
9. A method for operating a memory device, comprising:
storing device class dependent information in a memory array;
building a read command in the memory array with a host system processor based on the device class dependent information;
locating the read command in the memory array using a pointer stored in a register in a host controller, wherein the pointer indicates the location of the read command in the memory array;
sending the read command from the memory array to the memory device via a host system controller, wherein the memory device is external to a host system that includes the memory array; and
executing the read command with the memory device by initiating a direct memory access (DMA) write from the memory device to the memory array with the memory device.
10. The method of claim 9, wherein the method includes:
allocating data space in the memory array for the read command data with the host system processor; and
setting an identifier associated with the read command with the host system processor, wherein the identifier indicates a location of the data space in the memory array for the read command data.
11. The method of claim 10, wherein the identifier is a segment identifier (SID) and wherein the SID locates a base address in a SID table in the one or more of host system memory and host controller memory for the location of read command data.
12. The method of claim 11, wherein the SID identifies a range of addresses in the one or more of host system memory and host controller memory indicating valid locations for the read command data.
13. The method of claim 10, wherein the method includes initiating a memory write to the data space allocated in the memory array at the location indicated by the identifier associated with the read command by the memory device.
14. The method of claim 9, wherein the memory array is a memory array type selected from the group including host system memory and host controller memory.
15. The method of claim 9, wherein the method includes updating the read command with a completion status in the memory array with the memory device.
16. A memory system, comprising:
a host system that includes a host controller having host controller memory and a host processor coupled to the host controller; and
one or more memory devices each coupled to at least one other of the one or more memory devices and to the host system via a bus, wherein the one or more memory devices are external to the host system,
wherein the host controller locates commands in host system memory andor host controller memory using a pointer in a register in the host controller to the commands based on device class dependent information stored in the host system memory andor host controller memory, transmits the commands to the one or more memory devices, and the one or more memory devices execute the commands with a memory device controller on the one or more memory devices to read data from andor write data to the one or more memory devices.
17. The system of claim 16, wherein the host controller has a hardware port and a peripheral device is coupled to the hardware port.
18. The system of claim 17, wherein the host controller transmits commands built by the host processor in the host system memory andor the host controller memory to the number of memory devices to read data from andor write data to the peripheral device.
19. The system of claim 16, wherein the host controller transmits commands built by the host processor in the host system memory andor the host controller memory to the number of memory devices to read data from andor write data to system memory.
20. The system of claim 16, wherein the host controller transmits commands built by the host processor in the host system memory andor the host controller memory to the number of memory devices to read data from andor write data to host controller memory.

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 nuclear medicine scanner system, comprising:
an intelligent scheduler which schedules a plurality of patients, each for an ordered nuclear medicine scanning procedure with a nuclear medicine scanning device based on data mined from prior patients with like scanning procedures and in a time window which minimizes the patient dose.
2. The system according to claim 1, wherein the time window includes
a time and an amount of a unit radiopharmaceutical dosage based on the ordered procedure, at least one patient characteristic and the arrival time; and
a time and a duration of a scanning procedure based on the time and dosage of the radiopharmaceutical, patient loadingunloading requirements, and the ordered procedure.
3. The system according to claim 1, wherein the data mined includes selecting andor grouping of data from prior patients by at least one of:
radiopharmaceutical;
administered unit dose of radiopharmaceutical;
a region of interest;
patient weight;
disease;
a scan protocol;
wait times from administering a radiopharmaceutical until scanning;
image quality;
scan duration;
time of day;
type of scanning device;
device options;
hours of operation; and
best practice of nuclear medicine.
4. The system according to claim 1, wherein the schedule of the plurality of patients for a day is optimized which includes adjusting the schedule of at least one patient to fit into the available time window by extending the time window and reducing the administered dose.
5. The system according to claim 1, wherein the schedule of the plurality of patients is optimized over a span of a plurality of days based on the time needed for a quality image and a minimum dose for each patient compared with a group of prior patients, and including hours of operation and staffing breaks.
6. The system according to claim 1, wherein the patient characteristics includes at least one of:
a mass of the patient;
a maximum time the patient can spend on a patient support of the scanning device.
7. The system according to claim 2, wherein the loadingunloading requirements include a measure of patient mobility based on at least one of:
an age of the patient;
a mass of the patient;
a height-mass ratio of the patient;
whether the patient is ambulatory;
whether the patient is conscious.
8. The system according to claim 1, further including:
a tracking unit which tracks each received ordered unit dose of the radiopharmaceutical, the decay of each radiopharmaceutical with time, and the intended patient; and
a forecasting unit which based on tracked unit doses of radiopharmaceuticals, and the arrival of the plurality of scheduled patients, forecasts:
the time to administer to each tracked unit dosage of radiopharmaceutical to each patient;
the time and duration to load each patient into the scanning device;
the time and duration to perform the ordered scanning procedure; and
the time and duration to unload each patient from the scanning device.
9. The system according to claim 7, wherein the forecasting unit revises the forecast based on at least one of:
a cancellation to the schedule;
an emergency addition to the schedule;
a delay in receiving of the unit doses ordered radiopharmaceutical;
a loss of at least one unit dose of radiopharmaceutical;
a delay in administering the radiopharmaceutical to at least one patient;
a delay in loading andor unloading the patient; and
a delay due to an extended scan procedure.
10. The system according to claim 7, wherein the forecasting unit includes \u201cwhat if\u201d scenarios based on at least one proposed change to at least one patient schedule and the forecast unit forecasts the changes to all scheduled patients affected by the at least one proposed change to the at least one patient schedule as an alternative forecast.
11. A method of scheduling nuclear medicine imaging, comprising:
scheduling a plurality of patients, each for an ordered nuclear medicine scanning procedure with a nuclear medicine scanning device based on data mined from prior patients with like scanning procedures and in a time window which minimizes the patient dose.
12. The method according to claim 11, wherein the time window includes:
a time and an amount of a unit radiopharmaceutical dosage based on the ordered procedure, at least one patient characteristic and the arrival time; and
a time and a duration of a scanning procedure based on the time and dosage of the radiopharmaceutical, patient loadingunloading requirements, and the ordered procedure.
13. The method according to claim 14, wherein data mining further includes:
selecting times and durations of scanning procedures of prior patient scanning procedures based on at least one of:
radiopharmaceutical;
administered unit dose of radiopharmaceutical;
a region of interest;
a scan protocol;
scan duration;
time of day;
type of scanning device;
device options;
hours of operation; and
best practice guidelines.
14. The method according to claim 11, further including:
adjusting the schedule of at least one patient to fit into the available time window by extending the time window and reducing the administered dose.
15. The method according to claim 11, further including:
optimizing the schedule of the plurality of patients over a plurality of days based on the time needed for a quality image and a minimum dose for each patient compared with a group of prior patients, and including hours of operation and staffing breaks.
16. The method according to claim 11, further including:
tracking each received ordered unit dose of the radiopharmaceutical, the decay of each radiopharmaceutical with time, and the associated dosage for each patient; and
forecasting based on tracked unit doses of radiopharmaceuticals, and the arrival of the plurality of scheduled patients:
the time to administer to each tracked unit dosage of radiopharmaceutical to each associated patient;
the time and duration to load each patient into the scanning device;
the time and duration to perform the ordered scanning procedure; and
the time and duration to unload each patient from the scanning device.
17. The method according to claim 16, further including:
revising the forecast based on at least one of:
a cancellation to the schedule;
an emergency addition to the schedule;
a delay in receiving of the unit doses ordered radiopharmaceutical;
a loss of at least one unit dose of radiopharmaceutical;
a delay in administering the radiopharmaceutical to at least one patient;
a delay in loading andor unloading the patient; and
a delay due to an extended scan procedure.
18. A non-transitory computer-readable storage medium carrying software which controls one or more electronic data processing devices to perform the method according to claim 11.
19. An electronic data processing device configured to perform the method according to claim 11.
20. A nuclear medicine scanner system, comprising:
at least one processor configured to:
schedule a plurality of patients for an ordered nuclear medicine scanning procedure for a nuclear medicine scanning device, each patient scheduled for:
an arrival date and time;
a time and an amount of a unit radiopharmaceutical dosage based on the ordered procedure, the patient characteristics and the arrival time; and
a time and duration of a scanning procedure based on the time and dosage of the radiopharmaceutical, patient loadingunloading requirements, and the ordered procedure; and

forecast based on received unit doses of radiopharmaceuticals, and the arrival of the plurality of scheduled patients:
the time to administer to each received unit dosage of radiopharmaceutical to each patient;
the time and duration to load each patient into the scanning device;
the time and duration to perform the ordered scanning procedure; and
the time and duration to unload each patient from the scanning device.
21. A nuclear medicine scanner system, comprising:
at least one processor configured to:
track a plurality of received ordered unit doses of radiopharmaceuticals and each tracked unit dose includes a decay of each radiopharmaceutical dose with time, and an intended patient with a scheduled arrival time and with an ordered scanning procedure; and

forecast based on data mined about use of a scanning device, the tracked unit doses of radiopharmaceuticals, and an arrival of each patient:
a time to administer each tracked unit dose to each patient,
a time and duration to load each patient into the scanning device,
a time and duration to perform the ordered scanning procedure with the scanning device, and
a time and duration to unload each patient from the scanning device.