All The Claims

1. A system for processing video data, said system comprising:
a motion estimator operable for comparing a block of an input frame of video data and a block of a reference frame of video data to generate motion vectors according to a first encoding scheme, wherein said motion vectors are generated using a two-tap finite impulse response (FIR) filter, said motion vectors indicating the position of said block of said input frame relative to the position of said block of said reference frame;
a first encoder coupled to said motion estimator, said first encoder operable for receiving said motion vectors generated using said two-tap filter from said motion estimator and for using said motion vectors generated using said two-tap filter to produce a reconstructed first version of said block of said input frame according to said first encoding scheme; and
a second encoder coupled to said motion estimator, said second encoder operable for receiving said motion vectors generated using said two-tap filter from said motion estimator and for using said motion vectors generated using said two-tap FIR filter to produce a reconstructed second version of said block of said input frame according to a second encoding scheme that is different from said first encoding scheme, wherein said second encoder comprises a plurality of six-tap FIR filters configured to use said motion vectors generated using said two-tap FIR filter as inputs to generate half pel interpolated pixels for said reconstructed second version.
2. The system of claim 1 wherein said second encoder further comprises a circular buffer operable for concurrently storing both said block of said reference frame and said reconstructed second version of said block of said input frame.
3. The system of claim 1 wherein said second encoder further comprises a data packer operable for interleaving blocks of video data for said input frame and blocks of video data for said reference frame.
4. The system of claim 1 wherein said second encoder further comprises a digital signal processor operable for executing a plurality of software-based instructions that implement encoding operations, said encoding operations comprising transformation and quantization of a residual comprising a difference between said input frame and said reference frame, said encoding operations further comprising inverse quantization and inverse transformation of said residual.
5. The system of claim 1 wherein said second encoder comprises a plurality of luma row filters for operating on the x-component of a luma channel of said video data.
6. The system of claim 5 wherein said second encoder further comprises a cache that is coupled to said plurality of luma row filters, wherein for each clock cycle in which said cache is enabled a result from said plurality of luma row filters is loaded into a first row of said cache and a result already residing in said first row is shifted to a second row of said cache.
7. The system of claim 6 wherein said second encoder further comprises:
a plurality of chroma row filters for operating on the x-component of a chroma channel of said video data;
a plurality of chroma column filters for operating on the y-component of said chroma channel; and
a plurality of luma column filters for operating on the y-component of said luma channel;
wherein both said plurality of chroma row filters and said plurality of chroma column filters are coupled to both said first row and said second row of said cache and wherein each luma column filter of said plurality of luma column filters is coupled to each row of said cache.
8. The system of claim 1 wherein said first encoding scheme comprises an encoding scheme substantially compliant with MPEG-4 and said second encoding scheme comprises an encoding scheme substantially compliant with H.264.
9. A method for processing video data, said method comprising:
selecting motion vectors that are generated using a two-tap finite impulse response (FIR) filter, wherein said motion vectors are generated according to a first encoding scheme that compares a block of an input frame of video data and a block of a reference frame of video data, said motion vectors indicating the position of said block of said input frame relative to the position of said block of said reference frame;
receiving, at a first encoder, said motion vectors generated using said two-tap filter, said first encoder operable for using said motion vectors to produce a reconstructed first version of said block of said input frame according to said first encoding scheme; and
applying, with a second encoder, six-tap FIR filters to said motion vectors generated using said two-tap FIR filter, said six-tap FIR filters using said motion vectors as inputs to calculate half pel interpolated pixels to produce a reconstructed second version of said block of said input frame according to a second encoding scheme that is different from said first encoding scheme.
10. The method of claim 9 further comprising concurrently storing both said block of said reference frame and said reconstructed second version of said block of said input frame in a circular buffer.
11. The method of claim 9 further comprising interleaving blocks of video data for said input frame and blocks of video data for said reference frame.
12. The method of claim 9 further comprising:
operating on the x-component of a luma channel of said video data using a plurality of luma row six-tap FIR filters;
loading a result from said plurality of luma row filters into a first row of a cache; and
shifting a result already residing in said first row to a second row of said cache.
13. The method of claim 12 further comprising:
operating on the x-component of a chroma channel of said video data using a plurality of chroma row six-tap FIR filters;
operating on the y-component of said chroma channel using a plurality of chroma column six-tap FIR filters; and
operating on the y-component of said luma channel using a plurality of luma column six-tap FIR filters.
14. The method of claim 9 wherein said first encoding scheme comprises an encoding scheme substantially compliant with MPEG-4 and said second encoding scheme comprises an encoding scheme substantially compliant with H.264.
15. A system for processing video data, said system comprising:
a motion estimator operable for comparing a block of an input frame of video data and a block of a reference frame of video data to generate motion vectors according to a first encoding scheme, wherein said motion vectors are generated using a two-tap finite impulse response (FIR) filter, said motion vectors indicating the position of said block of said input frame relative to the position of said block of said reference frame;
a first motion compensator coupled to said motion estimator, said motion compensator comprising a first encoder operable for using said motion vectors to produce a reconstructed first version of said block of said input frame according to said first encoding scheme;
a second motion compensator coupled to said motion estimator, said second motion compensator comprising a second encoder comprising a plurality of six-tap filters and operable for using said motion vectors generated using said two-tap FIR filter to produce a reconstructed second version of said block of said input frame, said reconstructed second version produced according to a second encoding scheme that is different from said first encoding scheme, wherein said second encoder comprises a plurality of six-tap FIR filters configured to use said motion vectors generated using said two-tap FIR filter as inputs to generate half pel interpolated pixels for said reconstructed second version; and
a data packer coupled to said second motion compensator, said data packer operable for interleaving blocks of video data for said input frame with blocks of video data for said reference frame.
16. The system of claim 15 wherein said second motion compensator further comprises a circular buffer operable for concurrently storing both said block of said reference frame and said reconstructed second version of said block of said input frame.
17. The system of claim 15 further comprising a digital signal processor coupled to said data packer and operable for executing a plurality of software-based instructions that implement encoding operations, said encoding operations comprising transformation and quantization of a residual comprising a difference between said input frame and said reference frame, said encoding operations further comprising inverse quantization and inverse transformation of said residual.
18. The system of claim 15 further comprising:
a plurality of luma row filters for operating on the x-component of a luma channel of said video data;
a cache coupled to said plurality of luma row filters, wherein for each clock cycle in which said cache is enabled a result from said plurality of luma row filters is loaded into a first row of said cache and a result already residing in said first row is shifted to a second row of said cache;
a plurality of chroma row filters for operating on the x-component of a chroma channel of said video data;
a plurality of chroma column filters for operating on the y-component of said chroma channel; and
a plurality of luma column filters for operating on the y-component of said luma channel, wherein both said plurality of chroma row filters and said plurality of chroma column filters are coupled to both said first row and said second row of said cache and wherein each luma column filter of said plurality of luma column filters is coupled to each row of said cache.
19. The system of claim 1 wherein said second encoder comprises a data bypass, wherein if said motion vectors are zero then said operations of said six-tap FIR filters are bypassed.
20. The system of claim 1 wherein second encoder receives a signal that indicates data is for said reconstructed second version of said block of said input frame and that enables a state machine that controls and executes memory fetches and memory buffer reads for said second encoder.

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, comprising:
at a computing device having one or more processors and memory for storing one or more programs to be executed by the one or more processors,
clustering users in a community to form a community user circle according to original community data of the users, attributes of the users, a bulletin board system participated by the users, or a chat group of an instant messaging application participated by the users, wherein the original community data of a user comprise information about following-up of the user on another user in the community, and an amount of topics which both the user and the another user take part in; and
creating a knowledge graph corresponding to the community user circle according to user behavior data generated by users in the community user circle.
2. The method according to claim 1, wherein the clustering users in a community to form a community user circle according to original community data of the users comprises:
calculating a closeness score of a user in the community with respect to another user in the community according to original community data of the user, wherein the closeness score of the user is used for indicating a closeness degree of the user with respect to the another user; and
clustering users according to respective closeness scores of each user with respect to other users, so as to form the community user circle.
3. The method according to claim 2, wherein the calculating a closeness score of a user in the community with respect to another user in the community according to original community data of the user comprises:
calculating a following-up score of a user with respect to another user according to the information about following-up of the user on the another user in the community;
calculating a common-hot-topic score of the user according to the amount of topics which both the user and the another user take part in; and
calculating the closeness score of the user with respect to the another user according to the following-up score and common-hot-topic score of the user with respect to the another user.
4. The method according to claim 2, wherein the clustering users according to respective closeness scores of each user with respect to other users, so as to form the community user circle comprises:
selecting a user from the community;
calculating a distance between the user and each of remaining users in the community according to the closeness score of the user with respect to the each of remaining users in the community and the closeness score of the each of remaining users in the community with respect to the user; and
determining a user whose distance from the user is less than a preset user-distance threshold, the determined user and the user forming the community user circle.
5. The method according to claim 2, wherein the clustering users according to closeness scores of each user with respect to other users, so as to form the community user circles comprises:
selecting a user in the community, other users in the community forming a first user set;
calculating respective distances of the selected user with each user of the first user set according to respective closeness scores of the selected user with respect to the each user of the first user set and respective closeness scores of the each user of the first user set with respect to the selected user;
forming the selected user and a user contained in the first user set which has a minimum distance with the selected user into a second user set, wherein users contained in the first user set but not contained in the second user set are considered as a third user set;
calculating an amount of an effective party of a user contained in the third user set with respect to the second user set;
determining an amount of a user in the third user who has a greatest amount of effective parties with respect to the second user set;
in the case that the determined amount of the user in the third user who has the greatest amount of effective parties is greater than 0, adding the user who has the greatest number of effective parties into the second user set;
calculating an amount of an effective party of a user contained in the second user set with respect to each of other users contained in the second user set;
determining a user who has a least amount of the effective party;
in the case that an amount of the user who has the least amount of the effective party is less than one half of the amount of the user who has been added in the second user set, deleting from the second user set the user who has the least amount of the effective party;
in the case that the amount of users in the second user set is equal to or great than a first threshold and less than or equal to a second threshold, forming all users contained in the second user set into the community user circle.
6. The method according to claim 1, wherein the creating a knowledge graph corresponding to the community user circle according to user behavior data generated by users in the community user circle comprises:
obtaining user behavior data generated by each user in the community user circle, user behavior data which belong to a same theme forming a text; and
mining the text to obtain a knowledge graph corresponding to the community user circle.
7. The method according to claim 6, wherein the mining the text to obtain a knowledge graph corresponding to the community user circle comprises:
performing word segmentation on data contained in the text, so as to get segmented words of the text, the segmented words of the text forming eigenvectors of the text;
clustering texts according to eigenvectors of each text, texts with a same topic forming a text clustering; and
mining texts contained in the text clustering to obtain the knowledge graph corresponding to the community user circle.
8. A system, comprising at least a processor operating in conjunction with a memory and a plurality of components, the plurality of components comprising:
a clustering module, configured to cluster users in a community to form a community user circle according to original community data of the users, attributes of the users, a bulletin board system participated by the users, or a chat group of an instant messaging application participated by the users, wherein the original community data of a user comprise information about following-up of the user on another user in the community, and an amount of topics which both the user and the another user take part in; and
a creating module, configured to create a knowledge graph corresponding to the community user circle according to user behavior data generated by users in the community user circle.
9. The system according to claim 8, wherein the clustering module comprises:
a first calculating unit, configured to calculate a closeness score of a user in the community with respect to another user in the community according to original community data of the user, wherein the closeness score of the user is used for indicating a closeness degree of the user with respect to the another user; and
a clustering unit, configured to cluster users according to respective closeness scores of each user with respect to other users, so as to form the community user circle.
10. The system according to claim 9, wherein the first calculating unit comprises:
a first calculating sub-unit, configured to calculate a following-up score of a user with respect to another user according to the information about following-up of the user on the another user in the community;
a second calculating sub-unit, configured to calculate a common-hot-topic score of the user according to the amount of topics which both the user and the another user take part in; and
a third calculating sub-unit, configured to calculate the closeness score of the user with respect to the another user according to the following-up score and common-hot-topic score of the user with respect to the another user.
11. The system according to claim 9, wherein the clustering unit comprises:
a fourth calculating sub-unit, configured to select a user from the community, and to calculate a distance of the user from each of remaining users in the community according to the closeness score of the user with respect to the each of remaining users in the community and the closeness score of the each of remaining users in the community with respect to the user; and
a clustering sub-unit, configured to determine a user whose distance from the user is less than a preset user-distance threshold, the determined user and the user forming the community user circle.
12. The system according to claim 9, wherein the clustering unit comprises:
a selecting sub-unit, configured to:
select a user in the community, wherein other users in the community form a first user set; and
calculate respective distances of the selected user with each user of the first user set according to respective closeness scores of the selected user with respect to the each user of the first user set and respective closeness scores of the each user of the first user set with respect to the selected user;

a determining sub-unit, configured to:
form the selected user and a user contained in the first user set which has a minimum distance with the selected user into a second user set, wherein users contained in the first user set but not contained in the second user set are considered as a third user set;
calculate an amount of an effective party of a user contained in the third user set with respect to the second user set; and
determine an amount of a user in the third user who has a greatest amount of effective parties with respect to the second user set;

a deleting sub-unit, configured to:
in the case that the determined amount of the user in the third user who has the greatest amount of effective parties is greater than 0, add the user who has the greatest number of effective parties into the second user set;
calculate an amount of an effective party of a user contained in the second user set with respect to each of other users contained in the second user set;
determine a user who has a least amount of the effective party; and
in the case that an amount of the user who has the least amount of the effective party is less than one half of the amount of the user who has been added in the second user set, delete from the second user set the user who has the least amount of the effective party; and

a clustering sub-unit, configured to in the case that the amount of users in the second user set is equal to or great than a first threshold and less than or equal to a second threshold, form all users contained in the second user set into the community user circle.
13. The system according to claim 8, wherein the creating module comprises:
a forming unit, configured to obtain user behavior data generated by each user in the community user circle, and to form user behavior data which belong to a same theme into a text; and
a mining unit, configured to mine the text to obtain a knowledge graph corresponding to the community user circle.
14. The system according to claim 13, wherein the mining unit comprises:
a word-segmentation sub-unit, configured to perform word segmentation on data contained in the text, so as to get segmented words of the text, the segmented words of the text forming eigenvectors of the text;
a second clustering sub-unit, configured to cluster texts according to eigenvectors of each text, texts with a same topic forming a text clustering; and
a mining sub-unit, configured to mine texts contained in the text clustering to obtain the knowledge graph corresponding to the community user circle.
15. A non-transitory computer-readable storage medium storing instructions thereon for execution by at least one processing circuit, the instructions comprising:
clustering users in a community to form a community user circle according to original community data of the users, attributes of the users, a bulletin board system participated by the users, or a chat group of an instant messaging application participated by the users, wherein the original community data of a user comprise information about following-up of the user on another user in the community, and an amount of topics which both the user and the another user take part in; and
creating a knowledge graph corresponding to the community user circle according to user behavior data generated by users in the community user circle.
16. The non-transitory computer-readable storage medium according to claim 15, wherein the clustering users in a community to form a community user circle according to original community data of the users comprises:
calculating a closeness score of a user in the community with respect to another user in the community according to original community data of the user, wherein the closeness score of the user is used for indicating a closeness degree of the user with respect to the another user; and
clustering users according to respective closeness scores of each user with respect to other users, so as to form the community user circle.
17. The non-transitory computer-readable storage medium according to claim 16, wherein the calculating a closeness score of a user in the community with respect to another user in the community according to original community data of the user comprises:
calculating a following-up score of a user with respect to another user according to the information about following-up of the user on the another user in the community;
calculating a common-hot-topic score of the user according to the amount of topics which both the user and the another user take part in; and
calculating the closeness score of the user with respect to the another user according to the following-up score and common-hot-topic score of the user with respect to the another user.
18. The non-transitory computer-readable storage medium according to claim 16, wherein the clustering users according to respective closeness scores of each user with respect to other users, so as to form the community user circle comprises:
selecting a user from the community;
calculating a distance between the user and each of remaining users in the community according to the closeness score of the user with respect to the each of remaining users in the community and the closeness score of the each of remaining users in the community with respect to the user; and
determining a user whose distance from the user is less than a preset user-distance threshold, the determined user and the user forming the community user circle.
19. The non-transitory computer-readable storage medium according to claim 16, wherein the clustering users according to closeness scores of each user with respect to other users, so as to form the community user circles comprises:
selecting a user in the community, other users in the community forming a first user set;
calculating respective distances of the selected user with each user of the first user set according to respective closeness scores of the selected user with respect to the each user of the first user set and respective closeness scores of the each user of the first user set with respect to the selected user;
forming the selected user and a user contained in the first user set which has a minimum distance with the selected user into a second user set, wherein users contained in the first user set but not contained in the second user set are considered as a third user set;
calculating an amount of an effective party of a user contained in the third user set with respect to the second user set;
determining an amount of a user in the third user who has a greatest amount of effective parties with respect to the second user set;
in the case that the determined amount of the user in the third user who has the greatest amount of effective parties is greater than 0, adding the user who has the greatest number of effective parties into the second user set;
calculating an amount of an effective party of a user contained in the second user set with respect to each of other users contained in the second user set;
determining a user who has a least amount of the effective party;
in the case that an amount of the user who has the least amount of the effective party is less than one half of the amount of the user who has been added in the second user set, deleting from the second user set the user who has the least amount of the effective party;
in the case that the amount of users in the second user set is equal to or great than a first threshold and less than or equal to a second threshold, forming all users contained in the second user set into the community user circle.
20. The non-transitory computer-readable storage medium according to claim 15, wherein the creating a knowledge graph corresponding to the community user circle according to user behavior data generated by users in the community user circle comprises:
obtaining user behavior data generated by each user in the community user circle, user behavior data which belong to a same theme forming a text; and
mining the text to obtain a knowledge graph corresponding to the community user circle.
21. (canceled)

1. An apparatus to display a stereo image, comprising:
a feature-point extractor to extract feature points of graphics objects included in a left image and a right image, included in a stereo image;
a representative-vector determiner to determine a representative vector among vectors between a predetermined point and the feature points for the left image and the right image, respectively; and
an error-correction unit to correct at least one of a vertical error and a rotation error between the left image and the right image using a difference between the representative vector determined in the left image and the representative vector determined in the right image.
2. The apparatus of claim 1, wherein the feature points comprise feature points of the graphics objects included in predetermined areas of the left image and the right image.
3. The apparatus of claim 2, further comprising an area-generator for generating the predetermined areas.
4. The apparatus of claim 1, wherein respective representative vectors comprise a vector that is nearly vertical or horizontal, among the vectors between a center of a polygon, which is generated based on the feature points.
5. The apparatus of claim 1, wherein at least one representative vector is determined.
6. The apparatus of claim 1, wherein the error-correction unit sets one of the left image and the right image as a reference image and vertically shifts the corresponding right image or the left image, which is not the reference image, with respect to the reference image, thereby correcting the vertical error.
7. The apparatus of claim 1, wherein the error-correction unit sets one of the left image and the right image as a reference image, and rotates the corresponding right image or the left image, which is not the reference image, with respect to the reference image, thereby correcting the rotation error.
8. The apparatus of claim 1, wherein, the error-correction unit corrects the vertical error or the rotation error when the difference between the representative vectors meets a predetermined threshold value.
9. The apparatus of claim 1, further comprising a display unit to display the left image and the right image for which at least one of the vertical error and the rotation error has been corrected.
10. The apparatus of claim 1, further comprising a stereo-optical unit to optically divide the displayed left image and right image.
11. The apparatus of claim 10, wherein the stereo-optical unit optically divides the displayed left image and right image using at least one of a parallax barrier operation, a lenticular operation, a polarization operation and a time-division operation.
12. A method of displaying a stereo image, comprising:
extracting feature points of graphics objects included in a left image and a right image, included in a stereo image;
determining a representative vector among vectors between a predetermined point and the feature points for the left image and the right image, respectively; and
correcting at least one of a vertical error and a rotation error between the left image and the right image using a difference between the representative vector determined in the left image and the representative vector determined in the right image.
13. The method of claim 12, wherein the feature points comprise feature points of the graphics objects included in predetermined areas of the left image and the right image.
14. The method of claim 13, further comprising generating the predetermined areas.
15. The method of claim 12, wherein respective representative vectors comprise a vector, which is nearly vertical or horizontal, among the vectors between a center of a polygon, which is generated based on the feature points.
16. The method of claim 12, wherein at least one representative vector is determined.
17. The method of claim 12, wherein the correcting of the at least one of the vertical error and the rotation error involves one of the left image and the right image being set as a reference image, and the corresponding right image or the left image, which is not the reference image, being vertically shifted with respect to the reference image, so that the vertical error is corrected.
18. The method of claim 12, wherein the correcting of the at least one of the vertical error and the rotation error involves one of the left image and the right image being set as a reference image, and the corresponding right image or the left image, which is not the reference image, being rotated with respect to the reference image, so that the rotation error is corrected.
19. The method of claim 12, wherein, the correcting of the at least one of the vertical error and the rotation error involves the vertical error or the rotation error being corrected when the difference between the representative vectors meets a predetermined threshold value.
20. The method of claim 12, further comprising optically dividing the displayed left image and right image.
21. The method of claim 20, wherein the optically dividing involves the displayed left image and right image being optically divided using at least one of a parallax barrier method, a lenticular method, a polarization method and a time-division method.
22. The method of claim 12, further comprising displaying the left image and the right image, for which at least one of the vertical error and the rotation error has been corrected.
23. At least one medium comprising computer readable code to control at least one processing element to implement the method of claim 12.

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 creating orders for goods or services, comprising:
a business providing code for generating an order for goods or services from a plurality of order choices in a memory tag being a transponder device with a memory for storing digital content;
a user uploading the code from the memory tag to a handheld computing device, the code including at least executable code for generating an order on the handheld computing device in the form of digital data thereon;
providing the user with an order template on the handheld computing device, the order template for the user to place an order on the handheld computing device even when the user is out of range of the memory tag and initiate the order as soon as a connection is established;
the user sending the order generated using the uploaded code on the handheld computing device from the handheld computing device to the business; and
updating a pro forma order on the memory tag.