Author: claimsbot

1. A lithium polymer secondary battery comprising: an electrode group formed by laminating a positive electrode made of a lithium-containing composite oxide, a negative electrode made of a material capable of reversibly absorbing and desorbing lithium, and a separator containing an inorganic or organic filler; and a polymer electrolyte made of a nonaqueous electrolyte and a polymer material contained in said positive electrode, negative electrode and separator;
wherein 70 to 90% of a total void volume of said electrode group is filled with said nonaqueous electrolyte.
2. The lithium polymer secondary battery in accordance with claim 1, wherein said polymer material is polyvinylidene fluoride or a copolymer having vinylidene fluoride unit.
3. The lithium polymer secondary battery in accordance with claim 1, wherein said polymer material is a polymer of monomer unit having an acrylate group or methacrylate group at a terminal end of a polyalkylene oxide group, or a copolymer having said monomer unit.

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 for teaching how insurance is provided for an entity, comprising:
providing at least one factor associated with the entity, wherein the factor is associated with a potential likelihood of a claim by the entity for insurance reimbursement over a period of time;
bidding on a premium to provide insurance for the entity over the period of time, wherein the bid is based at least in part on the at least one factor associated with the entity;
generating at least one claim for the entity over the period of time; and
identifying a user that bid for the premium at a lowest cost over the period of time, and identifying a winner as at least one user with a largest positive reserve of funds after deducting the generated at least one claim from the premium that was lowest bid by the winner.
2. The method of claim 1, further comprising repeating the actions of the method until at least one of a predetermined period of time is over or the actions of the method have been repeated for a selected number of times.
3. The method of claim 2, wherein repeating the actions further comprises providing a reserve of the user that bid for the premium at the lowest cost with additional funds, wherein the additional funds are representative in part of a potential for renewal of the insurance by the entity with that user.
4. The method of claim 1; further comprising identifying the winner with the largest positive reserve of funds after deducting at least a cost of providing insurance and the at least one generated claim.
5. The method of claim 1, wherein the entity is at least one of a company, corporation, partnership, sole proprietorship, cooperative, non-profit organization, educational institution, government agency, limited liability corporation, limited liability partnership, or professional corporation.
6. The method of claim 1, wherein the factor includes at least one of participation, industry, age, gender, or family.
7. The method of claim 1, further comprising a plurality of potential claims that are included in a datastore, wherein the plurality of potential claims are arranged in the datastore based on at least one of probability, incidence or weighting.
8. The method of claim 1, further comprising enabling at least one user to change a coverage of the insurance for claims generated for the entity.
9. The method of claim 1, further comprising enabling at least one user to change the generated claims for the entity.
10. The method of claim 1, further comprising enabling at least one user to select an association with a company, and enabling at least one user to select a name for the company.
11. A game for teaching how insurance is provided for an entity, comprising:
a module for providing at least one factor associated with the entity, wherein the factor is associated with a potential likelihood of a claim by the entity for insurance reimbursement over a period of time;
a module for bidding on a premium to provide insurance for the entity over the period of time, wherein the bid is based at least in part on the at least one factor associated with the entity;
a module for generating at least one claim for the entity over the period of time; and
a module for identifying a user that bid for the premium at a lowest cost over the period of time, and identifying a winner as at least one user with a largest positive reserve of funds after deducting the generated at least one claim from the premium that was lowest bid by the winner.
12. The game of claim 11, further comprising a module for enabling a plurality of users to bid over a network on the premium for providing insurance for the entity.
13. The game of claim 11, further comprising a module for repeating the actions of the method until at least one of a predetermined period of time is over or the actions of the game have been repeated for a selected number of times.
14. The game of claim 13, wherein repeating the actions further comprises providing a reserve of the user that bid for the premium at the lowest cost with additional funds, wherein the additional funds are representative in part of a potential for renewal of the insurance by the entity with that user.
15. The game of claim 11, further comprising a module for identifying the winner with the largest positive reserve of funds after deducting at least a cost of providing insurance and the at least one generated claim.
16. The game of claim 11, wherein the actions are performed in at least one of a client-server architecture, peer architecture, or a stand alone application.
17. A processor readable medium that includes code for enabling actions for teaching how insurance is provided for an entity, comprising:
a module for providing at least one factor associated with the entity, wherein the factor is associated with a potential likelihood of a claim by the entity for insurance reimbursement over a period of time;
a module for bidding on a premium to provide insurance for the entity over the period of time, wherein the bid is based at least in part on the at least one factor associated with the entity;
a module for generating at least one claim for the entity over the period of time; and
a module for identifying a user that bid for the premium at a lowest cost over the period of time, and identifying a winner as at least one user with a largest positive reserve of funds after deducting the generated at least one claim from the premium that was lowest bid by the winner.
18. The processor readable medium of claim 17, further comprising a module for enabling a plurality of users to bid over a network on the premium for providing insurance for the entity.
19. The processor readable medium of claim 17, further comprising a module for repeating the actions until at least one of a predetermined period of time is over or the actions of the game have been repeated for a selected number of times.
20. An apparatus for enabling actions to determine a premium for insurance, comprising:
a memory for storing code that enables actions;
a processor for performing actions, including:
providing at least one factor associated with the entity, wherein the factor is associated with a potential likelihood of a claim by the entity for insurance reimbursement over a period of time;
bidding on a premium to provide insurance for the entity over the period of time, wherein the bid is based at least in part on the at least one factor associated with the entity;
generating at least one claim for the entity over the period of time; and
identifying a user that bid for the premium at a lowest cost over the period of time, and identifying a winner as at least one user with a largest positive reserve of funds after deducting the generated at least one claim from the premium that was lowest bid by the winner.

1. An image processing device adapted to correct distortion of an image projected on a projection screen with projection light emitted from a projector, comprising:
a detection section adapted to detect at least one of display positions of a plurality of predetermined figures displayed in a correction image projected by the projector on the projection screen, the plurality of figures arranged at densities varying in accordance with an incident condition of projection light to the projection screen;
a distortion correction information generation section adapted to generate distortion correction information used for distortion correction of an image to be projected on the projection screen, the distortion correction information being based on variance in the detected display position of the plurality of predetermined figures caused by non-uniformity of a projection surface of the projection screen when the projection light is projected on the projection surface at an incident angle, and correction image data representing the correction image;
an acquisition section adapted to acquire image data representing the image to be projected on the projection screen; and
a distortion correction section adapted to execute a distortion correction process using the distortion correction information on the image data acquired,
wherein the plurality of predetermined figures projected on the projection screen is arranged at densities varying in accordance with the incident angle of the projection light to the projection screen, and is arranged to have a higher density in accordance with a larger incident angle of the projection light to the projection screen.
2. The image processing device according to claim 1, wherein
the plurality of predetermined figures projected on the projection screen is arranged at densities varying in accordance with an incident direction of the projection light to the projection screen.
3. The image processing device according to claim 2, wherein
the plurality of predetermined figures projected on the projection screen is arranged at densities different between areas obtained by dividing in accordance with the incident direction of the projection light to the projection screen.
4. The image processing device according to claim 2, wherein
the plurality of predetermined figures projected on the projection screen is arranged at a higher density in a direction substantially perpendicular to the incident direction of the projection light to the projection screen than a density in a direction substantially parallel to the incident direction.
5. The image processing device according to claim 1, wherein
the predetermined figures in the correction image are arranged at densities varying in accordance with projection distances of the projection light from the projector to the projection screen.
6. The image processing device according to claim 1, further comprising:
a shot correction image data acquisition section adapted to acquire shot correction image data representing the projection correction image shot by a shooting section; and
a correction image data acquisition section adapted to acquire correction image data representing the correction image,
wherein the distortion correction information generation section compares the shot image represented by the shot correction image data and the correction image with each other, and generates the distortion correction information based on the difference in the projection positions of the predetermined figures.
7. The image processing device according to claim 1, further comprising:
a storage section adapted to previously store the correction image data corresponding to a configuration of an optical system of the projector.
8. A projector comprising:
a light source;
an image formation section adapted to modulate light emitted from the light source based on image data to form an image represented by the image data;
a projection optical system adapted to project light representing the image formed by the image formation section on the projection screen; and
the processing device according to claim 1.
9. The image processing device according to claim 1, wherein
the plurality of predetermined figures projected on the projection screen is arranged at a constant density in a direction substantially perpendicular to an incident direction of the projection light to the projection screen.
10. A distortion correction method comprising:
detecting at least one of display positions of a plurality of predetermined figures displayed in a correction image projected by a projector on a projection screen, the plurality of figures arranged at densities varying in accordance with an incident angle of projection light to the projection screen, and is arranged to have a higher density in accordance with a larger incident angle of the projection light to the projection screen;
generating distortion correction information used for distortion correction of an image to be projected on the projection screen, based on variance in the detected display position of the plurality of predetermined figures caused by non-uniformity of a projection surface of the projection screen when the projection light is projected on the projection surface at the incident angle, and correction image data representing the correction image;
acquiring image data representing the image to be projected on the projection screen; and
executing a distortion correction process using the distortion correction information on the image data acquired.

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. An optimization application for determining a distribution policy for a single period inventory system comprising a plurality of locations, the optimization application operating on a computer system comprising of at least a processor and a memory, the optimization application operable to:
allocate draw units of a consumer item to each location of the plurality of locations;
determine a location from the plurality of locations such that the determined location meets a pre-selected allocation decision criterion based on at least the draw units allocated to each location of the plurality of locations;
allocate one and only one additional draw unit of the consumer item to the determined location; and
repeat the determine the location step and the allocate the one and only one additional draw unit step until at least one constraint is satisfied.
2. The optimization application of claim 1 wherein the pre-selected allocation decision criterion is a maximum incremental availability, where availability is a probability of completely satisfying a demand for the consumer item at the location without occurrence of a sellout due to an insufficient draw at the location and is computed using a cumulative distribution function (cdf) of a mean demand and draw units of the consumer item allocated for the location, wherein
incremental availability for the location is a difference between a first availability of the consumer item at the location and a second availability of the consumer item at the location, the second availability computed with one additional draw unit of the consumer item allocated to the location in comparison to the draw units allocated for computation of the first availability and the maximum incremental availability is a maximum of the incremental availability of the consumer item computed for each location of the plurality of locations, and wherein the determined location has the maximum incremental availability.
3. The optimization application of claim 1 wherein the pre-selected allocation decision criterion is a minimum availability, where availability is a probability of completely satisfying a demand for the consumer item at the location without occurrence of a sellout due to insufficient draw at the location and is computed using a cumulative distribution function (cdf) of the mean demand and draw units of the consumer item allocated for the location, where minimum availability is a minimum of the availability of the consumer item computed for each location of the plurality of locations and wherein the determined location has the minimum availability.
4. The optimization application of claim 1 wherein the pre-selected allocation decision criterion is a maximum decremental expected stockout, where expected stockout is expected quantity of unsatisfied demand of the consumer item at the location and is computed using demand, mean demand and draw units of the consumer item allocated for the location wherein,
the decremental expected stockout is a difference between a first expected stockout and a second expected stockout, the first expected stockout computed for one additional draw unit of the consumer item allocated at the location in comparison to the draw units allocated for computation of the second expected stockout, where the maximum decremental expected stockout is a maximum of the decremental expected stockout of the consumer item computed for each location and wherein the determined location has the maximum decremental expected return.
5. The optimization application of claim 1 wherein the pre-selected allocation decision criterion is a sum of two weighted components, a first weighted component based on a difference between two expected return percentages with at least one expected return percentage calculated based on an initial allocation of the draw units and a second weighted component based on a difference between two expected sellout percentages with at least one expected sellout percentage calculated based on the initial allocation of the draw units.
6. The optimization application of claim 1 wherein the pre-selected allocation decision criterion is a sum of two weighted components, a first weighted component based on a difference between two expected return percentages with at least one expected return percentage calculated based on an initial allocation of the draw units and a second weighted component based on a difference between two expected stockout percentages with at least one expected stockout percentages calculated based on the initial allocation of the draw units.
7. The optimization application of claim 1 wherein the pre-selected allocation decision criterion comprises a maximum incremental availability criterion and a minimum decremental availability criterion and the determine the location step determines a first location that has a maximum incremental availability and a second location that has a minimum decremental availability and wherein the allocate the one and only one additional draw unit step allocates the one and only one additional draw unit from the second location to the first location.
8. The optimization application of claim 1 further comprising a forecast engine for forecasting the mean demand of the consumer item at each location of the plurality of locations.
9. The optimization application of claim 8 wherein the allocate the draw units of the consumer item to each location allocates draw units equal to the mean demand of the consumer item at that location.
10. The optimization application of claim 1 wherein the at least one constraint is based on performance metrics including at least one of safety stock, expected sellout, expected stockout, or expected return.
11. The optimization application of claim 10 wherein, the safety stock is a difference between an actual draw of the consumer item at the location and its demand forecast at that location and can assume one of positive, or negative value and wherein the optimization application is further operable to compute the draw units of the consumer item to be allocated to each location even when the safety stock assumes a negative value.
12. The optimization application of claim 1 wherein the consumer item is a printed media publication.
13. A computer implemented method for determining a distribution policy for a single period inventory system comprising a plurality of locations, the method comprising the steps of:
allocating draw units of a consumer item to each location of the plurality of locations;
determining, using at least one processor operatively coupled with a memory, a location from the plurality of locations such that the determined location meets a pre-selected allocation decision criterion based on at least the draw units allocated to each location of the plurality of locations;
allocating one and only one additional draw unit of the consumer item to the determined location; and
repeating the determining the location step and the allocating the one and only one additional draw unit step until at least one constraint is satisfied.
14. The computer implemented method of claim 13 wherein the pre-selected allocation decision criterion is a maximum incremental availability, where availability is a probability of completely satisfying a demand for the consumer item at the location without occurrence of a sellout due to an insufficient draw at the location and is computed using a cumulative distribution function (cdf) of a mean demand and draw units of the consumer item allocated for the location, wherein
incremental availability for the location is a difference between a first availability of the consumer item at the location and a second availability of the consumer item at the location, the second availability computed with one additional draw unit of the consumer item allocated to the location in comparison to the draw units allocated for computation of the first availability and the maximum incremental availability is a maximum of the incremental availability of the consumer item computed for each location of the plurality of locations, and wherein the determining the location step determines a location that has the maximum incremental availability.
15. The computer implemented method of claim 14 wherein the pre-selected allocation decision criterion is a minimum availability, where availability is a probability of completely satisfying a demand for the consumer item at the location without occurrence of a sellout due to insufficient draw at the location and is computed using a cumulative distribution function (cdf) of the mean demand and draw units of the consumer item allocated for the location, where minimum availability is a minimum of the availability of the consumer item computed for each location of the plurality of locations and wherein the determining the location step determines a location with the minimum availability.
16. The computer implemented method of claim 13 wherein the pre-selected allocation decision criterion is a minimum incremental expected return, where expected return is expected number of the consumer item unsold at the location and is computed using demand, mean demand and draw units of the consumer item allocated for the location wherein,
the incremental expected return is a difference between a first expected return and a second expected return, the second expected return computed for one additional draw unit of the consumer item allocated at the location in comparison to the draw units allocated for computation of the first expected return, where the minimum incremental expected return is a minimum of the incremental expected return of the consumer item computed for each location and wherein the determined location has the minimum incremental expected return.
17. The computer implemented method of claim 13 wherein the at least one constraint is based on performance metrics including at least one of safety stock, expected sellout, expected stockout, or expected return.
18. A machine readable storage medium storing a plurality of instructions execution of which by a processor causes the processor to determine a distribution policy for a single period inventory system comprising a plurality of locations, the execution of the plurality of instructions by the processor causing the processor to perform the actions of:
allocating draw units of a consumer item to each location of the plurality of locations;
determining a location from the plurality of locations such that the determined location meets a pre-selected allocation decision criterion based on at least the draw units allocated to each location of the plurality of locations;
allocating one and only one additional draw unit of the consumer item to the determined location; and
repeating the determining the location step and the allocating the one and only one additional draw unit step until at least one constraint is satisfied.
19. The machine readable storage medium of claim 18 wherein the pre-selected allocation decision criterion is a maximum incremental availability, where availability is a probability of completely satisfying a demand for the consumer item at the location without occurrence of a sellout due to an insufficient draw at the location and is computed using a cumulative distribution function (cdf) of a mean demand and draw units of the consumer item allocated for the location, wherein
incremental availability for the location is a difference between a first availability of the consumer item at the location and a second availability of the consumer item at the location, the second availability computed with one additional draw unit of the consumer item allocated to the location in comparison to the draw units allocated for computation of the first availability and the maximum incremental availability is a maximum of the incremental availability of the consumer item computed for each location of the plurality of locations, and wherein the determining the location step determines a location that has the maximum incremental availability.
20. The machine readable storage medium of claim 18 wherein the at least one constraint is based on performance metrics including at least one of safety stock, expected sellout, expected stockout, or expected return.