1. An information processing apparatus comprising:
a receiving device for receiving a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
a first adjusting device for adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
a second adjusting device for adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
a calculator for calculating a characteristic coefficient identifying a Pearson function from a moment of a function including the first and second functions joined at the position of the extreme value;
a storage device for storing the characteristic coefficient in a database in association with the data describing the condition under which the distribution data series has been obtained;
an interpolator for interpolating a characteristic coefficient for condition data of interest from the characteristic coefficient stored in the database; and
a distribution data calculator for calculating distribution data by a Pearson function identified by the interpolated characteristic coefficient.
2. An information processing apparatus comprising:
a receiving device for receiving a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
a first adjusting device for adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
a second adjusting device for adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
a storage device for storing a function parameter set for identifying the first and second functions in a database in association with the data describing the condition under which the distribution data series has been obtained, the function parameter set including at least one of the first function parameter set, the second function parameter set, a parameter set converted from the first function parameter set, and a parameter set converted from the second function parameter set;
an interpolator for interpolating a function parameter for condition data of interest from the function parameter set stored in the database; and
a calculator for calculating a distribution data series by a composite function of the first and second functions identified by the interpolated function parameter set.
3. The information processing apparatus according to claim 2, wherein the distribution data series includes a first data series in which data appear in a convex or concave curve with the extreme value at a peak or valley and a second data series having a smaller value-change rate than the first data series, the information processing apparatus further comprising:
a third adjusting device for adjusting at least one function parameter in a third function parameter set to reduce an error between distribution data generated by a third function and the second distribution data series, the third function including the third function parameter set, the third function parameter set specifying the extreme value, the position of the extreme value, and the ratio of a value at a third distance from the position of the extreme value in the direction of the second data series to the extreme value, and an order of an expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the third distance in the direction of the second data series.
4. The information processing apparatus according to claim 1, wherein the distribution data calculator comprises:
a first logarithmic distribution data calculator for calculating distribution data on a logarithmic axis;
a second logarithmic distribution data calculator for calculating a maximum value of the distribution data on the logarithmic axis;
a third logarithmic distribution data calculator for subtracting the maximum value of the distribution data on the logarithmic axis from the distribution data on the logarithmic axis; and
a third linear distribution data calculator for converting the distribution data on the logarithmic axis from which the maximum value has been subtracted to distribution data on a linear axis.
5. An information processing method performed by a computer, comprising:
inputting a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
calculating a characteristic coefficient identifying a Pearson function from a moment of a function including the first and second functions joined at the position of the extreme value;
storing the characteristic coefficient in a database in association with the data describing the condition under which the distribution data series has been obtained;
interpolating a characteristic coefficient for condition data of interest from the characteristic coefficient stored in the database; and
calculating distribution data by a Pearson function identified by the interpolated characteristic coefficient.
6. The information processing method according to claim 5, further comprising:
adjusting the first and second function parameter sets to reduce an error between data generated by the Pearson function identified by the characteristic coefficient calculated from the moment and the input distribution data series.
7. The information processing method according to claim 5, wherein the distribution data series includes a first data series in which data appear in a convex or concave curve with the extreme value at a peak or valley and a second data series having a smaller value-change rate than the first data series, the information processing method further comprises:
adjusting at least one function parameter in a third function parameter set to reduce an error between data generated by a third function and the second data series, the third function including the third function parameter set, the third function parameter set specifying the extreme value, the position of the extreme value, and the ratio of a value at a third distance from the position of the extreme value in the direction of the second data series to the extreme value, and an order of an expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the third distance in the direction of the second data series.
8. The information processing method according to claim 7, further comprising:
setting a factor for the Pearson function, the factor being determined from a condition that functions resulting from multiplication of the sum of the Pearson function and the third function by the factor are continuous; and
adjusting an extreme value of the third function so that the third function multiplied by the factor matches the second data series.
9. An information processing method performed by a computer, comprising:
inputting a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
storing a function parameter set for identifying the first and second functions in a database in association with the data describing the condition under which the distribution data series has been obtained, the function parameter set including at least one of the first function parameter set, the second function parameter set, a parameter set converted from the first function parameter set, and a parameter set converted from the second function parameter set;
interpolating a function parameter for condition data of interest from the function parameter set stored in the database; and
calculating a distribution data series by a composite function of the first and second functions identified by the interpolated function parameter set.
10. The information processing method according to claim 9, wherein the distribution data series includes a first data series in which data appear in a convex or concave curve with the extreme value at a peak or valley and a second data series having a smaller value-change rate than the first data series, the information processing method further comprises:
adjusting at least one function parameter in a third function parameter set to reduce an error between distribution data generated by a third function and the second data series, the third function including the third function parameter set, the third function parameter set specifying the extreme value, the position of the extreme value, and the ratio of a value at a third distance from the position of the extreme value in the direction of the second data series to the extreme value, and an order of an expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the third distance in the direction of the second data series.
11. The information processing method according to claim 10, further comprising:
calculating a second characteristic coefficient identifying a Pearson function from the third function parameter set; and
storing the second characteristic coefficient in a database in association with the data describing the condition under which the distribution data series has been obtained.
12. The information processing method according to claim 11, wherein the calculating a distribution data series by a composite function of the first and second functions identified by the interpolated function parameter set further comprises:
calculating distribution data on a logarithmic axis;
calculating a maximum value of the distribution data on the logarithmic axis;
subtracting the maximum value of the distribution data on the logarithmic axis from the distribution data on the logarithmic axis; and
converting the distribution data on the logarithmic axis from which the maximum value has been subtracted to distribution data on a linear axis.
13. A computer readable medium includes a program causing a computer to perform an information processing method, the information processing method comprising:
inputting a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
calculating a characteristic coefficient identifying a Pearson function from a moment of a function including the first and second functions joined at the position of the extreme value;
storing the characteristic coefficient in a database in association with the data describing the condition under which the distribution data series has been obtained;
interpolating a characteristic coefficient for condition data of interest from the characteristic coefficient stored in the database; and
calculating distribution data by a Pearson function identified by the interpolated characteristic coefficient.
14. The computer readable medium according to claim 13, the information processing method further comprising:
adjusting the first and second function parameter sets to reduce an error between data generated by the Pearson function identified by the characteristic coefficient calculated from the moment and the input distribution data series.
15. The computer readable medium according to claim 13, wherein the distribution data series includes a first data series in which data appear in a convex or concave curve with the extreme value at a peak or valley and a second data series having a smaller value-change rate than the first data series, the information processing method further comprising:
adjusting at least one function parameter in a third function parameter set to reduce an error between data generated by a third function and the second data series, the third function including the third function parameter set, the third function parameter set specifying the extreme value, the position of the extreme value, and the ratio of a value at a third distance from the position of the extreme value in the direction of the second data series to the extreme value, and an order of an expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the third distance in the direction of the second data series.
16. The computer readable medium according to claim 15, the information processing method further comprising:
setting a factor for the Pearson function, the factor being determined from a condition that functions resulting from multiplication of the sum of the Pearson function and the third function by the factor are continuous; and
adjusting an extreme value of the third function so that the third function multiplied by the factor matches the second data series.
17. A computer readable medium includes a program causing a computer to perform an information processing method, the information processing method comprising:
inputting a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
storing a function parameter set for identifying the first and second functions in a database in association with the data describing the condition under which the distribution data series has been obtained, the function parameter set including at least one of the first function parameter set, the second function parameter set, a parameter set converted from the first function parameter set, and a parameter set converted from the second function parameter set;
interpolating a function parameter for condition data of interest from the function parameter set stored in the database; and
calculating a distribution data series by a composite function of the first and second functions identified by the interpolated function parameter set.
18. The computer readable medium according to claim 17, wherein the distribution data series includes a first data series in which data appear in a convex or concave curve with the extreme value at a peak or valley and a second data series having a smaller value-change rate than the first data series, the information processing method further comprising:
adjusting at least one function parameter in a third function parameter set to reduce an error between distribution data generated by a third function and the second distribution data series, the third function including the third function parameter set, the third function parameter set specifying the extreme value, the position of the extreme value, and the ratio of a value at a third distance from the position of the extreme value in the direction of the second data series to the extreme value, and an order of an expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the third distance in the direction of the second data series.
19. The computer readable medium according to claim 19, wherein the calculating a distribution data series by a composite function of the first and second functions identified by the interpolated function parameter set further comprises:
calculating distribution data on a logarithmic axis;
calculating a maximum value of the distribution data on the logarithmic axis;
subtracting the maximum value of the distribution data on the logarithmic axis from the distribution data on the logarithmic axis; and
converting the distribution data on the logarithmic axis from which the maximum value has been subtracted to distribution data on a linear axis.
20. A system comprising:
a receiving device for inputting a distribution data series including an extreme value of a value corresponding to a position on a coordinate axis and data describing a condition under which the distribution data series has been obtained;
a first adjusting device for adjusting a first function parameter set to reduce an error between data generated by a first function and the distribution data series, the first function including the first function parameter set, the first function parameter set specifying the position of the extreme value, the ratio of a value at a first distance on the coordinate axis from the position of the extreme value in a first direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the first distance in the first direction;
a second adjusting device for adjusting a second function parameter set to reduce an error between data generated by a second function and the distribution data series, the second function including the second function parameter set, the second function parameter set specifying the position of the extreme value, the ratio of a value at a second distance on the coordinate axis from the position of the extreme value in a second direction to the extreme value, and an order of expression of an exponent part of a function curve including an exponential form in the interval from the position of the extreme value to the second distance in the second direction;
a calculator for calculating a characteristic coefficient identifying a Pearson function from a moment of a function including the first and second functions joined at the position of the extreme value;
a storage device for storing the characteristic coefficient in a database in association with the data describing the condition under which the distribution data series has been obtained;
an interpolator for interpolating a characteristic coefficient for condition data of interest from the characteristic coefficient stored in the database;
a distribution data calculator for calculating distribution data by a Pearson function identified by the interpolated characteristic coefficient; and
an ion implantation apparatus.
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 designing and planning a menu, wherein the menu comprises a plurality of dishes, and each dish in the plurality of dishes comprises a set of ingredients prepared according to a recipe, the method comprising:
obtaining, by a processor, a user-specified ingredient that must be included in the menu;
obtaining, by the processor, a user-specified criterion that must be satisfied by the menu;
automatically formulating, by the processor, an optimization problem that evaluates a plurality of potential dishes for inclusion in the menu by optimizing over the user-specified criterion; and
automatically selecting, by the processor, the plurality of dishes from among the plurality of potential dishes, wherein the automatically selecting is performed based at least in part on a solution to the optimization problem, and wherein at least one dish in the plurality of dishes includes the user-specified ingredient.
2. The method of claim 1, wherein the user-specified criterion is a type of cuisine that must be included in the menu.
3. The method of claim 1, wherein the user-specified criterion is an attribute that must be maximized in the menu.
4. The method of claim 3, wherein the attribute is novelty of the menu.
5. The method of claim 3, wherein the attribute is variety of the menu.
6. The method of claim 1, wherein the plurality of potential dishes includes dishes that satisfy the user-specified criterion and contain the user-specified ingredient.
7. The method of claim 1, wherein the optimization problem comprises a weighted linear function of scores including a score for the user-specified criterion.
8. The method of claim 7, wherein the optimization problem additionally attempts to satisfy a constraint on the menu.
9. The method of claim 8, wherein the constraint is user-specified.
10. The method of claim 9, wherein the constraint relates to a nutritional requirement of the menu.
11. The method of claim 8, wherein the constraint is an availability of a resource required to prepare the menu.
12. The method of claim 7, wherein at least the score for the user-specified criterion is generated in accordance with an assessment model.
13. The method of claim 12, wherein the assessment model is built using data from a plurality of recipes.
14. The method of claim 12, wherein the assessment model is built using data from a plurality of expert opinions relating at least to the user-specified criterion.
15. The method of claim 12, wherein the assessment model is built using data from a plurality of interviews relating at least to resource and facility availability.
16. The method of claim 1, further comprising:
automatically designing, by the processor, a work plan, wherein the work plan specifies a schedule of tasks for preparation of the menu.
17. The method of claim 16, wherein the work plan accounts for resources and facilities available for use in the preparation of the menu.
18. The method of claim 1, wherein the work plan accounts for a time constraint on the preparation of the menu.
19. A device for designing and planning a menu, wherein the menu comprises a plurality of dishes, and each dish in the plurality of dishes comprises a set of ingredients prepared according to a recipe, the device comprising:
a processor; and
a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising:
obtaining a user-specified ingredient that must be included in the menu;
obtaining a user-specified criterion that must be satisfied by the menu;
automatically formulating an optimization problem that evaluates a plurality of potential dishes for inclusion in the menu by optimizing over the user-specified criterion; and
automatically selecting the plurality of dishes from among the plurality of potential dishes, wherein the automatically selecting is performed based at least in part on a solution to the optimization problem, and wherein at least one dish in the plurality of dishes includes the user-specified ingredient.
20. A system for designing and planning a menu, wherein the menu comprises a plurality of dishes, and each dish in the plurality of dishes comprises a set of ingredients prepared according to a recipe, the system comprising:
a database storing recipes for a plurality of potential dishes; and
an application server for automatically selecting the plurality of dishes from among the plurality of potential dishes, wherein the automatically selecting is performed by automatically evaluating the plurality of dishes by optimizing over a user-specified criterion, wherein the plurality of dishes includes at least one dish that includes a user-specified ingredient.