DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-101825, filed on 06/21/2023.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2024-037478, filed on 03/11/2024.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 07/15/2026, 06/13/2024, and 03/04/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 9-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim (US20200275742).
In regards to Claim 1, Kim teaches “an input configured to receive an input of measurement data of the foot shape of the subject in a first loaded state (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot.” – [0133]; “In this case, the foot-related human biomechanical data may further measure 3D foot scan data, which is the entire scan of the subject's foot shape through a 3D scanner 102 , for the tilting controller 100 , and extract it from the 3D foot scan data.” – [0134]; “The data collector 210 collects foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel (rear foot portion) height from the tilting controller 100 and EMG sensor 101 (S 12 ).” – [0135]; “The data collector 210 generates a piece of qualitative data that matches the subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of the heel and stores it in the database 230 (S 13 )” - [0136]);
a processing circuitry configured to use a prediction model trained by machine learning to predict the foot shape of the subject in a second loaded state in which a load is different from the first loaded state based on the measurement data received by the input (“The data processor 220 performs machine learning based on the qualitative data contained in the raw data to generate quantitative data of tilting conditions in which comfort according to a foot shape is optimized” – [0148]; “For this, the data processor 220 may build a quantitative data generation model based on a multi-layered neural network using a machine learning engine” – [0150]; “The input parameters for the learning may include a tilting condition for each heel height, a foot pressure sum [i.e. each foot pressure sum corresponds to loads that are different], a foot pressure average, a foot pressure standard deviation, a number of pressure sensors (cells) for measuring foot pressure, a foot pressure ratio of the front foot portion mechanism and the rear foot portion mechanism, EMG similarity (similarity to flat-level EMG), foot comfort satisfaction, and a degree of foot tilt” – [0152]; “The database 230 stores raw data generated by the accumulation of qualitative data, stores quantitative data processed by learning raw data, and stores and manages 3D last model information optimized according to the stored information” – [0155]; “The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]); and
an output configured to output prediction data of the foot shape of the subject in the second loaded state predicted by the processing circuitry (“The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]),
wherein the prediction model is generated in advance through training by machine learning based on training measurement data of a foot shape of a training subject in the first loaded state and training shape data of the foot shape of the training subject in the second loaded state (“A user-customized last providing system according to one aspect of the present invention includes: a tilting controller that measures foot pressure data applied to a sole according to a tilting condition while a subjects stands thereupon; and a server that generates accumulated raw data by collecting qualitative data according to physical conditions of various subjects and foot pressure data measurement from the tilting controller, extracts quantitative data of an optimized tilting condition according to a foot shape through machine learning by utilizing the raw data, and provides last information generated based on quantitative data that matches foot-related human biomechanical data of the user” – [0019]; “In addition, the server may include: a data collector that collects qualitative data that includes foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel height from the tilting controller and EMG sensor; a data processor that generates quantitative data of tilting conditions in which comfort according to a foot shape is optimized by performing machine learning based on the qualitative data; a controller that generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data; and a database that stores the qualitative data and the quantitative data, and stores the 3D last information optimized according to stored information” – [0026]; “A method for offering user-customized last information according to a feet shape of a user by a server according to one aspect to the present invention, include: a) generating raw data by accumulating qualitative data that matches subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of a heel of the tilting controller; b) extracting quantitative data of tilting conditions optimized according to a foot shape through machine learning based on the raw data; c) acquiring personal information of a user, and foot-related human biomechanical data that includes at least one of a foot straight line length, a ball width of the foot, a height of the top of the foot, medial and lateral longitudinal arches, and a straight line length of the ball of the foot by image-processing a foot image; and d) deriving last information to which a tilting value that is the most similar information from quantitative data through a matching algorithm based on the personal information and the foot-related human biomechanical data of the user, and recommending the derived last information.” – [0034]).”
In regards to Claim 9, Kim discloses the claimed invention as detailed above in Claim 1. Kim further teaches “the prediction model is generated in advance through training by machine learning for each attribute information of the training subject (“A user-customized last providing system according to one aspect of the present invention includes: a tilting controller that measures foot pressure data applied to a sole according to a tilting condition while a subjects stands thereupon; and a server that generates accumulated raw data by collecting qualitative data according to physical conditions of various subjects and foot pressure data measurement from the tilting controller, extracts quantitative data of an optimized tilting condition according to a foot shape through machine learning by utilizing the raw data, and provides last information generated based on quantitative data that matches foot-related human biomechanical data of the user” – [0019]; “In addition, the server may include: a data collector that collects qualitative data that includes foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel height from the tilting controller and EMG sensor; a data processor that generates quantitative data of tilting conditions in which comfort according to a foot shape is optimized by performing machine learning based on the qualitative data; a controller that generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data; and a database that stores the qualitative data and the quantitative data, and stores the 3D last information optimized according to stored information” – [0026]; “A method for offering user-customized last information according to a feet shape of a user by a server according to one aspect to the present invention, include: a) generating raw data by accumulating qualitative data that matches subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of a heel of the tilting controller; b) extracting quantitative data of tilting conditions optimized according to a foot shape through machine learning based on the raw data; c) acquiring personal information of a user, and foot-related human biomechanical data that includes at least one of a foot straight line length, a ball width of the foot, a height of the top of the foot, medial and lateral longitudinal arches, and a straight line length of the ball of the foot by image-processing a foot image; and d) deriving last information to which a tilting value that is the most similar information from quantitative data through a matching algorithm based on the personal information and the foot-related human biomechanical data of the user, and recommending the derived last information.” – [0034]),
the input is configured to receive at least one of the attribute information of the subject (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot.” – [0133]; “In this case, the foot-related human biomechanical data may further measure 3D foot scan data, which is the entire scan of the subject's foot shape through a 3D scanner 102 , for the tilting controller 100 , and extract it from the 3D foot scan data.” – [0134]; “The data collector 210 collects foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel (rear foot portion) height from the tilting controller 100 and EMG sensor 101 (S 12 ).” – [0135]; “The data collector 210 generates a piece of qualitative data that matches the subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of the heel and stores it in the database 230 (S 13 )” - [0136]), and
the processing circuitry is configured to use the prediction model corresponding to the inputted attribute to predict the foot shape of the subject in the second loaded state based on the measurement data received by the input (“The data processor 220 performs machine learning based on the qualitative data contained in the raw data to generate quantitative data of tilting conditions in which comfort according to a foot shape is optimized” – [0148]; “For this, the data processor 220 may build a quantitative data generation model based on a multi-layered neural network using a machine learning engine” – [0150]; “The input parameters for the learning may include a tilting condition for each heel height, a foot pressure sum [i.e. each foot pressure sum corresponds to loads that are different], a foot pressure average, a foot pressure standard deviation, a number of pressure sensors (cells) for measuring foot pressure, a foot pressure ratio of the front foot portion mechanism and the rear foot portion mechanism, EMG similarity (similarity to flat-level EMG), foot comfort satisfaction, and a degree of foot tilt” – [0152]; “The database 230 stores raw data generated by the accumulation of qualitative data, stores quantitative data processed by learning raw data, and stores and manages 3D last model information optimized according to the stored information” – [0155]; “The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]).”
In regards to Claim 10, Kim discloses the prediction device according to claim 1 as detailed above. Kim further teaches “a measurement device configured to measure the foot shape of the subject in the first loaded state (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot.” – [0133]; “In this case, the foot-related human biomechanical data may further measure 3D foot scan data, which is the entire scan of the subject's foot shape through a 3D scanner 102 , for the tilting controller 100 , and extract it from the 3D foot scan data.” – [0134]; “The data collector 210 collects foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel (rear foot portion) height from the tilting controller 100 and EMG sensor 101 (S 12 ).” – [0135]; “The data collector 210 generates a piece of qualitative data that matches the subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of the heel and stores it in the database 230 (S 13 )” - [0136]).”
In regards to Claim 11, Kim teaches “receiving an input of measurement data of the foot shape of the subject in a first loaded state (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot.” – [0133]; “In this case, the foot-related human biomechanical data may further measure 3D foot scan data, which is the entire scan of the subject's foot shape through a 3D scanner 102 , for the tilting controller 100 , and extract it from the 3D foot scan data.” – [0134]; “The data collector 210 collects foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel (rear foot portion) height from the tilting controller 100 and EMG sensor 101 (S 12 ).” – [0135]; “The data collector 210 generates a piece of qualitative data that matches the subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of the heel and stores it in the database 230 (S 13 )” - [0136]);
using a prediction model trained by machine learning (“The data processor 220 performs machine learning based on the qualitative data contained in the raw data to generate quantitative data of tilting conditions in which comfort according to a foot shape is optimized” – [0148]);
predicting the foot shape of the subject in a second loaded state in which a load is different from the first loaded state based on the measurement data received (“The data processor 220 performs machine learning based on the qualitative data contained in the raw data to generate quantitative data of tilting conditions in which comfort according to a foot shape is optimized” – [0148]; “For this, the data processor 220 may build a quantitative data generation model based on a multi-layered neural network using a machine learning engine” – [0150]; “The input parameters for the learning may include a tilting condition for each heel height, a foot pressure sum [i.e. each foot pressure sum corresponds to loads that are different], a foot pressure average, a foot pressure standard deviation, a number of pressure sensors (cells) for measuring foot pressure, a foot pressure ratio of the front foot portion mechanism and the rear foot portion mechanism, EMG similarity (similarity to flat-level EMG), foot comfort satisfaction, and a degree of foot tilt” – [0152]; “The database 230 stores raw data generated by the accumulation of qualitative data, stores quantitative data processed by learning raw data, and stores and manages 3D last model information optimized according to the stored information” – [0155]; “The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]); and
outputting prediction data, which has been predicted, of the foot shape of the subject in the second loaded state (“The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]),
wherein the prediction model is generated in advance through training by machine learning based on training measurement data of a foot shape of a training subject in the first loaded state and training shape data of the foot shape of the training subject in the second loaded state (“A user-customized last providing system according to one aspect of the present invention includes: a tilting controller that measures foot pressure data applied to a sole according to a tilting condition while a subjects stands thereupon; and a server that generates accumulated raw data by collecting qualitative data according to physical conditions of various subjects and foot pressure data measurement from the tilting controller, extracts quantitative data of an optimized tilting condition according to a foot shape through machine learning by utilizing the raw data, and provides last information generated based on quantitative data that matches foot-related human biomechanical data of the user” – [0019]; “In addition, the server may include: a data collector that collects qualitative data that includes foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel height from the tilting controller and EMG sensor; a data processor that generates quantitative data of tilting conditions in which comfort according to a foot shape is optimized by performing machine learning based on the qualitative data; a controller that generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data; and a database that stores the qualitative data and the quantitative data, and stores the 3D last information optimized according to stored information” – [0026]; “A method for offering user-customized last information according to a feet shape of a user by a server according to one aspect to the present invention, include: a) generating raw data by accumulating qualitative data that matches subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of a heel of the tilting controller; b) extracting quantitative data of tilting conditions optimized according to a foot shape through machine learning based on the raw data; c) acquiring personal information of a user, and foot-related human biomechanical data that includes at least one of a foot straight line length, a ball width of the foot, a height of the top of the foot, medial and lateral longitudinal arches, and a straight line length of the ball of the foot by image-processing a foot image; and d) deriving last information to which a tilting value that is the most similar information from quantitative data through a matching algorithm based on the personal information and the foot-related human biomechanical data of the user, and recommending the derived last information.” – [0034]).”
In regards to Claim 12, Kim discloses the claimed invention as detailed above in Claim 1. Kim further teaches “the foot shape is a foot sole shape (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot [i.e. foot sole shape].” – [0133].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Kim as applied to claim 1 above, and further in view of Lowe (US20080086907)
In regards to Claim 2, Kim discloses the claimed invention as detailed above in Claim 1. Kim further teaches “the training measurement data and the training shape data are obtained from the training subject as three-dimensional data (“In this case, the data collector 210 can acquire personal information including the subject's name, date of birth, height, body weight, and presence or absence of foot disease history, and as FIG. 10 and FIG. 11, foot-related human biomechanical data including a foot straight line length (mm), a ball width of the foot (mm), medial and lateral longitudinal arches, and a straight line length (mm) of the ball of the foot.” – [0133]; “In this case, the foot-related human biomechanical data may further measure 3D foot scan data, which is the entire scan of the subject's foot shape through a 3D scanner 102 , for the tilting controller 100 , and extract it from the 3D foot scan data.” – [0134]; “The data collector 210 collects foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel (rear foot portion) height from the tilting controller 100 and EMG sensor 101 (S 12 ).” – [0135]; “The data collector 210 generates a piece of qualitative data that matches the subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of the heel and stores it in the database 230 (S 13 )” - [0136]),
the prediction model is generated in advance through training by machine learning based on dimensional training data converted from the training measurement data and the training shape data respectively into dimensional data including information on the foot shape in a height direction of the training subject (“A user-customized last providing system according to one aspect of the present invention includes: a tilting controller that measures foot pressure data applied to a sole according to a tilting condition while a subjects stands thereupon; and a server that generates accumulated raw data by collecting qualitative data according to physical conditions of various subjects and foot pressure data measurement from the tilting controller, extracts quantitative data of an optimized tilting condition according to a foot shape through machine learning by utilizing the raw data, and provides last information generated based on quantitative data that matches foot-related human biomechanical data of the user” – [0019]; “In addition, the server may include: a data collector that collects qualitative data that includes foot pressure data, EMG data, and subject satisfaction data for each tilting condition for each subject's heel height from the tilting controller and EMG sensor; a data processor that generates quantitative data of tilting conditions in which comfort according to a foot shape is optimized by performing machine learning based on the qualitative data; a controller that generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data; and a database that stores the qualitative data and the quantitative data, and stores the 3D last information optimized according to stored information” – [0026]; “A method for offering user-customized last information according to a feet shape of a user by a server according to one aspect to the present invention, include: a) generating raw data by accumulating qualitative data that matches subject's personal information, foot-related human biomechanical data, foot pressure data, EMG data, and satisfaction data for each height of a heel of the tilting controller; b) extracting quantitative data of tilting conditions optimized according to a foot shape through machine learning based on the raw data; c) acquiring personal information of a user, and foot-related human biomechanical data that includes at least one of a foot straight line length, a ball width of the foot, a height of the top of the foot, medial and lateral longitudinal arches, and a straight line length of the ball of the foot by image-processing a foot image; and d) deriving last information to which a tilting value that is the most similar information from quantitative data through a matching algorithm based on the personal information and the foot-related human biomechanical data of the user, and recommending the derived last information.” – [0034]).” Kim is silent with regards to the language of “two-dimensional training data converted from the training measurement data and the training shape data respectively into two-dimensional data including information on the foot shape in a height direction of the training subject”
Lowe teaches “two-dimensional training data converted from the training measurement data and the training shape data respectively into two-dimensional data including information on the foot shape in a height direction of the training subject (“In addition to capturing information using the footpad, the user terminal in accordance with an embodiment of the present invention can display the captured information. The information can be displayed in one of a number of manners. In one embodiment, three dimensional information concerning the shape of a patient's foot can be displayed as a two dimensional height information display, a two dimensional interpolated height information display or a three dimensional contour map” – [0062]; “An example of a two dimensional height information display generated in accordance with an embodiment of the present invention is illustrated in FIG. 8. The display includes a grid 120 of cells. Each cell corresponds to information collected by an electrode cell in the footpad. The majority of cells do not contain information concerning the shape of a patient's foot and a 16*33 cell grid is chosen that contains all of the pressure information generated by the patient's foot. The grid squares that contain pressure information concerning the patient's foot are indicated by a number 122 and a dot 124. The number indicates the height of the patient's foot at that point measured relative to the lowest point of the patient's foot. The size and color of the dot are assigned based on the height information. Numbers that are small are assigned large dots with a red color. As the number increases smaller dots with colors trending from red to yellow to green to blue are assigned. Cells where no pressure was detected from the patient's foot 126 are left empty” – [0063]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to incorporate the teaching of Lowe to utilize conversion of the three dimensional foot shape data into a two dimensional data with further details towards the height data (i.e. the third dimension). By utilizing two dimensional data of the foot, this is an improvement to the capturing and evaluation of shape of feet so that the improved foot function can be obtained with improved support from footwear for the foot.
In regards to Claim 3, Kim in view of Low discloses the claimed invention as detailed above in Claim 2. Kim is silent with regards to the language of “the two-dimensional training data includes the information on the foot shape in the height direction of the training subject as gradation information.”
Lowe further teaches “the two-dimensional training data includes the information on the foot shape in the height direction of the training subject as gradation information (“In addition to capturing information using the footpad, the user terminal in accordance with an embodiment of the present invention can display the captured information. The information can be displayed in one of a number of manners. In one embodiment, three dimensional information concerning the shape of a patient's foot can be displayed as a two dimensional height information display, a two dimensional interpolated height information display or a three dimensional contour map” – [0062]; “An example of a two dimensional height information display generated in accordance with an embodiment of the present invention is illustrated in FIG. 8. The display includes a grid 120 of cells. Each cell corresponds to information collected by an electrode cell in the footpad. The majority of cells do not contain information concerning the shape of a patient's foot and a 16*33 cell grid is chosen that contains all of the pressure information generated by the patient's foot. The grid squares that contain pressure information concerning the patient's foot are indicated by a number 122 and a dot 124. The number indicates the height of the patient's foot at that point measured relative to the lowest point of the patient's foot. The size and color of the dot are assigned based on the height information. Numbers that are small are assigned large dots with a red color. As the number increases smaller dots with colors trending from red to yellow to green to blue are assigned. Cells where no pressure was detected from the patient's foot 126 are left empty” – [0063]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to incorporate the teaching of Lowe to utilize conversion of the three dimensional foot shape data into a two dimensional data with further details towards the height data (i.e. the third dimension). By utilizing two dimensional data of the foot, this is an improvement to the capturing and evaluation of shape of feet so that the improved foot function can be obtained with improved support from footwear for the foot.
In regards to Claim 4, Kim in view of Low discloses the claimed invention as detailed above in Claim 2. Kim is silent with regards to the language of “the information on the foot shape in the height direction is information on a height from a reference plane to a sole of a foot.”
Lowe further teaches “the information on the foot shape in the height direction is information on a height from a reference plane to a sole of a foot (“An example of a two dimensional interpolated height information display generated in accordance with an embodiment of the present invention is illustrated in FIG. 10. The image 150 includes a grid 152 that depicts the scale of the information presented. Superimposed on the grid are pixels of color that indicate the height of the surface of the underside of a patient's foot above an arbitrary reference surface. The information displayed has a higher resolution than the information collected using the footpad. The increased resolution is obtained by interpolating the raw data. A black dot 156 is superimposed on the image to indicate the center of balance of the foot” – [0065]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to incorporate the teaching of Lowe to utilize conversion of the three dimensional foot shape data into a two dimensional data with further details towards the height data (i.e. the third dimension). By utilizing two dimensional data of the foot, this is an improvement to the capturing and evaluation of shape of feet so that the improved foot function can be obtained with improved support from footwear for the foot.
In regards to Claim 7, Kim discloses the claimed invention as detailed above in Claim 1. Kim further teaches “the processing circuitry is configured to predict the foot shape of the subject in the second loaded state based on the measurement data using the prediction model (“The data processor 220 performs machine learning based on the qualitative data contained in the raw data to generate quantitative data of tilting conditions in which comfort according to a foot shape is optimized” – [0148]; “For this, the data processor 220 may build a quantitative data generation model based on a multi-layered neural network using a machine learning engine” – [0150]; “The input parameters for the learning may include a tilting condition for each heel height, a foot pressure sum [i.e. each foot pressure sum corresponds to loads that are different], a foot pressure average, a foot pressure standard deviation, a number of pressure sensors (cells) for measuring foot pressure, a foot pressure ratio of the front foot portion mechanism and the rear foot portion mechanism, EMG similarity (similarity to flat-level EMG), foot comfort satisfaction, and a degree of foot tilt” – [0152]; “The database 230 stores raw data generated by the accumulation of qualitative data, stores quantitative data processed by learning raw data, and stores and manages 3D last model information optimized according to the stored information” – [0155]; “The controller 240 generates 3D last information in consideration of various foot-related physical conditions based on the quantitative data. The 3D last information includes last information optimized for tilting conditions for each heel height, matched with 3D foot scan data.” – [0157]; “The controller 240 may output (produce) the actual last through 3D printing using the 3D last information” - [0158]; “In addition, the controller 240 may collect information and foot-related human biomechanical data of a user who wants to purchase footwear and provides customized 3D last information based on the quantitative data” – [0159]).”
Kim is silent with regards to the language of “the measurement data, which has been converted into two-dimensional data”
Lowe teaches “the measurement data, which has been converted into two-dimensional data (“In addition to capturing information using the footpad, the user terminal in accordance with an embodiment of the present invention can display the captured information. The information can be displayed in one of a number of manners. In one embodiment, three dimensional information concerning the shape of a patient's foot can be displayed as a two dimensional height information display, a two dimensional interpolated height information display or a three dimensional contour map” – [0062]; “An example of a two dimensional height information display generated in accordance with an embodiment of the present invention is illustrated in FIG. 8. The display includes a grid 120 of cells. Each cell corresponds to information collected by an electrode cell in the footpad. The majority of cells do not contain information concerning the shape of a patient's foot and a 16*33 cell grid is chosen that contains all of the pressure information generated by the patient's foot. The grid squares that contain pressure information concerning the patient's foot are indicated by a number 122 and a dot 124. The number indicates the height of the patient's foot at that point measured relative to the lowest point of the patient's foot. The size and color of the dot are assigned based on the height information. Numbers that are small are assigned large dots with a red color. As the number increases smaller dots with colors trending from red to yellow to green to blue are assigned. Cells where no pressure was detected from the patient's foot 126 are left empty” – [0063]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to incorporate the teaching of Lowe to utilize conversion of the three dimensional foot shape data into a two dimensional data with further details towards the height data (i.e. the third dimension). By utilizing two dimensional data of the foot, this is an improvement to the capturing and evaluation of shape of feet so that the improved foot function can be obtained with improved support from footwear for the foot.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Lowe as applied to claim 7 above, and further in view of Lin (CN106901445A)
In regards to Claim 8, Kim in view of Lowe discloses the claimed invention as detailed above in Claim 7. Kim is silent with regards to the language of “the processing circuitry is configured to restore the prediction data which is two-dimensional data to three-dimensional data.”
Lin teaches “the processing circuitry is configured to restore the prediction data which is two-dimensional data to three-dimensional data (Orthotic shoes were made using the following methods: (1) Collect foot data from patients with flat feet: Use a planar testing device with capacitive pressure sensors to collect plantar pressure distribution data. The distribution density of the capacitive pressure sensors is 10/cm<sup>2</sup>. Take no less than 6 foot images using an imaging device. Collect other health data of the patients to obtain source data. (2) Collect foot data using a network: Collect source data to a data processing platform using a wireless network. (3) Image the pressure distribution data: Constrain the output value of each pressure sensor of the planar testing device to 0-3840. Divide the pressure data into 640 levels according to the output value, with each level including 6 amplitudes. The pressure unit is 1 degree. Simultaneously, according to the RGB model, the entire color transition from black to green to red is divided into 640 levels, each level corresponding to a color. The pressure data levels are mapped one-to-one with the color levels, and the plantar pressure data is converted into color. Then, the color is matched with the pressure acquisition point (i.e., the location of the pressure sensor) to form a virtual plantar color image corresponding to the plantar pressure data. The maximum arch height is calculated using the foot image, and this maximum height corresponds to the maximum level difference among all colors in the virtual plantar color image. Based on this, a mapping relationship between the virtual image color levels and the foot shape is established, and the virtual plantar color image is then converted into a three-dimensional plantar image.
(4) Foot image recognition and 3D modeling: The collected foot images are transformed into digital images with discrete spatial coordinates and gray levels for each foot image in the source data. After the image transformation step is completed, the images are compressed according to the JPEG standard. Image noise is removed by statistically analyzing Gaussian noise, Rayleigh noise, gamma noise, exponential distribution noise, uniform distribution noise and impulse noise using probability density functions. The noise image is then pre-filtered to obtain the noise variance. Low-pass filtering is then performed using mean filtering or median filtering based on the high-frequency characteristics of the noise. Finally, an image degradation system is established, which is expressed by the mathematical function relationship g(x,y)＝h(x,y)×f(x,y)+n(x,y), where x and y represent the two-dimensional coordinates of the digital image, g(x,y) represents the degraded image, h(x,y) represents the degradation function, f(x,y) represents the digitized static two-dimensional image, and n(x,y) represents the effect of external noise.
After performing a Fourier transform on h(x,y), we obtain H(x,y). We then assign a value to H(x,y) using the expression H(x,y)＝2.72^(-ρβ(x2+y2)<sup>5/6</sup>). After performing an inverse Fourier transform on the assigned H(x,y), we obtain the final solution of h(x,y), where ρ represents the number of gray levels per unit area in the local area of the digitized image, and β represents the pixel density of the local area of the digitized image. Then, based on the calculation results of h(x,y), the noise variance, and the noise removal status, we reverse-derive the image degradation model, ultimately completely eliminating or partially reducing the spatial degradation (blurring) and point degradation (noise) of the image, and obtaining the restored image.
The image is then enhanced using a computer. The foot image is graded by grayscale, and histogram equalization is used to divide the image into several regions. Histogram equalization is then applied to each region, traversing all regions to broaden grayscale levels with a large number of pixels and compress grayscale levels with a small number of pixels, creating locally high contrast. Different grayscale levels are then transformed into different colors using linear or non-linear mapping functions, making the details of the digitized foot image more apparent. Next, a Fourier transform is performed on the digitized image, followed by a frequency-mapping function to further enhance the details. A CMYK space model of the foot is established using cyan (C), magenta (M), yellow (Y), and black (K) as parameters. The RGB space of the digitized foot image is mapped onto the CMYK space model, making the details of the digitized foot image even more apparent.
The computer is then used to segment the image. The Roberts operator for grayscale is used to detect discontinuities in the image, and then the boundary between the foot and the background is extracted. The foot image is then separated from the background and segmented into non-overlapping sub-regions according to the grayscale, shape, texture and color of the image, so that each sub-region can be used to construct a 3D model.
Then, invariant features such as curvature, normal vector, and angle between normal vectors of two points are extracted from the foot surface. Corresponding points are searched in other point clouds and matching relationships are established. At the same time, the pairwise distance relationships of at least three points in the foot image are marked, and corresponding points are searched in other point clouds and matching relationships are established.
After removing bad pixel matches, the scale of each image is adjusted to ensure that the scale of all images is consistent. Then, the two-dimensional coordinates of the image data are converted into three-dimensional coordinates.
Then, the image is subjected to global optimization processing such as noise reduction, repair of model defects (hole filling), segmentation of local images, image structuring, and surface smoothing of three-dimensional images to form a primary three-dimensional foot model; (5) Model fitting: the three-dimensional foot image data obtained by using foot pressure and the primary three-dimensional foot model obtained by using foot images are converted into the same proportion, and then fitted into the same three-dimensional coordinate system to form a three-dimensional foot model that is consistent with or close to the shape of the real foot.
(6) Compare the shape of the arch of the three-dimensional model with the standard healthy arch of the same proportion and calculate the height difference between the two arch heights. Then, set a concave part with a certain arc at the arch position of the three-dimensional model of the foot. The height of the arc of the concave part is 1/3 of the height difference, forming a corrected three-dimensional model; (7) Make shoes: Make shoes according to the corrected three-dimensional model. The shoe making process is injection molding. The insole of the shoe is made of butadiene-styrene copolymer (ABS) at the position corresponding to the arch. The manufacturing precision of the insole is controlled by laser cutting so that the maximum diameter of any defect in the insole does not exceed 1mm. The insole of the shoe is completely fitted with the corrected three-dimensional model of the foot that is restored to the proportion of the real foot. The minimum straight distance between any point on the inner side of the shoe surface and the upper part of the foot is 0.1cm. A wireless communication device and a power supply and charging device are placed in the insole. The insole of the shoe is polished to make it smooth. – [0222]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim in view of Lowe to incorporate the teaching of Lin to perform a process to transform a two-dimensional image of a foot into a three dimensional image of a foot. By performing the conversion process to generate a three dimensional image, this is an improvement to the evaluation of image data of the foot is specialized to the individual’s foot.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kim as applied to claim 1 above, and further in view of Hu (US20230040650)
In regards to Claim 13, Kim discloses the invention as detailed above in Claim 1. Kim is silent with regards to the language of “the training measurement data and the training shape data is a paired image data.”
Hu teaches “the training measurement data and the training shape data is a paired image data (“In its training phase, the inputs are paired images, the output is the predicted label of an input pair” – [0061]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to incorporate the teaching of Hu to utilize paired images for training data. By using paired images this is an improvement to the evaluation of the foot shape under different conditions by using multiple data points.
Allowable Subject Matter
Claims 5-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to Claim 5, Kim in view of Lowe discloses the claimed invention as detailed above in Claim 4. Kim in view of Lowe is silent with regards to the language of “in the two-dimensional training data, data from the reference plane to a first height in a direction of the sole of the foot is converted from the training measurement data into two-dimensional data, and data from the reference plane to a second height higher than the first height in the direction of the sole of the foot is converted from the training shape data into two-dimensional data.”
Claim 6 is dependent on claim 5.
Examiner’s Note
The following prior art are considered of important to the application, but are not utilized in the 35 U.S.C. 102 or 35 U.S.C. 103 rejection of the claims.
Koh (US20220270297) teaches a system for generating pressure maps of feet using deep learning models.
Xiang (Liangling Xiang, et. al., “Automatic Classification of Barefoot and Shod Populations Based on the Foot Metrics and Plantar Pressure Patterns”, 03/23/2022, Frontiers in Bioengineering and biotechnology, https://pmc.ncbi.nlm.nih.gov/articles/PMC8984198/) teaches a method with machine learning model to classify foot function and performance.
Schwartz (US20200151594) teaches a method for foot differentiation scoring based on differences between feet.
Hakkala (US20210315323) teaches a method for foot analysis with three-dimensional scanning to determine the shape of the foot under different pressures applied to the foot.
Wang (US20240138523) teaches a method of providing personalized footwear by utilizing foot shape, pathology, gait and biomechanical, and context data.
Conclusion
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/YOSSEF KORANG-BEHESHTI/ Primary Examiner, Art Unit 2857
