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 .
Response to Amendment
Examiner acknowledges preliminary amendment to the claims and cancellation of claim 20, filed on 6/13/2023. They have been entered and considered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1, 3-4, 7-8, 10-11, 14-15, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Agostino (USPGPUB 20070011173) “Agostino”, in view of Kahlon (USPGPUB 20140285646) “Kahlon”.
Regarding claim 1, Agostino discloses a method comprising: providing, to a client device (108) by a server (112), access to a foot measurement data portal (Fig. 4) presented to a user of the client device (108) as a graphical user interface (GUI) (Figs. 5 and 6); receiving, by the server (112), one or more data selection criteria (504, 506, 608, 610) corresponding to input of the user via the GUI (Figs. 5 and 6); and transmitting, to the client device (108).
Agostino does not disclose a three-dimensional (3D) model of a representative foot or a representative foot last data for display.
Kahlon teaches in figure 1, a three-dimensional (3D) model of a representative foot or a representative foot last data for display (foot modelled and displayed on 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model of a foot displayed on Agostino’s client device with graphical user interface. This allows the user to see a visual representation of a foot based on Agostino’s data selection criteria, improving user experience and visualization of measurement data.
Regarding claim 3, Agostino and Kahlon disclose all the limitations of claim 1. Additionally, they disclose the one or more data selection criteria (Agostino; 504, 506, 608, 610) comprise a user selection of a subset of foot measurement data (608 and 610) from at least one data distribution (length and width).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino’s user foot measurement data in Agostino and Kahlon’s method to produce a more accurate foot last based on user measurements.
Regarding claim 4, Agostino and Kahlon disclose all the limitations of claim 1. Additionally, they disclose the one or more data selection criteria (Agostino; 504, 506, 608, 610) comprise a selection of one or more foot measurements selected from the group consisting of: length, width, girth, arch height, dorsal height, ankle girth, ball girth, ball height, ball width, heel width, instep width, length to first metatarsal head, length to fifth metatarsal head, long heel girth, short heal girth, and max toe height (Agostino; 608, 610 and Kahlon; [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino and Kahlon’s foot measurement selections in Agostino and Kahlon’s method to provide more accurate foot last modeling and better fit predictions.
Regarding claim 7, Agostino and Kahlon disclose all the limitations of claim 1. Additionally, they disclose transmitting, to the user device (Agostino; 108), data descriptive of the 3D model (Kahlon; foot modelled and displayed on 8) for display to the user via the GUI (Agostino; Fig. 5 and 6); receiving, by the server (Agostino; 112), a user selection of a planar slice through the 3D model (Kahlon; 560); and computing a length of a path corresponding to an intersection of the planar slice (Kahlon; 560) and the 3D model.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model measuring in Agostino and Kahlon’s method, providing more detailed and accurate sizing.
Regarding claim 8, Agostino discloses a system comprising: at least one memory unit (memory of 112, [0022]); and a processing device ([0021]) operatively coupled to the at least one memory unit (memory of 112, [0022]), wherein the processing device is configured to: provide, to a client device, access to a foot measurement data portal (Fig. 4) presented to a user of the client device (108) as a graphical user interface (GUI) (Figs. 5 and 6); receive one or more data selection criteria (404, 412, 420, 428, 436, 444, 452) corresponding to input of the user via the GUI.
Agostino does not disclose a three-dimensional (3D) model of a representative foot or a representative foot last.
Kahlon teaches in figure 1, a three-dimensional (3D) model of a representative foot or a representative foot last data for display (foot modelled and displayed on 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model of a foot displayed on Agostino’s client device with graphical user interface. This allows the user to see a visual representation of a foot based on Agostino’s data selection criteria, improving user experience and visualization of measurement data.
Regarding claim 10, Agostino and Kahlon disclose all the limitations of claim 8. Additionally, they disclose the one or more data selection criteria (Agostino; 504, 506, 608, 610) comprise a user selection of a subset of foot measurement data (608 and 610) from at least one data distribution (length and width).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino’s user foot measurement data in Agostino and Kahlon’s method to produce a more accurate foot last based on user measurements.
Regarding claim 11, Agostino and Kahlon disclose all the limitations of claim 8. Additionally, they disclose the one or more data selection criteria (Agostino; 504, 506, 608, 610) comprise a selection of one or more foot measurements selected from the group consisting of: length, width, girth, arch height, dorsal height, ankle girth, ball girth, ball height, ball width, heel width, instep width, length to first metatarsal head, length to fifth metatarsal head, long heel girth, short heal girth, and max toe height (Agostino; 608, 610 and Kahlon; [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino and Kahlon’s foot measurement selections in Agostino and Kahlon’s method to provide more accurate foot last modeling and better fit predictions.
Regarding claim 14, Agostino and Kahlon disclose all the limitations of claim 8. Additionally, they disclose processing device (Agostino; [0021]) transmits to the user device (Agostino; 108), data descriptive of the 3D model (Kahlon; foot modelled and displayed on 8) for display to the user via the GUI (Agostino; Fig. 5 and 6); receiving, by the server (Agostino; 112), a user selection of a planar slice through the 3D model; and computing a length of a path corresponding to an intersection of the planar slice and the 3D model (Kahlon; 560).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model measuring in Agostino and Kahlon’s method, providing more detailed and accurate sizing.
Regarding claim 15, Agostino discloses a non-transitory computer-readable medium having instructions encoded thereon ([0022]) that, when executed by a processing device ([0022]), cause the processing device to: provide, to a client device (108), access to a foot measurement data portal (Fig. 4) presented to a user of the client device as a graphical user interface (GUI) (Figs. 5 and 6); receive one or more data selection criteria (504, 506, 608, 610) corresponding to input of the user via the GUI (Figs. 5 and 6).
Agostino does not disclose a three-dimensional (3D) model of a representative foot or a representative foot last data for display.
Kahlon teaches in figure 1, a three-dimensional (3D) model of a representative foot or a representative foot last data for display (foot modelled and displayed on 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model of a foot displayed on Agostino’s client device with graphical user interface. This allows the user to see a visual representation of a foot based on Agostino’s data selection criteria, improving user experience and visualization of measurement data.
Regarding claim 17, Agostino and Kahlon disclose the non-transitory computer-readable medium of claim 15. Additionally, they disclose the one or more data selection criteria (Agostino; 504, 506, 608, 610) comprise a selection of one or more foot measurements selected from the group consisting of: length, width, girth, arch height, dorsal height, ankle girth, ball girth, ball height, ball width, heel width, instep width, length to first metatarsal head, length to fifth metatarsal head, long heel girth, short heal girth, and max toe height (Agostino; 608, 610 and Kahlon; [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino and Kahlon’s foot measurement selections in Agostino and Kahlon’s method to provide more accurate foot last modeling and better fit predictions.
Regarding claim 21, Agostino discloses a method comprising: providing, to a client device (108) by a server (112), access to a foot measurement data portal (Fig. 4) presented to a user of the client device (108) as a graphical user interface (GUI) (Figs. 5 and 6); presenting, for display in the GUI (Figs. 5 and 6);
Agostino does not disclose a planar slice of a 3D model.
Kahlon teaches a planar slice (560) of a 3D model (foot modelled and displayed on 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s planar slice of a 3D model to accurately measure a foot and generate better shoe size recommendations.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Agostino and Kahlon, in view of Schouwenburg et al. (USPGPUB 20160101571) “Schouwenburg”.
Regarding claim 2, Agostino and Kahlon disclose all the limitations of claim 1. Additionally, they disclose the 3D model corresponds to the representative foot last (Kahlon; foot modelled and displayed on 8), and data (Agostino; data acquired in Fig. 4) descriptive of the 3D model.
Agostino and Kahlon do not disclose transmitting to a fabrication device to fabricate.
Schouwenburg teaches in figure 1A, transmitting to a fabrication device (132A-Z) to fabricate.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino’s descriptive data to generate Kahlon’s 3D model, and to transmit to Schouwenburg’s fabrication device, facilitating faster and easier production of Agostino and Kahlon’s representative foot last.
Regarding claim 9, Agostino and Kahlon disclose all the limitations of claim 8. Additionally, they disclose the 3D model corresponds to the representative foot last (Kahlon; foot modelled and displayed on 8), and data (Agostino; data acquired in Fig. 4) descriptive of the 3D model.
Agostino and Kahlon do not disclose transmitting to a fabrication device to fabricate.
Schouwenburg teaches in figure 1A, transmitting to a fabrication device (132A-Z) to fabricate.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino’s descriptive data to generate Kahlon’s 3D model, and to transmit to Schouwenburg’s fabrication device, facilitating faster and easier production of Agostino and Kahlon’s representative foot last.
Regarding claim 16, Agostino and Kahlon disclose all the limitations of claim 15. Additionally, they disclose the non-transitory computer-readable medium (Agostino; [0022]) of claim 15, wherein the 3D model corresponds to the representative foot last (Kahlon; foot modelled and displayed on 8), and wherein the instructions (Agostino; [0022]) further cause the processing device to: transmit data (Agostino; data acquired in Fig. 4) descriptive of the 3D model.
Agostino and Kahlon do not disclose transmitting to a fabrication device to fabricate.
Schouwenburg teaches in figure 1A, transmitting to a fabrication device (132A-Z) to fabricate.
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Agostino’s descriptive data to generate Kahlon’s 3D model, and to transmit to Schouwenburg’s fabrication device, facilitating faster and easier production of Agostino and Kahlon’s representative foot last.
Claims 5-6, 12-13, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Agostino and Kahlon, in view of Schwartz et al. (USPGPUB 20200151594) “Schwartz”.
Regarding claim 5, Agostino and Kahlon disclose all the limitations of claim 1. Additionally, they disclose identifying the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) and the one or more data selection criteria (Agostino; 504, 506, 608, 610).
Agostino and Kahlon do not disclose a trained machine learning model.
Schwartz teaches in figure 6, a trained machine learning model (604).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Schwartz’s trained machine learning model in Agostino and Kahlon’s method to select the closest 3D model generated by Kahlon, improving the efficiency and accuracy of selecting a modelled foot last.
Regarding claim 6, Agostino, Kahlon, and Schwartz disclose all the limitations of claim 1. Additionally, they disclose generating the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) comprises generating the 3D model by applying one or more transformations (Kahlon; 550) to a 3D model of a foot identified or generated, by a trained machine learning model (Schwartz; 604), as a best fit (Kahlon; Fig. 6) to the one or more data selection criteria (Agostino; 504, 506, 608, 610).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model generation and Schwartz’s machine learning model in Agostino, Kahlon, and Schwartz’s method, allowing the system to adapt to subtle variations in foot shape and improve shoe fit.
Regarding claim 12, Agostino and Kahlon disclose all the limitations of claim 8. Additionally, they disclose identifying the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) and the one or more data selection criteria (Agostino; 504, 506, 608, 610).
Agostino and Kahlon do not disclose a trained machine learning model.
Schwartz teaches in figure 6, a trained machine learning model (604).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Schwartz’s trained machine learning model in Agostino and Kahlon’s method to select the closest 3D model generated by Kahlon, improving the efficiency and accuracy of selecting a modelled foot last.
Regarding claim 13, Agostino and Kahlon disclose all the limitations of claim 8.
Additionally, they disclose generating the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) comprises generating the 3D model by applying one or more transformations (Kahlon; 550) to a 3D model of a foot identified or generated, by a trained machine learning model (Schwartz; 604), as a best fit (Kahlon; Fig. 6) to the one or more data selection criteria (Agostino; 504, 506, 608, 610).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model generation and Schwartz’s machine learning model in Agostino, Kahlon, and Schwartz’s method, allowing the system to adapt to subtle variations in foot shape and improve shoe fit.
Regarding claim 18, Agostino and Kahlon disclose the non-transitory computer-readable medium of claim 15. Additionally, they disclose identifying the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) that best fits the one or more data selection criteria (Agostino; 504, 506, 608, 610).
Agostino and Kahlon do not disclose a trained machine learning model.
Schwartz teaches in figure 6, a trained machine learning model (604).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Schwartz’s trained machine learning model in Agostino and Kahlon’s method to select the closest 3D model generated by Kahlon, improving the efficiency and accuracy of selecting a modelled foot last.
Regarding claim 19, Agostino and Kahlon disclose the non-transitory computer-readable medium of claim 15. Additionally, they disclose generating the 3D model of the representative foot or the representative foot last (Kahlon; foot modelled and displayed on 8) comprises generating the 3D model by applying one or more transformations (Kahlon; 550) to a 3D model of a foot identified or generated, by a trained machine learning model (Schwartz; 604), as a best fit (Kahlon; Fig. 6) to the one or more data selection criteria (Agostino; 504, 506, 608, 610).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use Kahlon’s 3D model generation and Schwartz’s machine learning model in Agostino, Kahlon, and Schwartz’s method, allowing the system to adapt to subtle variations in foot shape and improve shoe fit.
Conclusion
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/ANNA JOSEPHINE SAUNDERS/Examiner, Art Unit 2855
/PETER J MACCHIAROLO/Supervisory Patent Examiner, Art Unit 2855