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 .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Status of Claims
Claims 1-11, 13-17, and 19-20 remain pending, and are rejected.
Claims 21-24 have been added, and are rejected.
Claims 12 and 18 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 3/2/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 3/2/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive.
Notably, on pages 10-11 of the Applicant’s Remarks, arguments are made that the claims are not directed to an abstract idea, but are directed to a technical system that processes raw image data to generate structured 3D digital representation, derives geometric measurements from that representation, applies predictive modelling to forecast dimensional change over time, and dynamically controls future image acquisition timing based on a calculated risk metric. The process is described as a time-dependent computational model that modifies system behavior as a function of predicted geometric change and constitutes a control mechanism that governs operation of the image capture system itself, and improves mobile image-based dimensional measurement systems by introducing predictive modelling and risk-based scan scheduling that reduces measurement degradation over time, and is rooted in computer technology and addresses a problem arising specifically in the context of image-derived 3D modelling and time-based dimensional variability. On pages 12-13, the Applicant argues that even if the claims recite an abstract idea, the ordered combination of elements provides significantly more than the abstract idea, the integration of multi-image 3D reconstruction, machine-learned-based growth prediction, temporally increasing risk computation, and threshold-triggered scan alerting is not conventional or routine arrangement. On pages 13-14, it is further argued that the generating of a three-dimensional geometric representation from multiple two-dimensional images is a technical computer-vision process that transforms raw pixel data into structured geometric data, and the risk calculation introduces a time-dependent predictive monitoring mechanism that is not a business risk or financial abstraction, but are computational measures of predicted geometric mismatch between footwear dimensions and a reconstructed 3D anatomical model. It is argued that the system implements a specific predictive geometric monitoring framework that improves the operation and reliability of a mobile scanning platform.
On pages 15-17, claim 20 is argued to be eligible as the claims transform raw image data captured by a mobile computing device into a three-dimensional geometric model and uses that model to generate predictive outputs tied to physical dimensional change over time, which is rooted in computer vision techniques, and improves the ability of a mobile computing device to perform dimensionally accurate foot measurement without specialized scanning hardware. Arguments are also made that machine learning is used to predict foot growth patterns based on 3D foot profile and user profile data form multiple scans and multiple users, and integrates predictive modelling with a reconstructed 3D geometric representation derived form image data. On pages 17-19, the Applicant argues that claim 21 is recites generating an alert that monitors predicted geometric deviation of a 3D model derived from image data and automatically initiates further image acquisition when a modelled dimensional tolerance condition is exceeded, and the focus of the claim is on maintaining geometric measurement accuracy of a 3D representation through automated device control. Pages 19-21 argue that claim 22 improves the ability of a mobile device to perform dimensional measurement without specialized hardware, and is rooted in computer vision and geometric modelling technology, and further utilizes cluster-based machine learning techniques to create a structured organization of user profiles aggregating phenotype features, dimensional features, preferences, and purchasing status. Similar arguments are made for independent claims 23 and 24 on pages 22-26 as for the other claims above.
Examiner respectfully disagrees. The claims do not recite any steps or underlying technology of how a structured 3D digital representation is generated, or to any technical method of how the dimensions/measurements are determined from the plurality of images. The claims recite these steps in passing without any particular detail. Furthermore, merely generating a 3D representation is not an inherent computer-rooted functionality, such as computer-generated graphics, and the specification does not disclose any detail to how the 3D foot profile is generated. Predictive modelling to forecast dimensional change over time is also not rooted in technology, but is more mathematical in using previous data to estimate calculations for future data using trends. Additionally, the claims do not recite any control or change in functionality of any scanning technology, but merely uses input data to estimate and recommend when another scan should be performed. The claims merely recite predicting a next time a scan should be performed, such as in claim 1, or generating an alert to a user to perform a next scan, such as in claim 24. The claims do not recite any particular control of a scanning system, merely providing a notification to users to take another measurement, and thus is directed to commercial processes of recommending footwear and estimating when another measurement should be taken, which are not technical endeavors, and does not govern operations of an image capture system. The accuracy of measuring feet with a standard computing device is also unaddressed, as the claims do not recite the underlying technology of how dimensions are determined from the image scan, and the claims only recite predicting a risk that a user may have overgrown their current size based on historical data and predicting a next time to perform another measurement. The claims do not address any particular problem/solution of 3D modelling, the 3D modelling is merely applied to the abstract idea of the claims in a very generic manner, and there are not particular additional activity regarding the 3D modelling. The time-based dimensional variability also does not represent any technical undertaking, but only represents a passage of time, such as determining a next measurement or product size for a growing customer. Furthermore, in view of the above, the claims do not recite significantly more than the abstract idea.
The claims merely apply generic technology, at a very high level of generality, to the calculating of a user’s dimensions and recommending footwear. The claims do not recite any particularity or any sort of detail regarding the integration of multi-image 3D reconstruction or machine learning, and any non-conventional or routine arrangement in the claims is only within the abstract idea itself, and not in the combination of additional elements, as was the case in Bascom. The claims nor the specifications provide any detail regarding the multi-image 3D reconstruction, merely disclosing that it happens to the received plurality of images, and there is not any disclosure to any transforming of raw pixel data. Any machine learning is also recited with a very high level of generality, the claims reciting no more than it is used within the predicting a foot growth pattern, which is just a mathematical calculation of trends in data. There is no disclosure in the specification about any particular functionalities of machine learning techniques, but the specification only discloses generic machine learning techniques that can be applied to the abstract data, such as in specification paragraph [0055], which merely discloses that the machine learning can possibly be k-means or DBSCAN. For the same rationale as discussed above, newly added claims 21-24 are also directed to an abstract idea, and merely apply additional elements to the abstract idea with a very high level of generality to automate the abstract idea, and provide a general link to a computing environment, but does not provide significantly more than the abstract idea.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Claim Objections
Regarding Claim 16: Claim 16 recites a system comprising:
calculate a risk that the user is wearing an incorrectly fitted footwear, based on the worn footwear, 3D foot profile, and the predicted foot growth pattern, wherein the risk increases with each day subsequent to day of the of the current scan;
calculate a risk associated with changing footwear size of the user based on the 3D foot profile and the predicted foot growth pattern, wherein the risk increases with each day subsequent to day of the current scan;
generate an alert for the user when the calculated risk exceeds a predetermined risk threshold.
Clarification is required as to which risk is being used to generate an alert when it exceeds a predetermined threshold. The claim language should be clarified so that is clear that the calculated risk exceeds a predetermined threshold is either one of the risk that the user is wearing an incorrectly fitted footwear or risk associated with changing footwear size. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, 13-17, and 19-24 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-11, 13-15, and 21-24 are directed to a method, which is a process. Claims 16-17 and 19 are directed to a system, which is an apparatus. Claim 20 is directed to a non-transitory computer readable medium, which is an article of manufacture. Therefore, claims 1-11, 13-17, and 19-24 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 24 as representative, claim 24 sets forth the following limitations reciting the abstract idea of predicting foot growth and recommending footwear sizes for a user:
receiving a plurality of images of a foot of a user;
retrieving metadata associated with the plurality of images and validating the metadata against one or more capture criteria, and outputting guidance to the user to adjust image capture when the metadata fails the one or more capture criteria;
analyzing the plurality of images using 3D data processing to generate a 3D foot profile, the 3D foot profile including at least two of: length, width, ankle width, foot height, and hallux angle;
extracting, from the 3D foot profile, measurement values in millimeters and converting the measurement values to a standardized footwear size;
predicting a foot growth pattern by:
representing (i) parameters of the current scan and (ii) parameters of a prior scan as an instance in an n-dimensional space that includes scan parameters and phenotype parameters;
identifying a subset of neighbor instances within a threshold distance from a query instance associated with the current scan, wherein the threshold distance evolves as additional scan data is received,
calculating daily growth values using short-time linear extrapolations derived from the subset of neighbor instances, and
outputting at least one forecasted prediction together with an associated confidence value based on distance in the n-dimensional space;
calculating a risk of ill-fitting footwear based on (i) the 3D foot profile, (ii) the forecasted prediction, and (iii) the confidence value, wherein the risk increases with time subsequent to a day of the current scan;
generating an alert to perform a next scan when the calculated risk exceeds a predetermined risk threshold;
recommending one or more footwear sizes for the user based on the standardized footwear size, the forecasted prediction, and manufacturer-specific footwear dimensions; and
predicting a time of the next scan based on the forecasted prediction and the confidence value.
The recited limitations above set forth the process for predicting foot growth and recommending footwear sizes for a user. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to receiving images of a foot profile to generate dimensional data of the foot, calculate risk of ill-fitting footwear, recommending footwear sizes, and predicting a time of next scan (see specification page 1, lines 11-33, disclosing the challenge of keeping track of footwear sizes, product returns, and inaccurate footwear recommendations), which is an advertising and marketing activity. These limitations also amount to mathematical concepts (e.g., mathematical calculations). The claims are directed to generating a 3D profile, converting measurement values to standardized footwear sizes, n-dimensional spaces, neighbor instances within a threshold distance from a query instance, calculating daily growth values suing short-time extrapolations, outputting forecasted predictions with confidence values, and calculating a risk compared to a threshold, which are mathematical calculations.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 24 recites additional elements, such as:
a mobile computing device;
computer vision;
using cluster-based machine learning;
Taken individually and as a whole, representative claim 24 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a mobile computing device, the mobile computing device is recited with a very high level of generality, the claims merely reciting receiving and outputting data to the mobile device. Specification page 11, lines 1-4, discloses the user computing device, as any of a personal computer, a mobile phone, a portable computing device, and the like. As such, it is evident that the mobile computing device is any generic computing device that represents the user within a computing environment. The computer vision is also recited in passing in the claims, merely reciting that it is used to generate a 3D profile without any further detail. The specification discloses no more detail that the claims themselves, such as in specification page 12, lines 19-21, which merely discloses using 3D data processing and computer vision to generate the 3D foot profile, without any further detail. As such, it is evident that the claims are not directed to any computer vision, and any generic computer vision technique is merely applied to the abstract idea. Any machine learning is also disclosed generally, merely disclosing the machine learning as possibly being k-means or DBSCAN without any further detail except to provide output for the abstract idea (specification: page14, lines 8-10).
In view of the above, under Step 2A (Prong 2), representative claim 24 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 24, taken individually or as a whole, the additional elements of claim 24 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 24 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 24 do not add anything further than when they are considered individually.
In view of the above, claim 24 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 1 (method): Claim 1 recites at least substantially similar concepts and elements as recited in claim 24 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 1 is rejected under at least similar rationale as provided above regarding claim 24.
Regarding Claim 16 (system): Claim 16 recites at least substantially similar concepts and elements as recited in claim 24 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 16 is rejected under at least similar rationale as provided above regarding claim 24.
Regarding Claim 20 (non-transitory computer readable medium): Claim 20 recites at least substantially similar concepts and elements as recited in claim 24 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 20 is rejected under at least similar rationale as provided above regarding claim 24.
Regarding Claim 22 (method): Claim 22 recites at least substantially similar concepts and elements as recited in claim 24 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 22 is rejected under at least similar rationale as provided above regarding claim 24.
Regarding Claim 23 (method): Claim 23 recites at least substantially similar concepts and elements as recited in claim 24 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 23 is rejected under at least similar rationale as provided above regarding claim 24.
Dependent claims 2-11, 13-15, 17, 19, and 21 recite further complexity to the judicial exception (abstract idea) of claim 24, such as by further defining the algorithm of predicting foot growth and recommending footwear sizes for a user, and do not recite any further additional elements. Thus, each of claims 2-11, 13-15, 17, 19, and 21 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-11, 13-15, 17, 19, and 21 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-11, 13-15, 17, 19, and 21 rely on at least similar elements as recited in claim 24. Further additional elements are also acknowledged; however, the additional elements of claims 2-11, 13-15, 17, 19, and 21 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-7, 9, 11-17, and 20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-11, 13-15, 17, 19, and 21 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 24. Thus, dependent claims 2-11, 13-15, 17, 19, and 21 do not add “significantly more” to the abstract idea.
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.
Claim 20 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Marks (US 20160081435 A1).
Regarding Claim 20: Marks discloses a method comprising:
receive a plurality of images of a foot of a user during a current scan of the user foot, wherein the scan is performed through a mobile computing device; (Marks: [0079] – “the footwear service 108 may obtain scan data describing a foot and/or feet of a user. The scan data may include an image(s) of the foot, a video(s) of the foot, depth information of the foot, pressure information of the foot on a surface (e.g., based on body weight distribution and/or foot physiology), a data point(s) of the foot (e.g., a point cloud, vector, matrix, measurement, etc.) and so on. In some instances, the user may upload the scan data through a website, client-side application or portal, in other instances the scanning device 106 may capture the scan data and provide the scan data to the footwear service”; Marks: [0020] – “the device 104 may include and/or be communicatively coupled to the scanning device 106. In some instances, the scanning device 106 is integral with the device 104, while in other instances the scanning device 106 is wirelessly coupled or wired to the device 104 (e.g., the scanning device 106 may be external to the device 104). The scanning device 106 may include one or more cameras, depth sensors (e.g., infrared laser projector and sensor), pressure sensors, microphones, projectors, speakers and so on, that may capture data. For example, the scanning device 106 may comprise a 3D scanner, range camera, x-ray device, thermal imaging camera and so on. In one example, the scanning device 106 comprises a Kinect® sensor. In some instances, the scanning device 106 may contact an object to scan the object (e.g., probe the object with arms or other items)”).
analyse the plurality of images to generate a 3D foot profile of the user; (Marks: [0080] – “the footwear service 108 may generate a 3D representation of the foot and/or feet of the user. The 3D representation may be generated based on the scan data. The 3D representation may include a 3D model, mathematical model/vector/matrix, a point cloud and so on of the foot or feet of the user. The operation 604 may include (i) using a standard 3D foot representation (e.g., template) and adjusting dimensions of the standard 3D foot representation to match the user's foot, (ii) creating a 3D representation of the user's foot without reference to a standard 3D foot representation, (iii) processing the scan data of the user's foot to remove outlining data (e.g., noise) and so on”).
determine a foot size of the user based on the 3D foot profile; (Marks: [0083] – “the footwear service 108 may determine or generate a footwear recommendation for the user. The recommendation may be based on the 3D representation of the user's foot or feet, user preferences for the user or other information about the user, footwear data for footwear items and so on. In some instances, the recommendation may account for historical data about the user's foot or feet over time (e.g., previous 3D representations of the user's feet). The recommendation may generally indicate a level of fit of a footwear item and/or how well the footwear item satisfies the user's preferences and other information. To illustrate, a level of fit of a footwear item associated with a 3D representation 612, may be relatively good for the user's foot, since the 3D representation of the user's foot fits within the 3D representation 612 of the footwear item according to particular tolerances”).
predict a foot growth pattern of the user, using machine learning techniques based on the 3D foot profile, and a user profile from the current and one or more previous scans of the user and one or more other users; (Marks: [0072] – “the shoes may be ranked according to a level of fit of the shoes for an estimated future growth of the user's feet. Here, historical data for the user's feet may be analyzed. The historical data may include 3D representations for the user's feet over time (e.g., a first 3D representation, a second 3D representation a year later, a third 3D representation two years later and so on). Average growth information for users that are deemed to be similar to the user may also be analyzed (e.g., growth information for users that are the same age). The analysis may estimate what the user's feet will be like at a particular future time (e.g., next year, in two years, etc.). Based on the estimated future growth, the recommendation interface 400 may rank shoes according to an estimated fit of the shoes at a future time”).
recommend one or more footwear sizes for the user based on at least one of: the foot size, the predicted foot growth pattern, one or more footwear brands and one or more footwear models; Examiner notes that Applicant recites at least one of in the claim. (Marks: [0083] – “the footwear service 108 may determine or generate a footwear recommendation for the user. The recommendation may be based on the 3D representation of the user's foot or feet, user preferences for the user or other information about the user, footwear data for footwear items and so on. In some instances, the recommendation may account for historical data about the user's foot or feet over time (e.g., previous 3D representations of the user's feet). The recommendation may generally indicate a level of fit of a footwear item and/or how well the footwear item satisfies the user's preferences and other information”).
predicting a time of next scan of the user based on the recommended footwear size and the predicted foot growth pattern of the user and one or more other users. (Marks: [0035] – “estimated future growth (e.g., how much a user's feet are estimated to grow over the next year, how tall the user is estimated to grow over the next year and so on)—in some instances the estimated future growth may be based on previous growth of the user (e.g., the user's foot grew less than an inch last year, so the user will likely grow more this year due to the user's age), while in other instances the estimated future growth may be based on information about the general population (e.g., generally kids grow 1.5 inches from age 8 to age 9)”).
Subject Matter Free of Prior Art
Claims 1-11, 13-17, 19, and 21-24 are determined to have overcome the prior art of rejection and are free of the prior art, however, the claims remain rejected under 35 U.S.C. 101, as set forth above.
Claims 1 recites calculating a risk associated with changing footwear size of the user based on the 3D foot profile, and the predicted foot growth pattern, wherein the risk increases with each day subsequent to day of the current scan;
The closes prior art was found to be as follows:
Marks (US 20160081435 A1) discloses [0083] – “the footwear service 108 may determine or generate a footwear recommendation for the user. The recommendation may be based on the 3D representation of the user's foot or feet, user preferences for the user or other information about the user, footwear data for footwear items and so on. In some instances, the recommendation may account for historical data about the user's foot or feet over time (e.g., previous 3D representations of the user's feet). The recommendation may generally indicate a level of fit of a footwear item and/or how well the footwear item satisfies the user's preferences and other information. To illustrate, a level of fit of a footwear item associated with a 3D representation 612, may be relatively good for the user's foot, since the 3D representation of the user's foot fits within the 3D representation 612 of the footwear item according to particular tolerances”.
Kagami (US 20210251345 A1) discloses [0015] – “the foot length information management system 10 includes a measurement unit 11 that measures foot length, and an information processing device 13 that processes information obtained by the measurement unit 11. The measurement unit 11 may be placed in a store, for example. A salesperson measures the foot length of a customer using the measurement unit 11. The foot length information 35 obtained by the measurement unit 11 is managed by the information processing device 13. Based on the foot length information 35, for example, the information processing device 13 calculates an optimum replacement time for shoes appropriately for the growth of a child and notifies a customer of the replacement time”).
Hlavacek (US 20130332107 A1) discloses [0028] – “this contains an input module 1 measuring the length of the feet and the input of information about the age of the monitored child to which is linked a prediction module 2 for the growth of children's feet implementing the laws of growth and including genetic and local influences, connected to a comparative and inferential module 3 establishing the predicted course of growth in the foot length of the monitored child. Linked to the comparative and inferential module 3 then is the output module 4 determining the nearest date for the necessary replacement of shoe size for the monitored child”.
NPL Reference U (see PTO-892 Reference U mailed on 8/28/2025) discloses measuring the accuracy of 3D scanning of feet to determine foot sizes for people.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
calculating a risk associated with changing footwear size of the user based on the 3D foot profile, and the predicted foot growth pattern, wherein the risk increases with each day subsequent to day of the current scan;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 1-11, 13-17, 19, and 21-24 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 7:30 - 5:00.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/10/2026
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