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 .
Claim Interpretation
Claims 1-13 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claims 14-16 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “memory”, “processor” and “instructions” provide sufficient structure to perform all claimed limitations.
Claims 17-20 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because each of these claims is an article of manufacture claim.
Claim Objections
Claims 2, 15 and 18 are objected to because of the following informalities: “hair types” is repeatedly recited or duplicated in each of these claims. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of pre-AIA 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.
Claim(s) 1-9, 11-12 and 14-20 is/are rejected under 35 U.S.C. 102(a/b) as being anticipated by Riley et al. (U.S. Pat. App. Pub. No. 2019/0384874A1, referred as Riley hereinafter).
Regarding claim 1 as a representative claim, Riley teaches a method for acquiring head dimensions using a depth-sensing electronic device, the method comprising:
capturing one or more images of a user's head positioned adjacent to a planar surface (see figures 7A-7C, paras. [0110] - [0112] (3D scanning module; 3D measurement data of rider’s head; head positioned adjacent to planar surface is inherently included in the images of the rider’s head));
extracting depth-related attributes of the user's head and the planar surface from the one or more images (see figures 7A-7C, paras. [0110] - [0112] (3D scanning module; 3D measurement data of rider’s head are used to determine the size/fit of the helmet as shown in figure 7C));
applying machine learning models to interpret the extracted depth-related attributes and generate a three-dimensional model of the user's head (see figures 2B, 3C-3D, and 5; paras. [0100] – [0101] (machine learning is used to generate rider model which includes head of a rider/child) and [0129]); and
determining specific head dimensions from the three-dimensional model (see figures 7A-7C, paras. [0110] - [0112] (3D scanning module; 3D measurement data of rider’s head are used to determine the size/fit of the helmet as shown in figure 7C), [0113] (matching the helmets to the head dimensions of the rider scanned in user interface), and [0100] – [0101] (estimating a head size of the child based on the rider dimension model)).
Regarding claim 2, Riley further teaches wherein the machine learning models are trained on a diverse dataset of head dimensions including one or more attributes selected from the group consisting of varying ages, genders, ethnicities, hairstyles, hair types, and hair types (see paras. [0073] (hair), [0095] (gender), and [0101] (age); as for ethnicities, rider himself or herself inherently includes his/her ethnicity).
Regarding claim 3, Riley further teaches comprising preprocessing the one or more captured images to enhance the quality and accuracy of depth data before applying machine learning models (see para. [0111] (obtaining images of an object and generating dimensions and representation of the scanned object, wherein the object is the rider’s head; images 722 and 724).
Regarding claim 4, Riley further teaches wherein the machine learning models account for variability introduced by different hairstyles to normalize hair-induced deviations from an actual head shape (see figure 7C (best fit inherently account for different hairstyles); para. [0112] (minimizing gaps between the user’s head and helmet; thus, accounting for different hairstyles is inherently included in order to provide “best fit”)).
Regarding claim 5, Riley further teaches comprising manufacturing a personalized head-worn product based on the specific head dimensions (see figure 7C (best fit to child’s head is helmets 1 and/or 2)).
Regarding claim 6, Riley further teaches comprising recommending a size of a head-worn product based on the specific head dimensions (see figure 7C (best fit to child’s head is helmets 1 and/or 2)).
Regarding claim 7, Riley further teaches wherein the machine learning models account for ambient lighting conditions during image capture to adjust and enhance depth data processing (see figure 7A (camera inherently includes ambient light conditions in order to scan and generate images of the child’s head); para. [0092] (optical measurement data of the rider)).
Regarding claim 8, Riley further teaches wherein the specific head dimensions are stored in a user profile, enabling subsequent retrievals for other personalized products or applications (see paras. [0052] – [0053], [0061] (sizing models), [0100] (updating rider’s sizing models; thus, rider’s profile is inherently includes)).
Regarding claim 9, Riley further teaches further comprising a feedback mechanism where the user can add measurements and provide input on the accuracy of the specific head dimensions, allowing continuous improvement of the machine learning models (see para. [0100] (updating sizing models due to growth rate of the child)).
Regarding claim 11, Riley further teaches comprising a step of using augmented reality to overlay visual feedback on the user's device during the capturing process, aiding the user in achieving acceptable angles and positions (see figures 7A (touch here to allow access to camera and scan child’s head) and 7B (Move phone to capture indicated areas of child’s head)).
Regarding claim 12, Riley further teaches comprising storing the specific head dimensions in a cloud based system where they can be accessed and retrieved by authorized entities or applications (see para. [0057] (cloud storage)).
Regarding claim 14, it is noted claim recites similar claim limitations called for in the counterpart claim 1. Thus, the advanced statements as applied to claim 1 above are incorporated hereinafter. Riley further teaches an electronic device with image capture capabilities (see figures 1 and para. [0060] (phone or tablet); figure 7A and paras. [0034] and [0049] (phone with camera); a user interface for instructing a user on image capture (see figure 7A and paras. [0110] [0111] (user interfaces and sizing software application)); at least one processor (see paras. [0130], [0139] [0140] and [0147]) (processors)); and memory storing instructions (see paras. [0117] (sizing software executing on the smart device; thus, such device inherently included memory) and [0129] (medium storing software and/or software instructions)).
Regarding claim 15, it is noted claim recites similar claim limitations called for in the counterpart claim 2 and thus is rejected for the same reasons as well.
Regarding claim 16, it is noted claim recites similar claim limitations called for in the counterpart claim 8 and thus is rejected for the same reasons as well.
Regarding claim 17, it is noted claim recites similar claim limitations called for in the counterpart claim 1. Thus, the advanced statements as applied to claim 1 above are incorporated hereinafter. Riley further teaches memory storing instructions (see paras. [0117] (sizing software executing on the smart device; thus, such device inherently included memory) and [0129] (medium storing software and/or software instructions)) and at one least one processor (see paras. [0130], [0139] [0140] and [0147]) (processors)).
Regarding claim 18, it is noted claim recites similar claim limitations called for in the counterpart claim 2 and thus is rejected for the same reasons as well.
Regarding claim 19, it is noted claim recites similar claim limitations called for in the counterpart claim 8 and thus is rejected for the same reasons as well.
Regarding claim 20, Riley further teaches comprising manufacturing a personalized head-worn product based on the specific head dimensions (see figure 7C (best fit to child’s head is helmets 1 and/or 2); para. [0112]) or recommending a size of a head-worn product based on the specific head dimensions (see figure 7C (best fit to child’s head is helmets 1 and/or 2)).
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.
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riley.
The advanced statements as applied to claims 1-9, 11-12 and 14-20 are incorporated hereinafter.
Regarding claim 10, Riley is silent in further teaching claim limitations “wherein wall distance loss optimization ensures that the back of the head's detected depth aligns with the planar surface's detected depth, providing a constraint for dimension extraction”.
However, such claim limitations are well known in the art (Official Notice).
The motivation for doing so is to obtain proper scanned images of the head so that it would improve head model generation.
Therefore, before the effective filing date of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations in combination with Riley for that reasons.
Regarding claim 13, Riley is silent in further teaching claim limitations “the extracted depth-related attributes include a resolution parameter, allowing for scalability in processing based on a required precision of the specific head dimensions”.
However, such claim limitations are well known in the art (Official Notice).
The motivation for doing so is to generate head model more accurately so that it would provide best fit helmet for the rider.
Therefore, before the effective filing date of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations in combination with Riley for that reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Siddique et al. (U. S. Pat. App. Pub. No. 2010/0030578 A1) teaches a system for measuring human head (figure 6A and para. [0135]) and machine learning for generating user model (para. [0117]).
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DMD
7/2026
/DUY M DANG/Primary Examiner, Art Unit 2662
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