CTNF 18/214,607 CTNF 86729 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim 14 is objected to because of the following informalities: In claim 14, line 6 should be amended to read: --causes the processor processing unit to:-- to remain consistent in terminology and avoid confusion . Appropriate correction is required. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “scanning unit” in claim 14, with corresponding structure described in the specification at paragraph [28] as “a 3-D optical scanner, a camera, a push-pin array, or any other device suitable for creating a 3D model of the patient's face …or other parts of a patient” “a processing unit” in claim 14, line 5, with corresponding structure described in the specification at paragraph [36] as “any type of processing device including a processor, such as a computer, laptop, tablet, mobile phone etc.” (The claim limitation is interpreted as invoke 112(f) because although the claim later recites the structure of “the processor” in line 6, this appears to be a typographical error and should read “the processing unit” as discussed in the above objection to claim 14. Thus, the claim is interpreted as though it reads “the processing unit”.) Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 5, 7, 8, 10, 11, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Xiaoli et al. (US 2016/0162604), in view of Gugino (US 2021/0322701) . As to claim 1, Xiaoli discloses a 3D data collection method (Fig. 27) with privacy protection, the method comprising: obtaining a 3D scan of at least a portion of a patient at a local site (see Fig. 1, paragraph [0053] showing/describing a virtual fitting station 110 having a 3D scanner and a mask fitting engine 170 which receives the facial scan data from the scanner, see also Fig. 27, paragraph [0110]: module 2700 captures a ROI point cloud forming a 3D image of a body part); receiving an avatar template (step 2710, paragraph [0111]: generic model 2710 retrieved from a database of generic models) and control point registration data (step 2715, paragraph [0111]: semantic information attributed to generic model which may be distinguishable body feature points of the corresponding body part such as the eyes, ears, mouth corners, nose tip, the vertices of the generic model, and/or a set of rules which define the semantic information); identifying control points on the 3D scan based on the control point registration data (paragraph [0112]: overlaying the point cloud of the ROI involves aligning the vertices of the ROI point cloud with the corresponding vertices of the generic face model, such as proximally similar or equivalent locations on the model, e.g., the nose portion of the point cloud with the nose portion of the generic model); and adjusting the avatar template to coincide control points on the avatar template with control points on the 3D scan (Fig. 27, step 2730, paragraph [0113]: vertices of the generic model may be deformed to fit the overlaid point cloud by a non-rigid registration method which may include blending the non-aligning vertices of the generic model with neighboring vertices or certain vertices of the generic model may be moved a predetermined allowable distance to reach an alignment with the point cloud of the ROI). While Xiaoli further discloses transmitting the adjusted avatar template (step 2740: the static model having the point cloud with semantic information may then be outputted to a comparator or the dynamic modeling module 2800 of Fig. 18A) they do not expressly disclose that the avatar template and control point registration data is received from an external site or that the adjusted avatar template is transmitted to the external site. However, Gugino teaches a mask fitting method which includes storing and/or processing data at an external site in communication with the system (paragraphs [0063]-[0066] describe that multiple systems or machines 700 (Fig. 710) may execute different portions of embodiments and communicate over a network, that items stored in memory may be transferred to/from other storage devices, data/instructions may be transmitted over a network 714, see also paragraph [0119] describing that fitting component 808 of system 800 may access on or more external databases). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Xiaoli so that the avatar template and control point data (generic face model and semantic information) is obtained from, and the adjusted model/template and control point data is transmitted to, one or more databases at an external site, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed. As to claim 5, modified Xiaoli discloses the method of claim 1, wherein the control point registration data associates control points with the avatar template (step 2715, paragraph [0111]: semantic information attributed to generic model which may be distinguishable body feature points of the corresponding body part such as the eyes, ears, mouth corners, nose tip, the vertices of the generic model, and/or a set of rules which define the semantic information). As to claim 7, modified Xiaoli discloses the method of claim 1, wherein identifying control points on the 3D scan based on the control point registration data includes: morphing the avatar template to fit the 3D scan; and projecting control points of the avatar template onto the 3D scan (step 2730, Fig. 27, paragraph [0113]: if the vertices do not align to an acceptable level, the vertices of the generic model may be deformed to fit the overlaid point cloud by a non-rigid registration method. In an exemplary embodiment, a non-rigid registration method may include blending the non-aligning vertices of the generic model with neighboring vertices. In another exemplary embodiment, certain vertices of the generic model may be moved a predetermined allowable distance to reach an alignment with the point cloud of the ROI). As to claim 8, modified Xiaoli discloses the method of claim 7, wherein morphing the avatar template to fit the 3D scan includes matching landmarks of the avatar template and the 3D scan (step 2730, Fig. 27, paragraph [0113]: if the vertices do not align to an acceptable level, the vertices of the generic model may be deformed to fit the overlaid point cloud by a non-rigid registration method. In an exemplary embodiment, a non-rigid registration method may include blending the non-aligning vertices of the generic model with neighboring vertices. In another exemplary embodiment, certain vertices of the generic model may be moved a predetermined allowable distance to reach an alignment with the point cloud of the ROI). As to claim 10, modified Xiaoli discloses the method of claim 1, further comprising retrieving adjusted control points from the adjusted avatar template (step 2735, Fig. 1, paragraph [0114]: Once alignment is reached with the vertices of the point cloud and vertices of the generic model, the semantic information of each vertex on the generic face model may be attributed to the point cloud. For example, the vertices of the point cloud may receive the semantic information and be stored within the properties of the point cloud such that each of the points in the point cloud may include identification properties corresponding to a location of the point in the point cloud. For example a point of the point cloud located at a position corresponding to a nose tip may include semantic information identifying the point as “nose tip”). As to claim 11, modified Xiaoli discloses the method of claim 10, further comprising using the adjusted control points to select or customize a patient interface device for the patient (see Fig. 26, paragraphs [0104]-[0106],[0109]: at comparator module 2655 where the static/dynamic model 2630/2635 is compared to PPE model candidate(s) 2620 and an optimal fit PPE is chosen based on simulated comfort and fit). As to claim 14 , Xiaoli discloses a system (Fig. 1) for data collection with privacy, the system 100 comprising: a scanning unit 105 (Fig. 1) structured to obtain a 3D scan of at least a portion of a patient at a local site (see Fig. 1, paragraph [0053] showing/describing a virtual fitting station 110 having a 3D scanner 105 and a mask fitting engine 170 which receives the facial scan data from the scanner, see also Fig. 27, paragraph [0110]: module 2700 captures a ROI point cloud forming a 3D image of a body part); a memory 175 structured to store one or more routines (Fig. 1, paragraph [0053]: instructions received from program memory locations 175); a processing unit (processor 164, Fig. 1) structured to execute the one or more routines (paragraph [0053], the process 2700 shown in Fig. 27), wherein execution of the one or more routines causes the processor to: receive an avatar template (step 2710, paragraph [0111]: generic model 2710 retrieved from a database of generic models) and control point registration data (step 2715, paragraph [0111]: semantic information attributed to generic model which may be distinguishable body feature points of the corresponding body part such as the eyes, ears, mouth corners, nose tip, the vertices of the generic model, and/or a set of rules which define the semantic information); identify control points on the 3D scan based on the control point registration data (paragraph [0112]: overlaying the point cloud of the ROI involves aligning the vertices of the ROI point cloud with the corresponding vertices of the generic face model, such as proximally similar or equivalent locations on the model, e.g., the nose portion of the point cloud with the nose portion of the generic model); and adjust the avatar template to coincide control points on the avatar template with control points on the 3D scan (Fig. 27, step 2730, paragraph [0113]: vertices of the generic model may be deformed to fit the overlaid point cloud by a non-rigid registration method which may include blending the non-aligning vertices of the generic model with neighboring vertices or certain vertices of the generic model may be moved a predetermined allowable distance to reach an alignment with the point cloud of the ROI). While Xiaoli further discloses that the adjusted avatar template is transmitted (step 2740: the static model having the point cloud with semantic information may then be outputted to a comparator or the dynamic modeling module 2800 of Fig. 18A), they do not expressly disclose that the avatar template and control point registration data is received from an external site or that the adjusted avatar template is transmitted to the external site. However, Gugino teaches a mask fitting system which includes storing and/or processing data at an external site in communication with the system (paragraphs [0063]-[0066] describe that multiple systems or machines 700 (Fig. 710) may execute different portions of embodiments and communicate over a network, that items stored in memory may be transferred to/from other storage devices, data/instructions may be transmitted over a network 714, see also paragraph [0119] describing that fitting component 808 of system 800 may access on or more external databases). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the system of Xiaoli so that the avatar template and control point data (generic face model and semantic information) is obtained from, and the adjusted model/template and control point data is transmitted to, one or more databases at an external site, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed. As to claim 15, Xiaoli discloses a non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform a method of 3D data collection with privacy protection (Fig. 1, paragraphs [0053],[0082], the method/process being shown in Fig. 27), the method comprising: receiving an avatar template (step 2710, Fig. 27, paragraph [0111]: generic model 2710 retrieved from a database of generic models) and control point registration data (step 2715, paragraph [0111]: semantic information attributed to generic model which may be distinguishable body feature points of the corresponding body part such as the eyes, ears, mouth corners, nose tip, the vertices of the generic model, and/or a set of rules which define the semantic information); identifying control points on the 3D scan based on the control point registration data (paragraph [0112]: overlaying the point cloud of the ROI involves aligning the vertices of the ROI point cloud with the corresponding vertices of the generic face model, such as proximally similar or equivalent locations on the model, e.g., the nose portion of the point cloud with the nose portion of the generic model); and adjusting the avatar template to coincide control points on the avatar template with control points on the 3D scan (Fig. 27, step 2730, paragraph [0113]: vertices of the generic model may be deformed to fit the overlaid point cloud by a non-rigid registration method which may include blending the non-aligning vertices of the generic model with neighboring vertices or certain vertices of the generic model may be moved a predetermined allowable distance to reach an alignment with the point cloud of the ROI). While Xiaoli further discloses transmitting the adjusted avatar template (step 2740: the static model having the point cloud with semantic information may then be outputted to a comparator or the dynamic modeling module 2800 of Fig. 18A) they do not expressly disclose that the avatar template and control point registration data is received from an external site or that the adjusted avatar template is transmitted to the external site. However, Gugino teaches a computer-implemented mask fitting method which includes storing and/or processing data at an external site in communication with the system (paragraphs [0063]-[0066] describe that multiple systems or machines 700 (Fig. 710) may execute different portions of embodiments and communicate over a network, that items stored in memory may be transferred to/from other storage devices, data/instructions may be transmitted over a network 714, see also paragraph [0119] describing that fitting component 808 of system 800 may access on or more external databases). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Xiaoli so that the avatar template and control point data (generic face model and semantic information) is obtained from, and the adjusted model/template and control point data is transmitted to, one or more databases at an external site, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Xiaoli et al. (US 2016/0162604), in view of Gugino (US 2021/0322701), as applied to claims 1 and 5, and further in view of Varanasi et al. (US 2017/0278302) . As to claim 6 , modified Xiaoli discloses the method of claim 5, but is silent as to the control point registration data including a sparse registration matrix. However, Varanasi teaches a method of registering an image to a 3D facial model, wherein control point registration data (facial landmark points 720, Fig. 7) includes a sparse registration matrix (paragraph [0047]-[0052]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Xiaoli so that the control point registration data includes a sparse registration matrix, as taught by Varanasi, in order to provide a suitable known means for simplifying the data for more efficient manipulation . 07-22-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Xiaoli et al. (US 2016/0162604), in view of Gugino (US 2021/0322701) , as applied to claim 1 above, and further in view of Chen et al. (US 2015/0332058) . As to claim 13 , modified Xiaoli discloses the method of claim 1, but is silent as to the avatar template and control point registration data being received in an encrypted communication and the adjusted avatar template being transmitted in an open communication. However, Chen teaches method for encrypting a 3D model file (Fig. 1, steps S1-S4, paragraph [0008]) and transmitting the file in its encrypted form or in its open/original form based on whether a user is authorized to view the unencrypted file or not (steps S5-S8, paragraph [0009]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Xiaoli so that communication between the local site and external site is an open form of communication or an encrypted form of communication based on whether the user at the external site is authorized to view the unencrypted/decrypted file, as taught by Chen, in order to protect the data from unauthorized users from accessing private information . 07-21-aia AIA Claim s 1, 2, 5, 7, 8, 10-12, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Oenning et al. (US 2018/0043122), in view of Gugino (US 2021/0322701) . As to claim 1, Oenning discloses a 3D data collection method with privacy protection, the method comprising: obtaining a 3D scan 102 (3D facial scan data 102, see Fig. 1) of at least a portion of a patient at a local site (paragraph [0048]: facial scan data can be obtained or retrieved locally); receiving an avatar template (base mask/face model 602) and control point registration data (paragraph [0055]: aggregate database 108 (Fig. 1) stores base mask models and determines landmark points, paragraph [0056]: can aggregate facial scan data to store surface models of a face instead of a mask model); identifying control points 102, 604 (Fig. 1, Fig. 6) on the 3D scan based on the control point registration data (landmark points 102,604 identified on facial scan data, Fig. 1, Fig. 6, paragraph [0058]); adjusting the avatar template 602 to coincide control points on the avatar template 602 with control points on the 3D scan (Fig, 6, base mask model 602 can be deformed based on landmark points determined from facial scan data to generate a deformed mask model 606, paragraph [0083]). While Oenning discloses receiving the avatar template, control point registration data from an aggregate mask database 108 (Fig. 1, paragraph [0054]-[0055]) and transmitting the adjusted avatar template/deformed mask model to the database (after geometry check 114, see Fig. 1), Oenning does not expressly disclose that the avatar template and control point registration data is received from an external site and that the adjusted avatar template is transmitted to the external site (i.e., Oenning does not disclose if the aggregate database 108 is at an external site). However, Gugino teaches a mask fitting method having a database of mask data that is external to the processor (paragraph [0119]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Oenning so that the aggregate mask database is external to the local processing system, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed. As to claim 2, modified Oenning discloses the method of claim 1, further comprising: generating the avatar template and control point registration data at the external site (the aggregate database 108, which has been modified to be external to the system 100, produces a base mask model that is the average of all facial scans fitting a selected criteria, paragraph [0054]-[0056]); and transmitting the avatar template and control point registration data to the local site (at 110, Fig. 1, paragraph [0051]). As to claim 5, modified Oenning discloses the method of claim 1, wherein the control point registration data associates control points with the avatar template (base mask model is an average of all facial scans fitting a selected criteria, updated as new facial scan data is entered, landmarks points are used to determine deviations between different base model types and face shapes of users, paragraph [0054]-[0056]), As to claim 7, modified Oenning discloses the method of claim 1, wherein identifying control points on the 3D scan based on the control point registration data includes: morphing the avatar template to fit the 3D scan; and projecting control points of the avatar template onto the 3D scan (paragraph [0058], base model is deformed to fit the landmark points of the facial scan data). As to claim 8, modified Oenning discloses the method of claim 7, wherein morphing the avatar template to fit the 3D scan includes matching landmarks of the avatar template and the 3D scan (base mask model is deformed based on landmark points from the facial scan data, (Fig. 6, paragraph [0058]). As to claim 10, modified Oenning discloses the method of claim 1, further comprising retrieving adjusted control points from the adjusted avatar template (a customized mask model is generated using the deformed model and landmark points, Fig. 7-9, paragraph [0051]). As to claim 11, modified Oenning discloses the method of claim 10, further comprising using the adjusted control points to select or customize a patient interface device for the patient (a customized mask model is generated using the deformed model, paragraphs [0051],[0058],[0059]). As to claim 12, modified Oenning discloses the method of claim 1, wherein the 3D scan includes a scan of the patient's face, and wherein the patient is not identifiable from the adjusted avatar template (see Oenning Fig. 6, the adjusted model 606 is not identifiable to the patient since it is just a nasal mask model adjusted from a generic model using 4 data points 604). As to claim 14 , Oenning discloses a system 100 (Fig. 1) for data collection with privacy, the system 100 comprising: a scanning unit structured to obtain a 3D scan of at least a portion of a patient at a local site (paragraph [0048]: facial scan data can be obtained locally, in some embodiments, facial scan data is obtained in real-time by scanning a face using 3D scanning technology); a memory structured to store one or more routines (a memory would be inherent in the computer which executes the modeling algorithms and process 100 disclosed in Fig. 1, paragraphs [0046],[0048]); a processing unit (the computer to which the disclosed system/algorithm 100 is applied, see paragraph [0046],[0048],[0049]) structured to execute the one or more routines (the process 100 shown in Fig. 1), wherein execution of the one or more routines causes the processor to: receive an avatar template (base mask/face model 602) and control point registration data (paragraph [0055]: aggregate database 108 (Fig. 1) stores base mask models and determines landmark points, paragraph [0056]: can aggregate facial scan data to store surface models of a face instead of a mask model); identify control points 102, 604 (Fig. 1, Fig. 6) on the 3D scan based on the control point registration data (landmark points 102,604 identified on facial scan data, Fig. 1, Fig. 6, paragraph [0058]); adjust the avatar template 602 to coincide control points on the avatar template 602 with control points on the 3D scan (Fig, 6, base mask model 602 can be deformed based on landmark points determined from facial scan data to generate a deformed mask model 606, paragraph [0083]). While Oenning discloses receiving the avatar template, control point registration data from an aggregate mask database 108 (Fig. 1, paragraph [0054]-[0055]) and transmitting the adjusted avatar template/deformed mask model to the database (after geometry check 114, see Fig. 1), Oenning does not expressly disclose that the avatar template and control point registration data is received from an external site and that the adjusted avatar template is transmitted to the external site (i.e., Oenning does not disclose if the aggregate database 108 is at an external site). However, Gugino teaches a mask fitting system having a database of mask data that is external to the processor (paragraph [0119]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the system of Oenning so that the aggregate mask database is external to the local processing system, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed. As to claim 15, Oenning discloses a non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform a method of 3D data collection with privacy protection (computer program that executes the process 100 of Fig. 1, see paragraph [0046],[0048],[0049]), the method comprising: receiving an avatar template (base mask/face model 602) and control point registration data (paragraph [0055]: aggregate database 108 (Fig. 1) stores base mask models and determines landmark points, paragraph [0056]: can aggregate facial scan data to store surface models of a face instead of a mask model); identifying control points 102, 604 (Fig. 1, Fig. 6) on the 3D scan based on the control point registration data (landmark points 102,604 identified on facial scan data, Fig. 1, Fig. 6, paragraph [0058]); adjusting the avatar template 602 to coincide control points on the avatar template 602 with control points on the 3D scan (Fig, 6, base mask model 602 can be deformed based on landmark points determined from facial scan data to generate a deformed mask model 606, paragraph [0083]). While Oenning discloses receiving the avatar template, control point registration data from an aggregate mask database 108 (Fig. 1, paragraph [0054]-[0055]) and transmitting the adjusted avatar template/deformed mask model to the database (after geometry check 114, see Fig. 1), Oenning does not expressly disclose that the avatar template and control point registration data is received from an external site and that the adjusted avatar template is transmitted to the external site (i.e., Oenning does not disclose if the aggregate database 108 is at an external site). However, Gugino teaches a mask fitting method having a database of mask data that is external to the processor (paragraph [0119]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the computer-readable medium of Oenning so that the aggregate mask database is external to the local processing system, as taught by Gugino, in order to save memory space at the local site, while preserving generated data for future retrieval if needed . 07-22-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Oenning et al. (US 2018/0043122), in view of Gugino (US 2021/0322701) , as applied to claim s 1 and 2 above, and further in view of Karpas et al. (US 2017/0080172) . As to claim 3, modified Oenning discloses the method of claim 2, wherein generating the avatar template includes: registering control points to an average template (base mask model is an average of all facial scans fitting a selected criteria, updated as new facial scan data is entered, landmarks points are used to determine deviations between different base model types and face shapes of users, paragraph [0054]-[0056]), but is silent as to blurring the average template to generate the avatar template. However, Karpas teaches blurring a template when merging data (the head data and/or face scan data are then scaled, rotated, stretched, smoothed, or otherwise morphed to merge the head data with the face data, paragraph [0097]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of modified Oenning to blur the average template, as taught by Karpas, in order to create a realistic base model/template which includes smooth, continuous transitions at the boundaries thus removing any sharp, unnatural looking edges resulting from the combination of data from multiple sources . 07-22-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Oenning et al. (US 2018/0043122), in view of Gugino (US 2021/0322701) and Karpas et al. (US 2017/0080172) , as applied to claim s 1-3 above, and further in view of Saban et al. (US 2014/0225978) . As to claim 4, modified Oenning discloses the method of claim 3, except that blurring the average template includes at least one of random vertex displacement and decimation of the average template. However, Saban (US 2014/0225978) teaches decimating a template (2D/3D model 410, Fig. 4) to improve the quality (paragraph [0048]: enhanced rendering techniques are performed to improve video quality, e.g., interpolating the object and the image to higher resolution and decimating after combining to smooth the edges and eliminate an aliasing effect, [0122]: interpolating the object into higher resolution, object and frame are combined, edges are smoothed, and then the object is decimated back to the lower resolution with better quality; [0143]: additional information techniques like interpolation and decimation or edge smoothing can be applied after processing via module 410 in order to improve the quality of the model). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Oenning so that blurring/smoothing of the average template/model includes decimation, as taught by Saban, in order to provide a known enhanced rendering technique to improve the quality of the model and remove any aliasing effect . 07-22-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Oenning et al. (US 2018/0043122), in view of Gugino (US 2021/0322701) , as applied to claim 1 above, and further in view of Karlsson et al. (US 2025/0322630) As to claim 9, modified Oenning discloses the method of claim 1, but is silent as to the adjusting of the avatar template to coincide control points on the avatar template with control points on the 3D scan includes morphing the avatar template using Laplacian mesh editing. However, Karlsson teaches Laplacian mesh editing as a technique for deforming a 3D model according to target landmark areas (paragraph [0065]: the deformation in this example is operated over an intrinsic surface representation (See Sorkine, et al.) based on the Laplac ian of a mesh so that the reconstruction of the global coordinates preserves local geometric details of the surface as much as possible, see also paragraph [0149]). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the invention to modify the method of Oenning so that adjusting the avatar template includes Laplacian mesh editing, as taught by Karlsson, in order to preserve the local geometric details of the face as much as possible . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Evans et al. (US 2023/0149647) discloses generating a 3D face model from face scan data which is used to customize a face mask for a patient. Varanasi et al. (US 2018/0225882 ) discloses a method and device for editing a facial image. Kundu et al. (US 2013/0004090) discloses image processing to prevent access to private information . Any inquiry concerning this communication or earlier communications from the examiner should be directed to VALERIE L WOODWARD whose telephone number is (571)270-1479. The examiner can normally be reached on Monday - Friday 8:30 am - 4:30 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KENDRA CARTER can be reached on 571-272-9034. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /VALERIE L WOODWARD/Primary Examiner, Art Unit 3785 Application/Control Number: 18/214,607 Page 2 Art Unit: 3785 Application/Control Number: 18/214,607 Page 3 Art Unit: 3785 Application/Control Number: 18/214,607 Page 4 Art Unit: 3785 Application/Control Number: 18/214,607 Page 5 Art Unit: 3785 Application/Control Number: 18/214,607 Page 6 Art Unit: 3785 Application/Control Number: 18/214,607 Page 7 Art Unit: 3785 Application/Control Number: 18/214,607 Page 8 Art Unit: 3785 Application/Control Number: 18/214,607 Page 9 Art Unit: 3785 Application/Control Number: 18/214,607 Page 10 Art Unit: 3785 Application/Control Number: 18/214,607 Page 11 Art Unit: 3785 Application/Control Number: 18/214,607 Page 12 Art Unit: 3785 Application/Control Number: 18/214,607 Page 13 Art Unit: 3785 Application/Control Number: 18/214,607 Page 14 Art Unit: 3785 Application/Control Number: 18/214,607 Page 16 Art Unit: 3785 Application/Control Number: 18/214,607 Page 17 Art Unit: 3785 Application/Control Number: 18/214,607 Page 18 Art Unit: 3785 Application/Control Number: 18/214,607 Page 19 Art Unit: 3785 Application/Control Number: 18/214,607 Page 20 Art Unit: 3785 Application/Control Number: 18/214,607 Page 21 Art Unit: 3785 Application/Control Number: 18/214,607 Page 22 Art Unit: 3785 Application/Control Number: 18/214,607 Page 23 Art Unit: 3785 Application/Control Number: 18/214,607 Page 24 Art Unit: 3785
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