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 .
Response to Amendment
This action is in response to the amendment filed on 29th June, 2026. Claims 1, 4, 9, 12, 14, and 19-20 have been amended. Claims 1-20 remain rejected in the application.
Response to Arguments
Applicant's arguments with respect to Claims 1, 12, and 20 filed on 29th June, 2026, with respect to the rejection under 35 U.S.C. § 103, regarding that the prior art does not teach the limitation(s): "modify the transmitted overlaid real-time image stream responsive to dynamic changes in one or more characteristics of the communication channel during the video call, including dynamically adjusting a quality of the virtual cosmetic look overlaid onto the depiction of the associated at least one facial feature of the user within the real-time image stream" has been fully considered, but are moot because of new grounds for rejection. It has now been taught by the combination of Iglehart and Mendez.
Regarding arguments to Claims 2-11 and 13-19, they directly/indirectly depend on independent Claims 1, 12, and 20 respectively. Applicant does not argue anything other than independent Claims 1, 12, and 20. The limitations in those claims, in conjunction with combination, was previously established as explained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-8, 10-13, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Iglehart et al. (US 20170256084 A1, previously cited), hereinafter referenced as Iglehart, in view of Lopez Mendez et al. (US 20220174111 A1), hereinafter referenced as Mendez.
Regarding Claim 1, Iglehart discloses a system for dynamically depicting users with virtual cosmetic looks during video calls (Iglehart, [0104]: teaches computing device 601 <read on system> including a looks ecosystem application, which is presented to a local user and one or more remote users <read on video calls>, where users can apply pre-defined looks or customized looks), the system comprising:
one or more processors (Iglehart, [0118]: teaches the computing device including processors); and
one or more non-transitory memories coupled to the one or more processors storing computer-executable instructions stored on the one or more non-transitory memories that, when executed by the one or more processors, cause the system to (Iglehart, [0119]: teaches the computer including computer program instructions <read on computer-executable instructions> and data being stored in non-volatile memory <read on non-transitory memories>):
receive, via a user interface, an indication of a virtual cosmetic look (Iglehart, [0056]: teaches "the term “makeup effect” includes pre-defined looks <read on indication of virtual cosmetic look> such as an eighties-look, goth-look, or Jennifer Aniston look," in addition to individual cosmetic options that one may use to create a customized look <read on indication of virtual cosmetic look>, where "makeup effects can include types and shades of cosmetics from which a user can select to define their own look, or a look for another user, through a user interface, such as though a looks ecosystem" as shown in FIG. 6; Note: it should be noted that any type of look is being interpreted as a virtual cosmetic look),
PNG
media_image1.png
384
509
media_image1.png
Greyscale
the virtual cosmetic look specifying respective application locations and application techniques of each virtual cosmetic included in a set of virtual cosmetics to generate the virtual cosmetic look (Iglehart, [0056]: teaches "a makeup effect can be defined by data identifying, for example, types, colors, shades, opacities, and amounts of RMAs that constitute the makeup effect and locations <read on application locations> on an area of skin for the RMAs to be deposited to apply the makeup effect to the skin," where "types, colors, shades, opacities, and amounts of RMAs <read on application techniques> to be applied to locations of the skin can be determined for individual users based comparing the reflectance of the makeup effect to the users' own skin"; [0056]: further teaches "makeup effects can include types and shades of cosmetics <read on virtual cosmetics> from which a user can select to define their own look <read on generate virtual cosmetic look>," where the makeup effects are selectable from a makeup options menu <read on set of virtual cosmetics>), and
the each virtual cosmetic of the set of virtual cosmetics associated with at least one facial feature (Iglehart, [0099]: teaches "features <read on facial feature> may include the pupils of both eyes, the center of the nose and lips, ears, chin, extent of cheeks, or other features"; FIG. 6 teaches a makeup options menu 608 <read on set of virtual cosmetics> that includes makeup <read on virtual cosmetic> for each part of the face);
overlay, in a real-time image stream being obtained via an image sensor, the each virtual cosmetic utilized in the virtual cosmetic look onto a depiction, within the real-time image stream, of the associated at least one facial feature of a user, the overlay in accordance with one or more characteristics of the at least one facial feature of the user and with the virtual cosmetic look (Iglehart, [0059]: teaches a user applying <read on overlaying> "makeup effects to a control layer of the image," where "the effects <read on depiction of facial feature of user> applied to the control layer can be mapped to a real-time video image <read on real-time image stream> of the user so as to preview the makeup effects on the use" and the video image is captured from one or more cameras <read on image sensor>; [0056]: teaches "a makeup effect can be defined by data identifying, for example, types, colors, shades, opacities, and amounts of RMAs <read on characteristics of facial feature of user> that constitute the makeup effect and locations on an area of skin for the RMAs to be deposited to apply the makeup effect to the skin");
cause the overlaid real-time image stream to be transmitted, via a communication channel, during a video call (Iglehart, [0092]: teaches the real-time video feed <read on transmitted via communication channel> of the user smiling being displayed, where a lipstick markup effect moves and bends with the user's mouth in real-time in the video <read on overlaid real-time image stream>; [0104]: teaches computing device 601 including a looks ecosystem application, which is presented to a local user and one or more remote users <read on video call>, where users can apply pre-defined looks or customized looks); and
modify the transmitted overlaid real-time image stream [[responsive to dynamic changes in one or more characteristics of the communication channel]] during the video call, including dynamically adjusting a quality of the virtual cosmetic look overlaid onto the depiction of the associated at least one facial feature of the user within the real-time image stream (Iglehart, [0111]: teaches a user applying RMA above their right eye and to their right cheek, where if each respective indicator shows that the user must apply more RMA, then the user is guided to apply the correct amount, thus updating the composited video <read on modify transmitted overlaid real-time image stream>; [0105]: teaches the local user and remote users <read on video call> editing the cosmetic look; [0056]: teaches "a more precise makeup effect <read on dynamically adjusting virtual cosmetic look quality> can also be defined by data identifying, for example, a reflectance, including intensity and color, for locations on an area of skin that constitute the makeup effect").
However, Iglehart does not expressly disclose
modify the transmitted overlaid real-time image stream responsive to dynamic changes in one or more characteristics of the communication channel during the video call, including dynamically adjusting a quality of the virtual cosmetic look overlaid onto the depiction of the associated at least one facial feature of the user within the real-time image stream.
Mendez discloses
modify the transmitted overlaid real-time image stream responsive to dynamic changes in one or more characteristics of the communication channel during the video call, including dynamically adjusting a quality of the virtual cosmetic look overlaid onto the depiction of the associated at least one facial feature of the user within the real-time image stream (Mendez, [0032]: teaches an extended-reality system being configured to "receive a request to obtain extended-reality data <read on virtual cosmetic look> not present on the extended reality system from a requesting device on the peer-to-peer network," where it determines whether to obtain the extended-reality data from a remote network-connected storage based on the quality of the connection <read on dynamic changes of communication channel> between the extended-reality system and the remote network-connected storage).
Mendez is analogous art with respect to Iglehart because they are from the same field of endeavor, namely providing augmented-reality experiences. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a cloud-based server that handles processing extended-reality data for a user's device in real-time as taught by Mendez into the teaching of Iglehart. The suggestion for doing so would allow for real-time monitoring of the network connection strength between clients and server, which would then determine which extended-reality data, such as AR effects (i.e., cosmetic filters), can be displayed over the network, thereby yielding predictable results. Therefore, it would have been obvious to combine Mendez with Iglehart.
Regarding Claim 12, it recites the limitations that are similar in scope to Claim 1, but in a computer-implemented method. As shown in the rejection, the combination of Iglehart and Ganju discloses the limitations of Claim 1. Additionally, Iglehart discloses a computer-implemented method for dynamically depicting users with virtual cosmetic looks during video calls, the computer-implemented method (Iglehart, [0104]: teaches a process <read on computer-implemented method> of a looks ecosystem application, which is presented to a local user and one or more remote users <read on video calls>, where users can apply pre-defined looks or customized looks) comprising:…
Thus, Claim 12 is met by Iglehart according to the mapping presented in the rejection of Claim 1, given the system corresponds to a computer-implemented method.
Regarding Claim 20, it recites the limitations that are similar in scope to Claim 1, but in a non-transitory computer readable medium. As shown in the rejection, the combination of Iglehart and Ganju discloses the limitations of Claim 1. Additionally, Iglehart discloses a non-transitory computer readable medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to (Iglehart, [0119]: teaches a computer including processors and computer program instructions <read on computer-executable instructions> and data being stored in non-volatile memory <read on non-transitory memories>):…
Thus, Claim 20 is met by Iglehart according to the mapping presented in the rejection of Claim 1, given the system corresponds to a non-transitory computer readable medium.
Regarding Claim 2, the combination of Iglehart and Mendez discloses the system of Claim 1. Additionally, Iglehart further discloses wherein the set of virtual cosmetics corresponding to the virtual cosmetic look includes
multiple virtual cosmetics (Iglehart, FIG. 6 teaches makeup options menu 608 <read on multiple virtual cosmetics>).
Regarding Claims 3 and 13, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Iglehart does not expressly disclose the limitations of Claims 3 and 13; however, Mendez discloses wherein the one or more characteristics of the communication channel include at least one of:
a stability of the communication channel, a bandwidth of the communication channel, a speed of the communication channel, an amount of interference on the communication channel, or a latency of the communication channel (Mendez, [0032]: teaches the quality of the connection (i.e., signal strength or communication bandwidth <read on bandwidth of communication channel>) between an extended-reality system and a remote network-connected storage).
Mendez is analogous art with respect to Iglehart because they are from the same field of endeavor, namely providing augmented-reality experiences. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a cloud-based server that handles processing extended-reality data for a user's device in real-time as taught by Mendez into the teaching of Iglehart. The suggestion for doing so would allow for real-time monitoring of the network connection strength between clients and server, which would then determine which extended-reality data, such as AR effects (i.e., cosmetic filters), can be displayed over the network, thereby yielding predictable results. Therefore, it would have been obvious to combine Mendez with Iglehart.
Regarding Claims 7 and 17, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Additionally, Iglehart further discloses wherein the one or more characteristics of the at least one facial feature of the user include at least one of:
a type of a facial feature, a color of the facial feature, a skin type of the facial feature, one or more dimensions of the facial feature, or a level of illumination of the facial feature (Iglehart, [0049]: teaches applying an RMA over skin area, such as the cheeks <read on skin type of facial feature>, where the opacity of the cosmetic would determine the resulting skin color (i.e., 0%, 50%, and 100% opacity)).
Regarding Claims 8 and 18, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Additionally, Iglehart further discloses additional computer-executable instructions that, when executed by the one or more processors, cause the system to:
determine the one or more characteristics of the at least one facial feature of the user based upon one or more of:the real-time image stream,depth sensor data, orfacial recognition of the user (Iglehart, [0061]: teaches modifying the characteristics of cosmetic indicators in the real-time video <read on real-time image stream> as the user applies cosmetics to the indicated locations on their skin, such as their cheeks <read on facial feature of user>).
Regarding Claim 10, the combination of Iglehart and Mendez discloses the system of Claim 1. Additionally, Iglehart further discloses
a virtual look data store (Iglehart, [0087]: teaches a user selecting to preview makeup effects, looks or makeup options from menus 404, 406, and 408 <read on virtual look data store> by applying them to the image as shown in FIG. 4), and wherein
PNG
media_image2.png
389
518
media_image2.png
Greyscale
the virtual cosmetic look is obtained from the virtual look data store (Iglehart, [0087]: teaches a user selecting to preview makeup effects, looks or makeup options <read on virtual cosmetic look> from menus 404, 406, and 408 <read on virtual look data store> by applying them to the image as shown in FIG. 4).
Regarding Claim 11, the combination of Iglehart and Mendez discloses the system of Claim 1. Additionally, Iglehart further discloses wherein
the virtual cosmetic look is customized for the user based upon preferences of the user (Iglehart, [0088]: teaches a user creating a customized look <read on virtual cosmetic look> by selecting "one or more makeup options <read on user preferences> from the makeup options menu 408").
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Iglehart et al. (US 20170256084 A1, previously cited), hereinafter referenced as Iglehart, in view of Lopez Mendez et al. (US 20220174111 A1), hereinafter referenced as Mendez as applied to Claims 1 and 12 above respectively, and further in view of Kim et al. (US 20140119618 A1, previously cited), hereinafter referenced as Kim.
Regarding Claims 4 and 14, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Additionally, Iglehart further discloses wherein at least one of:
(i) the dynamic changes in the one or more characteristics of the communication channel include a degradation of a characteristic of the communication channel past a first threshold, andthe modification to the transmitted overlaid real-time image stream includes a reduction of a complexity of a representation, within the transmitted overlaid real-time image stream, of respective application locations and/or application techniques of one or more virtual cosmetics included in the set of virtual cosmetics specified by the virtual cosmetic look; or[[(ii) the dynamic changes in the one or more characteristics of the communication channel include an improvement to the characteristic of the communication channel past a second threshold]], andthe modification to the transmitted overlaid real-time image stream includes an increase in the complexity of the representation, within the transmitted overlaid real-time image stream, of the respective application locations and/or application techniques of the one or more virtual cosmetics included in the set of virtual cosmetics specified by the virtual cosmetic look (Iglehart, [0111]: teaches a user applying RMA above their right eye and to their right cheek, where if each respective indicator shows that the user must apply more RMA, then the user is guided to apply the correct amount, thus updating the composited video <read on modification of transmitted overlaid real-time image stream>; [0056]: teaches "a more precise makeup effect <read on increase in complexity of representation> can also be defined by data identifying, for example, a reflectance, including intensity and color, for locations on an area of skin that constitute the makeup effect").
However, the combination of Iglehart and Mendez does not expressly disclose
(ii) the dynamic changes in the one or more characteristics of the communication channel include an improvement to the characteristic of the communication channel past a second threshold.
Kim discloses
(ii) the dynamic changes in the one or more characteristics of the communication channel include an improvement to the characteristic of the communication channel past a second threshold (Kim, [0079]: teaches an image acquirer obtaining a facial image of the user, where a quality determiner determines whether the extracted facial image meets a predetermined quality condition <read on second threshold> as shown in FIG. 3; FIG. 3 teaches if the facial image fails to meet the predetermined quality, then the process repeats until the quality improves to a sufficient point, where facial recognition processes are performed; improvement is being interpreted as finding a better quality image; [0082]: teaches operation 330 from FIG. 3 "may include, e.g., an operation of performing a predetermined pre-processing with respect to the facial image meeting the quality condition (operation 430)," where pre-processing includes correcting gamma, correcting contrast, and removing noise from the extracted facial image; the process of extracting facial image features repeatedly until the quality improves is being interpreted as an increase in complexity of the representation; Note: the "representation of the respective application locations and/or application techniques of the one or more virtual cosmetics" is being interpreted as the representation of the user's face).
PNG
media_image3.png
477
300
media_image3.png
Greyscale
Kim is analogous art with respect to Iglehart, in view of Mendez because they are from the same field of endeavor, namely performing face detection for processing. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to incorporate an image quality detector to monitor the image quality of facial images as taught by Kim into the teaching of Iglehart, in view of Mendez. The suggestion for doing so would allow the system to process and correct facial images, such as facial regions, to provide a better output, where virtual makeup filters can be added more precisely, thereby improving the overall user experience. Therefore, it would have been obvious to combine Kim with Iglehart, in view of Mendez.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Iglehart et al. (US 20170256084 A1, previously cited), hereinafter referenced as Iglehart, in view of Lopez Mendez et al. (US 20220174111 A1), hereinafter referenced as Mendez as applied to Claims 1 and 12 above respectively, and further in view of Ganju et al. (US 12167169 B1, previously cited), hereinafter referenced as Ganju.
Regarding Claims 5 and 15, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. The combination of Iglehart and Mendez does not expressly disclose the limitations of Claims 5 and 15; however, Ganju discloses additional computer-executable instructions that, when executed by the one or more processors, cause the system to:
responsive to a degradation of a characteristic of the communication channel corresponding to a particular threshold, replace a depiction of the user within the real-time image stream with at least one of a static image of the user or an avatar of the user (Ganju, [Column 12, Lines 62-64]: teaches low-bandwidth operation, where the digital avatar system operates "using only audio and/or text and (in some cases) a single image <read on static image> of the associated participant <read on depiction of user>"; [Column 16, Lines 19-21]: teaches a bandwidth threshold parameter <read on particular threshold> being used for comparison, "such that any bandwidth, or performance metric, below this parameter may trigger activation of a digital avatar for one or more participants," whenever one or more participant devices are experiencing sufficiently low bandwidth <read on degradation of characteristic of communication channel>).
Ganju is analogous art with respect to Iglehart, in view of Mendez because they are from the same field of endeavor, namely real-time facial detection of users. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a system that keeps track of video call connection quality as taught by Ganju into the teaching of Iglehart, in view of Mendez. The suggestion for doing so would allow the system to determine when to switch to different representations of the user, such as an avatar or a static image, thereby yielding predictable results. Therefore, it would have been obvious to combine Ganju with Iglehart, in view of Mendez.
Regarding Claims 6 and 16, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Additionally, Iglehart further discloses additional computer-executable instructions that, when executed by the one or more processors, cause the system to:
modify the overlay corresponding to the virtual cosmetic look (Iglehart, [0111]: teaches a user applying RMA above their right eye and to their right cheek, where if each respective indicator shows that the user must apply more RMA, then the user is guided to apply the correct amount <read on modify overlay of virtual cosmetic look>, thus updating the whole cosmetic look) and
[[the user responsive to at least one of:movements of the user depicted within the real-time image stream orchanges in lighting depicted within the real-time image stream.]]
However, the combination of Iglehart and Mendez does not expressly disclose
the user responsive to at least one of:movements of the user depicted within the real-time image stream orchanges in lighting depicted within the real-time image stream.
Ganju discloses
the user responsive to at least one of:movements of the user depicted within the real-time image stream orchanges in lighting depicted within the real-time image stream (Ganju, [Column 16, Lines 47-63]: teaches "in addition to detecting low light conditions <read on changes in lighting>, video quality analysis submodule 727 could be used to automatically detect blurring, poor contrast, or other image quality issues, and automatically trigger the digital avatar mode in response").
Ganju is analogous art with respect to Iglehart, in view of Mendez because they are from the same field of endeavor, namely real-time facial detection of users. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a system that keeps track of video call connection quality by tracking environmental conditions, such as environmental lighting as taught by Ganju into the teaching of Iglehart, in view of Mendez. The suggestion for doing so would allow the system to determine the resulting image quality, where it can use this information to determine when to switch to different representations of the user, such as an avatar or a static image, thereby improving results. Therefore, it would have been obvious to combine Ganju with Iglehart, in view of Mendez.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Iglehart et al. (US 20170256084 A1, previously cited), hereinafter referenced as Iglehart, in view of Lopez Mendez et al. (US 20220174111 A1), hereinafter referenced as Mendez as applied to Claims 1 and 12 above respectively, and further in view of Jhou (US 20210015242 A1, previously cited).
Regarding Claims 9 and 19, the combination of Iglehart and Mendez discloses the system and the computer-implemented method of Claims 1 and 12 respectively. Additionally, Iglehart further discloses
[[a machine learning model stored on the one or more non-transitory memories,]]
[[the machine learning model trained using model training data to determine associations between historical characteristics of historical facial features of respective faces of historical users and historical overlays of historical virtual cosmetics on the historical facial features of the respective faces of the historical users corresponding to historical virtual cometic looks; and wherein]]
[[the system utilizes the machine learning model to]] overlay the each virtual cosmetic of the set of virtual cosmetics utilized in the virtual cosmetic look onto the associated at least one facial feature of the user depicted in the real-time image stream in accordance with the one or more characteristics of the at least one facial feature of the user and with the virtual cosmetic look (Iglehart, [0059]: teaches a user applying <read on overlaying> "makeup effects to a control layer of the image," where "the effects <read on depiction of facial feature of user> applied to the control layer can be mapped to a real-time video image <read on real-time image stream> of the user so as to preview the makeup effects on the use" and the video image is captured from one or more cameras; [0056]: teaches "a makeup effect can be defined by data identifying, for example, types, colors, shades, opacities, and amounts of RMAs <read on characteristics of facial feature of user> that constitute the makeup effect and locations on an area of skin for the RMAs to be deposited to apply the makeup effect to the skin").
However, the combination of Iglehart and Mendez does not expressly disclose
a machine learning model stored on the one or more non-transitory memories,
the machine learning model trained using model training data to determine associations between historical characteristics of historical facial features of respective faces of historical users and historical overlays of historical virtual cosmetics on the historical facial features of the respective faces of the historical users corresponding to historical virtual cometic looks; and wherein
the system utilizes the machine learning model to overlay the each virtual cosmetic of the set of virtual cosmetics utilized in the virtual cosmetic look onto the associated at least one facial feature of the user depicted in the real-time image stream in accordance with the one or more characteristics of the at least one facial feature of the user and with the virtual cosmetic look.
Jhou discloses
a machine learning model stored on the one or more non-transitory memories (Jhou, [0039]: teaches a deep neural network model <read on machine learning model>),
the machine learning model trained using model training data to determine associations between historical characteristics of historical facial features of respective faces of historical users and historical overlays of historical virtual cosmetics on the historical facial features of the respective faces of the historical users corresponding to historical virtual cosmetic looks (Jhou, [0039]: teaches the deep neural network model analyzing a collection of digital images and extracting the target attributes, where the plurality of samples correspond to identities of different individual and the target attributes include "a high dimensional vector characterizing global facial features, landmark facial features, size and shape attributes of facial features <read on historical characteristics of historical facial features>, makeup effects <read on historical virtual cosmetics>, hairstyle <read on historical virtual cosmetic looks>, and so on"; [0040]: teaches the deep neural network model being pre-trained/initialized to different faces that correspond to different identities of individuals <read on using model training data>; [0041]: teaches the computing device 102 constructing a database 116 of entries comprising the collection of digital images 118 <read on historical users> and extracted attributes; [0042]: teaches the computing device 102 merging recommendations among the first number of recommendations 120 to generate a second number of recommendations, by grouping faces in the database 116, where "each face in the database 116 depicts a corresponding makeup effect <read on determining associations>"); and wherein
the system utilizes the machine learning model to overlay the each virtual cosmetic of the set of virtual cosmetics utilized in the virtual cosmetic look onto the associated at least one facial feature of the user depicted in the real-time image stream in accordance with the one or more characteristics of the at least one facial feature of the user and with the virtual cosmetic look (Jhou, [0043]: teaches the computing device 102, which utilizes a deep neural network <read on machine learning model>, performing a virtual application of a makeup effect that corresponds to the selection).
Jhou is analogous art with respect to Iglehart, in view of Mendez because they are from the same field of endeavor, namely analyzing and processing facial images. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a deep neural network model that correlates facial features with database facial images as taught by Jhou into the teaching of Iglehart, in view of Mendez. The suggestion for doing so would allow the system to provide a set number of recommendation based on categorized facial features, thereby optimizing storage and access of makeup data. Therefore, it would have been obvious to combine Jhou with Iglehart, in view of Mendez.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Asgekar et al. (US 20230147584 A1) discloses a system that receives a plurality of frames from a video stream, where poses of the user can be identified;
Daya et al. (US 20230376525 A1) discloses generating virtual skin tones based on user queries;
Huang et al. (US 20190166980 A1) discloses identifying cosmetic products and simulating application of said cosmetic products;
Kosecoff (US 20230101374 A1) discloses an augmented-reality system that presents AR cosmetic design filters;
Luo et al. (US 20220101419 A1) discloses generating a 3D model to be placed in an augmented reality environment;
Miller et al. (US 20180180448 A1) discloses a virtual-reality system that determines the transmission strength of a magnetic field strength;
Odizzio et al. (US 20180075524 A1) discloses applying virtual makeup products using layered visual filters;
Santos et al. (US 20130169827 A1) discloses performing make-up virtual images based on detected points of interest on a user's face;
Wang et al. (US 20140043329 A1) discloses generating a personalized 3D morphable model of a user's face; and
Lotti et al. (US 12198289 B1) discloses an augmented-reality system that overlays fake eye lashes using an AR effect.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 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, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
Prosecution Insights