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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/13/2024, 10/07/2025, 06/04/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) extracting partial images, determining a partial image to use, and further generated a composite image of the determined portions.
The limitations of extracting, determining, and generating, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “executed by an image processing device,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “image processing device” language, “extracting partial images” in the context of this claim encompasses the user manually looking at the partial images, such as the faces within the images. Additionally, “determining one of the partial images” in the context of this claim encompasses the user manually or mentally selecting which partial image, such as the face, is the face they want to use. Lastly, “generating a partial image” in the context of this claim encompasses the user changing the partial regions, such as the faces, with the faces they deemed to be better. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an image processing device to perform the steps listed amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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 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 9 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by YOSHIZAWA (JP 5136245 B2).
RE claim 9, Yoshizawa teaches an image editing system that modifies captured images to produce a combined image so that all face are displayed with a high face evaluation value. Yoshizawa teaches an image processing method executed by an image processing device comprising:
(a)
a step of extracting partial images of multiple subjects to be imaged who are contained in each of multiple images captured at different times;
Yoshizawa teaches an extraction means (said partial image extractor) for extracting a partial image of an object to be photographed (said configured to extract partial images) [0006]. In the image synthesizing apparatus, the acquiring means is characterized by acquiring the plurality of photographed images continuously in time (said multiple images captured at different times) [0007, 0025]. As further described in the specification of Yoshizawa, Figs. 4-7 image frames that are captured continuously. Each frame is analyzed. In the example of the first frame shown in Fig. 4(a), a partial image of a face portion in the captured image is detected by scanning the captured image [0032, 0034]. As shown in the example of Fig. 4(a), three faces are determined (said multiple subjects to be imaged). Fig. 4(b) illustrates individual table (T1) that provides information of each face portion determined [0032, 0034, 0035]. The information includes a “face evaluation” ranked “a-c” [0035], with “face evaluation a” corresponding to a smile feature but can also require additional features [0029-0030]. The same analysis is performed on image 2 (Fig. 5(a-b)) [0046-0048], image 3 (Fig. 6(a-b)) [0052-0054], and image 4 (Fig. 7(a-b)) [0058-0062].
(b)
a step of determining one of the partial images from the partial images of each of the subjects to be imaged among the extracted partial images;
As disclosed in the rationale of claim 1(a), each frame is analyzed and individual tables (T1) (Figs. 4(b), 5(b), 6(b), 7(b)) for each image includes a “face evaluation” with rank “a-c” [0035]. Fig. 8 provides the comprehensive table (132) that includes fields “face No.”, “photographed image of evaluation a”, combination candidate”, and “final combination” [0041]. As the images 1-4 are being analyzed, the comprehensive table (132) is filled in with which image number has a specific face number with a face evaluation of “a”. As shown in Fig. 8(d), the comprehensive table (132) is completed after image 4 has been analyzed. The comprehensive table (132) is used to determine a combination of captured images that includes all face images of “1-3” (said partial image) that has an evaluation “a” and that is a minimum number of captured images (said determining one of the partial images)[0065]. Therefore, based on comprehensive table (132), image 2 and image 3 are shown to be valid for face 1 and 2, while image 4 is best for face 3 [0065].
(c)
a step of generating a partial image of the subject based on the determined partial image.
Ultimately, once a base image is determined [0066], the information from comprehensive table (132) is used to produce the final image. As shown in Fig. 8(d), image 2 and image 4 are used to produce the composite image shown in Fig. 11(d). As discussed in the rationale of claims 9(a-b), it is determined that face 3 in image 2 is not smiling. Face 3 of image 4 is smiling. The method/system of Yoshizawa teaches cutting out the face partial image 3 of image 2 and overwrite and synthesize the face partial image 3 from image 4 into image 2 [0074]. This results in a composite image shown in Fig. 11(d) of each face partial image (1-3) smiling.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over YOSHIZAWA (JP 5136245 B2).
RE claim 1, Yoshizawa teaches an image editing system that modifies captured images to produce a combined image so that all face are displayed with a high face evaluation value. Yoshizawa teaches an image processing device comprising:
(a)
a partial image extractor configured to extract partial images of multiple subjects to be imaged who are contained in each of multiple images captured at different times;
Yoshizawa teaches an extraction means (said partial image extractor) for extracting a partial image of an object to be photographed (said configured to extract partial images) [0006]. In the image synthesizing apparatus, the acquiring means is characterized by acquiring the plurality of photographed images continuously in time (said multiple images captured at different times) [0007, 0025]. As further described in the specification of Yoshizawa, Figs. 4-7 image frames that are captured continuously. Each frame is analyzed. In the example of the first frame shown in Fig. 4(a), a partial image of a face portion in the captured image is detected by scanning the captured image [0032, 0034]. As shown in the example of Fig. 4(a), three faces are determined (said multiple subjects to be imaged). Fig. 4(b) illustrates individual table (T1) that provides information of each face portion determined [0032, 0034, 0035]. The information includes a “face evaluation” ranked “a-c” [0035], with “face evaluation a” corresponding to a smile feature but can also require additional features [0029-0030]. The same analysis is performed on image 2 (Fig. 5(a-b)) [0046-0048], image 3 (Fig. 6(a-b)) [0052-0054], and image 4 (Fig. 7(a-b)) [0058-0062].
(b)
a vote unit configured to accept a vote on the partial images of each of the subjects to be imaged among the partial images that are extracted by the partial image extractor;
As disclosed in the rationale of claim 1(a), each frame is analyzed and individual tables (T1) (Figs. 4(b), 5(b), 6(b), 7(b)) for each image includes a “face evaluation” with rank “a-c” [0035]. Fig. 8 provides the comprehensive table (132) that includes fields “face No.”, “photographed image of evaluation a”, combination candidate”, and “final combination” [0041]. As the images 1-4 are being analyzed, the comprehensive table (132) is filled in with which image number has a specific face number with a face evaluation of “a”. As shown in Fig. 8(d), the comprehensive table (132) is completed after image 4 has been analyzed. Yoshizawa does not specifically recite “voting” however, it would have been obvious before the effective filing date of the claimed invention to conclude the listing of the images with evaluation “a” for a particular face can be considered a vote since the results of column 2, “photographed image of evaluation a”, is used to determine which image(s) containing the best face should be used for the final composite image. Thus, image 2 and image 3 are shown to be valid for face 1 and 2, while image 4 is best for face 3 [0065].
(c)
a partial image determination unit configured to determine one of the partial images based on a result of the votes on the partial images that are accepted by the vote unit; and
As discussed in the rationale of claim 1(b), the comprehensive table (132) is used to determine a combination of captured images that includes all face images of “1-3” (said partial image) that has an evaluation “a” and that is a minimum number of captured images [0065]. Therefore, based on comprehensive table (132), image 2 and image 3 are shown to be valid for face 1 and 2, while image 4 is best for face 3 [0065].
(d)
an image processor configured to generate a partial image of the subject based on the partial image that is determined by the partial image determination unit.
Ultimately, once a base image is determined [0066], the information from comprehensive table (132) is used to produce the final image. As shown in Fig. 8(d), image 2 and image 4 are used to produce the composite image shown in Fig. 11(d). As discussed in the rationale of claims 1(a-c), it is determined that face 3 in image 2 is not smiling. Face 3 of image 4 is smiling. The method/system of Yoshizawa teaches cutting out the face partial image 3 of image 2 and overwrite and synthesize the face partial image 3 from image 4 into image 2 [0074]. This results in a composite image shown in Fig. 11(d) of each face partial image (1-3) smiling.
RE claim 2, Yoshizawa teaches wherein the image processor is further configured to
(a)
combine the generated partial image as the partial image of the subject in the captured image.
As shown in Fig. 8(d), image 2 and image 4 are used to produce the composite image shown in Fig. 11(d). As discussed in the rationale of claims 1(a-c), it is determined that face 3 in image 2 is not smiling. Face 3 of image 4 is smiling. The method/system of Yoshizawa teaches cutting out the face partial image 3 of image 2 and overwrite and synthesize the face partial image 3 from image 4 into image 2 [0074]. This results in a composite image shown in Fig. 11(d) of each face partial image (1-3) smiling.
RE claim 3, Yoshizawa teaches wherein
(a)
the image processor is further configured to deform the partial image of the subject in the captured image such that the partial image of the subject in the captured image is approximated to the determined partial image.
Yoshizawa teaches the face region extraction unit (12) normalizes the size of the image. Specifically, conversion is performed so that the number of pixels of the image data becomes the number of pixels set in advance as the number of pixels appropriate for extracting the face region or the mouth region [0021].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over YOSHIZAWA (JP 5136245 B2) in view of HIBI et al. (JP 2005234686).
RE claim 4, Yoshizawa teaches performing face evaluations using the system/method of Hibi. Yoshizawa in view of Hibi further comprising
(a)
a facial expression analyzer configured to output a determination result by analyzing a facial expression of the subject to be imaged,
As in the example of image 1 shown in Figs. 4(a-b) of Yoshizawa, partial image of face portion is detected [0034] and each face is evaluated based on face evaluations rank “a-c” (said determination result) [0035]. Yoshizawa teaches using the system/method of Hibi to perform the evaluation (said facial expression analyzer) [0035].
In further view of Hibi, Hibi teaches the system (said facial expression analyzer) includes a face region extraction unit (12) that extracts a region corresponding to a face in an image, and a mouth region extraction unit (13) that extracts a region corresponding to a lip [0017]. The mouth region analysis unit (14) analyzes a mouth region and extracts a value used to generate expression identification information [0017]. A smile index is a unique value added to the image data as supplementary information [Fig. 3, 0020]. Figs. 5 and 6 illustrate how the method/system of Hibi determines the smile index [0024-0026]. A smile index table [Fig. 7] indicates “high degree of smile”, “low degree of smile” and “not smiling”, based on the comparison of parameter A and parameter B to threshold values ThA and ThB (said determination result) [0026].
wherein the partial image determination unit is further configured to determine the partial image by weighting the vote according to the determination result of the facial expression analyzer.
As discussed in the rationale of claim 1(b), the comprehensive table (132) of Yoshizawa is used to determine a combination of captured images that includes all face images of “1-3” (said partial image) that has an evaluation “a” and that is a minimum number of captured images [0065]. As seen in the comprehensive table (132), the more times an image is listed that encompasses a face image with an evaluation “a”, the greater chance that that image will be used (said weighting the vote). Thus, as seen in Fig. 8(d), the comprehensive table (132) lists images 2 and 3 are valid for face 1 and face 2, while image 4 is best for face 3 [0065]. Furthermore, as taught the in the rationale of claim 4(a), the evaluation value is determined by the system/method of Hibi (said determination result of the facial expression analyzer).
It would have been obvious before the effective filing date of the claimed invention to utilize the expression recognition device of Hibi with the system/method of Yoshizawa. As Yoshizawa teaches, the face evaluation is performed as indicated by the system/method of Hibi [0036].
Claims 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over YOSHIZAWA (JP 5136245 B2) in view of LU et al. (US 2014/0081717 A1).
RE claim 5, Yoshizawa teaches the limitations of claim 5 with the exception of disclosing multiple votes by multiple voters. Lu is made of record as teaching the art of generating an option decisions based on the input from multiple users [abstract]. Yoshizawa in view of Lu teaches
(a)
wherein the vote unit is further configured to accept multiple votes by multiple voters on the partial images of each of the subjects to be imaged among the partial images that are extracted by the partial image extractor, and, e
Yoshizawa teaches selecting a face by an expression rating as disclosed in the rationale of claim 1. As discussed in the rationale of claim 1(b), the comprehensive table (132) is used to determine a combination of captured images that includes all face images of “1-3” (said partial image) that has an evaluation “a” and that is a minimum number of captured images [0065]. The table provides which images are best for each face (said vote). However, Yoshizawa does not disclose accepting multiple votes by multiple voters.
Lu teaches a web presentment utility (56) is provided to present one or more web pages that include a user interface for enabling voters to provide the input to the voting method (said accept votes) [0090]. The intelligent voting manager (54) is operable to implement one or more tools for enabling group decision making [0093]. The method/system of Lu allows any group (said multiple voters) to form a collective decision [0094]. A form may be provided to the “event organizer” to provide a group decision problem, a list of alternatives from which the group may choose, and a list of group members [0098]. Each group member may input their preferences (said vote) [0101]. The server may update the preferences and may store the information in a database [0101].
(b)
the partial image determination unit is further configured to determine one of the partial images for each of the subjects to be imaged based on the result of the votes by the multiple voters on the partial images that are accepted by the vote unit.
Yoshizawa teaches selecting the best face for the composite image. In further view of Lu, Lu teaches the “winner” may be defined to be the alternative that has minimum maximum (i.e., minimax) regret [0110].
It would have been obvious before the effective filing date of the claimed invention to further include receiving votes from multiple users in order to determine the best face of Yoshizawa as taught by Lu. Even though computers recognize objective data, they often struggle to understand subjection emotions, cultural context or humor. By allowing users to vote or select an image to use rather than an algorithm guarantees that the chosen visual image authentically resonates with the target audience. The system/method of Lu generates an efficient optimal, or near-optimal choice without obtaining entire complete rankings from one or more votes, which may be able to reduce the cognitive and time demands imposed on voters [Lu: 109].
RE claim 10, Yoshizawa teaches an image editing system that modifies captured images to produce a combined image so that all face are displayed with a high face evaluation value. Yoshizawa teaches a non-transitory storage medium that stores a program causing a controller of an image processing device to function processes comprising of (Yoshizawa teaches a ROM for storing programs and the like [0027]):
(a)
a step of extracting partial images of multiple subjects to be imaged who are contained in each of multiple images captured at different times;
Yoshizawa teaches an extraction means (said partial image extractor) for extracting a partial image of an object to be photographed (said configured to extract partial images) [0006]. In the image synthesizing apparatus, the acquiring means is characterized by acquiring the plurality of photographed images continuously in time (said multiple images captured at different times) [0007, 0025]. As further described in the specification of Yoshizawa, Figs. 4-7 image frames that are captured continuously. Each frame is analyzed. In the example of the first frame shown in Fig. 4(a), a partial image of a face portion in the captured image is detected by scanning the captured image [0032, 0034]. As shown in the example of Fig. 4(a), three faces are determined (said multiple subjects to be imaged). Fig. 4(b) illustrates individual table (T1) that provides information of each face portion determined [0032, 0034, 0035]. The information includes a “face evaluation” ranked “a-c” [0035], with “face evaluation a” corresponding to a smile feature but can also require additional features [0029-0030]. The same analysis is performed on image 2 (Fig. 5(a-b)) [0046-0048], image 3 (Fig. 6(a-b)) [0052-0054], and image 4 (Fig. 7(a-b)) [0058-0062].
(b)
a step of determining one of the partial images for each of the subjects to be imaged from the partial images of each of the subjects to be imaged among the extracted partial images based on multiple votes by multiple voters; and
As disclosed in the rationale of claim 1(a), each frame is analyzed and individual tables (T1) (Figs. 4(b), 5(b), 6(b), 7(b)) for each image includes a “face evaluation” with rank “a-c” [0035]. Fig. 8 provides the comprehensive table (132) that includes fields “face No.”, “photographed image of evaluation a”, combination candidate”, and “final combination” [0041]. As the images 1-4 are being analyzed, the comprehensive table (132) is filled in with which image number has a specific face number with a face evaluation of “a”. As shown in Fig. 8(d), the comprehensive table (132) is completed after image 4 has been analyzed. Thus, the comprehensive table (132) is used to determine a combination of captured images that includes all face images of “1-3” (said partial image) that has an evaluation “a” and that is a minimum number of captured images [0065]. Based on comprehensive table (132), image 2 and image 3 are shown to be valid for face 1 and 2, while image 4 is best for face 3 [0065]. However, Yoshizawa does not disclose accepting multiple votes by multiple voters.
Lu teaches a web presentment utility (56) is provided to present one or more web pages that include a user interface for enabling voters to provide the input to the voting method [0090]. The intelligent voting manager (54) is operable to implement one or more tools for enabling group decision making [0093]. The method/system of Lu allows any group (said multiple voters) to form a collective decision [0094]. A form may be provided to the “event organizer” to provide a group decision problem, a list of alternatives from which the group may choose, and a list of group members [0098]. Each group member may input their preferences (said vote) [0101]. The server may update the preferences and may store the information in a database [0101].
It would have been obvious before the effective filing date of the claimed invention to further include receiving votes from multiple users in order to determine the best face of Yoshizawa as taught by Lu. Even though computers recognize objective data, they often struggle to understand subjection emotions, cultural context or humor. By allowing users to vote or select an image to use rather than an algorithm guarantees that the chosen visual image authentically resonates with the target audience. The system/method of Lu generates an efficient optimal, or near-optimal choice without obtaining entire complete rankings from one or more votes, which may be able to reduce the cognitive and time demands imposed on voters [Lu: 109].
(c)
a step of generating a partial image of the subject based on the determined partial image.
Ultimately, once a base image is determined [0066], the information from comprehensive table (132) is used to produce the final image. As shown in Fig. 8(d), image 2 and image 4 are used to produce the composite image shown in Fig. 11(d). As discussed in the rationale of claims 10(a-b), it is determined that face 3 in image 2 is not smiling. Face 3 of image 4 is smiling. The method/system of Yoshizawa teaches cutting out the face partial image 3 of image 2 and overwrite and synthesize the face partial image 3 from image 4 into image 2 [0074]. This results in a composite image shown in Fig. 11(d) of each face partial image (1-3) smiling.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over YOSHIZAWA (JP 5136245 B2) in view of LU et al. (US 2014/0081717 A1) as applied to claim 5, and in further view of RICHARDSON (US 2015/0039404 A1).
RE claim 6, Yoshizawa in view of Lu teaches multiple voters choosing the best face for a composite image. However, Yoshizawa in view of Lu fail to disclose a weighted vote. Richardson is made of record as teaching a system/method for applying a mathematical weight to the vote of each user in respect of any given voting event [abstract]. In further view of Richardson, Richardson teaches wherein
(a)
the partial image determination unit is further configured to determine the partial image by weighting the vote according to each of the multiple voters.
Richardson teaches users (10) can be encouraged to vote and give a score relating to items on a web site (12) [0051]. A vote database (15) is used to collect the votes made by people visiting the website. An item database (14) contains information about items being presented. A weighting system (16) is used to ensure that intentional manipulation of votes is detected and avoided and also that users who are active in giving of their time to provide votes are rewarded with a higher weighting [0051]. Richardson teaches the voting system gives each user (20) a larger number of votes (21) which are calculated based on the credibility of the voter and on their voting history (said weighting the vote according to each of the multiple voters) [0054]. Weighting system (22) uses rules (23-25) to calculate the users’ (20) weighting (321). Each rule helps define how many votes (30, 31) the user can use to give their opinion [0054].
It would have been obvious before the effective filing date of the claimed invention to modify the system/method of Yoshizawa in view of Lu to include the weighting system of Richardson in regards to the voting. Richardson teaches using a weighting system (16) ensures that intentional manipulation of votes is detected and avoided and also so that users who are active in giving of their time to provide votes are rewarded with a higher weighting [0051].
RE claim 7, in further view of Richardson, Richardson teaches wherein,
(a)
when the voter votes on the partial image of the voter herself/himself, the partial image determination unit is further configured to weight the vote by a value greater than that when the voter votes on the partial images of the voters other than the voter herself/himself, and thereby determine the partial image.
Richardson teaches users (10) can be encouraged to vote and give a score relating to items on a web site (12) [0051]. A vote database (15) is used to collect the votes made by people visiting the website. An item database (14) contains information about items being presented. A weighting system (16) is used to ensure that intentional manipulation of votes is detected and avoided and also that users who are active in giving of their time to provide votes are rewarded with a higher weighting [0051]. Richardson teaches the voting system gives each user (20) a larger number of votes (21) which are calculated based on the credibility of the voter and on their voting history [0054]. Weighting system (22) uses rules (23-25) to calculate the users’ (20) weighting (321). Each rule helps define how many votes (30, 31) the user can use to give their opinion [0054].
Thus, Richardson teaches applying different weighting rules based on characteristics of the voter rather than assigning equal value to every vote. It would have been obvious before the effective filing date of the claimed invention to assign additional weight to a person’s evaluation of his or her own facial expression (said weight the vote by a value greater) because that individual is uniquely situated to judge whether his or her appearance is satisfactory, thereby improving user satisfaction in the resulting composite image of Yoshizawa.
RE claim 8, in further view of Richardson, Richardson teaches wherein,
(a)
the partial image determination unit is further configured to set a person who is in a neutral position as a manger, and when the manager votes, weight the vote by a value greater than that when persons other than the manager vote, and thereby determine the partial image.
Richardson teaches users (10) can be encouraged to vote and give a score relating to items on a web site (12) [0051]. A vote database (15) is used to collect the votes made by people visiting the website. An item database (14) contains information about items being presented. A weighting system (16) is used to ensure that intentional manipulation of votes is detected and avoided and also that users who are active in giving of their time to provide votes are rewarded with a higher weighting [0051]. Richardson teaches the voting system gives each user (20) a larger number of votes (21) which are calculated based on the credibility of the voter and on their voting history [0054]. Weighting system (22) uses rules (23-25) to calculate the users’ (20) weighting (321). Each rule helps define how many votes (30, 31) the user can use to give their opinion [0054].
Thus, Richardson teaches applying different weighting rules based on characteristics of the voter rather than assigning equal value to every vote. It would have been obvious before the effective filing date of the claimed invention to assign additional weight to a neutral party, such as a manager, because the manager would vote without prejudice. As discussed in Richardson, measuring characteristics of a user’s voting against the other users of the site means that there is an opportunity to tag extreme activities of people that have a hidden agenda [0071]. Thus, a manager/neutral party would try to maintain a common sense evaluation and rating [0071]. Therefore, providing a neutral party with more weight can help the outcome of the vote with true results, eliminating extreme activities of people that have a hidden agenda.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. (US 9336583 B2) is made of record as teaching an image editing system [abstract]. A utilization score is assigned to the frame based on the detected facial characteristics, and a determination of whether to utilize the frame is made based on the utilization score. Regions from the frames are combined to generate a composite image [abstract]. Figs. 4-5 provide a good illustration of the general steps of the system of Huang.
BITOUK et al. (US 8712189 B2) is made of record as teaching the system/method of swapping faces in images. The swapped-face image is aligned to the input image to form an output image [abstract]. The face-swapped copies are ranked to determine which copy is best [6:56-58].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE L SAMS:
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michelle.sams@uspto.gov
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/MICHELLE L SAMS/
Primary Examiner, Art Unit 2611
10 July 2026
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