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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP 23219477.9, filed on 02/12/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application.
Claims 1-13 and 15-20 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim(s) 1-4 and 9-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Begun et al (U.S. 20210044864 A1; Begun).
Claim(s) 5-8 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Begun et al (U.S. 20210044864 A1; Begun), in view of Thorwirth et al (U.S. 20130097625 A1; Thorwirth).
Drawings
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because in Fig. 1 , drawings are difficult to review with respect to the specification. The drawings are objected to because the Examiner may require and is requiring descriptive text labels, “Fig.1: box 105, 110, 120, 130, 140, 150, 155, 160, 170, 180, 190 respectively … 1) in the drawings do not show every feature of the invention specified in the claims-- or 2) is an unlabeled rectangular box(es) shown in the drawings should be provided with descriptive text labels” [MPEP 608.02(b) examiner note]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 and 15-20 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “a positive event” in line 17. There is insufficient antecedent basis for this limitation in the claim. It is not clear to the Examiner if this the same as “ a positive event” in claim 1, line 8.
Claims 2-13 is/are also rejected under 35 USC 112(b) because they are dependent on claim 1.
Appropriate correction is required.
Claim 1 recites the limitation “a negative event” in line 18. There is insufficient antecedent basis for this limitation in the claim. It is not clear to the Examiner if this the same as “ a negative event” in claim 1, line 6.
Claims 2-13 is/are also rejected under 35 USC 112(b) because they are dependent on claim 1.
Appropriate correction is required.
Claim 15 recites the limitation “a positive event” in line 18. There is insufficient antecedent basis for this limitation in the claim. It is not clear to the Examiner if this the same as “ a positive event” in claim 15, line 9.
Claims 16-20 is/are also rejected under 35 USC 112(b) because they are dependent on claim 15.
Appropriate correction is required.
Claim 15 recites the limitation “a negative event” in line 19. There is insufficient antecedent basis for this limitation in the claim. It is not clear to the Examiner if this the same as “ a negative event” in claim 15, line 7.
Claims 16-20 is/are also rejected under 35 USC 112(b) because they are dependent on claim 15.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4 and 9-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Begun et al (U.S. 20210044864 A1; Begun).
Regarding claim 1, Begun discloses A computer-implemented method for identifying a video content, (Paragraph 2: “a method and apparatus for identifying video content based on biometric features of characters.”) comprising:
a) obtaining a portion of the video content to be identified; (Fig.6 – S510; Paragraph 61: “in operation S510, the processor 320 may receive video content. Hereinafter, without special limitation, the term ‘video content’ indicates the video content received in operation S510, i.e., video content to be currently analyzed, and may indicate ‘present video content’ when the video content may be configured with other video content”; Paragraph 71: “In a description of FIG. 6, the description made in relation to of FIG. 5 may be omitted to prevent redundancy.”)
b) extracting a frame from the video portion;(Fig.6 – S610; Paragraph 71: “n operation S610, the processor 320 may extract a frame from received video content. The extracted frame may include a single frame or a plurality of frames.”)
c) detecting if the frame includes at least one human face; (Fig.5 – step S520 and Paragraph 61: “the processor 320 may detect biometric features of characters in the video content. The biometric features may include at least one of face shapes, voices, gaits, hand shapes, or ear shapes.”; Fig.6 – S620; Paragraphs 71-72: “when a face or hand shape is detected from video content, one frame may be extracted, and when voices or gaits are detected from video content, a plurality of frames may be extracted. … In operation S620, the processor 320 may detect biometric features of characters from the extracted frame.”)
dl) in a negative event, executing the steps b) and c) on at least one other frame of the video portion; (Fig.6 – S630 with NO ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”
d2) in a positive event, determining if the at least one human face can be identified and, if so, updating a query list including at least one identification information of the human face detected in the video content to be identified; (Fig.6 – S630 with YES ; Step 530 ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”; Paragraph 121: “An operation of updating the person-specific biometric feature database 410 is described below. The person-specific biometric feature database update operation may be performed in a video content identification operation or independently from the video content identification operation. FIG. 14 shows an operation of updating the person-specific biometric feature database 410, according to an example embodiment. Referring to FIG. 14, when a character is identified based on a biometric feature detected from the present video content and the content-specific non-identified biometric feature database 430, in operation S1410, the processor 320 may match the biometric feature detected from the present video content with a person identifier of the identified character and store the matched biometric feature in the person-specific biometric feature database 410”)
e) comparing the query list with at least one predetermined reference list in a database, including identification information of human faces appearing in a known video content to determine if a matching criterion is satisfied by the at least one predetermined reference list; (Fig.6 -S540 and Paragraph 65-68: “The processor 320 may identify characters by applying machine learning to the biometric features detected from the present video content and the person-specific biometric feature database 410 … In operation S540, the processor 320 may identify the video content based on a result of identifying the characters. The processor 320 may use the content-specific character database 420 to identify video content based on identified characters. That is, the processor 320 may identify video content based on an identification result of characters being matched with a character list stored in the content-specific character database 420. The content-specific character database 420 may store a character list of known video content. Herein, a character list stored in a database may include person identifiers of characters. A character list of each video content may be matched with a content identifier of corresponding video content and stored. A content identifier is identification information uniquely indicating video content and may conform to an arbitrary format such as a number, a character string, or a binary code.”)
f) determining if an identification result can be finalized at least based on a comparison. Fig.6 -S660 and Paragraphs 73-75: “In operation S640, the processor 320 may proceed to the video content identification operation S540 when characters are identified based on the detected biometric features, and proceed back to the frame extraction operation S610 when no characters are identified … In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”; Paragraph 70: “The processor 320 may specify a content identifier of identified video content when the video content is successfully identified. In operation S550, the processor 320 may output a content identifier of the video content based on a result of identifying the video content.”)
g1) in a positive event, outputting the identification result; (Fig.6 -S660 with YES ; S550 Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”) and
g2) in a negative event, executing the steps b) to f) on at least one other frame of the video portion, in an iterative manner. (Fig.6 -S660 with NO ; Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified. The processor 320 may then perform the biometric feature detection operation S620, the character identification operation S530, the video content identification operation S540, and the like on a newly extracted frame.”)
Regarding claim 2, Begun discloses further comprising detecting a change of scene between two frames successively extracted. (Paragraph 72: “ Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like.”)
Regarding claim 3 , Begun discloses a duration or number of frames of the video portion is set to a predetermined value. (Paragraph 72: “ Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like.”)
Regarding claim 4 , Begun discloses if no predetermined reference list in the database satisfies the matching criterion after executing steps b. to e. on all the frames extracted from the video portion, outputting the identification result that the video content to be identified does not correspond to known video content from the database. (Paragraph 78: “ the processor 320 may extract a first frame from received video content and identify the video content based on the extracted first frame, and when the video content is not identified based on the first frame, extract a second frame from the video content and identify the video content based on the extracted second frame. Each of the first frame and the second frame may include a single frame or a plurality of frames.”; Paragraph 74-75: “he processor 320 may perform a non-identified biometric feature processing operation S650 when no characters are identified based on the detected biometric features. Hereinafter, a biometric feature by which a character is not identified among biometric features of characters, which have been detected from video content, is referred to as ‘non-identified biometric feature’. …In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified.”; Paragraph 76: “A case where frame extraction on the entire video content is completed may include a case where all the frames of the video content are extracted and a case where a certain condition indicating completion of frame extraction is satisfied, such as a case where the video content ends while sequentially extracting frames at constant time intervals. The processor 320 may end the video content identification process when frame extraction on the entire video content is completed even though the video content is not identified. )
Regarding claim 9, Begun discloses the database is a generic database of information related to known video contents such as films, series, TV programs or shows, and online streaming video contents. (Paragraph 3: “Along with the development of computer and Internet technologies, many pieces of video content such as movies and TV series are distributed, and thus, there is demand for a technique of identifying video content. The technique of identifying video content may be used for statistical analysis of consumption of video content, detection of consumers' tendencies, recommendation of video content or other products, and the like.”; Fig.21: movie frames)
Regarding claim 10, Begun discloses the database contains a predetermined reference list of a known video content that is currently being distributed, such as a sport event, and step a) is performed on a video content to be identified that is being transmitted concomitantly to the distribution of said known video content. (Paragraph 3: “Along with the development of computer and Internet technologies, many pieces of video content such as movies and TV series are distributed, and thus, there is demand for a technique of identifying video content. The technique of identifying video content may be used for statistical analysis of consumption of video content, detection of consumers' tendencies, recommendation of video content or other products, and the like.”; Paragraph 87: “he processor 320 may store biometric features belonging to a same person in the content-specific non-identified biometric feature database 430 by matching the biometric features belonging to the same person with each other. The biometric features belonging to the same person may be the same type of biometric features or different types of biometric features.”)
Regarding claim 11, Begun discloses the method is executed on a plurality of video contents to be identified. (Fig.12: Video Content A; Video Content B; Video Content C; Paragraph 109: “When certain video content is identified, a character list of the video content may be obtained from the content-specific character database 420. … a list of characters, of which biometric feature data is not stored in the person-specific biometric feature database 410 is referred to as a ‘non-identified character list’. The processor 320 may store a non-identified character list for each video content in the content-specific non-identified biometric feature database 430.”)
Regarding claim 12, Begun discloses further comprising generating the predetermined reference list of at least one known video content from the database during broadcast of said at least one known video content.( Fig.12: Video Content A; Video Content B; Video Content C; Paragraph 109: “When certain video content is identified, a character list of the video content may be obtained from the content-specific character database 420. … a list of characters, of which biometric feature data is not stored in the person-specific biometric feature database 410 is referred to as a ‘non-identified character list’. The processor 320 may store a non-identified character list for each video content in the content-specific non-identified biometric feature database 430.”; Paragraph 3: “Along with the development of computer and Internet technologies, many pieces of video content such as movies and TV series are distributed, and thus, there is demand for a technique of identifying video content. The technique of identifying video content may be used for statistical analysis of consumption of video content, detection of consumers' tendencies, recommendation of video content or other products, and the like.”;)
Regarding claim 13, Begun discloses step f. further comprises determining a broadcast time information of the video content to be identified; and determining a set of one or more known video contents broadcast at the same time as the video to be identified, based on the broadcast time information. (Paragraph 3: “Along with the development of computer and Internet technologies, many pieces of video content such as movies and TV series are distributed, and thus, there is demand for a technique of identifying video content. The technique of identifying video content may be used for statistical analysis of consumption of video content, detection of consumers' tendencies, recommendation of video content or other products, and the like.”)
Regarding claim 14, Begun discloses A system for identifying a video content (Paragraph 2: “a method and apparatus for identifying video content based on biometric features of characters.”) comprising: processing circuitry configured to:
obtain a portion of the video content to be identified; (Fig.6 – S510; Paragraph 61: “in operation S510, the processor 320 may receive video content. Hereinafter, without special limitation, the term ‘video content’ indicates the video content received in operation S510, i.e., video content to be currently analyzed, and may indicate ‘present video content’ when the video content may be configured with other video content”; Paragraph 71: “In a description of FIG. 6, the description made in relation to of FIG. 5 may be omitted to prevent redundancy.”)
extract a frame from the video portion; (Fig.6 – S610; Paragraph 71: “n operation S610, the processor 320 may extract a frame from received video content. The extracted frame may include a single frame or a plurality of frames.”)
detect whether the frame includes at least one human face; (Fig.5 – step S520 and Paragraph 61: “the processor 320 may detect biometric features of characters in the video content. The biometric features may include at least one of face shapes, voices, gaits, hand shapes, or ear shapes.”; Fig.6 – S620; Paragraphs 71-72: “when a face or hand shape is detected from video content, one frame may be extracted, and when voices or gaits are detected from video content, a plurality of frames may be extracted. … In operation S620, the processor 320 may detect biometric features of characters from the extracted frame.”)
when the frame does not include at least one human face, the processing circuitry is further configured to obtain the portion of the video content to be identified and extract the frame from the video portion using at least one other frame of the video portion; Fig.6 – S630 with NO ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”)
when the frame does include at least one human face, the processing circuitry is further configured to determine whether the at least one human face is identifiable and, if so, updating a query list including at least one identification information of the human face detected in the video content to be identified; (Fig.6 – S630 with YES ; Step 530 ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”; Paragraph 121: “An operation of updating the person-specific biometric feature database 410 is described below. The person-specific biometric feature database update operation may be performed in a video content identification operation or independently from the video content identification operation. FIG. 14 shows an operation of updating the person-specific biometric feature database 410, according to an example embodiment. Referring to FIG. 14, when a character is identified based on a biometric feature detected from the present video content and the content-specific non-identified biometric feature database 430, in operation S1410, the processor 320 may match the biometric feature detected from the present video content with a person identifier of the identified character and store the matched biometric feature in the person-specific biometric feature database 410”)
compare the query list with at least one predetermined reference list in a database, including identification information of human faces appearing in a known video content to determine whether a matching criterion is satisfied by the at least one predetermined reference list; (Fig.6 -S540 and Paragraph 65-68: “The processor 320 may identify characters by applying machine learning to the biometric features detected from the present video content and the person-specific biometric feature database 410 … In operation S540, the processor 320 may identify the video content based on a result of identifying the characters. The processor 320 may use the content-specific character database 420 to identify video content based on identified characters. That is, the processor 320 may identify video content based on an identification result of characters being matched with a character list stored in the content-specific character database 420. The content-specific character database 420 may store a character list of known video content. Herein, a character list stored in a database may include person identifiers of characters. A character list of each video content may be matched with a content identifier of corresponding video content and stored. A content identifier is identification information uniquely indicating video content and may conform to an arbitrary format such as a number, a character string, or a binary code.”)
determine whether an identification result is finalizable at least based on a comparison; (Fig.6 -S660 and Paragraphs 73-75: “In operation S640, the processor 320 may proceed to the video content identification operation S540 when characters are identified based on the detected biometric features, and proceed back to the frame extraction operation S610 when no characters are identified … In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”; Paragraph 70: “The processor 320 may specify a content identifier of identified video content when the video content is successfully identified. In operation S550, the processor 320 may output a content identifier of the video content based on a result of identifying the video content.”)
when the identification result is finalizable, output the identification result; (Fig.6 -S660 with YES ; S550 Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”) and
when the identification result is not finalizable, (Fig.6 -S660 with NO ; Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified. The processor 320 may then perform the biometric feature detection operation S620, the character identification operation S530, the video content identification operation S540, and the like on a newly extracted frame.”) the processing circuitry is further configured to extract the frame from the video portion, (Fig.6 – S610) detect whether the frame includes at least one human face, (Fig.5 – step S520; Fig.6 – S620;
when the frame does not include at least one human face, the processing circuitry is further configured to obtain the portion of the video content to be identified and extract the frame from the video portion using at least one other frame of the video portion, (Fig.6 – S630 with NO) when the frame does include at least one human face, the processing circuitry is further configured to determine whether the at least one human face is identifiable and, if so, updating a query list including at least one identification information of the human face detected in the video content to be identified, (Fig.6 – S630 with YES) compare the query list with at least one predetermined reference list in the database, including identification information of human faces appearing in the known video content to determine whether the matching criterion is satisfied by the at least one predetermined reference list, (Fig.6 -S540) determine whether the identification result is finalizable at least based on the comparison using at least one other frame of the video portion, Fig.6 -S660 and Paragraphs 73-75: “In operation S640, the processor 320 may proceed to the video content identification operation S540 when characters are identified based on the detected biometric features, and proceed back to the frame extraction operation S610 when no characters are identified … In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”; Paragraph 70: “The processor 320 may specify a content identifier of identified video content when the video content is successfully identified. In operation S550, the processor 320 may output a content identifier of the video content based on a result of identifying the video content.”) in an iterative manner; (Fig.6 -S660 with NO ; Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified. The processor 320 may then perform the biometric feature detection operation S620, the character identification operation S530, the video content identification operation S540, and the like on a newly extracted frame.”) and having access to or including a reference database, store in said reference database storing at least one predetermined reference list including identification information of human faces appearing in the known video content. (Paragraphs 63-68: “The person-specific biometric feature database 410 may store biometric features such as face shapes, voices, gaits, hand shapes, and ear shapes of known people. Biometric features stored in a database may include the biometric features, as well as any type of information indicating unique biometric features. … , the processor 320 may identify video content based on an identification result of characters being matched with a character list stored in the content-specific character database 420. … e content-specific character database 420 may store a character list of known video content. Herein, a character list stored in a database may include person identifiers of characters. A character list of each video content may be matched with a content identifier of corresponding video content and stored.”)
Regarding claim 15, Begun discloses A non-transitory computer-readable storage medium including instructions which, when executed by a computer, cause the computer to carry out a method (Paragraph 134: “a recording medium including computer-executable instructions such as a program module executed by a computer system. … the non-transitory computer-readable medium may include all types of computer storage media and communication media”) for identifying a video content, (Paragraph 2: “a method and apparatus for identifying video content based on biometric features of characters.”) comprising:
a) obtaining a portion of the video content to be identified; (Fig.6 – S510; Paragraph 61: “in operation S510, the processor 320 may receive video content. Hereinafter, without special limitation, the term ‘video content’ indicates the video content received in operation S510, i.e., video content to be currently analyzed, and may indicate ‘present video content’ when the video content may be configured with other video content”; Paragraph 71: “In a description of FIG. 6, the description made in relation to of FIG. 5 may be omitted to prevent redundancy.”)
b) extracting a frame from the video portion;(Fig.6 – S610; Paragraph 71: “n operation S610, the processor 320 may extract a frame from received video content. The extracted frame may include a single frame or a plurality of frames.”)
c) detecting if the frame includes at least one human face; (Fig.5 – step S520 and Paragraph 61: “the processor 320 may detect biometric features of characters in the video content. The biometric features may include at least one of face shapes, voices, gaits, hand shapes, or ear shapes.”; Fig.6 – S620; Paragraphs 71-72: “when a face or hand shape is detected from video content, one frame may be extracted, and when voices or gaits are detected from video content, a plurality of frames may be extracted. … In operation S620, the processor 320 may detect biometric features of characters from the extracted frame.”)
dl) in a negative event, executing the steps b) and c) on at least one other frame of the video portion; (Fig.6 – S630 with NO ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”
d2) in a positive event, determining if the at least one human face can be identified and, if so, updating a query list including at least one identification information of the human face detected in the video content to be identified; (Fig.6 – S630 with YES ; Step 530 ; Paragraph 72: “In operation S630, the processor 320 may proceed to a character identification operation S530 when biometric features of characters are detected from the extracted frame, and proceed back to the frame extraction operation S610 to extract another frame when no biometric features of characters are detected. Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like. The processor 320 may perform the biometric feature detection operation S620 on a newly extracted frame, and perform the character identification operation S530 when biometric features are detected.”; Paragraph 121: “An operation of updating the person-specific biometric feature database 410 is described below. The person-specific biometric feature database update operation may be performed in a video content identification operation or independently from the video content identification operation. FIG. 14 shows an operation of updating the person-specific biometric feature database 410, according to an example embodiment. Referring to FIG. 14, when a character is identified based on a biometric feature detected from the present video content and the content-specific non-identified biometric feature database 430, in operation S1410, the processor 320 may match the biometric feature detected from the present video content with a person identifier of the identified character and store the matched biometric feature in the person-specific biometric feature database 410”)
e) comparing the query list with at least one predetermined reference list in a database, including identification information of human faces appearing in a known video content to determine if a matching criterion is satisfied by the at least one predetermined reference list; (Fig.6 -S540 and Paragraph 65-68: “The processor 320 may identify characters by applying machine learning to the biometric features detected from the present video content and the person-specific biometric feature database 410 … In operation S540, the processor 320 may identify the video content based on a result of identifying the characters. The processor 320 may use the content-specific character database 420 to identify video content based on identified characters. That is, the processor 320 may identify video content based on an identification result of characters being matched with a character list stored in the content-specific character database 420. The content-specific character database 420 may store a character list of known video content. Herein, a character list stored in a database may include person identifiers of characters. A character list of each video content may be matched with a content identifier of corresponding video content and stored. A content identifier is identification information uniquely indicating video content and may conform to an arbitrary format such as a number, a character string, or a binary code.”)
f) determining if an identification result can be finalized at least based on a comparison. Fig.6 -S660 and Paragraphs 73-75: “In operation S640, the processor 320 may proceed to the video content identification operation S540 when characters are identified based on the detected biometric features, and proceed back to the frame extraction operation S610 when no characters are identified … In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”; Paragraph 70: “The processor 320 may specify a content identifier of identified video content when the video content is successfully identified. In operation S550, the processor 320 may output a content identifier of the video content based on a result of identifying the video content.”)
g1) in a positive event, outputting the identification result; (Fig.6 -S660 with YES ; S550 Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters,”) and
g2) in a negative event, executing the steps b) to f) on at least one other frame of the video portion, in an iterative manner. (Fig.6 -S660 with NO ; Paragraph 75: “In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified. The processor 320 may then perform the biometric feature detection operation S620, the character identification operation S530, the video content identification operation S540, and the like on a newly extracted frame.”)
Regarding claim 16, Begun discloses further comprises detecting a change of scene between two frames successively extracted. (Paragraph 72: “ Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like.”)
Regarding claim 17, Begun discloses a duration or number of frames of the video portion is set to a predetermined value. (Paragraph 72: “ Another frame may be extracted sequentially or though other various ways. For example, another frame may be a next frame, a next key frame, a frame after a certain time, a frame in which a scene is changed, or the like.”)
Regarding claim 18, Begun discloses if no predetermined reference list in the database satisfies the matching criterion after executing steps b) to e) on all the frames extracted from the video portion, the method further comprises outputting the identification result that the video content to be identified does not correspond to the known video content from the database. (Paragraph 78: “ the processor 320 may extract a first frame from received video content and identify the video content based on the extracted first frame, and when the video content is not identified based on the first frame, extract a second frame from the video content and identify the video content based on the extracted second frame. Each of the first frame and the second frame may include a single frame or a plurality of frames.”; Paragraph 74-75: “he processor 320 may perform a non-identified biometric feature processing operation S650 when no characters are identified based on the detected biometric features. Hereinafter, a biometric feature by which a character is not identified among biometric features of characters, which have been detected from video content, is referred to as ‘non-identified biometric feature’. …In operation S660, the processor 320 may proceed to the content identifier output operation S550 when the video content is identified based on a result of identifying the characters, and proceed back to the frame extraction operation S610 when the video content is not identified.”; Paragraph 76: “A case where frame extraction on the entire video content is completed may include a case where all the frames of the video content are extracted and a case where a certain condition indicating completion of frame extraction is satisfied, such as a case where the video content ends while sequentially extracting frames at constant time intervals. The processor 320 may end the video content identification process when frame extraction on the entire video content is completed even though the video content is not identified. )
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 5-8 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Begun et al (U.S. 20210044864 A1; Begun), in view of Thorwirth et al (U.S. 20130097625 A1; Thorwirth).
Regarding claim 5, Begun discloses all the claims invention except further comprising: extracting additional information from the video portion by performing an image processing operation such as Optical Character Recognition; and comparing the extracted additional information with metadata stored in the database in association with the at least one predetermined reference list.
Thorwirth discloses extracting additional information from the video portion by performing an image processing operation such as Optical Character Recognition; and comparing the extracted additional information with metadata stored in the database in association with the at least one predetermined reference list. (Paragraph 65: “each piece of semantic information is indicative of a match probability for the content and a copyright owner. For example, the identification of the TV station logo indicates a specific copyright owner with a probability according to the quality of the match of the logo in the video with the logo stored in a reference database. The extraction of keywords from OCR and speech recognition can result in a different match probability to that copyright owner. The probabilities are combined in order to obtain a better estimation of the likely copyright owner. The resulting probability is compared to a set threshold value that determines if the match is good enough to conclude if a copyright owner has been successfully identified.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 6, Begun discloses further comprising, in step f., determining that the identification result cannot be finalized if a number of predetermined reference lists satisfying the matching criterion is above a first predetermined threshold value. (Paragraph 74: “the processor 320 may perform a non-identified biometric feature processing operation S650 when no characters are identified based on the detected biometric features. Hereinafter, a biometric feature by which a character is not identified among biometric features of characters, which have been detected from video content, is referred to as ‘non-identified biometric feature’. A non-identified biometric feature may be a biometric feature by which a character is not identified based on the person-specific biometric feature database 410.”)
However, Begun does not disclose matching criterion is above a first predetermined threshold value
Thorwirth discloses matching criterion is above a first predetermined threshold value (Paragraph 25: “extracting at least one semantic property from a specified media object, matching said at least one semantic property of the specified media object against known semantic properties of known media objects, comparing matches between said at least one semantic property and known semantic properties to a threshold value”; Paragraph 47: “A determination (262) is made based upon the comparisons concerning whether the extracted area contains a known logo image. The match can be established with a resulting matching score that is compared to a given threshold. If several matching scores are above the threshold, the match with the highest score is determined to be the corresponding logo and returned (264) as a match; otherwise the extracted area is determined to not be present in the logo database.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 7, Begun discloses the matching criterion includes a number of same human faces identified in both the query list and the predetermined reference list, that is above a predetermined threshold value. (Fig. 18 Paragraph 126-127: “ the processor 320 may detect a first type of biometric feature and a second type of biometric feature of a same character from the present video content, identify the character based on the detected first type of biometric feature and a match in the person-specific biometric feature database 410, match the detected second type of biometric feature with a person identifier of the identified character, and store the matched second type of biometric feature in the person-specific biometric feature database 410. … when the character cannot be identified based on the second type of biometric feature and a match in the person-specific biometric feature database 410, the second type of biometric feature may be matched with another non-identified biometric feature stored in the content-specific non-identified biometric feature database 430. In this case, the person-specific biometric feature database 410 may be updated based on the matched non-identified biometric feature. The matched non-identified biometric feature may be a different type from the second type of biometric feature. For example, when a face shape and a hand shape of an actor are detected from video content, the actor is identified based on the detected face shape and the person-specific biometric feature database 410,”)
However, Begun does not disclose the matching criterion that is above a predetermined threshold value.
Thorwirth discloses the matching criterion that is above a predetermined threshold value. (Paragraph 25: “extracting at least one semantic property from a specified media object, matching said at least one semantic property of the specified media object against known semantic properties of known media objects, comparing matches between said at least one semantic property and known semantic properties to a threshold value”; Paragraph 47: “A determination (262) is made based upon the comparisons concerning whether the extracted area contains a known logo image. The match can be established with a resulting matching score that is compared to a given threshold. If several matching scores are above the threshold, the match with the highest score is determined to be the corresponding logo and returned (264) as a match; otherwise the extracted area is determined to not be present in the logo database.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 8, Begun discloses the matching criterion includes a first number of same human faces identified in both the query list and a first part of the reference list, and a second number of same human faces identified in both the query list and a second part of the reference list, that are both above a predetermined threshold value. (Fig. 18 Paragraph 126-127: “ the processor 320 may detect a first type of biometric feature and a second type of biometric feature of a same character from the present video content, identify the character based on the detected first type of biometric feature and a match in the person-specific biometric feature database 410, match the detected second type of biometric feature with a person identifier of the identified character, and store the matched second type of biometric feature in the person-specific biometric feature database 410. … when the character cannot be identified based on the second type of biometric feature and a match in the person-specific biometric feature database 410, the second type of biometric feature may be matched with another non-identified biometric feature stored in the content-specific non-identified biometric feature database 430. In this case, the person-specific biometric feature database 410 may be updated based on the matched non-identified biometric feature. The matched non-identified biometric feature may be a different type from the second type of biometric feature. For example, when a face shape and a hand shape of an actor are detected from video content, the actor is identified based on the detected face shape and the person-specific biometric feature database 410,”)
However, Begun does not disclose the matching criterion that is above a predetermined threshold value.
Thorwirth discloses the matching criterion that is above a predetermined threshold value. (Paragraph 25: “extracting at least one semantic property from a specified media object, matching said at least one semantic property of the specified media object against known semantic properties of known media objects, comparing matches between said at least one semantic property and known semantic properties to a threshold value”; Paragraph 47: “A determination (262) is made based upon the comparisons concerning whether the extracted area contains a known logo image. The match can be established with a resulting matching score that is compared to a given threshold. If several matching scores are above the threshold, the match with the highest score is determined to be the corresponding logo and returned (264) as a match; otherwise the extracted area is determined to not be present in the logo database.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 19, Begun discloses all the claims invention except further comprising: extracting additional information from the video portion by performing an image processing operation such as Optical Character Recognition; and comparing the extracted additional information with metadata stored in the database in association with the at least one predetermined reference list.
Thorwirth discloses extracting additional information from the video portion by performing an image processing operation such as Optical Character Recognition; and comparing the extracted additional information with metadata stored in the database in association with the at least one predetermined reference list. (Paragraph 65: “each piece of semantic information is indicative of a match probability for the content and a copyright owner. For example, the identification of the TV station logo indicates a specific copyright owner with a probability according to the quality of the match of the logo in the video with the logo stored in a reference database. The extraction of keywords from OCR and speech recognition can result in a different match probability to that copyright owner. The probabilities are combined in order to obtain a better estimation of the likely copyright owner. The resulting probability is compared to a set threshold value that determines if the match is good enough to conclude if a copyright owner has been successfully identified.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 20, Begun discloses further comprising, in step f., determining that the identification result cannot be finalized if a number of predetermined reference lists satisfying the matching criterion is above a first predetermined threshold value. (Paragraph 74: “the processor 320 may perform a non-identified biometric feature processing operation S650 when no characters are identified based on the detected biometric features. Hereinafter, a biometric feature by which a character is not identified among biometric features of characters, which have been detected from video content, is referred to as ‘non-identified biometric feature’. A non-identified biometric feature may be a biometric feature by which a character is not identified based on the person-specific biometric feature database 410.”)
However, Begun does not disclose matching criterion is above a first predetermined threshold value
Thorwirth discloses matching criterion is above a first predetermined threshold value (Paragraph 25: “extracting at least one semantic property from a specified media object, matching said at least one semantic property of the specified media object against known semantic properties of known media objects, comparing matches between said at least one semantic property and known semantic properties to a threshold value”; Paragraph 47: “A determination (262) is made based upon the comparisons concerning whether the extracted area contains a known logo image. The match can be established with a resulting matching score that is compared to a given threshold. If several matching scores are above the threshold, the match with the highest score is determined to be the corresponding logo and returned (264) as a match; otherwise the extracted area is determined to not be present in the logo database.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Begun by including analysis of semantic information in the media objects that is taught by Thorwirth, to make the invention that System and method for identification of ownership to media objects; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving estimates of content origin and ownership, such as fingerprinting, by using the pre-selection, to limit the number of possible matches that the comparison of the fingerprinting system has to perform, or by combining match probabilities for a more reliable detection.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharon et al (U.S. 20130326573 A1), “Video Identification And Search”, teaches about Visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. The visual fingerprints include a bit-wise representation of a frame of video based on the brightness of cells of the frame in one example. Confidence measures are generated at the bit level to assist in query signature processing. Bit confidence measures can be used to adjust thresholds in determining whether query signatures have a sufficient probability to match a true base result, to terminate searches where the probability is low, and/or to determine a number of subsets or buckets for base set signatures against which the query signatures are matched.
Schneiderman et al (U.S. 20080080743 A1), “Video Retrieval System For Human Face Content”, teaches about a method for processing video data comprises detecting human faces in a plurality of video frames in the video data, and, for at least one detected human face, identifying all frames in which this face is present irrespective of whether the detected human face is present in these "face-specific" set of video frames in a substantially temporally continuous manner. The method also comprises enabling a user to view face-specific video segments in the video data based on the face-specific set of video frames identified.
Feng et al (U.S. 20210287006 A1), Video Query Method, Apparatus, and Device , and Storage Medium” teaches about a video query method includes obtaining a media feature of a query media and a static image feature corresponding to a candidate video. The query media includes the target object, and the candidate video includes the moving object. A video feature of the candidate video is determined according to the static image feature and motion time sequence information of the moving object in the candidate video. Whether the moving object in the candidate video is related to the target object in the query media can be determined according to the media feature and the video feature.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Duy A Tran whose telephone number is (571)272-4887. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm.
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/DUY TRAN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
