DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/16/2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see Remarks pg. 12, filed 3/16/2026, with respect to the summary of the Office Action and status of the claims have been fully considered and are herein acknowledged.
Applicant’s arguments, see Remarks pg. 12, filed 3/16/2026, with respect to the interview request is herein acknowledged. The examiner contacted applicant on 12/12/2025 and conducted an interview regarding the outstanding obviousness rejection.
Applicant’s arguments, see Remarks pg. 13, filed 3/16/2026, with respect to the Double Patenting rejection have been fully considered. The applicant’s arguments regarding agreeing to filing a terminal disclaimer is herein acknowledged, however, the examiner notes that a terminal disclaimer has not been filed to date. MPEP 804 states: As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated. Therefore, an application must not be allowed unless the required compliant terminal disclaimer(s) is/are filed and/or the withdrawal of the nonstatutory double patenting rejection(s) is made of record by the examiner. See MPEP § 804.02, subsection VI., for filing terminal disclaimers required to overcome nonstatutory double patenting rejections in applications filed on or after June 8, 1995. Therefore, the rejection is maintained.
Applicant’s arguments, see Remarks pg. 13-14, filed 3/16/2026, with respect to the rejected claims 21-40 under 35 U.S.C. 103 over Nelson, McMillan, Kalampoukas, and Mitra and Ramaswamy have been fully considered. The examiner notes that the applicant’s arguments are directed to the claims as amended, however, the amendments to the claims comprise the limitations similar in scope as previously recited. For example, prior to the current amendments, claim 1 recited “generating, by the computing system, using the extracted features, repetition data for respective portions of the media content; determining, by the computing system, transition data for the media content; selecting, by the computing system, a portion within the media content using the transition data, wherein the generated repetition data for the selected portion comprises a list of other portions of the media content matching the selected portion and further comprises respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented.” Claim 1 is now amended to read "generating, by the computing system, using the extracted features, repetition data for respective portions of the media content, wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion, and wherein the list includes respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented; determining, by the computing system, transition data for the media content; selecting, by the computing system, a portion within the media content using the transition data."
First, in response to applicant’s arguments regarding the teachings of the prior art of record, the examiner incorporates by references the findings of fact as cited in the parent application (17/796,297) and the Patent Board Decision affirming the examiner’s obviousness rejection. Furthermore, in response to applicant’s arguments, test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). More importantly, on the issue of obviousness, the Supreme Court stated that when a patent simply arranges old elements with each performing the same function it had been known to perform and yields no more than one would expect from such an arrangement, the combination is obvious. KSR International Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385 (2007) (citing Sakraida v. AG Pro, Inc., 425 U.S. 273, 96 S. Ct. 1532, 47 L. Ed. 2d 784 (1976)). The Court further reiterated that in circumstances where the combination of two pre-existing elements did no more than they would in separate, sequential operation, the patent failed under 35 U.S.C. 103. See id. at 416-417 (citing Anderson's-Black Rock, Inc. v. Pavement Salvage Co., 396 U.S. 57, 90 S. Ct. 305, 24 L. Ed. 2d 258 (1969)). The analysis of a rejection on obviousness grounds need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ. See id. at 418. The obvious analysis cannot be confined by a formalistic conception of the words teaching, suggestion, and motivation. Id. at 419. Further, the Court stated that common sense teaches, however, that familiar items may have obvious uses beyond their primary purposes, and in many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle. See id. at 420.
With respect to applicant’s arguments regarding Nelson, Nelson teaches extracting features from media content comprising media identifying information (i.e., media identifying metadata, codes, signatures, watermarks, and/or other information that may be used to identify presented media) wherein
watermarks that are encoded with media and signatures extracted or derived from media may be used to identify the media. See Nelson col. 1, 11. 61-6 5, col. 3, 11. 24-3 4. The Examiner finds that Nelson teaches that fingerprint or signature-based media monitoring may require a series of signatures collected in series over a time interval wherein a "good signature is repeatable when processing the same media presentation, but is unique relative to other (e.g., different) presentations of other (e.g., different) media." (Nelson col. 2, 11. 26-42). The Examiner further finds that Figure 3 of Nelson teaches or suggests media content that transitions from a first Program ID # 1 portion to a first commercial/advertisement content portion, then to second Program ID # 1 portion, then to a second commercial/advertisement content portion, and then to Program ID #2 portion.
Based on the above teachings of Nelson, a person of ordinary skill in the art would understand and/ or reasonably infer that when comparing repetition data extracted from media content, the same media content will have the same series of signatures collected in series over a time interval based, in part, based on the teaching that "a good signature is repeatable when processing the same media presentation, but is unique relative to other (e.g., different) presentations of other ( e.g., different) media." The Examiner finds that stated differently the same media content will have the same identifiers when processing the same media presentation but the identifiers are unique relative to other ( e.g., different) presentations of other ( e.g., different) media. Id.
Furthermore, the Examiner finds that Nelson teaches using a hybrid of watermark detection and signature generation to identify media which correspond to 1) signature generation (i.e., repetition data) and 2) watermarks/codes that are unique to media content. Nelson col. 3, 11. 9-10, col. 9, 11. 36-40. Nelson also teaches that detecting watermarks/codes comprises identifying a unique identifier such that a person of ordinary skill in the art would reasonably infer that a change in the detected unique identifier (i.e., a transition from one unique identifier to a second (different) unique identifier) would mean a change in media content because each media content is provided with a unique identifier such that two different types of media content would each have a different unique identifier. Id. at 13-14 (citing Nelson col. 5, 11. 3-18). The Examiner finds that Nelson recognizes benefits of utilizing a hybrid of watermark/code detection (i.e., unique identifier) and a signature detection (repetition data), as comprising but are not limited to, reduce processing resources needed to identify media because only signatures in a subset of signatures are compared to identify the media in question (i.e., advertisements) rather than a comparison of signatures against an entire library of signatures. Id. at 14 (citing Nelson col. 3, 11. 30-34).
Furthermore, the teachings of McMillan further establish that utilizing a "list" (i.e., log) as claimed is further rendered obvious in view of the combined teachings of Nelson and McMillan. In particular the Examiner finds that McMillan paragraph 43 teaches that the linear/non-linear media identifier determines whether media in a media presentation detected at a presentation site ( e.g., a panelist site, household, etc.) is program media or non-program media and the log generator generates reference logs (i.e., list) to include indications of linear presentations of program media and nonprogram media (i.e., unique identifiers of program and non-program media) and the times at which the program media (i.e., program segment) and non-program media (i.e., advertisement segment) were presented. (McMillan 43). The Examiner further finds that McMillan teaches that "code matcher 216 of FIG. 2 may combine multiple consecutive codes into a single entry if, for example, the consecutive codes are associated with the same media ( e.g., program, commercial, etc.)." McMillan 47. Therefore, a person of ordinary skill in the art would understand that McMillan's invention is able to identify program media (i.e., program) or non-program media (i.e., commercials/advertisements) based, in part, on comparing a plurality of media identifiers extracted from media content. McMillan teaches the claimed limitation "based at least in part on a number of unique reference identifiers within the list of other portions of the media content relative to a total number of reference identifiers within the list of other portions of the media content" because McMillian classifies portions of media content based on a number of unique reference identifiers with a contiguous duration and wherein at least a threshold portion of the duration of events and for any duration of metered viewing where non-program media occurs in the reference, wherein the meter data must show that at least a threshold portion (i.e., eighty percent) of the non-program media was detected in the same order as detected on the reference system. McMillan 27, 72-74). Whereas McMillan does not refer to the disclosed “log” as a “list”, in an analogous art, Kalampoukas Fig. 5 and para 43-52 teaches tracking a list of segment representations indicating when the segment was aired and tracking re-airings of content and interpreted as the same advertisement be re-aired during two different programs. Lastly, Mitra para 68 and 81 teaches identifying advertisements in media content and reports the frequency of each advertisement broadcasted for the first time and the frequency of each advertisement broadcasted repetitively. All things considered, the applicant’s arguments are not persuasive.
Double Patenting Rejection
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Independent claims 21, 29, and 35 and their dependent claims 22-28, 30-34, and 36-46 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US 12132953 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the current application are broader in scope and all the elements of the claims read on the claims of US 12132953 B2.
Current Application 18/548,610 US Patent 12132953 B2
21. (Currently Amended) A method comprising: extracting, by a computing system, features from media content; generating, by the computing system, using the extracted features, repetition data for respective portions of the media content, wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion, and wherein the list includes respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented; determining, by the computing system, transition data for the media content; selecting, by the computing system, a portion within the media content using the transition data, classifying, by the computing system, using the generated repetition data for the selected
portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content, and wherein the classifying is based at least in part on (i) a number of unique reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion relative to (ii) a total number of reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion; and outputting, by the computing system, data indicating a result of the classifying for the selected portion.
22. The method of claim 21, wherein: extracting the features comprises extracting fingerprints, and generating the repetition data comprises generating the repetition data using the fingerprints.
23. The method of claim 21, wherein: extracting the features comprises extracting closed captioning, and generating the repetition data comprises generating the repetition data using the closed captioning.
24. The method of claim 21, wherein: extracting the features comprises extracting keyframes, and generating the repetition data comprises (i) identifying a portion between two adjacent keyframes of the keyframes and (ii) searching for other portions within the media content having features matching features for the identified portion.
25. The method of claim 21, wherein: the transition data comprises predicted transitions between different content segments, and selecting the portion comprises selecting the portion based on the portion being between two adjacent predicted transitions of the predicted transitions.
26. The method of claim 21, wherein: classifying the selected portion comprises classifying the selected portion as a program segment, the method further comprises determining that the selected portion classified as a program segment corresponds to a program specified in an electronic program guide using a timestamp of the selected portion, and the data indicating the result of the classifying comprises a data file for the program that includes an indication of the selected portion.
27. The method of claim 21, wherein: classifying the selected portion comprises classifying the selected portion as an advertisement segment, the features comprises metadata for the selected portion, and the data indicating the result of the classifying comprises a data file that includes the metadata and an indication of the selected portion.
28. The method of claim 21, further comprising: determining, by the computing system, logo coverage data indicative of a percent of time that a logo overlays the selected portion; and comparing, by the computing system, the percent of time to a threshold, wherein classifying the selected portion as either an advertisement segment or a program segment using the generated repetition data for the selected portion is further based on an output of the comparison of the percent of time to the threshold.
29. (Currently Amended) A non-transitory computer-readable medium having stored thereon program instructions that, upon execution by a processor, cause performance of a set of acts comprising: extracting features from media content; generating, using the extracted features, repetition data for respective portions of the media content, wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion, and wherein the list includes respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented; determining transition data for the media content;
selecting a portion within the media content using the transition data classifying, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement
segment is presented during two or more unique programs within the media content, and wherein the classifying is based at least in part on (i) a number of unique reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion relative to (ii) a total number of reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion; and outputting data indicating a result of the classifying for the selected portion.
30. The non-transitory computer-readable medium of claim 29, wherein: extracting the features comprises extracting fingerprints, and generating the repetition data comprises generating the repetition data using the fingerprints.
31. The non-transitory computer-readable medium of claim 29, wherein: extracting the features comprises extracting closed captioning, and generating the repetition data comprises generating the repetition data using the closed captioning.
32. The non-transitory computer-readable medium of claim 29, wherein: extracting the features comprises extracting keyframes, and generating the repetition data comprises (i) identifying a portion between two adjacent keyframes of the keyframes and (ii) searching for other portions within the media content having features matching features for the identified portion.
33. The non-transitory computer-readable medium of claim 29, wherein: classifying the selected portion comprises classifying the selected portion as a program segment, the set of acts further comprises determining that the selected portion classified as a program segment corresponds to a program specified in an electronic program guide using a timestamp of the selected portion, and the data indicating the result of the classifying comprises a data file for the program that includes an indication of the selected portion.
34. The non-transitory computer-readable medium of claim 29, wherein: classifying the selected portion comprises classifying the selected portion as an advertisement segment, the features comprises metadata for the selected portion, and the data indicating the result of the classifying comprises a data file that includes the metadata and an indication of the selected portion.
35. (Currently Amended) A computing system configured for performing a set of acts comprising: extracting features from media content; generating, using the extracted features, repetition data for respective portions of the media content, wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion, and wherein the list includes respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented; determining transition data for the media content; selecting a portion within the media content using the transition data classifying, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content, and wherein the classifying is based at least in part on (i) a number of unique reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion relative to (ii) a total number of reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion; and outputting data indicating a result of the classifying for the selected portion.
36. The computing system of claim 35, wherein: extracting the features comprises extracting fingerprints, and generating the repetition data comprises generating the repetition data using the fingerprints.
37. The computing system of claim 35, wherein: extracting the features comprises extracting closed captioning, and generating the repetition data comprises generating the repetition data using the closed captioning.
38. The computing system of claim 35, wherein: extracting the features comprises extracting keyframes, and generating the repetition data comprises (i) identifying a portion between two adjacent keyframes of the keyframes and (ii) searching for other portions within the media content having features matching features for the identified portion.
39. The computing system of claim 35, wherein: the transition data comprises predicted transitions between different content segments, and selecting the portion comprises selecting the portion based on the portion being between two adjacent predicted transitions of the predicted transitions.
40. The computing system of claim 35, wherein: classifying the selected portion comprises classifying the selected portion as a program segment, the set of acts further comprises determining that the selected portion classified as a program segment corresponds to a program specified in an electronic program guide using a timestamp of the selected portion, and the data indicating the result of the classifying comprises a data file for the program that includes an indication of the selected portion.
41. (New) The method of claim 25, wherein the transition data reflects a probability that that a transition exists between the respective portions of the media content.
42. (New) The method of claim 21, wherein determining transition data for the media content includes implementing a recurrent neural network.
43. (New) The method of claim 42, wherein the recurrent neural network includes an extraction layer, and a classification layer.
44. (New) The method of claim 21 wherein generating repetition data further includes generating repetition data according to a plurality of detection tiers.
45. (New) The method of claim 44, wherein generating repetition data further includes generating repetition data according to the plurality of detection tiers includes an audio tier, a video tier, and a closed caption tier.
46. (New) The method of claim 45, wherein the repetition data is generated based on closed caption text data.
1. A method comprising: obtaining, by a computing system, fingerprint repetition data for a portion of video content, wherein the fingerprint repetition data comprises a list of other portions of video content matching the portion of video content and respective reference identifiers for the other portions of video content, each reference identifier corresponding to a respective different time the other portion of video content was presented; identifying, by the computing system, the portion of video content as a program segment rather than an advertisement segment based at least on a number of unique reference identifiers within the list of other portions of video content relative to a total number of reference identifiers within the list of other portions of video content, wherein the total number of reference identifiers within the list of other portions of video content further includes a plurality of reference identifiers that are the same; determining, by the computing system, that the portion of video content corresponds to a program specified in an electronic program guide using a timestamp of the portion of video content; and based on the identifying of the portion of video content as a program segment and the determining that the portion of video content corresponds to the program, storing, by the computing system, an indication of the portion of video content in a data file for the program.
2. The method of claim 1, wherein obtaining the fingerprint repetition data comprises searching for matches to fingerprints of the portion of video content within a video database so as to obtain the list of other portions of video content.
3. The method of claim 1, wherein identifying the portion of video content as a program segment rather than an advertisement segment based at least on the number of unique reference identifiers relative to the total number of reference identifiers comprises determining that a ratio of the number of unique reference identifiers to the total number of reference identifiers satisfies a threshold.
4. The method of claim 1, further comprising obtaining logo coverage data for the portion of video content, wherein the logo coverage data is indicative of a percent of time that a logo overlays the portion of video content, and wherein the identifying the portion of video content as a program segment rather than an advertisement segment is further based on the logo coverage data.
5. The method of claim 4, wherein the identifying the portion of video content as a program segment rather than an advertisement segment is further based on a number of portions of video content in the list of other portions of video content and a length of the portion of video content.
6. The method of claim 1, further comprising: obtaining transition data for a section of video content that includes the portion of video content; and identifying boundaries of the portion of video content using the transition data.
7. The method of claim 1, further comprising after identifying the portion of video content as a program segment, merging the portion of video content with an adjacent portion of video content that is identified as a program segment.
8. The method of claim 7, wherein merging the portion of video content with the adjacent portion of video content comprises: obtaining a first list of matching portions for the portion of video content; obtaining a second list of matching portions for the adjacent portion of video content; identifying correspondences between the first list and the second list using timestamps for matching portions of the first list of matching portions and timestamps for matching portions of the second list of matching portions; and based on the correspondences, merging the portion of video content with the adjacent portion of video content.
9. The method of claim 1, further comprising generating a copy of the program using the data file for the program.
10. The method of claim 1, further comprising obtaining closed captioning repetition data for the portion of video content, wherein the identifying the portion of video content as a program segment rather than an advertisement segment is further based on the closed captioning repetition data.
11. The method of claim 10, further comprising: generating features using the closed captioning repetition data; and providing the features as input to a classification model, wherein the classification model is configured to output classification data indicative of a likelihood of the features being characteristic of a program segment, wherein the identifying the portion of video content as a program segment rather than an advertisement segment is further based on the classification data.
12. The method of claim 1, further comprising: obtaining fingerprint repetition data for another portion of video content; identifying the other portion of video content as an advertisement segment rather than a program segment using the fingerprint repetition data; identifying metadata for the other portion of video content; and storing an indication of the other portion of video content and the metadata in another data file.
13. A non-transitory computer-readable medium having stored thereon program instructions that upon execution by a processor, cause performance of a set of acts comprising: obtaining fingerprint repetition data for a portion of video content, wherein the fingerprint repetition data comprises a list of other portions of video content matching the portion of video content and respective reference identifiers for the other portions of video content, each reference identifier corresponding to a respective different time the other portion of video content was presented; identifying the portion of video content as a program segment rather than an advertisement segment based at least on a number of unique reference identifiers within the list of other portions of video content relative to a total number of reference identifiers within the list of other portions of video content, wherein the total number of reference identifiers within the list of other portions of video content further includes a plurality of reference identifiers that are the same; determining that the portion of video content corresponds to a program specified in an electronic program guide using a timestamp of the portion of video content; and based on the identifying of the portion of video content as a program segment and the determining that the portion of video content corresponds to the program, storing an indication of the portion of video content in a data file for the program.
14. The non-transitory computer-readable medium of claim 13, wherein obtaining the fingerprint repetition data comprises searching for matches to fingerprints of the portion of video content within a video database so as to obtain the list of other portions of video content.
15. The non-transitory computer-readable medium of claim 13, wherein identifying the portion of video content as a program segment rather than an advertisement segment based at least on the number of unique reference identifiers relative to the total number of reference identifiers comprises determining that a ratio of the number of unique reference identifiers to the total number of reference identifiers satisfies a threshold.
16. A computing system configured for performing a set of acts comprising: obtaining fingerprint repetition data for a portion of video content, wherein the fingerprint repetition data comprises a list of other portions of video content matching the portion of video content and respective reference identifiers for the other portions of video content, each reference identifier corresponding to a respective different time the other portion of video content was presented; identifying the portion of video content as a program segment rather than an advertisement segment based at least on a number of unique reference identifiers within the list of other portions of video content relative to a total number of reference identifiers within the list of other portions of video content, wherein the total number of reference identifiers within the list of other portions of video content further includes a plurality of reference identifiers that are the same; determining that the portion of video content corresponds to a program specified in an electronic program guide using a timestamp of the portion of video content; and based on the identifying of the portion of video content as a program segment and the determining that the portion of video content corresponds to the program, storing an indication of the portion of video content in a data file for the program.
17. The computing system of claim 16, wherein obtaining the fingerprint repetition data comprises searching for matches to fingerprints of the portion of video content within a video database so as to obtain the list of other portions of video content.
18. The computing system of claim 16, wherein identifying the portion of video content as a program segment rather than an advertisement segment based at least on the number of unique reference identifiers relative to the total number of reference identifiers comprises determining that a ratio of the number of unique reference identifiers to the total number of reference identifiers satisfies a threshold.
19. The computing system of claim 16, wherein: the set of acts further comprises obtaining logo coverage data for the portion of video content, the logo coverage data is indicative of a percent of time that a logo overlays the portion of video content, and the identifying the portion of video content as a program segment rather than an advertisement segment is further based on the logo coverage data.
20. The computing system of claim 16, wherein the set of acts further comprises: obtaining transition data for a section of video content that includes the portion of video content; and identifying boundaries of the portion of video content using the transition data.
Although independent claims 21, 29, and 35 of the current application, at issue in the current application, and the claims 1-20 of US 12132953 B2 are not identical, they are not patentably distinct from each other because the claims of the current application are broader in scope and all the elements of the claims read on the claims of said reference application. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify claims 1-20 of US 12132953 B2 because the claims merely broaden the scope of elements already taught in claims 1-20 of US 12132953 B2 and the modification or rearrangement of known elements is obvious since it has been held that omission of elements and its function in a combination where the remaining elements perform the same function as before involves only routine skill in the art. In re Karlson, 136 USPQ 184.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 21-22, 25-26, 28-30, 33, 35-36, 39-44 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson; Daniel et al. US 10581541 B1 (hereafter Nelson) and in further view of McMillan; F. Gavin US 20140282672 A1 (hereafter McMillan) and further in view of KALAMPOUKAS; Lampros et al. US 20180121541 A1 (hereafter Kalampoukas) and in further view of MITRA; Debasish et al. US 20170264930 A1 (hereafter Mitra).
Regarding claim 21, “a method comprising: extracting, by a computing system, features from media content; generating, by the computing system, using the extracted features, repetition data for respective portions of the media content,” Nelson col. 2:4-44 watermarks/identifying information are encoded with media signatures extracted or derived from media and used to identify the media (e.g., program or advertisements) wherein a signature may be a series of signatures collected in a series over a time interval and a good signature is repeatable when processing the same media presentation (wherein fingerprint and signature are used interchangeably). Whereas Nelson teaches collecting media monitoring information (col. 4:20-35 and 5:19-42), Nelson does not reference a “list of other portions” as claimed (i.e., wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion, and wherein the list includes respective reference identifiers for the other portions of the media content, each reference identifier corresponding to a respective different time that the other portion of the media content was presented; determining, by the computing system, transition data for the media content; selecting, by the computing system, a portion within the media content using the transition data, classifying, by the computing system, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content, and wherein the classifying is based at least in part on (i) a number of unique reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion relative to (ii) a total number of reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion; and outputting, by the computing system, data indicating a result of the classifying for the selected portion).
In an analogous art, McMillan teaches creating media presentation logs of events representative of media presentations comprising programs and non-programs(para 38-44, 47, 50, 68-69); see also para 94 pertaining wherein the generated repetition data for the respective portions comprises a list of other portions of the media content matching the selected portion (McMillian teaches “’…to code matcher 216 may detect multiple codes (e.g., program media codes and/or non-program media codes) and/or multiple instances of the same code(s) during a media presentation. In some examples, determining the source information in block 804 is based on decoding codes captured by the code matcher 216….).
With respect to “determining, by the computing system, transition data for the media content; selecting, by the computing system, a portion within the media content using the transition data, classifying, by the computing system, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content, and wherein the classifying is based at least in part on (i) a number of unique reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion relative to (ii) a total number of reference identifiers within the list of other portions of the media content in the generated repetition data for the selected portion; and outputting, by the computing system, data indicating a result of the classifying for the selected portion” McMillian para 96-104 – determining cue tones (transition data) in media content wherein the cue tones indicate an advertisement portions is identified and wherein the cue tone data is utilized to log the identification of linear and non-linear program and/or program and non-program content; “example log generator 202 determines that it is to process the stored media identifying information (block 820), the log generator 202 classifies media as program media and/or non-program media using the stored media identifying information (block 822).” McMillan does not reference two or more unique programs within the media content as recited in “classifying, by the computing system, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content.”
Whereas McMillan does not refer to the disclosed “log” as a “list”, in an analogous art, Kalampoukas Fig. 5 and para 43-52 teaches tracking a list of segment representations indicating when the segment was aired and tracking re-airings of content wherein re-airings corresponds to “classifying, by the computing system, using the generated repetition data for the selected portion, the selected portion as being either an advertisement segment or a program segment wherein the advertisement segment is presented during two or more unique programs within the media content” and interpreted as the same advertisement be re-aired during two different programs.
The motivation to modify Nelson, McMillan, and Kalampoukas is further evidenced by Mitra para 68 and 81 identifying advertisements in media content and reports the frequency of each advertisement broadcasted for the first time and the frequency of each advertisement broadcasted repetitively.
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine the teachings of Nelson for extracting/deriving watermarks/identifying information encoded with media signatures from media and used to identify the media (e.g., program or advertisements), wherein a signature may be a series of signatures collected in a series over a time interval and a good signature is repeatable when processing the same media presentation, by further incorporating known elements of McMillan’s for creating media presentation logs of events representative of media presentations comprising programs and non-programs by extracting multiple codes (e.g., program media codes and/or non-program media codes) and/or multiple instances of the same code(s) during a media presentation, such as signatures, because both inventions relate to extracting signature data from broadcast content in order to identify content presented to viewers and the combination of known elements according to their known function is likely to be obvious when rendering predictable results. It would have been obvious before the effective filing date of the claimed invention to combine Nelson and McMillan by further incorporating known elements of the teachings Kalampoukas’ invention for utilizing segment fingerprints/signatures for creating and tracking a list of segment representations indicating when the segment was aired and tracking re-airings of content because Mitra teaches identifying advertisements in media content and reports the frequency of each advertisement broadcasted for the first time and the frequency of each advertisement broadcasted repetitively and the combination of references would result to accurately bill the advertisers for multiple presentations of content and further reduce the presentation of the same commercials to avoid advertisement saturation.
Regarding claim 22, “wherein: extracting the features comprises extracting fingerprints, and generating the repetition data comprises generating the repetition data using the fingerprints” is further rejected on obviousness grounds as discussed in the rejection of claim 21 wherein Nelson col. 2:4-44 watermarks/identifying information are encoded with media signatures extracted or derived from media and used to identify the media (e.g., program or advertisements) wherein a signature may be a series of signatures collected in a series over a time interval and a good signature is repeatable when processing the same media presentation (wherein fingerprint and signature are used interchangeably); See also McMillan para 29 disclosing fingerprints/signatures. See also Kalampoukas para 45, 70 – segment signature and segment fingerprint.
Regarding claim 25, “wherein: the transition data comprises predicted transitions between different content segments, and selecting the portion comprises selecting a portion between two adjacent predicted transitions of the predicted transitions” is further rejected on obviousness grounds as discussed in the rejection of claims 21-22 wherein para 96-104 –cue tones (transition data) in media content are understood to be previously encoded with broadcast media before transmission and wherein the cue tones indicate an advertisement portions is identified and wherein the cue tone data is utilized to log the identification of linear and non-linear program and/or program and non-program content; “example log generator 202 determines that it is to process the stored media identifying information (block 820), the log generator 202 classifies media as program media and/or non-program media using the stored media identifying information (block 822).”
Regarding claim 26, “wherein: classifying the portion comprises classifying the portion as a program segment, the method further comprises determining that the portion classified as a program segment corresponds to a program specified in an electronic program guide using a timestamp of the portion, and the data indicating the result of the classifying comprises a data file for the program that includes an indication of the portion” is further rejected on obviousness grounds as discussed in the rejection of claims 1-2, 5 wherein McMillan para 57 further teaches identifying program or non-program media wherein a schedule of media to match identifiers and time to the schedule (which includes a mapping of media sources and times to media and/or media identifiers). See also para 45 logs type of the event corresponds to data indicating the result of the classifying comprises a data file for the program that includes an indication of the portion.
Regarding claim 28, “further comprising: determining, by the computing system, logo coverage data indicative of a percent of time that a logo overlays the portion; and comparing, by the computing system, the percent of time to a threshold, wherein classifying the portion as either an advertisement segment or a program segment using repetition data for the portion is further based on an output of the comparison of the percent of time to the threshold” is further rejected on obviousness grounds as discussed in the rejection of claims 21 wherein McMillian para 55-57, 149-152 teaches “The example logo identifier 226 of FIG. 2 detects logos present in a media stream. Some programmers and/or media distributors overlay a small logo on the program media. In contrast, the logos may not be placed on commercials. For media sources that perform such logo overlays, the example logo identifier 226 detects logo images that do not change for at least a threshold time (e.g., the logo remains for several minutes). By detecting the logos, the example logo identifier 226 derives the time(s) during which program media is presented and the time(s) during which non-program media is presented. On identifying the logo and determining the time(s) at which the logo is present, the example logo identifier 226 of FIG. 2 timestamps and/or records the time range of the event and stores the event in a reference log or presentation log. Example methods and apparatus for detecting a logo are described in U.S. Pat. No. 7,643,090.”
Regarding the non-transitory computer readable media claims 29-30, 33, and computing system 35-36, 39-400 the claims are grouped and rejected with the method claims 21-22, 25-26 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 21-22, 25-26 and because the steps of the method are easily converted into elements of computer implemented methods by one of ordinary skill in the art.
Regarding claim 41, “wherein the transition data reflects a probability that that a transition exists between the respective portions of the media content” is further rejected on obviousness grounds as discussed in the rejection of claims 21-40 wherein Gordon col. 57:16 to col. 60:1-7 disclosing analysis of fame fingerprints to determine a probability that the match is correct.
Regarding claim 42, “wherein determining transition data for the media content includes implementing a recurrent neural network” is further rejected on obviousness grounds as discussed in the rejection of claims 21-41 wherein Nelson col. 11:17-50 disclosing processor platform comprising neural network.
Regarding claim 43, “wherein the recurrent neural network includes an extraction layer, and a classification layer” is further rejected on obviousness grounds as discussed in the rejection of claims 21-41 wherein Nelson col. 11:17-50 disclosing processor platform comprising neural network. See also the rejection of claim 1 wherein the combination of Nelson, McMillan, Kalampoukas, and Mitra render obvious the limitations related to extracting and classifying (i.e., extracting, by a computing system, features from media content; classifying, by the computing system, using the generated repetition data for the selected portion).
Regarding claim 44, “wherein generating repetition data further includes generating repetition data according to a plurality of detection tiers” is further rejected on obviousness grounds as discussed in the rejection of claims 21-43 wherein McMillan para 27-28 teaches audio and video watermarks/signatures/fingerprints interpreted as identifier tiers.
Claims 23-24, 31-32, 34, 37-38, 45-46 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson; Daniel et al. US 10581541 B1 (hereafter Nelson) and in further view of McMillan; F. Gavin US 20140282671 A1 (hereafter McMillan) and further in view of KALAMPOUKAS; Lampros et al. US 20180121541 A1 (hereafter Kalampoukas) and in further view of MITRA; Debasish et al. US 20170264930 A1 (hereafter Mitra) and in further view of Ramaswamy; Arun et al. US 20090256972 A1 (hereafter Ramaswamy).
Regarding claim 23, “wherein: extracting the features comprises extracting closed captioning, and generating the repetition data comprises generating the repetition data using the closed captioning” the combination of Nelson, McMillan, Kalampoukas, and Mitra teaches the extracting the features to generate repetition data but does not disclose extracting closed captioning. In an analogous, Ramaswamy extracting either fingerprint data or closed captioning data in order to identify media content presented to viewers (para 20-21, 29, 33-34, 48 all paragraphs disclosing encoding metadata comprising close captioning within a signature as disclosed in para 46-52).
A person of ordinary skill in the art would have understood that the combination of Nelson, McMillan, and Kalampoukas render obvious first identifying the content that is presented utilizing signatures/fingerprints and then generating repetition data based off the identified media content.
Therefore, a person of ordinary skill would have seen the obvious benefit of not only utilizing fingerprint/signature data extracted from media content to identify segments of media content but to also utilizing either closed caption data that is encoded within a fingerprint/signature to identify media content first and then applying the combined improvement of Nelson, McMillan, Kalampoukas, and Mitra before generating repetition data because the substitution of one feature of another known feature to accomplish the same function of identifying media content segments would render predictable results. It would have been obvious before the effective filing date of the claimed invention to combine Nelson, McMillan, Kalampoukas, and Mitra’s by further incorporating known elements of Ramaswamy’s invention for utilizing segment fingerprints/signatures and/or closed captioning for identifying when program content or advertisement content is presented to viewers to accurately track content that was actually consumed by viewers based on their interaction with media content.
Regarding claim 24, “wherein: extracting the features comprises extracting keyframes, and generating the repetition data comprises: identifying a portion between two adjacent keyframes of the keyframes; and searching for other portions within the media content having features matching features for the portion” is further rejected on obviousness grounds based on the same analysis discussed in the rejection of claim 23 (i.e., closed captioning) wherein Ramaswamy’s also teaches utilizing keyframes encoded within signatures (para 20-21, 29, 33-34, 48 all paragraphs disclosing encoding metadata comprising keyframe information within a signature as disclosed in para 46-52) and wherein the advertisements have a beginning keyframe and an end keyframe such that the data between keyframes matches.
Regarding claim 27, “wherein: classifying the portion comprises classifying the portion as an advertisement segment, the features comprises metadata for the portion, and the data indicating the result of the classifying comprises a data file that includes the metadata and an indication of the portion” is further rejected on obviousness grounds as discussed in the rejection of claims 1-6 wherein Ramaswamy’s para 20-21, 29, 33-34, 48 all paragraphs disclosing encoding metadata within a signature as disclosed in para 46-52) and wherein McMillan para 45 logs type of the event corresponds to and the data indicating the result of the classifying comprises a data file that includes the metadata and an indication of the portion.
Regarding the non-transitory computer readable media claims 31-32, 34 and computing system 37-38 the claims are grouped and rejected with the method claims 23-24, 27 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 23-24 and 27 and because the steps of the method are easily converted into elements of computer implemented methods by one of ordinary skill in the art.
Regarding claim 45, “wherein generating repetition data further includes generating repetition data according to the plurality of detection tiers includes an audio tier, a video tier, and a closed caption tier” is further rejected on obviousness grounds as discussed in the rejection of claims 21-44 wherein McMillan para 27-28 teaches audio and video watermarks/signatures/fingerprints interpreted as identifier tiers. See also Ramaswamy para 20-21, 32-39 disclosing close caption tier.
Regarding claim 46, “wherein the repetition data is generated based on closed caption text data” is further rejected on obviousness grounds as discussed in the rejection of claims 21-44 wherein McMillan para 27-28 teaches audio and video watermarks/signatures/fingerprints interpreted as identifier tiers. See also Ramaswamy para 20-21, 32-39 disclosing close caption tier.
Conclusion
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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