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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/798,725 filed on August 8, 2024. Claims 1-20 are currently pending with the application.
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-4, 6-10, 12, and 15-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-11 and 15-19 of U.S. Patent No. 12130824.
Instant Application
Copending Application 16/288,508
1. A method comprising: providing a media item associated with a user of a platform as input to a machine learning model; obtaining one or more outputs of the machine learning model, wherein the one or more outputs indicate a level of confidence that at least one content segment of the media item matches content of a reference media item associated with another user of the platform in view of a content category associated with the media item; determining, based on the one or more obtained outputs, whether the at least one content segment of the media item matches the content of the reference media item in view of the content category; and responsive to determining that the at least one content segment of the media item matches the content of the referenced media item in view of the content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item.
1. A method comprising:obtaining a set of candidate matches for a media item of a platform, wherein each of the set of candidate matches indicates a respective reference media item including a content segment that corresponds to at least one content segment of the media item; providing similarity data associated with the media item and each reference media item indicated by the set of candidate matches as input to a machine learning model; obtaining one or more outputs of the machine learning model, wherein the one or more outputs indicate one or more content categories and, for each content category, a first level of confidence that the media item is associated with the content category, and a second level of confidence that the at least one content segment of the media item matches content of the respective reference media item indicated by the set of candidate matches in view of the content category; determining, based on the one or more obtained outputs: a content category associated with the media item, and whether the at least one content segment of the media item matches the content of the respective reference media item indicated by the set of candidate matches in view of the determined content category; and responsive to determining that the at least one content segment of the media item matches the content of the respective referenced media item indicated by the set of candidate matches in view of the determined content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item.2
2. The method of claim 1, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, whether content of the media items matches content of the reference media items in view of content categories associated with the media items.
2. The method of claim 1, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, content of the media items that matches content of the reference media items in view of content categories associated with the media items.
3. The method of claim 1, wherein the one or more outputs of the machine learning model further comprise similarity data indicating a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by a respective candidate match of a set of candidate matches.
3. The method of claim 1, wherein the similarity data associated with the media item and each of the set of candidate matches indicates a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by the respective candidate match of the set of candidate matches.
4. The method of claim 3, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by a set of candidate matches.
4. The method of claim 3, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by the set of candidate matches.
6. The method of claim 1, wherein determining whether the at least one content segment of the media item matches the content of the reference media item in view of the content category comprises: determining whether the level of confidence associated with the content category satisfies a confidence criterion.
6. The method of claim 1, wherein determining whether the at least one content segment of the media item matches the content of the respective reference media item indicated by the set of candidate matches in view of the determined content category comprises: determining whether the second level of confidence associated with the content category satisfies a second confidence criterion.
7. The method of claim 1, further comprising: obtaining feature data associated with the at least one content segment of the media item, wherein the feature data comprises at least one of spectral feature data, temporal feature data, or structural feature data for the at least one content segment; providing the obtained feature data as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item; obtaining one or more additional outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more reference media items at the platform and, for each of the one or more reference media items, an additional level of confidence that a content segment of the respective reference media item corresponds to a content segment of the media item; selecting the reference media item based on the additional level of confidence associated with the reference media item; and providing data associated with the selected reference media item with the media item as an input to the machine learning model.
7. The method of claim 1, wherein obtaining the set of candidate matches for the media item comprises: obtaining feature data associated with the at least one content segment of the media item, wherein the feature data comprises at least one of spectral feature data, temporal feature data, or structural feature data for the at least one content segment; providing the obtained feature data as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item; obtaining one or more additional outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more reference media items at the platform and, for each of the one or more reference media items, a third level of confidence that a content segment of the respective reference media item corresponds to a content segment of the media item; and selecting a reference media item to be indicated by the set of candidate matches based on the third level of confidence associated with the reference media item..
8. The method of claim 1, further comprising: responsive to determining that the at least one content segment of the media item does not match the content of the referenced media item, providing the media item, including the at least one content segment, for access to the one or more users of the platform.
8. The method of claim 1, further comprising: responsive to determining that the at least one content segment of the media item does not match the content of the respective referenced media item, providing the media item, including the at least one content segment, for access to the one or more users of the platform.
9. A system comprising: a memory device; and a processing device coupled to the memory device, the processing device to perform operations comprising: providing a media item associated with a user of a platform as input to a machine learning model; obtaining one or more outputs of the machine learning model, wherein the one or more outputs indicate a level of confidence that at least one content segment of the media item matches content of a reference media item associated with another user of the platform in view of a content category associated with the media item; determining, based on the one or more obtained outputs, whether the at least one content segment of the media item matches the content of the reference media item in view of the content category; and responsive to determining that the at least one content segment of the media item matches the content of the referenced media item in view of the content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item.
9. A system comprising: a memory device; and a processing device coupled to the memory device, the processing device to perform operations comprising: generating training data for a machine learning model, wherein generating the training data comprises: identifying a historical media item and one or more historical reference media items of a platform; determining a content category associated with the historical media item; obtaining historical similarity data associated with the historical media item and each of the one or more historical reference media items; generating a training input comprising an indication of the historical media item, an indication of the one or more historical reference media items, and the obtained historical similarity data; and generating a target output comprising the content category associated with the historical media item and an indication of whether content of the historical media item matches content of the one or more historical reference media items; and providing the training data to train the machine learning model to predict, based on given similarity data for a current media item and one or more current reference media items at the platform, content of the current media item that matches content of the one or more current referenced media items in view of a content category associated with the current media item, wherein the machine learning model is trained on (i) a set of training inputs comprising the training input, and (ii) a set of target outputs comprising the target output.
10. The system of claim 9, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, whether content of the media items matches content of the reference media items in view of content categories associated with the media items.
10. The system of claim 9, wherein the historical similarity data associated with the historical media item and the one or more historical reference media items indicates a degree of similarity between one or more features of each content segment of the historical media item and one or more features of each content segment of the one or more historical reference media items.
12. The system of claim 11, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by a set of candidate matches.
11.The system of claim 10, wherein the historical similarity data corresponds to a historical heat map, wherein each region of the historical heat map indicates a degree of similarity between a content segment of the historical media item and an additional content segment a historical reference media item of the one or more historical reference media items.
15. The system of claim 9, wherein the operations further comprise: obtaining feature data associated with the at least one content segment of the media item, wherein the feature data comprises at least one of spectral feature data, temporal feature data, or structural feature data for the at least one content segment; providing the obtained feature data as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item; obtaining one or more additional outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more reference media items at the platform and, for each of the one or more reference media items, an additional level of confidence that a content segment of the respective reference media item corresponds to a content segment of the media item; selecting one or more reference media items based on the additional level of confidence associated with the reference media item; and providing data associated with the selected one or more reference media items with the media item as an input to the machine learning model.
15. The system of claim 9, wherein generating the training data for the machine learning model further comprises: obtaining historical feature data associated with one or more content segments of the historical media item, wherein the feature data comprises at least one of spectral feature data, temporal feature data, or structural feature data for the one or more content segments; providing the obtained feature data as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item; and obtaining one or more outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more historical reference media items at the platform and, for each of the one or more historical reference media items, a level of confidence that a content segment of the respective historical reference media item corresponds to a content segment of the historical media item; and extracting the one or more historical reference media items from the one or more additional outputs in view of the level of confidence associated with each of the one or more historical reference media items.
16. A non-transitory computer readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: providing a media item associated with a user of a platform as input to a machine learning model; obtaining one or more outputs of the machine learning model, wherein the one or more outputs indicate a level of confidence that at least one content segment of the media item matches content of a reference media item associated with another user of the platform in view of a content category associated with the media item; determining, based on the one or more obtained outputs, whether the at least one content segment of the media item matches the content of the reference media item in view of the content category; and responsive to determining that the at least one content segment of the media item matches the content of the referenced media item in view of the content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item.
16. A non-transitory computer readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: obtaining a set of candidate matches for a media item of a platform, wherein each of the set of candidate matches indicates a respective reference media item including a content segment that corresponds to at least one content segment of the media item; providing similarity data associated with the media item and each of the set of candidate matches as input to a machine learning model, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, content of the media items that matches content of the reference media items in view of content categories associated with the media items; obtaining one or more outputs of the machine learning model, wherein the one or more outputs indicate one or more content categories and, for each content category, a first level of confidence that the media item is associated with the content category, and a second level of confidence that the at least one content segment of the media item matches content of the respective reference media item indicated by the set of candidate matches in view of the content category; determining, based on the one or more obtained outputs: a content category associated with the media item, and whether the at least one content segment of the media item matches the content of the respective reference media item indicated by the set of candidate matches in view of the determined content category; and responsive to determining that the at least one content segment of the media item matches the content of the respective referenced media item indicated by the set of candidate matches in view of the determined content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item.
17. The non-transitory computer readable storage medium of claim 16, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, whether content of the media items matches content of the reference media items in view of content categories associated with the media items.
17. The non-transitory computer readable storage medium of claim 16, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, content of the media items that matches content of the reference media items in view of content categories associated with the media items.
18. The non-transitory computer readable storage medium of claim 16, wherein the one or more outputs of the machine learning model further comprise similarity data indicating a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by a respective candidate match of a set of candidate matches.
18. The non-transitory computer readable storage medium of claim 16, wherein the similarity data associated with the media item and each of the set of candidate matches indicates a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by the respective candidate match of the set of candidate matches.
19. The non-transitory computer readable storage medium of claim 18, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by a set of candidate matches.
19. The non-transitory computer readable storage medium of claim 18, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by the set of candidate matches.
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.
Claim(s) 1, 2, 5, 6, 8-10, 13-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boksha (U.S. Publication No.: US 20220122191 A1) hereinafter Boksha, in view of Gouneili et al. (U.S. Patent No.: US 10979745 B1) hereinafter Gouneili.
As to claim 1:
Boksha discloses:
A method comprising: providing a media item associated with a user of a platform as input to a machine learning model [Paragraph 0040 teaches the user profile can display a user name, unique user identifier, user contact (e.g., email, telephone number, etc.), picture (e.g., photo)… the user profile can display one or more media files (e.g., audio, video, picture, and document). For example, the one or more media files can be uploaded by the user. Paragraph 0046 teaches the match engine 405 identifies matches (e.g., potential mentors and/or mentees) for the user using a machine learning model. The machine learning model can be trained with the user profiles of pairs of users who have matched via the platform.]; obtaining one or more outputs of the machine learning model [Paragraph 0046 teaches the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users.], wherein the one or more outputs indicate a level of confidence that at least one content segment of the media item matches content of a reference media item associated with another user of the platform in view of a content category associated with the media item [Paragraph 0046 the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. Paragraph 0048 teaches the rules-based model includes one or more rules that, if satisfied by the user profiles of two users, recommends a match between the two users. Some of the rules may match users if the users have a similar profile attribute. Paragraph 0077 teaches the upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: Scores generated indicating a likelihood (confidence) that a first and second user’s media contents included in each respective user’s profile are similar, wherein the examiner interprets similar also includes an exact match and the user profile includes user tags (categories) reads on the claims.]; determining, based on the one or more obtained outputs, whether the at least one content segment of the media item matches the content of the reference media item [Paragraph 0046 the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. Paragraph 0048 teaches the rules-based model includes one or more rules that, if satisfied by the user profiles of two users, recommends a match between the two users. Some of the rules may match users if the users have a similar profile attribute. Paragraph 0077 teaches the upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: A machine learning model generating scores indicating a likelihood (confidence) that a first and second user’s media contents included in each respective user’s profile are similar, wherein the examiner interprets similar also includes an exact match.];
Boksha discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose responsive to determining that the at least one content segment of the media item matches the content of the referenced media item in view of the content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item and in view of the content category.
Gouneili discloses:
responsive to determining that the at least one content segment of the media item matches the content of the referenced media item in view of the content category, causing one or more actions to be initiated to prevent one or more users of the platform from accessing the at least one content segment of the media item and in view of the content category [Column 21 Lines 64-67 teach if the first user plays or uses the one or more contents from the second user more than n times in an m hour period, raise a flag and stop serving the stream to the first user device 102. Note: Content viewed (browsing history) for a first and second user that matches within a specific time period (content category) and based on that matching preventing further access to those one or more contents reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha, by incorporating content viewed (browsing history) for a first and second user that match within a specific time period (content category),and based on that matching preventing further access to that one or more contents (see Gouneili Column 21 Lines 64-67), because the two applications are directed to media matching; incorporating content viewed (browsing history) for a first and second user that match within a specific time period (content category),and based on that matching preventing further access to that one or more contents allows significant reduction in possibility of third party fraud and provides precise and up-to-the-minute tracking, management, governance, and accounting of the streams of a multimedia (see Gouneili Column 2 Lines 25-28).
Claim(s) 9 and 16 are similarly rejected because they are similar in scope.
As to claim 2:
Boksha discloses:
The method of claim 1, wherein the machine learning model is trained to predict, based on given similarity data for media items and reference media items at the platform, whether content of the media items matches content of the reference media items in view of content categories associated with the media items [Paragraph 0046 the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. Paragraph 0048 teaches the rules-based model includes one or more rules that, if satisfied by the user profiles of two users, recommends a match between the two users. Some of the rules may match users if the users have a similar profile attribute. Paragraph 0077 teaches the upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: A machine learning model generating scores indicating a likelihood (confidence) that a first and second user’s media contents included in each respective user’s profile are similar, wherein the examiner interprets similar also includes an exact match.]
Claim(s) 10 and 17 are similarly rejected because they are similar in scope.
As to claim 5:
Boksha discloses:
The method of claim 1, further comprising: determining the content category associated with the media item based on the one or more outputs of the machine learning model [Paragraph 0046 the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. Paragraph 0048 teaches the rules-based model includes one or more rules that, if satisfied by the user profiles of two users, recommends a match between the two users. Some of the rules may match users if the users have a similar profile attribute. Paragraph 0077 teaches the upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: Scores generated from a machine learning model indicating a likelihood (confidence) that a first and second user’s media contents included in each respective user’s profile are similar, wherein the examiner interprets similar also includes an exact match and the user profile includes user tags (categories) reads on the claims.]
Claim(s) 13 and 20 are similarly rejected because they are similar in scope.
As to claim 6:
Boksha discloses:
The method of claim 1, wherein determining whether the at least one content segment of the media item matches the content of the reference media item in view of the content category [Paragraph 0046 the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. Paragraph 0048 teaches the rules-based model includes one or more rules that, if satisfied by the user profiles of two users, recommends a match between the two users. Some of the rules may match users if the users have a similar profile attribute. Paragraph 0077 teaches the upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: Scores generated indicating a likelihood (confidence) that a first and second user’s media contents included in each respective user’s profile are similar, wherein the examiner interprets similar also includes an exact match and the user profile includes user tags (categories) reads on the claims.] comprises: determining whether the level of confidence associated with the content category satisfies a confidence criterion [Paragraph 0046 teaches the model can be trained to generate scores representing a likelihood that a first user will be a good mentoring match for a second user, based on the user profiles of the first and second users. If the score is greater than a specified threshold, the match engine 405 recommends the first user to the second user or vice versa.]
Claim(s) 14 is similarly rejected because it is similar in scope.
As to claim 8:
Boksha and Gouneili discloses all of the limitations as set forth in claim 1.
Gouneili also discloses:
The method of claim 1, further comprising: responsive to determining that the at least one content segment of the media item does not match the content of the referenced media item, providing the media item, including the at least one content segment, for access to the one or more users of the platform [Column 21 Lines 64-67 teach if the first user plays or uses the one or more contents from the second user more than n times in an m hour period, raise a flag and stop serving the stream to the first user device 102. Note: Content viewed (browsing history) for a first and second user that matches within a specific time period (content category) and based on that matching preventing further access to those one or more contents, wherein if there is no raised flag based on the first user playing one more contents of the second user access to the stream of content is continued reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha, by incorporating content viewed (browsing history) for a first and second user that match within a specific time period (content category),and based on that matching preventing further access to that one or more contents (see Gouneili Column 21 Lines 64-67), because the two applications are directed to media matching; incorporating content viewed (browsing history) for a first and second user that match within a specific time period (content category),and based on that matching preventing further access to that one or more contents allows significant reduction in possibility of third party fraud and provides precise and up-to-the-minute tracking, management, governance, and accounting of the streams of a multimedia (see Gouneili Column 2 Lines 25-28).
Claim(s) 3, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boksha (U.S. Publication No.: US 20220122191 A1) hereinafter Boksha, in view of Gouneili et al. (U.S. Patent No.: US 10979745 B1) hereinafter Gouneili, and further in view of Yamada (U.S. Publication No.: US 20230114374 A1) hereinafter Yamada.
As to claim 3:
Boksha and Gouneili discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the one or more outputs of the machine learning model further comprise similarity data indicating a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by a respective candidate match of a set of candidate matches.
Yamada discloses:
The method of claim 1, wherein the one or more outputs of the machine learning model further comprise similarity data indicating a degree of similarity between one or more features of each content segment of the media item and one or more features of each content segment of the reference media item indicated by a respective candidate match of a set of candidate matches [Paragraph 0070 teaches a method of calculating the degree of similarity is similar to that of the updating unit 16 of the machine learning apparatus 10. The output unit 36 sequences the candidate images in a descending sequence of the calculated degrees of similarity and outputs the candidate images having undergone the sequencing as a search result of the images similar to the query text.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha and Gouneili, by incorporating a machine learning apparatrus for calculating degrees of similarity between media items and candidate media items (see Yamada Paragraph 0070), because the three applications are directed to media matching; incorporating a machine learning apparatrus for calculating degrees of similarity between media items and candidate media items increase in speed of the processing at the time of search may be achieved (see Yamada Paragraph 0095).
Claim(s) 11 and 18 are similarly rejected because they are similar in scope.
Claim(s) 4, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boksha (U.S. Publication No.: US 20220122191 A1) hereinafter Boksha, in view of Gouneili et al. (U.S. Patent No.: US 10979745 B1) hereinafter Gouneili, and further in view of Melapudi et al. (U.S. Publication No.: US 20250095826 A1) hereinafter Melapudi.
As to claim 4:
Boksha, Gouneili, and Yamada discloses all of the limitations as set forth in claim 1 and 3 but does not appear to expressly disclose wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by a set of candidate matches.
Melapudi discloses:
The method of claim 3, wherein the similarity data corresponds to a heat map, wherein each region of the heat map indicates the degree of similarity between a content segment of the media item and an additional content segment of the reference media item indicated by a set of candidate matches [Paragraph 0050 teaches the heat map can be considered as comprising similarity scores (e.g., cosine similarity values) that are computed between the flagged patch embedding of the given example medical image and each of the set of patch embeddings of the particular medical image.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha and Gouneili, by incorporating a heat map comprising similarity scores between each image item and the set of image items of that particular image (see Melapudi Paragraph 0050), because the three applications are directed to media matching; incorporating a heat map comprising similarity scores between each image item and the set of image items of that particular image provide concrete and tangible technical improvements in the field of deep learning (see Melapudi Paragraph 0063).
Claim(s) 12 and 19 are similarly rejected because they are similar in scope.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boksha (U.S. Publication No.: US 20220122191 A1) hereinafter Boksha, in view of Gouneili et al. (U.S. Patent No.: US 10979745 B1) hereinafter Gouneili, in view of Yu et al. (U.S. Publication No.: US 20220300555 A1) hereinafter Yu, and further in view of Staudinger et al. (U.S. Publication No.: US 20210264261 A1) hereinafter Staudinger.
As to claim 7:
Boksha discloses:
The method of claim 1, further comprising: obtaining feature data associated with the at least one content segment of the media item [Paragraph 0040 teaches the user profile can display a user name, unique user identifier, user contact (e.g., email, telephone number, etc.), picture (e.g., photo)… the user profile can display one or more media files (e.g., audio, video, picture, and document). For example, the one or more media files can be uploaded by the user. Paragraph 0046 teaches the match engine 405 identifies matches (e.g., potential mentors and/or mentees) for the user using a machine learning model. The machine learning model can be trained with the user profiles of pairs of users who have matched via the platform.], wherein the feature data comprises at least one of spectral feature data, temporal feature data, or structural feature data for the at least one content segment [Paragraph 0077 teaches the media files can include, but is not limited to, audio files (e.g., MP3, FLAC, WAV, etc.), video files (e.g., AVI, MOV, SWF, FLV, MP4, etc.), and picture files (e.g., JPEG, TIFF, BMP, GIF, PNG, etc.), and/or documents (e.g., DOC, PDF, RTF, PPT, XLS, etc.). The upload module 705 can store the media files in the database 120. The user can include one or more tags associated with the media file (e.g., sunset, flag, nature, people, etc.). Note: Various structural differences included in the media files such as the file extensions and formats for the media items reads on the claims.];
Boksha and Gouneili discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose providing the obtained feature data as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item, obtaining one or more additional outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more reference media items at the platform and, for each of the one or more reference media items, an additional level of confidence that a content segment of the respective reference media item corresponds to a content segment of the media item, selecting the reference media item based on the additional level of confidence associated with the reference media item, providing data associated with the selected reference media item with the media item as an input to the machine learning model.
Yu discloses:
providing the obtained feature data as input to an additional machine learning model [Paragraph 0087 teaches to train the one or more computational models 321 to predict which segments in a text are non-narrative segments, the one or more computational models 321 are trained using a plurality of annotated texts 510.], wherein the additional machine learning model is trained to predict, in view of feature data for a media item at the platform, content segments of reference media items at the platform that correspond to content segments of the media item [Paragraph 0087 teaches the one or more computational models 321 using annotated texts 510, in accordance with some embodiments. The one or more computational models in-training 520 correspond to the one or more computational model 321 during their training stage. In order to train the one or more computational models 321 to predict which segments in a text are non-narrative segments, the one or more computational models 321 are trained using a plurality of annotated texts 510. The plurality of annotated texts 510 are provided to the one or more computational models in-training 520.]; obtaining one or more additional outputs from the additional machine learning model, wherein the one or more additional outputs indicate one or more reference media items at the platform and, for each of the one or more reference media items, an additional level of confidence that a content segment of the respective reference media item corresponds to a content segment of the media item [Paragraph 0090 teaches the electronic device assigns (620) a score 412 (e.g., scores 412-1 to 412-4) for each segment in the text by applying the text to a trained computational model 321 (e.g., one or more trained computational models 321). The score corresponds to a predicted relevance of the respective segment to a narrative of the media content item.]; selecting the reference media item based on the additional level of confidence associated with the reference media item [Paragraph 0072 teaches the one or more trained computational models 321 provide outputs 410 for the text, including segment scores 412 for segments (e.g., sentences) of the text. In some embodiments, the computational model outputs 410 include a segment score 412 for each segment (e.g., sentence) in the text (e.g., segment-level scores, sentence-level scores). Paragraph 0103 teaches the electronic device provides (670) the media content item associated with the text to a user (e.g., user 440) of the media providing service based at least in part on the generated clean text (e.g., clean text 452, 462, 472). For example, the media content item may be provided as a recommendation to one or more users of the media providing service.]; and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha and Gouneili, by incorporating a plurality of models to determine whether or segments of media content item correspond to another segment of media content items (see Yu Paragraph 0072, 0087, 0090, and 0103), because the three applications are directed to media matching; incorporating a plurality of models to determine whether or segments of media content item correspond to another segment of media content items provides improved methods for detecting (e.g., identifying) non-narrative segments in texts (see Yu Paragraph 0010).
Boksha, Gouneili, and Yu discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose providing data associated with the selected reference media item with the media item as an input to the machine learning model.
Staudinger discloses:
providing data associated with the selected reference media item with the media item as an input to the machine learning model [Paragraph 0093 teaches a region of each of at least a rare object and one or more candidate objects may be first-detected, via a first ML model, from among each of a plurality of images. Paragraph 0094 teaches an object having a regional similarity score that satisfies a criterion may be second-detected, from among the first-detected regions via a second ML model. Note: Selecting an image and inputting that image into a second ML model reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Boksha, Gouneili, and Yu, by incorporating selecting an image and inputting that image into a second ML model (see Staudinger Paragraph 0093), because the four applications are directed to media matching; incorporating selecting an image and inputting that image into a second ML model provides an improvement to the accuracy or completeness of predictions (see Staudinger Paragraph 0042).
Claim(s) 14 is similarly rejected because it is similar in scope.
Conclusion
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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