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 .
DETAILED ACTION
This office action is responsive to application No. 18/919,287 filed on 12/24/2025. Claim(s) 1-30 have been cancelled. Claim(s) 31-50 is/are pending and have been examined.
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) 31-35, 38-39, 41-45, and 48-49 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1-3, 8-9, 10-12, and 17-18 of U.S. Patent No. 11,438,664 in view of Smith (US 2019/0080347) in view of Clark II et al. (US 2004/0155977). Although the claims at issue are not identical, they are not patentably distinct from each other because they recite similar subject matter which is obvious over one another.
For example, note the following relationship between the instant application claim and patented application claims.
Claims 31-32 and 41-42 of the instant application corresponds to that of Claims 1 and 10 of patented application except that pending application in claims 31-32 and 41-42 contains additional limitation “iteratively applying a plurality of virality enhancement techniques”, “automatically repeating steps (c)-(e) until the updated predicted virality score of the updated content item meets the virality score criterion”.
In an analogous art, Smith teaches “iteratively applying a plurality of virality enhancement techniques”, “repeating steps (c)-(e) until the updated predicted virality score of the updated content item meets the virality score criterion” (Paragraph 0105 teaches asset management system allowing a content creator to select a target asset score, i.e., a minimum asset score. The content creator wants to achieve a minimum asset score of “85” on a 100-point scale. System can identify a plurality of attributes that will produce the desired minimum score of “85”. System can recommend combinations of attributes that result in higher asset scores. Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5C-E, Paragraph 0092-0102 teaches user may interact with analyze option 510 and improve score option 518. Thus, users can go through the interface of Fig.5C-E where they could also iteratively go through the analyze option and improve score options, and iteratively apply further attributes to eventually reach a desired asset score that user(s) may have in mind). Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system to include “iteratively applying a plurality of virality enhancement techniques”, “repeating steps (c)-(e) until the updated predicted virality score meets the virality score criterion”, as taught by Smith, for the advantage of aiding in generating improved digital design assets that are more likely to obtain desired outcomes when disseminated to various client devices (Smith – Paragraph 0005).
In an analogous art, Clark II teaches automatically repeating steps until criterion is met (Paragraph 0031-0033, Claim 11 teaches automatically repeating steps until at least one image feature meets a threshold value of goodness). Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Smith to include automatically repeating steps until criterion is met, as taught by Clark II, for the advantage of providing an automated system that helps to efficiently eliminate repetitive actions, while still achieving similar results, freeing up user(s) to be able to perform other tasks.
Claims 33 and 43 of the instant application corresponds to that of Claims 2 and 11 of patented application.
Claims 34 and 44 of the instant application corresponds to that of Claims 3 and 12 of patented application.
Claims 35, 45, 38, and 48 of the instant application corresponds to that of Claims 8 and 17 of patented application.
Claims 39 and 49 of the instant application corresponds to that of Claims 9 and 18 of patented application.
Claim(s) 31, 35-40, 41, 45-50 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1, 2, 4, 8, 9, and 11 of U.S. Patent No. 12,149,797 in view of Smith (US 2019/0080347) in view of Clark II et al. (US 2004/0155977). Although the claims at issue are not identical, they are not patentably distinct from each other because they recite similar subject matter which is obvious over one another.
For example, note the following relationship between the instant application claim and patented application claims.
Claims 31, 35, 36, 38, 39, 41, 45, 46, 48, 49 of the instant application corresponds to that of Claims 1, 2, 8, and 9 of patented application except that pending application in claims 31, 35, 36, 38, 39, 41, 45, 46, 48, 49 contains additional limitation “iteratively applying a plurality of virality enhancement techniques”, “automatically repeating steps (c)-(e) until the updated predicted virality score of the updated content item meets the virality score criterion”.
In an analogous art, Smith teaches “iteratively applying a plurality of virality enhancement techniques”, “repeating steps (c)-(e) until the updated predicted virality score of the updated content item meets the virality score criterion” (Paragraph 0105 teaches asset management system allowing a content creator to select a target asset score, i.e., a minimum asset score. The content creator wants to achieve a minimum asset score of “85” on a 100-point scale. System can identify a plurality of attributes that will produce the desired minimum score of “85”. System can recommend combinations of attributes that result in higher asset scores. Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5C-E, Paragraph 0092-0102 teaches user may interact with analyze option 510 and improve score option 518. Thus, users can go through the interface of Fig.5C-E where they could also iteratively go through the analyze option and improve score options, and iteratively apply further attributes to eventually reach a desired asset score that user(s) may have in mind). Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system to include “iteratively applying a plurality of virality enhancement techniques”, “repeating steps (c)-(e) until the updated predicted virality score meets the virality score criterion”, as taught by Smith, for the advantage of aiding in generating improved digital design assets that are more likely to obtain desired outcomes when disseminated to various client devices (Smith – Paragraph 0005).
In an analogous art, Clark II teaches automatically repeating steps until criterion is met (Paragraph 0031-0033, Claim 11 teaches automatically repeating steps until at least one image feature meets a threshold value of goodness). Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Smith to include automatically repeating steps until criterion is met, as taught by Clark II, for the advantage of providing an automated system that helps to efficiently eliminate repetitive actions, while still achieving similar results, freeing up user(s) to be able to perform other tasks.
Claims 37 and 47 of the instant application corresponds to that of Claims 2 and 9 of patented application.
Claims 40 and 50 of the instant application corresponds to that of Claims 4 and 11 of patented application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 31-50 have been considered but are moot in view of the new ground(s) of rejection.
Although a new ground(s) of rejection has been made, some of Applicant’s arguments need to be addressed.
Applicants assert on P.8 that “Smith, at best, teaches the manual generation of enhanced images by the user. Thus, Applicant respectfully submits that Smith does not teach an automatic content item enhancement process, and that one of ordinary skill in the art would not consider the mental "desired asset score" suggested by the Office Action to teach a termination condition of an automatic content item enhancement process. The claimed iterative enhancement process enables more accurate prediction of content item virality by generating and evaluating intermediate updated content items, which Smith does not teach. For example, the iterative enhancement process may account for differences in a layering order of filters (described in Paragraph [0027] of the instant specification) that the machine learning model in Smith, which does not apply any virality enhancement techniques, may be unable to discern. Thus, Smith does not teach all of the features of Applicant's independent claims.”
In response, the Examiner respectfully disagrees. Smith teaches the manual generation of enhanced images (as provided below in the rejection below), however, did not explicitly teach automatically repeating steps until criterion is met. For which Clark II was brought in to teach in Paragraph 0031-0033, Claim 11. Therefore, the combination of Smith and Clark II teaches the pending claim limitation(s).
Additionally, Applicant has asserted that “The claimed iterative enhancement process enables more accurate prediction of content item virality by generating and evaluating intermediate updated content items… For example, the iterative enhancement process may account for differences in a layering order of filters (described in Paragraph [0027] of the instant specification)”. Even if Applicant’s invention may enable more accurate prediction of content item virality by generating and evaluating intermediate updated content items, where iterative enhancement process may account for differences in a layering order of filters, the claimed invention does not go into such depth, as those particular points are not explicitly claimed nor recited in the pending claim limitations. Therefore, unless Applicant amends claims to better capture the difference and additional depth of the invention provided in Applicant’s arguments, under broadest reasonable interpretation, the combination of Smith and Clark II teaches the claimed limitation.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 31, 35-41, and 45-50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 2019/0080347) in view of Clark II et al. (US 2004/0155977).
Consider claims 31 and 41, Smith teaches a method and a system (Figs. 1, 3A-C, 6; Paragraph 0005; Paragraph 0041 teaches asset score and/or attribute score is the success of the corresponding asset) comprising:
a communication port coupled to a communication network (Fig.1, Paragraph 0032 teaches client device(s) and server device(s) communicating over network 111. Paragraph 0034 teaches network 111 can include an Internet connection or other network connection. Communication interface 810-Fig.8, Paragraph 0149, 0152);
a virality enhancement database configured to store one or more virality enhancement techniques (Paragraph 0132 teaches asset management system that includes attribute manager 710 that facilitates management of plurality of attributes associated with digital design assets. Various digital design assets can be identified and/or classified by accessing a database that includes mappings of attributes to digital design assets. Paragraph 0096 teaches providing recommendations of one or more attributes for use in creating or modifying a digital design asset. Paragraph 0140 teaches instructions and/or data structures utilized in carrying out the invention. A variety of enhancement techniques, are known and stored in a type of data structure(s), by the system in order for the system to analyze, and recommend such modification of particular attributes, in order to improve the asset score); and
control circuitry configured to (computing device 800-Fig.8; Paragraph 0140, 0145, 0146, 0149):
receive a content item at a content sharing platform, wherein the content item is received from a user device via the communication network (Paragraph 0125 teaches client device 110 can communicate with asset management system 102 to send and receive data related to creation and management of digital design assets. Client device 110 can send digital design assets to the asset management system 102. Paragraph 0034 teaches server device(s), store, manage, and provide various types of digital content that may be provide to user 109 via distribution medium 112 at the client device 108. Paragraph 0028, 0076 teaches distribution media can include, but are not limited to, websites, emails, mobile applications, social applications, etc.);
determine a predicted virality score for the content item (Paragraph 0016 teaches using a machine-learning model to analyze attributes of a user-generated asset. Paragraph 0019 teaches asset management system determines the attributes of the user-generated asset and analyzes the attributes using the machine-learned model to predict performance of the user-generated asset based on previously used digital design assets. Asset management system analyzes the attributes to determine individual attribute scores that predict the performance of the attributes individually, which can then be used to generate an asset score for the user-generated asset as a whole. Paragraph 0038 teaches asset performance predictor 107 uses machine learning to predict performance of an asset that the user 113 has created, or is currently creating. Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute score, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300. Paragraph 0125 teaches client device 110 can send digital design assets to the asset management system 102 and receive analytics data corresponding to the digital design assets to aid the content creator in creating successful digital design assets);
based on determining that the predicted virality score does not meet a virality score criterion, iteratively apply a plurality of virality enhancement techniques selected from the virality enhancement database, wherein iteratively applying the plurality of virality enhancement techniques (Paragraph 0105 teaches asset management system allowing a content creator to select a target asset score, i.e., a minimum asset score. The content creator wants to achieve a minimum asset score of “85” on a 100-point scale. System can identify a plurality of attributes that will produce the desired minimum score of “85”. System can recommend combinations of attributes that result in higher asset scores. Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5C-E, Paragraph 0092-0102 teaches user may interact with analyze option 510 and improve score option 518. Thus, users can go through the interface of Fig.5C-E where they could also iteratively go through the analyze option and improve score options, and iteratively apply further attributes to eventually reach a desired asset score that user(s) may have in mind) comprises:
(a) selecting a first virality enhancement technique from the virality enhancement database based on the predicted virality score for the content item (Paragraph 0132 teaches asset management system that includes attribute manager 710 that facilitates management of plurality of attributes associated with digital design assets. Various digital design assets can be identified and/or classified by accessing a database that includes mappings of attributes to digital design assets. Paragraph 0096 teaches providing recommendations of one or more attributes for use in creating or modifying a digital design asset. Asset management system 102 can automatically use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image improves the asset score for the image. Paragraph 0140 teaches instructions and/or data structures utilized in carrying out the invention. A variety of enhancement techniques, are known and stored in a type of data structure(s), by the system in order for the system to analyze, and recommend such modification of particular attributes, in order to improve the asset score. Paragraph 0096 teaches providing recommendations of one or more attributes for use in creating or modifying a digital design asset. Paragraph 0097 teaches recommendation attribute values that improve the asset score 512, are provided in a recommendation portion 520. Paragraph 0102 teaches although user can manually selection options to view recommendations and select replacement attributes, the system can also provide an option to automatically modify one or more attributes, without user input for selecting attributes to change or selecting replacement attribute values. System may analyze the asset, determine one or more attribute values that improve the asset score, and generate the new digital design asset);
(b) applying the first virality enhancement technique to generate an updated content item; (c) determining an updated predicted virality score for the updated content item after the first virality enhancement technique has been applied (Paragraph 0100 teaches user can select one or more attribute values from the recommendation portion 520 to transform the digital design asset into a new design asset that reflects the selected attribute values. Paragraph 0101 teaches asset score 512 is updated along with the updated digital design asset. Paragraph 0102 teaches although user can manually select options to view recommendations and select replacement attributes, the system can also provide an option to automatically modify one or more attributes, without user input for selecting attributes to change or selecting replacement attribute values. System may analyze the asset, determine one or more attribute values that improve the asset score, and generate the new digital design asset. Fig.5C, Paragraph 0095 display a first asset score 512 for the content. Fig.5E, Paragraph 0101 teaches an updated asset score 512 for the updated content);
(d) selecting a next virality enhancement technique from the virality enhancement database based on the updated predicted virality score; (e) applying the next virality enhancement technique to the updated content item (Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5E continues to display options analyze and improve score. Thus, as taught in paragraph 0092-0102, user(s) can continue to select improve score option, and be provided with further recommendations in which to modify the content to further improve the asset score. Users may select and apply desired recommendations); and
(f) repeating steps (c)-(e) until the updated predicted virality score of the updated content item meets the virality score criterion (Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5C-E, Paragraph 0092-0102 teaches user may interact with analyze option 510 and improve score option 518. Thus, users can go through the interface of Fig.5C-E where they could also iteratively go through the analyze option and improve score options, and iteratively apply further attributes to eventually reach a desired asset score that user(s) may have in mind); and
provide the updated content item for consumption by a plurality of user devices via the communication network (Paragraph 0085 teaches allowing a user to create and/or modify digital design assets. Paragraph 0087 teaches user can perform one or more modifications to an image, and then store the modified image to the asset repository. Paragraph 0096 teaches recommendations provided for use in creating or modifying a digital design asset. Paragraph 0125 teaches client device 110 sending digital design assets to the asset management system. Paragraph 0074 teaches uploading of assets to the asset repository. Paragraph 0127 teaches tracking the usage and performance of one or more digital design assets provided, as well as identify interactions by one or more users with the digital design assets, to see how well the digital design assets are performing).
Smith does not explicitly teach automatically repeating steps until criterion is met.
In an analogous art, Clark II teaches automatically repeating steps until criterion is met (Paragraph 0031-0033, Claim 11 teaches automatically repeating steps until at least one image feature meets a threshold value of goodness).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Smith to include automatically repeating steps until criterion is met, as taught by Clark II, for the advantage of providing an automated system that helps to efficiently eliminate repetitive actions, while still achieving similar results, freeing up user(s) to be able to perform other tasks.
Consider claims 35 and 45, Smith and Clark II teach wherein determining the predicted virality score comprises determining the predicted virality score based on information about a plurality of virality factors present in the content item (Smith - Paragraph 0016 teaches using a machine-learning model to analyze attributes of a user-generated asset. Paragraph 0019 teaches asset management system determines the attributes of the user-generated asset and analyzes the attributes using the machine-learned model to predict performance of the user-generated asset based on previously used digital design assets. Asset management system analyzes the attributes to determine individual attribute scores that predict the performance of the attributes individually, which can then be used to generate an asset score for the user-generated asset as a whole. Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute sore, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300).
Consider claims 36 and 46, Smith and Clark II teach wherein the plurality of virality factors are not entered via user accounts onto the content sharing platform (Smith - Paragraph 0016 teaches using a machine-learning model to analyze attributes of a user-generated asset. Paragraph 0019 teaches asset management system determines the attributes of the user-generated asset and analyzes the attributes using the machine-learned model to predict performance of the user-generated asset based on previously used digital design assets. Asset management system analyzes the attributes to determine individual attribute scores that predict the performance of the attributes individually, which can then be used to generate an asset score for the user-generated asset as a whole. Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute sore, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300).
Consider claims 37 and 47, Smith and Clark II teach wherein the plurality of virality factors comprise at least one of a filter effect, a genre combination, a speed variation, background music, or a surprise element (Smith - Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute sore, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300. Paragraph 0027 teaches attribute(s) of a design asset may refer to audio visual characteristics of a digital design asset. Visual component attribute categories, may be, but not limited to, background, color, color scheme, font, font, weight, font size, font color, subject, e.g. object, person, idea, theme, gender/age of individuals in asset, dimension shape, content type, visual scheme, or layout of asset. Audible component attribute categories, may be, but not limited to music genre/type, audio feature, rhythm, tempo, pitch, etc, or length of asset. Paragraph 0137 teaches attributes of a digital design asset can include, but are not limited to, characteristics associated with categories such as colors, subjects, backgrounds, objects, audio features, e.g., music features, sound features, dimensions, video features, e.g., playback length, filming style, tone, and/or any combination thereof).
Consider claims 38 and 48, Smith and Clark II teach wherein determining the predicted virality score comprises:
determining respective numbers of the plurality of virality factors present in the content item (Smith - Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute sore, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300. Paragraph 0027 teaches attribute(s) of a design asset may refer to audio visual characteristics of a digital design asset. Visual component attribute categories, may be, but not limited to, background, color, color scheme, font, font, weight, font size, font color, subject, e.g. object, person, idea, theme, gender/age of individuals in asset, dimension shape, content type, visual scheme, or layout of asset. Audible component attribute categories, may be, but not limited to music genre/type, audio feature, rhythm, tempo, pitch, etc, or length of asset);
retrieving respective weights for the plurality of virality factors (Smith - Paragraph 0058 teaches trained machine-learning model 302 can weight the various performance categories according to an importance of the categories. Paragraph 0060-0061 teaches asset management system 102 applies a weight algorithm 310 to the attribute sores 308a-308c to obtain the asset score 304. A greater weight may be applied to particular attributes that may be deemed as more predictive of success than one or more other attributes); and
computing, as the predicted virality score, a weighted combination of the plurality of virality factors based on the respective weights (Smith - Paragraph 0019 teaches asset management system analyzes the attributes to determine individual attribute scores that predict the performance of the attributes individually, which can then be used to generate an asset score for the user-generated asset as a whole. Paragraph 0060-0061 teaches applying the weighting algorithm to determine the asset score. Paragraph 0094 teaches an analyze option 510 that causes the asset management system 102 to analyze the image 504 using a machine-learning model to generate the asset score 412, which indicates a prediction of future performance of the image 504. Paragraph 0095 teaches generating an asset score 512 for the image 504 in connection with the selected audience segment by predicting the performance of the image 504 in one or more marketing campaigns directed to the selected audience segment).
Consider claims 39 and 49, Smith and Clark II teach wherein the plurality of virality enhancement techniques comprise one or more of applying a filter effect, applying a genre combination, applying a speed variation, applying background music, or applying a surprise element (Smith - Paragraph 0055 teaches asset management system 102 analyzes the asset 300 to identify a plurality of attributes of the asset 300. To illustrate, asset management system can identify that the asset 300 has a specific font, color scheme, background, subject, etc. Paragraph 0056 teaches generating an asset score by calculating a plurality of attribute scores for the plurality of attributes, by using the trained machine-learning model to generate first attribute sore, second attribute score, etc., up to an nth attribute for nth attribute of the asset 300. Paragraph 0027 teaches attribute(s) of a design asset may refer to audio visual characteristics of a digital design asset. Visual component attribute categories, may be, but not limited to, background, color, color scheme, font, font, weight, font size, font color, subject, e.g. object, person, idea, theme, gender/age of individuals in asset, dimension shape, content type, visual scheme, or layout of asset. Audible component attribute categories, may be, but not limited to music genre/type, audio feature, rhythm, tempo, pitch, etc, or length of asset. Paragraph 0100 teaches user can select one or more attribute values from the recommendation portion 520 to transform the digital design asset into a new design asset that reflects the selected attribute values. Paragraph 0102 teaches although user can manually selection options to view recommendations and select replacement attributes, the system can also provide an option to automatically modify one or more attributes, without user input for selecting attributes to change or selecting replacement attribute values. System may analyze the asset, determine one or more attribute values that improve the asset score, and generate the new digital design asset. Paragraph 0137 teaches attributes of a digital design asset can include, but are not limited to, characteristics associated with categories such as colors, subjects, backgrounds, objects, audio features, e.g., music features, sound features, dimensions, video features, e.g., playback length, filming style, tone, and/or any combination thereof).
Consider claims 40 and 50, Smith and Clark II teach wherein the virality score criterion comprises a virality score threshold (Smith - Paragraph 0105 teaches asset management system allowing a content creator to select a target asset score, i.e., a minimum asset score. The content creator wants to achieve a minimum asset score of “85” on a 100-point scale. System can identify a plurality of attributes that will produce the desired minimum score of “85”. System can recommend combinations of attributes that result in higher asset scores. Paragraph 0096 teaches asset management system 102 can automatically, or in response to an indication of user interaction with an improve score option 518 use a machine-learning model to determine whether one or more alternative attribute values of one or more attributes of the image 504 improves the asset score 512 for the image. Fig.5C-E, Paragraph 0092-0102 teaches user may interact with analyze option 510 and improve score option 518. Thus, users can go through the interface of Fig.5C-E where they could also iteratively go through the analyze option and improve score options, and iteratively apply further attributes to eventually reach a desired asset score that user(s) may have in mind).
Claim(s) 32, 33, 42, and 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 2019/0080347), in view of Clark II et al. (US 2004/0155977), and further in view of Ma et al. (US 2019/0197125).
Consider claims 32 and 42, Smith and Clark II teach wherein determining the predicted virality score comprises determining the predicted virality score (Smith - Paragraph 0016, 0019, 0038, 0055-0056, 0125), but does not explicitly teach comprises determining based on feedback corresponding to the content item received by the content sharing platform from one or more user accounts via the communication network.
In an analogous art, Ma teaches determining based on feedback corresponding to the content item received by the content sharing platform from one or more user accounts via the communication network (Paragraph 0017 teaches analyzing the popularity of online content by measuring user engagement with the online content. User engagement may include indicators that online users like the content, share the online content with other users, write online comments associated with the online content, etc. The more likes, shares, associated with the online content item, the more popular the online content item is considered to be. Paragraph 0126 teaches viral analytics system collects comments, likes of the content, etc in order to utilize it as a weight factor for a viral mention score. Paragraph 0021 teaches viral analytics system identifies the original item of digital content, generates a viral strength value, which may represent the viral mention number, such as number of threads that started after original message, and are talking about the same topic, the number of times the item of digital content was mentioned, etc. Paragraph 0027 teaches viral analytics system determines strength value associated with original item of digital content. Strength value associated with original content may be based on number of one or more subsequent items of digital content included in the group. Fig.1A, Paragraph 0035 teaches a user interface displaying virality of digital content. Fig.1B, Paragraph 0036 teaches another user interface that displays virality of digital content and associated strength values of the digital content. Digital content may be published/uploaded by social network users as taught in paragraph 0047-0048. And user interface provided, can be used to show the virality of digital content items in the social network, which would also include, the digital content items authored by those social network user(s))
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Smith and Clark II to include determining based on feedback corresponding to the content item received by the content sharing platform from one or more user accounts via the communication network, as taught by Ma, for the advantage of analyzing the popularity of online content based on different indicators of user engagement with the online content (Ma – Paragraph 0017), providing and taking into account real life input, to determine how a content item will really do in reality.
Consider claims 33 and 43, Smith, Clark, and Ma teach wherein the feedback corresponding to the content item comprises respective values for a plurality of feedback metrics comprising at least one of a number of likes, dislikes, shares, comments, or viewed for the content item (Ma - Paragraph 0017 teaches analyzing the popularity of online content by measuring user engagement with the online content. User engagement may include indicators that online users like the content, share the online content with other users, write online comments associated with the online content, etc. The more likes, shares, associated with the online content item, the more popular the online content item is considered to be. Paragraph 0126 teaches viral analytics system collects comments, likes of the content, etc. in order to utilize it as a weight factor for a viral mention score. Paragraph 0021 teaches viral analytics system identifies the original item of digital content, generates a viral strength value, which may represent the viral mention number, such as number of threads that started after original message, and are talking about the same topic, the number of times the item of digital content was mentioned, etc.; Smith - Paragraph 0109 teaches attribute score related to impressions, clicks, user actions, likes, e.g. when user selects a “like” element corresponding to a social media site to indicate interest of the user in digital content, shares, e.g. when user selects a “share” element associated with digital content to present to one or more other co-users on social media site or other forum, associated with the user-generated digital design asset. Paragraph 0041 teaches asset score and/or attribute score is the success of the corresponding asset).
Claim(s) 34 and 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 2019/0080347), in view of Clark II et al. (US 2004/0155977), in view of Ma et al. (US 2019/0197125), and further in view of Komuves (US 2013/0066885).
Consider claims 34 and 44, Smith, Clark II, and Ma teach wherein determining the predicted virality score comprises computing, as the predicted virality score (Ma - Paragraph 0017 teaches analyzing the popularity of online content by measuring user engagement with the online content. User engagement may include indicators that online users like the content, share the online content with other users, write online comments associated with the online content, etc. The more likes, shares, associated with the online content item, the more popular the online content item is considered to be. Paragraph 0126 teaches viral analytics system collects comments, likes of the content, etc in order to utilize it as a weight factor for a viral mention score. Paragraph 0021 teaches viral analytics system identifies the original item of digital content, generates a viral strength value, which may represent the viral mention number, such as number of threads that started after original message, and are talking about the same topic, the number of times the item of digital content was mentioned, etc.; Smith - Paragraph 0005; Paragraph 0109 teaches attribute score related to impressions, clicks, user actions, likes, e.g. when user selects a “like” element corresponding to a social media site to indicate interest of the user in digital content, shares, e.g. when user selects a “share” element associated with digital content to present to one or more other co-users on social media site or other forum, associated with the user-generated digital design asset. Paragraph 0094-0095 teaches an assert score 512 which indicates performance of the image 504), but do not explicitly teach determining the score comprises computing, as the score, a cumulative number of likes, dislikes, shares, comments, and views for the content item indicated by the content sharing platform.
In an analogous art, Komuves teaches determining a score comprises computing, as the score, a cumulative number of likes, dislikes, shares, comments, and views for the content item indicated by a content sharing platform (Paragraph 0020 teaches a popularity score that accounts for total number of users that like, dislike, and overall number of users that rated the objects. Paragraph 0021 teaches popularity score is based on user responses or input. Paragraph 0025 teaches popularity scores are generated based on total number of expressed opinions, including number of likes and number of dislikes. Fig.6, Paragraph 0040 teaches popularity score for an item calculated based on number of likes, number of dislikes, total number of views, etc. Additional considerations when generating the score, may include, e.g. views, comments, shares, etc. Shares may include the number and frequency that users share the content objects, e.g., via social media applications, email functions, and other media sharing applications. Each of these additional considerations may be used by score calculation module to further refine the generated score).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the system of Smith, Clark II, and Ma to include determining a score comprises computing, as the score, a cumulative number of likes, dislikes, shares, comments, and views for the content item indicated by a content sharing platform, as taught by Komuves, for the advantage of providing a system that accounts for the overall number of ratings with respect to other content objects, improving rating (Komuves – Paragraph 0007), enabling the system to get a better overall snapshot regarding the performance and/or popularity of the content as a whole, by incorporating and taking into account a multitude of factors, providing greater accuracy and determination.
Cited Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. discloses automatically adjusting and repeating steps, until image quality value of restored image reaches the predetermined threshold value, so as to enhance the image quality of the generated restored image in (US 20008/0232707).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/JASON K LIN/Primary Examiner, Art Unit 2425