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 in response to communication filed 6/30/2023. Claims 1-20 are pending for examination, the rejection cited as stated below.
Examiner’s Claim Construction Comments
2. Regarding claims 8-14, Examiner interprets the claimed “a computer-readable storage medium” as “a non-transitory computer-readable storage medium” In light of specification paragraph [0017], “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.” As a result, claims 8-14 are considered statutory.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-12 and 14-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Evans et al (US 2021/0150222, hereafter Evans).
As to claim 1, Evans discloses a method for identifying video clips for inclusion in a targeted communication, the method comprising:
receiving video footage comprising a plurality of clips (Fig. 5, “accessing a first video”, “predicting a first noteworthy portion for the first video, wherein the first noteworthy portion is a portion of the first video”, “extracting a highlight from the first video, wherein the first highlight corresponds to the first noteworthy portion”, wherein portions of the video including the highlight portion are considered a plurality of clips, see also [0028], “As an example and not by way of limitation, the highlight may be a video clip ( e.g., an MPEG video)”);
receiving a plurality of information streams that are associated with the video footage (Fig. 5, “predicting a first noteworthy portion for the first video, wherein the first noteworthy portion is a portion of the first video that is predicted based on user-engagement information associated with the portion of the first video”; [0021], “a noteworthy portion of the first video may be identified by a publisher or a creator of the first video. As an example and not by way of limitation, a creator of a video of a concert ( e.g., a user who captured the video on a mobile device) may specify portions of a video that are noteworthy when uploading the video ( or after uploading the video). For example, the user may specify one or more time ranges during which band members performed a solo as corresponding to a noteworthy portion. In particular embodiments, a noteworthy portion of the first video may be identified by a viewer user-i.e., a person who has viewed or is viewing the first video. As an example and not by way of limitation, a viewer user viewing a football game may specify a time range during which the user's favorite player catches the ball as corresponding to a noteworthy portion”; [0022], “calculate a noteworthiness-score for a portion based on any combination of suitable information, such as the information described herein. In particular embodiments, the noteworthiness-score for a portion may be a weighted sum of different sub-scores, each sub-score being calculated based on different combinations of factors. As an example and not by way of limitation, a first sub-score may be calculated based on a number of reactions that occur during a portion, and a second sub-score may be calculated based on a number of comments that occur during the portion, while a third sub-score may be calculated based on a number of reactions and a number of shares that occur during the portion. In this example, the noteworthiness-score may be based on the result of the following expression Af1 +Bf2+Cf3 , where f1 is a function that calculates the first sub-score, f2 is a function that calculates the second subscore, f3 is a function that calculates the third sub-score, and where A, B, and C are weights. In particular embodiments, a portion may be noteworthy if it exceeds a threshold”.
Here, the creator user’s designation for noteworthy portion(s), the viewer user’s designation for noteworthy portion(s), the user reactions, the user comments, the user shares, or any sub combination(s), are a plurality of information streams that are associated with the video footage);
synchronizing the information streams with the video footage (see citation above, since the number of views, number of comments, and number of shares are counted “during the portion” of the video, synchronizing the information streams with video footage is implied);
applying weights to the information streams (see citation in the preceding limitations, e.g., [0022]);
aggregating the weighted information streams and identifying peaks therein (see citation in the preceding limitations, e.g., [0022], wherein the portions with respective noteworthy scores exceeding a threshold correspond to identified peaks. Also see [0022], “the computing system may rank portions of a video based on their noteworthiness-score and predict that portions above a threshold rank are noteworthy (e.g., the top three scores)”);
selecting clips from the video footage that correspond to the peaks for inclusion in a targeted communication ([0019], “The computing system may extract a highlight from the video that corresponds to the predicted noteworthy portion. In particular embodiments, this process may be performed multiple times for a video to predict multiple noteworthy portions and extract multiple highlights for the video”; Fig. 5, “sending, to a client system of a user, information configured to render the first highlight and a first interactive element that is configured to launch the first video on the client system”; [0070], “the coefficient may be used to generate advertisements for the user, where the user may be presented with advertisements for which the user has a high overall coefficient with respect to the advertised object”. See also [0022], “the computing system may rank portions of a video based on their noteworthiness-score and predict that portions above a threshold rank are noteworthy (e.g., the top three scores)); and
analyzing metrics from the targeted communication to provide feedback in order to optimize the weights ([0066], “The weights for each factor may be static or the weights may change according to, for example, the user, the type of relationship, the type of action, the user's location, and so forth”; [0066], “The ratings and weights may be continuously updated based on continued tracking of the actions upon which the coefficient is based. Any type of process or algorithm may be employed for assigning, combining, averaging, and so forth the ratings for each factor and the weights assigned to the factors. In particular embodiments, social-networking system 660 may determine coefficients using machine-learning algorithms trained on historical actions and past user responses, or data farmed from users by exposing them to various options and measuring responses”; Fig. 5, “sending, to a client system of a user, information configured to render the first highlight and a first interactive element that is configured to launch the first video on the client system”. Since the weights may be continuously updated and based continued tracking of the actions, the user’s action on the targeted communication, i.e., the launched video, is also tracked, to provide feedback to optimize the weights via., e.g., machine learning training, See also Fig. 2 and Fig. 3 and [0031], the user’s sharing the highlight, wherein sharing is a type of tracked user action as discussed in [0022]).
As to claim 8, see similar rejection to claim 1, wherein a memory is implied.
As to claim 15, see similar rejection to claim 1, wherein processor and memory are implied.
As to claim 2, Evans discloses the method of claim 1, further comprising recording the weights in a database (see citation in rejection to claim 1, e.g., [0066], “The ratings and weights may be continuously updated based on continued tracking of the actions upon which the coefficient is based. Any type of process or algorithm may be employed for assigning, combining, averaging, and so forth the ratings for each factor and the weights assigned to the factors. In particular embodiments, social-networking system 660 may determine coefficients using machine-learning algorithms trained” indicating that the weights are recorded in a database/datastore in order to be continuously updated).
As to claim 9, see similar rejection to claim 2.
As to claim 16, see similar rejection to claim 2.
As to claim 3, Evans discloses the method of claim 1, wherein the targeted communication is an advertisement ([0070], “the coefficient may be used to generate advertisements for the user, where the user may be presented with advertisements for which the user has a high overall coefficient with respect to the advertised object”).
As to claim 10, see similar rejection to claim 3.
As to claim 17, see similar rejection to claim 3.
As to claim 4, Evans discloses the method of claim 3, further comprising testing the advertisement in an advertising marketplace in order to generate the metrics ([0066], “The ratings and weights may be continuously updated based on continued tracking of the actions upon which the coefficient is based. Any type of process or algorithm may be employed for assigning, combining, averaging, and so forth the ratings for each factor and the weights assigned to the factors. In particular embodiments, social-networking system 660 may determine coefficients using machine-learning algorithms trained on historical actions and past user responses, or data farmed from users by exposing them to various options and measuring responses”; Fig. 5, “sending, to a client system of a user, information configured to render the first highlight and a first interactive element that is configured to launch the first video on the client system”. Since the weights may be continuously updated and based continued tracking of the actions, the user’s action on the targeted communication, i.e., the launched video, is also tracked, to provide feedback to optimize the weights via., e.g., machine learning training, which can be considered testing the advertisement in an advertisement market place, when the content is an advertisement, see 0070], “the coefficient may be used to generate advertisements for the user, where the user may be presented with advertisements for which the user has a high overall coefficient with respect to the advertised object”).
As to claim 11, see similar rejection to claim 4.
As to claim 18, see similar rejection to claim 4.
As to claim 5, Evans discloses the method of claim 1, wherein the information streams comprise at least one of social media streams, recorded event streams, and audio streams extracted from the video footage (see citation in rejection to claim 1, e.g., [0022], “a number of shares that occur during the portion“ indicates social media streams).
As to claim 12, see similar rejection to claim 5.
As to claim 19, see similar rejection to claim 5.
As to claim 7, Evans discloses the method of claim 1, wherein optimizing the weights leads to selecting different clips from the video footage for inclusion in the targeted communication (see citation in rejection to claim 1, e.g., [0022], “calculate a noteworthiness-score for a portion based on any combination of suitable information, such as the information described herein. In particular embodiments, the noteworthiness-score for a portion may be a weighted sum of different sub-scores, each sub-score being calculated based on different combinations of factors. As an example and not by way of limitation, a first sub-score may be calculated based on a number of reactions that occur during a portion, and a second sub-score may be calculated based on a number of comments that occur during the portion, while a third sub-score may be calculated based on a number of reactions and a number of shares that occur during the portion. In this example, the noteworthiness-score may be based on the result of the following expression Af1 +Bf2+Cf3 , where f1 is a function that calculates the first sub-score, f2 is a function that calculates the second subscore, f3 is a function that calculates the third sub-score, and where A, B, and C are weights. In particular embodiments, a portion may be noteworthy if it exceeds a threshold”, wherein a noteworthiness-score for a respective portion changes when any of the weights A, B, or C is updated, therefore whether a portion is considered noteworthy (i.e., whether the respective noteworthy-score exceeds a threshold) may change when any of the weights is updated. For example, the ranking may change accordingly, see [0022], “the computing system may rank portions of a video based on their noteworthiness-score and predict that portions above a threshold rank are noteworthy (e.g., the top three scores).”).
As to claim 14, see similar rejection to claim 7.
As to claim 20, see similar rejection to claim 7.
Claim Rejections - 35 USC § 103
6. 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.
7. 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 of this title, 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.
8. 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.
9. Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Evans, as applied to claim 1 above, and further in view of Cronje (US 2021/0248400).
As to claim 6, Evans discloses the claimed invention substantially as discussed in claim 1, including using machine learning to optimize weights (see citation in rejection to claim 1, e.g., [0066]), but does not expressly disclose that optimizing the weights comprises using a heuristic method to optimize the weights, the heuristic method selected from the group consisting of simulated annealing, particle swarm optimization, solvers, and quantum approximate optimization algorithms (QAOA). Cronje discloses a concept of using particle swam optimization to optimize weights ([0067]).
Before the effective filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Evans with Cronje. The suggestion/motivation of the combination would have been to optimize a classifier (Cronje, [0067]).
As to claim 13, see similar rejection to claim 6.
Prior Art Cited but not Applied in the Rejection
10. Below is a list of prior art reference(s) cited but not applied in the rejection:
a) Amer (US 2016/0071024), disclosing analyzing temporal components of multi-modal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using deep leaning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUA FAN whose telephone number is (571)270-5311. The examiner can normally be reached on 9-6.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nasser Goodarzi, can be reached at (571) 272-4195. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUA FAN/Primary Examiner, Art Unit 2426
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