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 .
Response to Arguments
Applicant’s arguments, see Remarks pg. 8, filed 3/17/2026, with respect to the status of the claims is hereby acknowledged.
Applicant’s arguments, see Remarks pg. 8, filed 3/17/2026, with respect to the rejection(s) of claim(s) 1-20 on obviousness grounds under 35 U.S.C. 103 have been fully considered. The examiner notes that the applicant’s arguments are directed to the newly amended limitations. Therefore, examiner will set forth a new grounds of rejection in order to address the newly added limitations.
With respect to the applicant’s arguments regarding the deficiencies of the prior art, the applicant argues, inter alia, the following:
The cited references fail to teach or suggest, at least, (1) "performing, by the at least one computer processor, automatic content recognition on the media stream content to generate potential optimal supplemental content items," and (2) "identifying, by the at least one computer processor, a subset of potential optimal supplemental content items from the potential optimal supplemental content items based on a relative conversion rate value of the subset of potential optimal supplemental content items being greater than the relative conversion rate threshold value," as recited in independent claims 1, 8, and 13.
Regarding feature (1), the Office Action acknowledges that Burstein does not teach "automatic content recognition" and relies on paragraphs 35-40 and 49 of Gilmore for allegedly teaching "conversion rate in the context of automatic content recognition in order to establish impressions." Office Action, 3. Paragraph 39 of Gilmore describes measuring device level frequency using automatic content recognition.
However, Gilmore does not teach "performing...automatic content recognition on the media stream content to generate potential optimal supplemental content items," as now recited by claims 1, 8, and 13. Burstein is similarly deficient because it also fail to teach or suggest automatic content recognition, much less using automatic content recognition on media content in order to generate potential optimal supplemental content items. This conclusion is reinforced by the Office Action relying on Burstein's disclosure of "the set of eligible ads" as the claimed "potential optimal supplemental content items." Office Action, 4.
Burstein's "eligible ads" are merely advertisements associated with content and they are not generated based on automatic content recognition on any content. Accordingly, Burstein fails to teach the claimed "potential optimal supplemental content items."
The remaining references are also deficient because they fail to teach or suggest "performing, by the at least one computer processor, automatic content recognition on the media stream content to generate potential optimal supplemental content items."
The examiner respectfully disagrees. In response to Appellants’ argument that the Examiner did not establish that the invention including the second of these interrelated functions would have been obvious, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Additionally, on the issue of obviousness, the Supreme Court stated the analysis of a rejection on obviousness grounds need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 418, 82 USPQ2d 1385 (2007). The obvious analysis cannot be confined by a formalistic conception of the words teaching, suggestion, and motivation. Id. at 419. Further, the Court stated that common sense teaches, however, that familiar items may have obvious uses beyond their primary purposes, and in many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle. Id. at 420.
The examiner will rely, in part, on the prior art of record and further cite newly found prior art in order to address the newly amended limitations. For example, With respect to calculating, by the at least one computer processor, a relative conversion rate threshold value based on the calculated relative conversion rate, wherein the relative conversion rate threshold value comprises a value less than a maximum relative conversion rate value, Burnstein Col. 13:4-14 teaches “the cached user information may include a pre-ordered ranking of eligible ads for a user. The pre-ordered ranking may be based at least in part on respective ad scores for the eligible ads, historical data indicative of a presentation history of respective ads, such that a user is not repeatedly presented with the same ad impression, or other factors, such as ad campaign performance information. In one example of ad campaign performance information, an ad may be ranked higher than normal if the ad is behind a desired rate of ad impression delivery.” Stated differently, Burnstein continuously tracks the campaign goals of an advertiser to determine what the status of the desired rate of ad impression delivery is and the success of ad viewing.
All things considered, a person of ordinary skill in the art would have been able to draw particular inferences relating to the claimed limitation (i.e., a relative conversion rate threshold value based on the calculated relative conversion rate, wherein the relative conversion rate threshold value comprises a value less than a maximum relative conversion rate value) and also (identifying, by the at least one computer processor, a subset of potential optimal supplemental content items from the potential optimal supplemental content items based on a relative conversion rate value of the subset of potential optimal supplemental content items being greater than the relative conversion rate threshold value. For example, Burstein col. 12:42-67 to col. 13:1-14 and col. 16:46-67 to col. 17:1-42 teaches periodically determining conversion events after a predetermined threshold number of monitored responses and utilizing the conversion events to identify ad values using dynamic data related to conversion events to enable an ad exchange server to update a new set of advertisements contents comprising new ads for underperforming ads and previous effective ads that resulted in conversion events. In an analogous art, Kennedy further teaches elements relating to advertisement opportunities and tracking conversion rates that are cited for supporting the inferences that a person of ordinary skill in the art would draw from the teachings of Burstein. See Kennedy teaching predetermined delivery commitment corresponds to guaranteed ad delivery teaches that particular slots are allocated before being made available to non-guaranteed contracts for ad placement and once the contract has been satisfied the available slots are made available to dynamic content advertisement (see para 15, 35-36, 55-58 disclosing “…allocation of increments of a supply of advertisements to meet demand may be optimized in a market for use of advertising opportunities (ad impressions) by establishing a proportion of revenue and/or quantity to be shared between distinct categories of demand with potentially different marginal values. A programmable technique divides all allocations that are projected and later the allocations that actually arise, between a category of pre-committed increments, typically contractually committed ad insertion opportunities with predetermined characteristics (e.g., guaranteed delivery (GD) of ads contracts), and a category of spot sales, such as via ad exchange auctions (e.g., non-guaranteed delivery (NGD) delivery of ads). Click through rates are considered in supplying the advertisements, such that the user that views the advertisement clicks on the advertisement, which may bring the user to a website of the advertiser. The embodiments also consider conversion to a sale from the advertisement, without the user actively clicking on the served ad.” Kennedy does not disclose elements with respect to content recognition. More importantly, a person of ordinary skill in the art would have appreciated the obvious benefit of utilizing content recognition in the known prior art inventions, wherein Gilmore teaches, inter alia, conversion rate in the context of automatic content recognition in order to establish impressions (para 35-40, 49).
All things considered, the examiner will set forth a new grounds of rejection in order to address
the new limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-6, 8-11 and 13-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Burstein; Jonathan Lee et al. US10565622B1 (hereafter Burstein) and in further view of and in further view of Kennedy; Oliver et al. US 20100114689 A1 (hereafter Kennedy) and in further view of Gilmore; Rudy C. et al. US 20240364950 A1 (hereafter Gilmore) and in further view of Lax; Reuven et al. US 20090006375 A1 (herein after Lax).
Regarding claim 1, “a computer-implemented method for generating an optimal supplemental content item in a media stream menu interface, comprising: calculating, by at least one computer processor, a relative conversion rate for a potential optimal supplemental content item, wherein the relative conversion rate for the potential optimal supplemental content item represents how often content is consumed based on supplemental content in the menu interface; calculating, by the at least one computer processor, a relative conversion rate threshold value based on the calculated relative conversion rate, wherein the relative conversion rate threshold value comprises a value less than a maximum relative conversion rate value; performing, by the at least one computer processor, automatic content recognition on the media stream content to generate potential optimal supplemental content items;” Burstein col. 12:42-67 to col. 13:1-14 and col. 16:46-67 to col. 17:1-42 teaches cached user information may include a set of eligible ads selected from the set of candidate ads or available advertising inventory that are pre-matched or otherwise identified as being potentially servable to a particular users and identifying ads as preset supplemental content items to be included as part of digital streaming digital content and further teaches periodically determining conversion events after a predetermined threshold number of monitored responses and utilizing the conversion events to identify ad values using dynamic data related to conversion events to enable an ad exchange server to update a new set of advertisements contents comprising new ads for underperforming ads and previous effective ads that resulted in conversion events; Burstein col. 12:42-67 to col. 13:1-14 and col. 16:46-67 to col. 17:1-42 teaches periodically determining conversion events after a predetermined threshold number of monitored responses and utilizing the conversion events to identify ad values using dynamic data related to conversion events to enable an ad exchange server to update a new set of advertisements contents comprising new ads for underperforming ads and previous effective ads that resulted in conversion events, Burstein’s teachings read on the terms “dynamic supplemental content” and “dynamic supplemental content values.” Whereas Burstein discloses impressions with respect to conversion rates (col. 8:12-47, col. 10:48-67 to col. 11:1-39) but does not reference automatic content recognition. With respect to calculating, by the at least one computer processor, a relative conversion rate threshold value based on the calculated relative conversion rate, wherein the relative conversion rate threshold value comprises a value less than a maximum relative conversion rate value, Burnstein Col. 13:4-14 teaches “the cached user information may include a pre-ordered ranking of eligible ads for a user. The pre-ordered ranking may be based at least in part on respective ad scores for the eligible ads, historical data indicative of a presentation history of respective ads, such that a user is not repeatedly presented with the same ad impression, or other factors, such as ad campaign performance information. In one example of ad campaign performance information, an ad may be ranked higher than normal if the ad is behind a desired rate of ad impression delivery.” Stated differently, Burnstein continuously tracks the campaign goals of an advertiser to determine what the status of the desired rate of ad impression delivery is and the success of ad viewing.
All things considered, a person of ordinary skill in the art would have been able to draw particular inferences relating to the claimed limitation (i.e., a relative conversion rate threshold value based on the calculated relative conversion rate, wherein the relative conversion rate threshold value comprises a value less than a maximum relative conversion rate value) and also (identifying, by the at least one computer processor, a subset of potential optimal supplemental content items from the potential optimal supplemental content items based on a relative conversion rate value of the subset of potential optimal supplemental content items being greater than the relative conversion rate threshold value. For example, Burstein col. 12:42-67 to col. 13:1-14 and col. 16:46-67 to col. 17:1-42 teaches periodically determining conversion events after a predetermined threshold number of monitored responses and utilizing the conversion events to identify ad values using dynamic data related to conversion events to enable an ad exchange server to update a new set of advertisements contents comprising new ads for underperforming ads and previous effective ads that resulted in conversion events. In an analogous art, Kennedy further teaches elements relating to advertisement opportunities and tracking conversion rates that are cited for supporting the inferences that a person of ordinary skill in the art would draw from the teachings of Burstein. See Kennedy teaching predetermined delivery commitment corresponds to guaranteed ad delivery teaches that particular slots are allocated before being made available to non-guaranteed contracts for ad placement and once the contract has been satisfied the available slots are made available to dynamic content advertisement (see para 15, 35-36, 55-58 disclosing “…allocation of increments of a supply of advertisements to meet demand may be optimized in a market for use of advertising opportunities (ad impressions) by establishing a proportion of revenue and/or quantity to be shared between distinct categories of demand with potentially different marginal values. A programmable technique divides all allocations that are projected and later the allocations that actually arise, between a category of pre-committed increments, typically contractually committed ad insertion opportunities with predetermined characteristics (e.g., guaranteed delivery (GD) of ads contracts), and a category of spot sales, such as via ad exchange auctions (e.g., non-guaranteed delivery (NGD) delivery of ads). Click through rates are considered in supplying the advertisements, such that the user that views the advertisement clicks on the advertisement, which may bring the user to a website of the advertiser. The embodiments also consider conversion to a sale from the advertisement, without the user actively clicking on the served ad.” Kennedy does not disclose elements with respect to content recognition.
In an analogous art, Gilmore teaches conversion rate in the context of automatic content recognition in order to establish impressions (para 35-40, 49). Gilmore is silent with respect to clustering as recited in “performing, by the at least one computer processor, automatic content recognition on the media stream content to generate potential optimal supplemental content items; clustering, by the at least one computer processor, the potential optimal supplemental content items according to a characteristic associated with the potential optimal supplemental content items; identifying, by the at least one computer processor, a subset of potential optimal supplemental content items from the potential optimal supplemental content items based on a relative conversion rate value of the subset of potential optimal supplemental content items being greater than the relative conversion rate threshold value; and transmitting the subset of potential optimal supplemental content items to a subset of media devices” Burstein teaches “the set of eligible ads may for the user of user device 202 may include a first ad and a second ad, where the set of eligible ads is indicative of ads with targeting criteria for which the user meets a threshold level of the targeting criteria. The threshold level may be represented by meeting some, a majority of, substantially all of, or all targeting criteria, such as over half of Boolean true/false queries, or by another metric. Additional examples of thresholds may include a relative comparison of target criteria matching amongst available users. For instance, while no users meet each targeting criteria, a user that meets most targeting criteria relative to other users may meet the threshold” Burstein col. 12:42-67 to col. 13:1-3 and col. 14:1-17; see also col. 2:49-67 to col. 3:1-4 advertisements selected for potential presentation to specific users based on user attributes.
The prior art to Lax discloses a motivation of for utilizing content recognition when determining conversion rate (para 33, 95) wherein a user interface comprises an option menu and an advertisement display portion (para 56).
See also prior art to Loheide; Donald Jude et al. US 20180343484 A1 (hereafter Loheide) made of record in the rejection of claim 4 and not recited in the rejection of the independent claim to avoid duplicative references. Loheide para 23-24 disclosing a menu of options. See also prior art made of record but not relied upon to Ramer; Jorey et al. US 20180025010 A1.
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the teachings of Burstein’s invention for determining one or more dynamic optimal supplemental content based on content characteristics to determine conversion rates that takes into consideration data reflecting the one or more digital supplemental content opportunities based on viewer characteristics to a supplemental content exchange because Gilmore teaches conversion rate in the context of automatic content recognition in order to establish impressions because the modification of utilizing known elements according to their known benefits as discussed Kennedy for monitoring ad performance and campaign goal and periodically determining conversion events after a predetermined threshold number of monitored responses and utilizing the conversion events to identify ad values using dynamic data related to conversion events to enable an ad exchange server to update a new set of advertisements contents comprising new ads for underperforming ads and previous effective ads that resulted in conversion events and Lax recognizes the benefit of utilizing content recognition when determining conversion rates which is likely to lead to predictable results for enabling an advertiser and content delivery exchange to improve the conversion rate of provided advertisements.
Regarding claim 2, “wherein the potential supplemental content items comprise one or more of an image or a content clip from the media stream content” is further rejected on obviousness grounds as discussed in the rejection of claim 1 wherein Burstein col. 3:14-35 teaches a movie advertised in an ad impression is interpreted as an image or content clip from a media stream.
Regarding claim 3, “further comprising: building, by the at least one computer processor, a random sample of potential optimal supplemental content items from the media stream content having a predicted relative conversion rate greater than a predetermined value; storing the random sample of potential optimal supplemental content items in a memory connected to the at least one computer processor; and selecting, by the at least one computer processor, the subset of media devices from a plurality of media devices based on one or more characteristics of the plurality of media devices and a relation between one or more characteristics of the subset of media devices and the relative conversion rate of the potential optimal supplemental content” is further rejected on obviousness grounds as discussed in the rejection of claims 1-2 wherein Burstein col. 3:14-67 disclosing probabilistic models and select an advertisement for which an expected value and/or probability of action may be highest, resulting in improved ad campaign effectiveness. Probability of action may be indicative of the chance that the potential consumer to which the ad impression was served will act or react in a certain manner intended by the advertiser. See also col. 13:14-67 disclosing pool of advertisements comprises random advertisements.
Regarding claim 5, “wherein the selecting comprises selecting the subset of media devices based on historical playback information” is further rejected on obviousness grounds as discussed in the rejection of claims 1-3 wherein Burstein col. 7:39-64 and col. 10:48-67 to col. 11:1-18 and col. 13:4-13 disclosing historical playback for each viewer.
Regarding claim 6, “further comprising receiving an indication from each media device of the subset of media devices confirming that the potential optimal supplemental content was displayed in the menu interface” is further rejected on obviousness grounds as discussed in the rejection of claims 1-3, 5 wherein Burstein col. 7:39-64 and col. 10:48-67 to col. 11:1-18 and col. 13:4-13 disclosing historical playback for each viewer suggests the supplemental content was presented to the viewer.
Regarding the system claims 8-11 and the method claims 13-18, 20 the claims are grouped and rejected with the method claims 1-3, 5-6 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 1-3, 5-6 and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art. With respect to claims 13-14, the claim recites a limitation relating to closed-caption and live media stream not discussed in the rejection of claims 1-3, 5-6, however, the prior art to Lax discussed in the rejection of claims Lax further renders obvious the limitation regarding closed-caption data and live media stream used to detect automatic content recognition. See Lax para 16-20, 33. Regarding the rejection of claims 16-17 and the element regarding large language model not discussed in the rejection of claims 1-3, 5-6, the prior art to Lax para 34 discloses content recognition comprising neural networks understood to comprise large language models and thus renders the limitations of claims 16-17 obvious.
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Burstein; Jonathan Lee et al. US10565622B1 (hereafter Burstein) and in further view of and in further view of Kennedy; Oliver et al. US 20100114689 A1 (hereafter Kennedy) and in further view of Gilmore; Rudy C. et al. US 20240364950 A1 (hereafter Gilmore) and in further view of Lax; Reuven et al. US 20090006375 A1 (herein after Lax) and in further view of Loheide; Donald Jude et al. US 20180343484 A1 (hereafter Loheide).
Regarding claim 4, Burstein and Gilmore are silent with respect to “wherein the characteristic comprises content genre, content personality, content director, content subject matter, content time length, content country of origin, or any combination thereof. Lax para 18 does disclose utilizing genre information. In an analogous art, Loheide teaches the deficiency with respect to genre (para 100-103 presenting optimal advertisements to improve conversion rate and take viewer genre preference into consideration when presenting advertisements.
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the teachings of Burstein, Gilmore, and Lax delivering dynamic optimal supplemental content by further incorporating known elements of Loheide for presenting optimal advertisements to improve conversion rate and take viewer genre preference into consideration when presenting advertisements in order to optimize the advertiser’s return on investment for providing advertisement content.
Regarding the system claim 19 the claim is grouped and rejected with the method claims 1-6 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 1-6 and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art.
Claims 7 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Burstein; Jonathan Lee et al. US10565622B1 (hereafter Burstein) and in further view of and in further view of Kennedy; Oliver et al. US 20100114689 A1 (hereafter Kennedy) and in further view of Gilmore; Rudy C. et al. US 20240364950 A1 (hereafter Gilmore) and in further view of Lax; Reuven et al. US 20090006375 A1 (herein after Lax) and in further view of Ciabarra, JR.; Mario Luciano US 20210176294 A1 (hereafter Ciabarra).
Regarding claim 7, Burstein, Gilmore, and Lax are silent with respect to “wherein the calculating the relative conversion rate comprises dividing an existing conversion rate by an average conversion rate based on the existing supplemental content, wherein the average conversion rate based on the existing supplemental content comprises an average across all tracked existing supplemental content. In an analogous art, Ciabarra teaches the deficiency (para 31, 46, 86).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the teachings of Burstein, Gilmore, and Lax delivering dynamic optimal supplemental content by further incorporating known elements of Ciabarra for establishing a relative conversion rate based on a ration of an existing conversion rate and average conversion rate in order to calculate an optimal advertiser’s return on investment for providing advertisement content.
Regarding the system claim 12 the claim is grouped and rejected with the method claims 1-6 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 1-6 and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALFONSO CASTRO whose telephone number is (571)270-3950. The examiner can normally be reached on Monday to Friday from 10am to 6pm.
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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