3DETAILED 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The examiner previously identified the IDS as submitted on 2/8/2025.
Response to Arguments
Applicant’s arguments, see Remarks pg. 11, filed 3/26/2026, with respect to the status of the claims and the interview summary are hereby acknowledged.
Applicant’s arguments, see Remarks pg. 11-12, filed 3/26/2026, with respect to the rejection(s) of claim(s) 21-40 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 not previously presented. The applicant argues the following in addressing the newly amended limitations:
Kitts discloses a system for ad targeting that automatically matches advertisements to media based on the demographic signatures. While the system in Kitts discloses using vector matches generally, such as when determining media matches in paragraph [0041 ]-[0042], Kitts fails to disclose "generating, by the one or more processors, a vector for each unique viewer based on what is viewed by each unique viewer'' and "selecting, by the one or more processors, a first package of unique viewers of the one or more packages of unique viewers based on the rank of each unique viewer and the vector for each unique vector." Kitts is limited to correlating predefined demographic attributes with media content and does not describe individual viewer-level vectors based on actual viewing behavior. Kitts also does not rank such viewers for package selection based on the behavior-derived vectors.
Blume, a the secondary reference, also discloses utilizing consumer vectors to determine nearest-neighbor matching, but fails to cure the deficiencies of Kitts as Blume is also silent regarding a vector based on what is viewed, and then selecting a first package of unique viewers based on the rank of each unique viewer and the vector for each unique vector." Thus, none of the cited art teaches or suggests, in combination or individually, the underlined features above.
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.
In the prior art of record, Blume paragraphs 18-21 teaches, inter alia, the following:
[0022] Preferably, each consumer is also given a profile that includes various demographic data, and summary data on spending habits. In addition, each consumer is preferably given a consumer vector. From the spending data, the merchants from whom the consumer has most frequently or recently purchased are determined. The consumer vector is then the summation of these merchant vectors. As new purchases are made, the consumer vector is updated, preferably decaying the influence of older purchases. In essence, like the expression "you are what you eat," the present invention reveals "you are whom you shop at," since the vectors of the merchants are used to construct the vectors of the consumers.
Furthermore, Kitts teaches utilizing demographic vectors in relation to viewed content wherein the demographic viewership of media assets are predicted (see Kitts para 121-130). More importantly, the examiner will rely on newly found prior art to Eldering which teaches that “[0075] By processing the recorded viewing habits in conjunction with programming related information and heuristic rules similar to those illustrated in FIG. 7 but related to programming rather than purchases, it is possible to construct a subscriber characterization vector which contains a probabilistic demographic profile of the household.” Eldering also teaches the following which is pertinent to the teachings of Kitts and Blume:
[0070] Profiler 140 may be a retailer who collects data from its stores, but can also be a third party who contracts with consumer 100 and the retailer to receive point of purchase data and profile consumer 100. Consumer 100 may agree to such an arrangement based on the increased convenience offered by targeted ads, or through a compensation arrangement in which they are paid on a periodic basis for revealing their specific purchase records.
[0071] Consumer profile server 130 can contain a consumer profile which is determined from observation of the consumer's viewing habits on television 108 or consumer PC 104. Determination of demographic and product preference information based on the consumer's use of services such as cable television and Internet access can be performed by monitoring the channel selections that a subscriber makes, and determining household demographics based on the subscriber selections and information associated with the programming being watched.
[0072] In one embodiment the channel selections are recorded, and based on the time of day during which the programming is watched and duration of viewing, heuristic rules are applied to make probabilistic determinations regarding the household demographics including age, gender, household size and income, as illustrated in FIG. 2A. This can be accomplished by applying heuristic rules which associate the programming with known and assumed characteristics for viewers of the programming. As an example, it is known that the probability that the viewer of a cartoon in the morning is in the 3-8 year old age group is high, thus if the household viewing habits consistently record viewing of cartoons the probability that the household will contain one or more viewers in the 3-8 year old age group is high.
[0073] In one embodiment information regarding the program is extracted from the Electronic Program Guide (EPG) which contains information regarding the scheduled programming. In another embodiment information regarding the programming is retrieved from the closed caption channel transmitted in the broadcast signal.
All things considered, the examiner will set forth a new grounds of rejection in order to address the newly amended limitations not previously presented.
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) 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Kitts; Brendan et al. US 20120042339 A1 (hereafter Kitts) and in further view of Blume; Matthias et al. US 20070244741 A1 (hereafter Blume) and in further view of Eldering; Charles US 20060230053 A1 (hereinafter Eldering).
Regarding claim 21, “a computer-implemented method of targeting electronic media content using an analytics platform, the method comprising: associating, by one or more processors of the analytics platform, a rank with each unique viewer of a plurality of viewers based on a determined match score of each unique viewer of the plurality of viewers; generating, by the one or more processors, a vector for each unique viewer based on what is viewed by each unique viewer; selecting, by the one or more processors, a first package of unique viewers of the one or more packages of unique viewers based on the rank of each unique viewer and the vector for each unique vector” Kitts Fig 2, 7, 8 and para 221-223 disclosing targeting electronic media content using an analytics platform; para 185 teaches clustering of viewer targeted profiles wherein users within each cluster are ranked.; para 147-148 disclosing match between product and panelists corresponds to Nielsen viewers watching content. Regarding “determining, by the one or more processors, an expected lift value of the first package of unique viewers; and transmitting, by the one or more processors and to a user interface, a graphical representation of the first package of unique viewers based on the expected lift value” Kitts para 107-109 FIG. 16 shows lift scores for customer demographic attributes for product buyers wherein lift scores are generated automatically, and using "key insights" the most distinctive demographics are provided in a summary view of an analytics platform including the lift distributions for the natural clusters that were automatically generated by the system. The system generates these clusters and lift-scores automatically and provides them to the user. Kitts does disclose the user of vectors for each viewer (e.g., para 123-124, 129) and is directed to the limitation of a vector for each unique viewer based on what is viewed by each unique viewer.
Whereas Kitts does not refer to the disclosed lift values as “expected lift value,” a person of ordinary skill in the art would have reasonably inferred that Kitts’ disclosure reads on “expected lift value” because in an analogous art, Blume discloses an invention for predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments and wherein consumer profiles allow for various levels and types of segment analysis comprising ranking of consumer’s profiles (see Blume para 109-115 disclosing the consumers with the highest levels (or rankings) of predicted spending in the segment may be identified, or the consumers having consumer vectors closest to the segment vector may be selected).
In an analogous art, Eldering teaches that “[0075] By processing the recorded viewing habits in conjunction with programming related information and heuristic rules similar to those illustrated in FIG. 7 but related to programming rather than purchases, it is possible to construct a subscriber characterization vector which contains a probabilistic demographic profile of the household.” Eldering also teaches the following which is pertinent to the teachings of Kitts and Blume:
[0070] Profiler 140 may be a retailer who collects data from its stores, but can also be a third party who contracts with consumer 100 and the retailer to receive point of purchase data and profile consumer 100. Consumer 100 may agree to such an arrangement based on the increased convenience offered by targeted ads, or through a compensation arrangement in which they are paid on a periodic basis for revealing their specific purchase records.
[0071] Consumer profile server 130 can contain a consumer profile which is determined from observation of the consumer's viewing habits on television 108 or consumer PC 104. Determination of demographic and product preference information based on the consumer's use of services such as cable television and Internet access can be performed by monitoring the channel selections that a subscriber makes, and determining household demographics based on the subscriber selections and information associated with the programming being watched.
[0072] In one embodiment the channel selections are recorded, and based on the time of day during which the programming is watched and duration of viewing, heuristic rules are applied to make probabilistic determinations regarding the household demographics including age, gender, household size and income, as illustrated in FIG. 2A. This can be accomplished by applying heuristic rules which associate the programming with known and assumed characteristics for viewers of the programming. As an example, it is known that the probability that the viewer of a cartoon in the morning is in the 3-8 year old age group is high, thus if the household viewing habits consistently record viewing of cartoons the probability that the household will contain one or more viewers in the 3-8 year old age group is high.
[0073] In one embodiment information regarding the program is extracted from the Electronic Program Guide (EPG) which contains information regarding the scheduled programming. In another embodiment information regarding the programming is retrieved from the closed caption channel transmitted in the broadcast signal.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Kitts’ invention for targeting electronic media content using an analytics platform comprising viewer vectors for analyzing a rank with each unique viewer of a plurality of viewers based on a determined match score of each unique viewer of the plurality of viewers to determine a lift value of the first package/cluster of unique viewers in order to tailor media content by further incorporating known elements of Blume’s invention for predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts wherein consumer profiles allow for various levels and types of segment analysis comprising ranking of consumer’s profiles and the consumers with the highest levels (or rankings) in the segment may be identified, or the consumers having consumer vectors closest to the segment vector may be selected in order to specifically tailor media content to improve advertising campaign an maximize advertising costs as return on investment because Eldering recognizes the benefits of processing the recorded viewing habits in conjunction with programming related information and heuristic rules but related to programming rather than purchases and construct a subscriber characterization vector which contains a probabilistic demographic profile of the household in order to delivered more effective advertisement targeting.
Regarding claim 22, “further comprising: receiving, by the one or more processors, a plurality of set top box data, the plurality of set top box data including a set of viewing behavior data of a unique user device associated with the unique viewer of the plurality of viewers; and determining, by the one or more processors, the match score based on a similarity factor between a set of product purchase data of a plurality of product purchasers and the set of viewing behavior data” is further rejected on obviousness grounds as discussed in the rejection of claim 21 wherein Kitts Fig 2, 7, 8 and para 221-223 disclosing targeting electronic media content using an analytics platform; para 185 teaches clustering of viewer targeted profiles wherein users within each cluster are ranked.; para 147-148 disclosing match between product and panelists corresponds to Nielsen viewers watching content; see also para 121-131 disclosing the media profile is calculated using information obtained from unique set-top boxes that have been assigned television viewers to record the television viewing history of the viewers and using demographic information provided by the viewers.
Regarding claim 23, “further comprising: determining, by the one or more processors, the expected lift based on a comparison of the set of product purchase data to the first package of unique viewers” is further rejected on obviousness grounds as discussed in the rejection of claims 21-22 wherein Kitts para 97-98 teaches customer data comprises customers who purchased a particular product.
Regarding claim 24, “further comprising: receiving, by the one or more processors, a plurality of demographic data of the unique viewer; and determining, by the one or more processors, the match score based on a similarity factor between the set of product purchase data of the plurality of product purchasers, the set of viewing behavior data, and the plurality of demographic data of the unique viewer.
is further rejected on obviousness grounds as discussed in the rejection of claims 21-23 wherein Kitts Fig 2, 7, 8 and para 221-223 disclosing targeting electronic media content using an analytics platform; para 185 teaches clustering of viewer targeted profiles wherein users within each cluster are ranked.; para 147-148 disclosing match between product and panelists corresponds to Nielsen viewers watching content; see also Kitts para 97-98, 112, 123 teaches customer demographics data comprises customers who purchased a particular product and targeting ads to predefined segments.
Regarding claim 25, “further comprising: initiating, by the one or more processors and in response to a user interaction with the user interface, an electronic transaction associated with the first package of unique viewers” is further rejected on obviousness grounds as discussed in the rejection of claims 21-24 wherein Kitts para 77-82, 174, 196 enables the user to purchase interact with interface to purchase media for distribution to viewers.
Regarding claim 26, “further comprising: selecting, by the one or more processors, a second package of unique viewers of the one or more packages of unique viewers; determining, by the one or more processors, a second expected lift value of the second package of unique viewers; and transmitting, by the one or more processors and to the user interface, a second graphical representation of the second package of unique viewers based on the second expected lift value” is further rejected on obviousness grounds as discussed in the rejection of claims 21-26 wherein Kitts para 107-109 FIG. 16 shows lift scores for customer demographic attributes for product buyers wherein lift scores are generated automatically, and using "key insights" the most distinctive demographics are provided in a summary view of an analytics platform including the lift distributions for the natural clusters that were automatically generated by the system. The system generates these clusters and lift-scores automatically and provides them to the user would be understood by a person of ordinary skill in the art and reasonably infer that multiple packages and/or clusters of viewers can be analyzed to establish predicted lift values in order to target different media instances.
Regarding claim 27, “further comprising: initiating, by the one or more processors and in response to a user interaction with the user interface, an electronic transaction associated with the second package of unique viewers” is further rejected on obviousness grounds as discussed in the rejection of claims 21-24 wherein Kitts para 77-82, 174, 196 enables the user to purchase interact with interface to purchase media for distribution to viewers. See also Kitts para 107-109 FIG. 16 shows lift scores for customer demographic attributes for product buyers wherein lift scores are generated automatically, and using "key insights" the most distinctive demographics are provided in a summary view of an analytics platform including the lift distributions for the natural clusters that were automatically generated by the system. The system generates these clusters and lift-scores automatically and provides them to the user would be understood by a person of ordinary skill in the art and reasonably infer that multiple packages and/or clusters of viewers can be analyzed to establish predicted lift values in order to target different media instances.
Regarding the system claims 28-34 and the non-transitory computer readable media claims 35-40, the claims are grouped and rejected with the method claims 21-27 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 21-27 and because the steps of the method are easily converted into elements of computer implemented systems and non-transitory computer readable media claims 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Flynn can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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