DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4 December 2025 has been entered.
Status
This First Action Final Office Action is in response to the communication filed on 4 December 2025. Claims 2-3, 5-6, 14-15, 22, and 27-29 have been cancelled currently or previously, no claims have been amended, and no new claims have been added. Therefore, claims 1, 4, 7-13, 16-21, 23-26, and 30 are pending and presented for examination.
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 Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment overcomes the claim objection(s); therefore, the Examiner withdraws the objection(s).
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 112 regarding demographic or behavior data relating to the first audience; therefore, the Examiner maintains the rejection(s) with further explanation.
Upon further consideration, the 112 rejection regarding a data indicative of categories is withdraw since now considered a breadth issue rather than indefiniteness; please see the claim interpretation below.
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment does not overcome the prior art rejection(s) under 35 USC §§ 102 or 103; therefore, the Examiner maintains the rejection(s) as below.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Claim Interpretation
The Examiner notes that independent claims 1, 19, and 20 recite “receiving data indicative of a plurality of consumer categories, wherein each consumer category of the plurality of consumer categories comprises an indication of at least one of a demographic category, an interest, or a lifestyle characteristic of a type of consumer represented by the respective consumer category” (quoting claim 1, claims 19 and 20 being the same or parallel phrasing). Where this had been rejected as indefinite, further consideration indicates that this is apparently indicating the receiving of ANY data that can or could be classified in some manner. There is no indication of any preset or predetermined categories such that the data received would have to fit within any of those categories, so “data indicative of … categories”, including a demographic, interest or lifestyle of a consumer would appear to include any data the Examiner has been able to imagine or consider reasonable as received. For instance, the data collected from the panel of users would appear (since being indicative of content consumed) to be able to be categorized however someone may want, including at least by demographics and/or interest (i.e., consumption indicating interest). This is to say that since there is no indication of predetermined categories, the claim apparently includes any data that can be classified – that data indicates the categories, and it appears that the recited types of categories would generally encompass the data that can be received.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4, 7-13, 16-21, 23-26, and 30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 19, and 20 reach recite “determining to insert supplemental content … wherein neither audience demographic information nor audience behavior information related to the first audience of users is available, and wherein subject matter of the supplemental content is determined, based at least in part, on consumer categories included in contextual profiles associated with the one or more content categories associated with the second content”. The apparent best description of what constitutes “audience data” is Applicant ¶ 0012 indicating that “it may be desirable to target audiences based on audience data, such as audience demographic information or audience behavior, it may not always be possible to do so” – indicating that audience data includes demographic or behavior information. Applicant ¶ 0049 indicates that “Consumer categories, such as demographic categories, ethnicity categories, interest or lifestyle categories, or viewing habit categories, that over-index with the content category may be associated to a profile associated with the content category.” Therefore, the consumer categories included in contextual profiles associated with the one or more content categories associated with the second content is, by Applicant’s description or definition, audience data. Since the first audience is associated to the second content via the content categories and those content categories are correlated to the demographic and audience behavior information (the claim indicates a threshold based on average correlation must be satisfied), there is (by definition) audience demographic information and audience behavior information that is “RELATED TO” the first audience. The term “related to” does NOT mean or require that the first audience be identified and tracked such that there is direct and specific information on that particular identified audience. But rather, the term “related to” would appear to mean that there is some demographic or audience behavior information available (from some source) that is somehow merely “related to” the prospective audience – and in the instant claims, the panelist demographic and behavior information is specifically indicated as being related to a prospective audience for the second content by the content categories that are required to have that correlation.
It is indefinite how, or whether, it is possible to not have audience data available, but the same data that is “unavailable” is apparently provided and used as a basis for determining the subject matter of the of the supplemental content.
The Examiner has no idea how to interpret these limitations, other than to interpret them as providing the information, such as through contextual profiles even though the particular, actual, specific users included in the first audience, and their specific data information, is not available.
Claims 4, 7-13, 16-18, 21, 23-26, and 30 depend from claim 1, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 4, 7-14, 16-18, 21, 23-26, and 30 are also indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 7-13, 16-21, 23-26, and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1, 4, 7-13, 16-18, 21, 23-26, and 30), system (claim 19), and non-transitory computer-readable medium (claim 20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites collecting, using a monitoring application installed on each device associated with users of a panel of users, data indicative of content consumed by the panel of users; determining a plurality of content categories associated with the content consumed by the panel of users, wherein each content category of the plurality of content categories comprises an indication of at least one of a theme, a subject matter, or a topic associated with the respective content category; receiving data indicative of a plurality of consumer categories, wherein each consumer category of the plurality of consumer categories comprises an indication of at least one of a demographic category, an interest, or a lifestyle characteristic of a type of consumer represented by the respective consumer category; receiving a contextual profile associated with a content category of the plurality of content categories; determining a correlation value between each of the plurality of consumer categories and the content category; averaging the correlation values of the plurality of consumer categories and the content category; determining that a correlation value of a consumer category of the plurality of consumer categories and the content category satisfies a threshold based on the average correlation; including, based on the correlation value satisfying the threshold, the consumer category in the contextual profile; and determining to insert supplemental content into second content provided to a first audience of users over a second period of time based, at least in part, on one or more content categories of the plurality of content categories that are associated with the second content, wherein neither audience demographic information nor audience behavior information related to the first audience of users is available, and wherein subject matter of the supplemental content is determined, based at least in part, on consumer categories included in contextual profiles associated with the one or more content categories of the plurality of content categories that are associated with the second content.
Independent claims 19 and 20 are parallel to claim 1 above, requiring the same activities, except being directed to a system comprising: at least one processor; and at least one memory storing instructions that, when executed, cause the at least one processor to perform the activities (at claim 19) and a non-transitory computer-readable medium storing instructions that, when executed, cause the activities to be performed (at claim 20).
The dependent claims (claims 4, 7-13, 16-18, 21, 23-26, and 30) appear to be encompassed by the abstract idea of the independent claims since they merely indicate to satisfy the threshold is based on comparing the consumer-content correlation to the threshold (claim 4), determining a correlation between a different consumer category does not satisfy the threshold and therefore determining that the different consumer category should not be associated with the profile associated with the content category (claim 7) where the threshold for determining the correlation with the different category is based on being less than or equal to the threshold (claim 8), determining the correlation with the different consumer category (at claim 7) satisfies the threshold and associating the different consumer category to the profile associated with the different content category (claim 9), at least one consumer category indicating a viewing habit category (claim 10), the data indicative of consumer categories comprises purchasing behavior (claim 11), web browsing behavior (claim 12), television watching habits (claim 13), HTTP requests (claim 21), where the device identifier is indicative of demographic information (claim 23), the consumed content being websites and the data indicative of that content being a URL, times the URL was consumed, or device ID (claim 16), the data indicative of consumer categories is accessed from a database (claim 17), using a regression model to determine correlation (claim 18), the monitoring application has access to the network stack of each device (claim 24), determining correlation comprises the correlation being greater than the threshold (claim 25), the supplemental content being obtained from a supplemental content provider (claim 26) and/or data indicative of content consumed comprises a language of the content (claim 30).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of “contextual targeting” per Applicant ¶ 2 – i.e., categorizing consumer segments and content to correlate them and associate a consumer category to a profile of a content category; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the certain methods of organizing human activity (e.g., … commercial or legal interactions such as … advertising, marketing or sales activities/behaviors, or business relations; …) grouping of subject matter, but also implicates mathematical concepts (e.g., … calculations) in the calculations of average correlations and satisfying a threshold.
The Examiner notes that How to Write Advertisements That Sell, specific author unknown, A.W. Shaw Co., 1912, advocates for classifying or categorizing both consumers and venues such as content types, summarizing the concepts in various charts to track the advertising (see, e.g., pp. 6, 40, 56, 64-65, etc.) where, for example, “The Advertising Chart has an important place here. By it a book publisher cleverly classifies his volumes in such a way that different groups of books, as advertised in the fiction monthly, the farm paper, the literary monthly, the news weekly, the morning paper, the religious weekly, the woman's journal and various class and trade publications respectively, make the widest possible appeal to those who are prospects for each.” (Id. at 28). The book is replete with other indications of categorizing consumers and content. This indicates that the content of various publications is categorized so that consumers that are categorized as correlating to that content category fit the associated profile for targeting.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are using a monitoring application installed on each device (at claim 1), the claim to a system comprising: at least one processor; and at least one memory storing instructions that, when executed, cause the at least one processor to perform the activities (at claim 19), a non-transitory computer-readable medium storing instructions that, when executed, cause the activities to be performed (at claim 20), and the data being collected as including HTTP requests (at claim 21), URLs of web pages accessed, times the URL was accessed/consumed, and device identifier (at claim 16), and/or having access to a network stack of each device (at claim 24). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The indication of using an application installed on a panelist device is literally just a form of “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
The collecting and forwarding of particular data (e.g., HTTP requests, URLs, times of access, device IDs, and/or content categories) as indicated at claims 1, 10-14, 16, 19-21, and 30 may be computer-related data, but there is no change or improvement to the collecting or gathering of data, nor the forwarding of the collected/gathered data. This is considered, based on MPEP § 2016.05(g) as insignificant extra-solution activity since “Mere Data Gathering” and/or “Selecting a particular data source or type of data to be manipulated”.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
No additional elements are identified as well-understood, routine, conventional (“WURC”) activity for consideration under the WURC rubric and/or Berkheimer.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore, the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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.
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 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.
Claims 1, 4, 7-10, 12, 14, 16-17, 19-21, 23-26, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Mielechowicz et al. (U.S. Patent Application Publication No. 2018/0047048, hereinafter Mielechowicz) in view of Huang et al. (U.S. Patent Application Publication No. 2019/0155916, hereinafter Huang) and in further view of Attenberg et al. (U.S. Patent Application Publication No. 2012/0010927, hereinafter Attenberg)
Claim 1: Mielechowicz discloses a method comprising:
collecting, using a monitoring application installed on each device associated with users of a panel of users, data indicative of content consumed by the panel of users (see Mielechowicz at least, e.g., ¶¶ 0026, “Panelists agree for the tracking and monitoring service to install one or more tracking applications on their computing devices to collect computing activity. In the example disclosed herein, panelists agree to have a plug-in application installed in one or more web browsers operating on a panelist device. The plug-in application is configured to acquire information regarding browsing behavior of the panelist including websites visited and advertisements displayed on each webpage. The plug-in application is also configured to identify social media websites and determine advertisements within posts, tweets, etc. In addition to enabling their computing activity to be monitored, panelists provide demographic information, which is later used downstream to correlate monitored activity with demographics for a plurality of panelists” – indicating that the computing activity is monitoring those websites and advertisements viewed, 0030, “the example categorization server is configured to analyze the creative file in connection with the information from the panelists to determine a corresponding brand for each of the advertisements (block 106). After determining the brand, the categorization server is configured to determine whether the advertisement is part of an advertising campaign using, for example metadata, audio signature analysis, and/or video signature analysis. The categorization server groups together different advertisements associated with the same campaign. In addition, the categorization server correlates demographics of panelists that viewed each of the advertisements. At this point, the categorization server has matched which groups of panelists (based on demographics) have viewed which advertisements” – indicating each brand and each campaign as categories of content, 0038, “The panelist devices 202 each include a monitoring application 208 configured to track and/or monitor computing activity”; citation by number only hereinafter) ;
determining a plurality of content categories associated with the content consumed by the panel of users, wherein each content category of the plurality of content categories comprises an indication of at least one of a theme, a subject matter, or a topic associated with the respective content category (Mielechowicz at 0002-0003, brand and advertising campaign as based on a “theme”, 0030, “the example categorization server is configured to analyze the creative file in connection with the information from the panelists to determine a corresponding brand for each of the advertisements”, brand as the category from the categorization server, 0070, “Specifically, the monitoring server 204 is configured to include a categorization interface 236 and a categorization engine 238 to match an advertisement to an advertising campaign”. It is noted that Applicant makes no distinction, nor defines any meaning or difference between “theme”, “subject matter”, and/or “topic” – they are interpreted within the light of the specification as synonyms of one another);
receiving data indicative of a plurality of consumer categories, wherein each consumer category of the plurality of consumer categories comprises an indication of at least one of a demographic category, an interest, or a lifestyle characteristic of a type of consumer represented by the respective consumer category (Mielechowicz at 0080, “real users and real browsers” explained and “the user analysis engine 240 is configured to determine a number of real browsers and real users”, 0081, “Real users are accordingly estimated users of a population of a defined area that have accessed a certain website or viewed certain advertisements. Demographics of the panelists that viewed the same websites and/or advertisements may be extrapolated to the real users” 0083 and Fig. 9 as indicating the information and calculation used in the real user and real browser determination; the received data being the browser/user information that is used for extrapolation to the real users);
receiving a contextual profile associated with a content category of the plurality of content categories (Mielechowicz at 0083-0084 and 0087-0089, the target browser and its history being the user profile);
determining a correlation value between each of the plurality of consumer categories and the content category (Mielechowicz at 0026, “correlate monitored activity with demographics for a plurality of panelists”, 0081, “Demographics of the panelists that viewed the same websites and/or advertisements may be extrapolated to the real users”).
Mielechowicz, however, does not appear to explicitly disclose averaging the correlation values of the plurality of consumer categories and the content category; determining that a correlation value of a consumer category of the plurality of consumer categories and the content category satisfies a threshold based on the average correlation, including, based on the correlation value satisfying the threshold, the consumer category in the user profile, and determining to insert supplemental content into second content provided to a first audience of users over a second period of time based, at least in part, on one or more content categories of the plurality of content categories that are associated with the second content, wherein neither audience demographic information nor audience behavior information related to the first audience of users is available, and wherein subject matter of the supplemental content is determined, based at least in part, on consumer categories included in contextual profiles associated with the one or more content categories associated with the second content. Where Mielechowicz associates the user profile to a category (as cited above per the correlating and targeting, the user profile being the profile indicated by the browser and browser history), Huang teaches content correlation to “select one or more related terms that have correlation coefficients greater than the stored average value of the correlation coefficients by a threshold value” (Huang at 0065) the Examiner noting that Huang at 0065 conveys two separate thresholds: 1) “correlation coefficients greater than the stored average value of the correlation coefficients”, and 2) that there is a margin or threshold above the first threshold by which the coefficient must be), where user profiles are stored by Huang (see, e.g., Huang at 0027, 0034), the correlation coefficients having statistical analysis applied so as to rate and score, “For example, a user may search with a query ‘donald duck’ and the social-networking system 160 may identify posts matching the query. The social-networking system 160 may determine, using the real-time query-post log, that the terms ‘huey’, ‘dewey’, and ‘louie’ are significant in predicting user interaction based on a statistical analysis. The social-networking system 160 may then score the posts based on the statistical analysis and rank the posts accordingly. Although this example is described in the context of post-type objects, the statistical analysis may be applied to any content objects containing text (e.g., user profiles, news, articles, comments on audio/visual content, text extracted by speech recognition on audio/visual content, etc.)” (Huang at 0062) – indicating the categories (such as ‘donald duck”, “huey”, ‘”dewey”, and “louie”) are included in a user profile, and this is “for achieving the goal of ranking the search results by presenting the most relevant, comprehensive, and/or popular results” (Huang at 0010). Therefore, the Examiner understands and finds that determining correlation based on average correlation satisfying a threshold is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to present relevant, comprehensive and/or popular content.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the collection and analysis of Mielechowicz with the correlating of Huang in order to determine correlation based on average correlation satisfying a threshold so as to present relevant, comprehensive and/or popular content.
The rationale for combining in this manner is that determining correlation based on average correlation satisfying a threshold is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to present relevant, comprehensive and/or popular content as explained above.
Mielechowicz in view of Huang, however, does not appear to explicitly disclose determining to insert supplemental content into second content provided to an audience of users over a second period of time based, at least in part, on one or more content categories associated with the second content, wherein neither audience demographic information nor audience behavior information related to the first audience of users is available, and wherein subject matter of the supplemental content is determined, based at least in part, on consumer categories included in contextual profiles associated with the one or more content categories associated with the second content. Attenberg, though, teaches “mechanisms for scoring and rating web pages, web sites, and other pieces of content of interest to advertisers or content providers for safe and effective online advertising” (Attenberg at 0008), “by determining a risk rating that is based on the probability of encountering different severity levels from a given URL and that is based on the types of estimated severity exhibited in the past (Attenberg at 0010), including “several categories of objectionable content that may be of interest. For example, … these categories can include offensive language (e.g., sites that contain swear words, profanity, hard language, inappropriate phrases and/or expressions)”, “select[ing] a uniform resource locator (URL) for rating” (Attenberg at 0044), “the text of the URL, image analysis, HyperText Markup Language (HTML) source code, site or domain registration information, ratings, categories, and/or labeling from partner or third party analysis systems (e.g., site content categories), “source information of the images on the page, page text or any other suitable semantic analysis of the page content, metadata associated with the page, anchor text on other pages that point to the page of interest” (Attenberg at 0045), ”example combination functions include weighted averaging, where the weights are set to the importance of particular objectionable content categories, Bayesian mixing, a secondary combining model, and/or a simple minimum function that determines the most risky category in the case of a brand safety model” (Attenberg at 0077), “a rating for each of a number of objectionable content categories … [or] the risk rating can be determined across several objectionable content categories, across multiple pieces of content” (Attenberg at 0009), “the risk rating identifies whether the web content is likely to contain objectionable content of a given category” (Attenberg at 0012), “the generated risk rating that encodes whether the web content is likely to contain objectionable content of the given category with a second risk rating that encodes whether the web content is likely to contain objectionable content of a second category” (Attenberg at 0020). Therefore, the base system and/or methods of determining whether to insert supplemental content as in Mielechowicz in view of Huang would be predictably improved or modified by the information providing techniques indicated in Attenberg so as to yield the predictable result of providing URL, brand safety, content and consumer category information, and additional information such as language so as to allow advertisers to place “safe and effective online advertising”. As such, the Examiner understands and finds that to determine whether to insert supplemental content in second content based on profiles of content categories associated with the second content is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to enable the placement of safe and effective online advertising.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the advertising analysis of Mielechowicz in view of Huang with the data provision of Attenberg so as to determine whether to insert supplemental content in second content based on profiles of content categories associated with the second content in order to enable the placement of safe and effective online advertising.
The rationale for combining in this manner is that to determine whether to insert supplemental content in second content based on profiles of content categories associated with the second content is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to enable the placement of safe and effective online advertising as explained above.
Claim 4: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein determining that the correlation value of the consumer category and the content category satisfies the threshold comprises comparing the correlation value of the consumer category and the content category to the threshold (Huang at 0065, the correlation coefficients and averaging being used in, or as, the correlating of Mielechowicz per the combination above and using the rationale as combined above).
Claim 7: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, further comprising:
determining that a correlation value between a different consumer category of the plurality of consumer categories and the content category does not satisfy the threshold (Huang at 0065, where, when/if the threshold is not met, correlation is not found, as per the combination above and using the rationale as combined above); and
determining, based on the correlation value not satisfying the threshold, that the different consumer category should not be included in the contextual profile (Huang at 0065, where, when/if the threshold is not met, correlation is not found, as per the combination above and using the rationale as combined above).
Claim 8: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1 wherein determining that the correlation value between the different consumer category and the content category does not satisfy the threshold comprises:
determining that the correlation between the different consumer category and the content category is less than or equal to an average correlation between other consumer categories of the plurality of consumer categories and the at least one content category (Huang at 0065, where, when/if the threshold is not met, correlation is not found, as per the combination above and using the rationale as combined above).
Claim 9: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 7, further comprising:
determining that a correlation value between the different consumer category and a different content category satisfies the threshold (Huang at 0065, where, when/if the threshold is not met, correlation is not found, as per the combination above and using the rationale as combined above); and
including, based on the correlation value satisfying the threshold, the different consumer category in a contextual profile associated with the different content category (Huang at 0065, where, when/if the threshold is not met, correlation is not found, as per the combination above and using the rationale as combined above).
Claim 10: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1 wherein at least one consumer category of the plurality of consumer categories further indicates a viewing habit category (Mielechowicz at 0023, “panelist and advertisement information to determine a number of real users that viewed an advertising campaign”, 0058, “the browser cookies may be used to determine how frequently a panelist visits particular website”, 0082, “an estimated cookies approach may be used to takes into account how often a website is accessed”, 0092, “the user analysis engine 240 may chart how often a particular advertisement (or advertisements of a campaign) was viewed during a specified time period”).
Claim 12: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1 wherein the data indicative of the plurality of consumer categories further comprises data indicative of consumer web browsing behavior associated with each of the plurality of consumer categories (Mielechowicz at 0046, “browsing history”).
Claim 16: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein the content consumed by the panel of users over the first period of time comprises websites, and wherein collecting the data indicative of the content consumed by the panel of users over the first period of time comprises receiving universal resource locators (URLs) associated with the websites (Mielechowicz at 0050, “rules may include, for example, … URL match rules”), times that the URLs were consumed (Mielechowicz at 0032, “statistical analysis server is configured to enable a third-party to filter the data based on different criteria including, time period, … advertisement in-screen time,” etc. – where to enable such filtering, the time URLs are consumed must be received and recorded, 0055, “browser interface 306 may further determine a date/time the advertisement was viewed”), or identifiers associated with the devices associated with the panel of users (Mielechowicz at 0080-0081, real browsers as identifiers associated with the devices).
Claim 17: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein receiving the data indicative of the plurality of consumer categories comprises accessing, from a database, the data indicative of the plurality of consumer categories (Mielechowicz at 0005, 0039, 0060).
Claims 19-20 are rejected on the same basis as claim 1 above since performing the same operations as at claim 1, but directed to a system comprising: at least one processor; and at least one memory storing instructions that, when executed, cause the at least one processor to perform the operations (claim 19) and a non-transitory computer-readable medium storing instructions that, when executed, cause the same operations (claim 20) (Mielechowicz at 0112).
Claim 21: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein the monitoring application is configured to collect information regarding HTTP requests and subsequent HTTP responses (Mielechowicz at 0046, “page request” and “ad request” with response, 0050, “URL match rules”, where “rules 312 to 316 may also include rules for comparing browser traffic after a webpage is loaded to identify ad request and response message” and the call for rules is using HTTP, so the request and responses are also interpreted as being or encompassing HTTP requests and responses).
Claim 23: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 22, wherein the identifier associated with the particular device is indicative of demographic information associated with one or more users associated with the particular device, the one or more users belonging to the panel of users (Mielechowicz at 0003, 0023, “the example method, apparatus, and system are configured to correlate panelist demographic information”, 0026, “panelists provide demographic information”).
Claim 24: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein the monitoring application installed on each device has access to the network stack of each device (Mielechowicz at 0051, “the example monitoring application 208 is configured to communicate with the monitoring server 204”).
Claim 25: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein determining that the correlation value satisfies the threshold comprises determining that the correlation value is greater than the threshold (Huang at 0065, the correlation coefficients and averaging being used in, or as, the correlating of Mielechowicz per the combination above and using the rationale as combined above).
Claim 26: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein the supplemental content is obtained from a supplemental content provider (Mielechowicz at 0004, “advertising campaign analysis apparatus includes a categorization interface connected to panelist devices and ad servers”, 0029, “categorization server disclosed herein obtains an IP address and advertisement identifier from the source and creative identifying information to obtain the corresponding creative file from the ad server” – the source of supplemental content being, by definition, a supplemental content provider).
Claim 30: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, wherein the data indicative of the content consumed by the panel of users comprises a language associated with the content consumed by the panel of users (Mielechowicz at 0026, “correlate monitored activity with demographics for a plurality of panelists. Demographic information may include, for example, … primary language”, etc.).
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Mielechowicz in view of Huang and in further view of Attenberg and in still further view of Schrader (U.S. Patent Application Publication No. 2004/0059625).
Claims 11 and 13: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, but does not appear to explicitly disclose wherein the data indicative of the plurality of consumer categories further comprises data indicative of consumer purchasing behavior associated with each of the plurality of consumer categories (claim 11), and wherein the data indicative of the plurality of consumer categories comprises data indicative of consumer television watching habits associated with each of the plurality of consumer categories (claim 13). Where Mielechowicz discloses various demographic information, such as “age, gender, race, ethnicity, nationality, primary language, geographic location, religious affiliation, sexual orientation, marital status, relationship status, children status, education level, income level, occupation, hobbies, and/or personal preferences (e.g., sports teams, dining, shopping, etc.)” (Mielechowicz at 0026, 0039, citing to 0026), Mielechowicz does not appear to specifically include purchasing behavior and/or television watching habits. Schrader, however, teaches that a “target market includes one or more consumers selected by age, income, browsing or purchase history, geography, or other demographic information, or randomly, that the marketer has identified to receive advertising under the campaign plan” (Schrader at 0026) and that “Channel distributors 203 include … television and radio networks, etc. [and] various current and imminent interactive channels 205 include the Internet and World Wide Web, Interactive Television, and self service devices” (Schrader at 0026) so as to allow advertisers to categorize consumer responses (Schrader at 0093). Therefore, although all of the indicated limitations are considered to be fields of use that may be granted little if any patentable weight (see MPEP § 2103), the Examiner understands and finds that also using data indicative of purchasing behavior and/or television watching habits are each and all applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to categorize consumers and responses.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the contextual advertising of Mielechowicz in view of Huang with the data of Schrader in order to also use data indicative of purchasing behavior and/or television watching habits so as to categorize consumers and responses.
The rationale for combining in this manner is that also using data indicative of purchasing behavior and/or television watching habits are each and all applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to categorize consumers and responses as explained above.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mielechowicz in view of Huang and in further view of Attenberg and in still further view of Fanelli et al. (U.S. Patent Application Publication No. 2006/0004622, hereinafter Fanelli).
Claim 18: Mielechowicz in view of Huang and in further view of Attenberg discloses the method of claim 1, but does not appear to explicitly disclose further comprising determining the correlation value between each of the plurality of consumer categories and the content category by fitting a regression model to data associated with each of the plurality of consumer categories and data associated with the content category. Fanelli, however, teaches “content classifications are determined by … [in part] performing a regression analysis to create the plurality of predictive attitudinal models; and using each predictive attitudinal model of the plurality of predictive attitudinal models, scoring the plurality of entities represented in the data repository, as the reference population, to create the plurality of predictive message content classifications” (Fanelli at 0138). Where Fanelli is determining classifications rather than correlation of classifications, the technique of fitting a regression model is the same technique and it is used in the same manner, just perhaps on different data. Therefore, the Examiner understands and finds that fitting a regression model to determine correlation between content and consumer categories is the use of known techniques to improve similar devices, methods, or products in the same way so as to score and create the correlations as are indicated at Mielechowicz in view of Huang.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the contextual advertising of Mielechowicz in view of Huang with the regression models of Fanelli in order to fit a regression model to determine correlation between content and consumer categories so as to score and create the correlations as are indicated at Mielechowicz in view of Huang.
The rationale for combining in this manner is that fitting a regression model to determine correlation between content and consumer categories is the use of known techniques to improve similar devices, methods, or products in the same way so as to score and create the correlations as are indicated at Mielechowicz in view of Huang as explained above.
Response to Arguments
Applicant's arguments filed 4 December 2025 have been fully considered but they are not persuasive.
Applicant first argues the 112 rejections (Remarks at 9-13), first alleging that “all that is known is the "one or more content categories" associated with the second content that the first audience of users is consuming. Again, there is no knowledge of the demographic information, purchasing behavior, web browsing behavior, etc., of any of the individual members of the first audience of users.” (Remarks at 10-11). However, the claims do not identify any particular individuals in the “the first audience”, that “first audience” is defined at the claims as being any user(s) viewing the second content “over a second period of time” at claim 1. Therefore, the Examiner agrees that what is known (but not necessarily “ALL that is known” – emphasis added from the argument) is that “the ‘one or more content categories’ associated with the second content that the first audience of users is consuming”; however those content categories that Applicant agrees are known, are in fact demographic and behavior information “RELATED TO” the first audience of users (emphasis added to the claim phrasing). Therefore, Applicant's argument that the references fail to show certain features of the invention (i.e., that individual members of the first audience are identified in particular, and that based on those identities, the particular data of those identified persons is not available, it is noted that the features upon which applicant relies are not recited in the rejected claim(s).
Applicant then argues the 101 rejections (Remarks at 11-15), alleging that since the claims use the content categories, the audience is predicted (since specific audience information is unavailable) and therefore “an improvement to prior art data collecting/gathering techniques, solving the technical problem of determining contextually-relevant content to serve to a particular audience of users, when the entity serving the content only knows the caterogry(ies) [sic] of content that it is serving to the particular audience-but it does not know any relevant demographic/behavioral information about the particular audience of users it is serving said content to” (Remarks at 14). However, first, prior art analysis and whether there is an improvement to prior art is not used in eligibility analysis. Second, this reflects the abstract idea of contextual targeting, and is included with the abstract idea – using that data (context, such as categories of users that have or tend to view content or a page) does not take the claims outside the realm of abstract ideas. The claims are still specifically in the advertising, marketing, etc. areas that are included in the certain methods of organizing human activity grouping.
Applicant then argues that “even if the individual components (processor, memory, monitoring applications, supplemental content insertion, etc.) are conventional, the specific arrangement and interaction of these components to allow audiences to be predicted by using the aforementioned created contextual profiles-especially in situations where audience data (i.e., audience demographic information and audience behavior information, in particular) for the audience in question is unavailable is non-conventional and non-generic” (Id at 15). However, there is nothing that is apparently “non-generic” about any configurations or components. The argument does not provide any information about any configuration that may be non-conventional – the argument does not even discuss any configuration. The argument is that “conventional targeted advertising systems are not able to work effectively when audience data, such as audience demographic information and audience behavioral data, is not available” and “The claimed invention departs from this conventional approach by using the aforementioned created contextual profiles” and performing contextual targeting (Id.). This indicates that Applicant apparently believes that ONLY other types of advertising such as search term targeted advertising, or location-based targeted advertising, or specific user profile targeting, are the only “conventional approach[es]” to be considered. The Examiner, at the rejection, indicated that at least by 1912 (in “How to Write Advertisements that Sell”) it was being advocated and taught to consider the profile of persons that receive publications such as journals or magazines when placing advertising, such that tractor and agricultural books and products should or would be advertised in “the farm paper”, mystery books may be advertised in “the fiction monthly”, etc. – to “make the widest possible appeal to those who are prospects for each” (Id. at 28). These advertisers from a century ago did not apparently have specific individual profiles of each subscriber of the journal, magazine, or publication they considered for advertising (it is understood that their demographics and/or behavior information was not available), but rather, the book indicates to rely on the profile of the persons that would or have receive(d) such publications. The concept Applicant argues as “inventive” and patent eligible is long-standing and well-established – it is NOT “non-conventional”.
Applicant then argues a “technical solution” as analogous to Bascom (Remarks at 15). However, there is no apparent “technical solution” presented by the claims – including (at least at the claimed method) of receiving hard-copy panelist information and/or data indicative of consumer categories, and manually performing/making the determinations and correlation value calculations. At the independent claims, only the system and medium claims of claims 19-20 appear to require or indicate any use of any technology whatsoever, and even there the only requirements are a basic, generic processor, memory, and/or non-transitory computer-readable medium storing instructions. Dependent claims 16, 21, and 23 do also indicate some technology use (i.e., e.g., websites, URLs, HTTP, etc.); however, that also is generic use of computers in performing the abstract idea. The claim to a technical solution is illusory at best.
Applicant next argues the § 103 rejections (Remarks at 15-19), alleging that Mielechowicz “is not the same as (or similar to) the claims of the present application” since the claims indicate “information regarding the audience members themselves in unavailable” (Id. at 16). However, the claims do not recite this requirement – the claims are far broader than this in reciting “information RELATED TO the first audience of users” not being available. However, the claims recite that the contextual profile information that includes demographic and behavior information IS available and used, and that information most certainly is “information RELATED TO the first audience of users” since that is the basis for defining the first audience (i.e., “a first audience of users … based, at least in part, on one or more content categories of the plurality of content categories that are associated with the second content”). Applicant argues that “Mielechowicz's teachings in the cited portions are related to estimating the demographics of ‘real users’ that have already viewed a particular advertising campaign - and not trying to figure out what subject matter of advertisement should be served to an audience for which no demographic information is known, i.e., based on the categories of content (i.e., the non-advertising/supplemental content) that the particular audience is consuming” (Id. at 16-17, emphasis at original argument). However, the breadth of the claims is that the demographics (and behavior) of those that already viewed the content and are recorded as a profile for the second content is, in fact, “information related to the first audience of users” – the contextual profile IS the basis for “predicted types of consumers that may have interest in the primary content being served” that Applicant argues the claims are trying to target (at Remarks at 16, with similar allegation at 18).
Therefore, Applicant’s arguments are not persuasive.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ho, Betty, Targeting 101: Contextual vs. Behavioral Targeting, Criteo.com, 1 November 2018, downloaded from https://www.criteo.com/blog/contextual-vs-behavioral-targeting/ on 21 January 2022, indicating contextual targeting as a common and long-established form of advertising.
Nettleton, David, Pearson Correlation, Commercial Data Mining, 2014, downloaded from https://www.sciencedirect.com/topics/computer-science/pearson-correlation on 22 January 2022, indicating that “The Pearson correlation method is the most common method to use for numerical variables; it assigns a value between − 1 and 1, where 0 is no correlation, 1 is total positive correlation, and − 1 is total negative correlation. This is interpreted as follows: a correlation value of 0.7 between two variables would indicate that a significant and positive relationship exists between the two. A positive correlation signifies that if variable A goes up, then B will also go up, whereas if the value of the correlation is negative, then if A increases, B decreases.” (at 1), which appears to be a/the average correlation claimed, or is related to it.
Sheppard et al. (U.S. Patent Application Publication No. 2020/0202370, hereinafter Sheppard) discusses collecting panelist user impression information via an app of a client device, including HTTP requests and responses, URLs, mobile equipment identifier, and associated demographic information (Sheppard at least at 0021, 0024-0026, 0029-0030, 0034, 0039-0041, 0044-0045, 0104).
Brown (U.S. Patent No. 10,123,063) indicates that “user access to web content may be monitored using a panel-based approach or a beacon-based approach. A panel-based approach generally entails installing a monitoring application on the user devices of a panel of users that have agreed, in advance with informed consent, to have their devices monitored. The monitoring application then collects information about the webpage or other resource accesses and sends that information to a collection server” (Brown at 4:36-64).
Kurland et al. (U.S. Patent No. 4,603,232, hereinafter Kurland) indicates that as far back as 1984, “Market survey data collection systems are well known in the art” (Kurland at 1:13-14), and discloses “a method for independently centrally electronically accumulating market survey data from different content rapidly disseminated market surveys from a plurality of panelist stations located at diverse locations” (Kurland at 1:6-10), i.e., gathering content data from panelist devices.
Burbank et al. (U.S. Patent No. 8,930,701, hereinafter Burbank) discusses “decoding information from a mobile device into a plurality of encrypted identifiers identifying at least one of the mobile device or a user of the mobile device, sending ones of the encrypted identifiers to corresponding database proprietors, receiving a plurality of user information corresponding to the ones of the encrypted identifiers from the corresponding database proprietors, and associating the plurality of user information with at least one of a search term collected at the mobile device or a media impression logged for media presented at the mobile device” (at Abstract).
Sullivan et al. (U.S. Patent Application Publication No. 2017/0127133, hereinafter Sullivan) discusses “Methods, apparatus, and articles of manufacture are disclosed to categorize audience members by age. An example method disclosed herein includes assigning a weight to each audience member record. In the example method, the weight is based on a quantity of audience members in a same age group as the audience member record. The example method includes, at the child nodes, calculating an effective quantity of audience member records based on the weights of the audience member record assigned to the corresponding child node. In the example method, when the effective quantity of audience member records satisfies a minimum leaf size, splitting the corresponding child node into additional ones of the child nodes based on a corresponding child node attribute-value pair. In the example method, when the effective quantity of audience member records does not satisfy the minimum leaf size, designating the corresponding child node as a terminal node.” (at Abstract).
Laurent et al., Measuring Consumer Involvement Profiles, Journal of Marketing Research, Vol, XXII (February 1985), pp. 41-53, downloaded from https://web.p.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=b5226b61-9d48-4dca-b157-d1f9aa637a74%40redis indicating at least that “There is more than one kind of consumer involvement. Depending on the antecedents of involvement (e.g., the product's pleasure value, the product's sign or symbolic value, risk importance, and probability of purchase error), consequences on consumer behavior differ. The authors therefore recommend measuring an involvement profile, rather than a single involvement level. These conclusions are based on an empirical analysis of 14 product categories.” (at 41).
Nakano et al, Customer segmentation with purchase channels and media touchpoints using single source panel data, Journal of Retailing and Consumer Services, Vol. 41, March 2018, pp. 142-152, downloaded from https://www.sciencedirect.com/science/article/pii/S096969891730471X on 2 August 2023, indicating that “This study examines how customers use multiple channels and media in modern retail environments. It segments customers by using Latent-Class Cluster Analysis, which focuses on the purchase channels of bricks-and-mortar and online stores, media touchpoints of PC, mobile, and social media, and psychographic and demographic characteristics. It extends the framework of prior research by analyzing 2595 Japanese single source panelists’ data in which purchase scan panel data on low-involvement, more frequently purchased categories, media contact log data, and survey data are tied to the same ID. The analyses reveal seven segments including the properties of research shoppers and multichannel enthusiasts.” (at 142, Abstract).
Leary et al. (U.S. Patent Application Publication No. 2012/0278064, hereinafter Leary) discusses “The determined sentiment value(s) 123 may be stored in association with the user text content 105 and represent what is determined to be sentiment for the subject. After analysis, the given user text content 105 may be associated with sentiment value 123 that correlates to (i) the users sentiment for a particular facet or category of the subject in the user content (e.g. business establishment), and/or (ii) the users sentiment in general, on average, or overall when all facets and categories are considered. The sentiment value(s) 123 that is determined for the particular item of user text content 105 may be based on the sentiment score 104 for salient terms 111 that are relevant to the subject and or the subject categories. For example, the sentiment score 104 for individual terms that are extracted from the text content may be averaged (or categorized and then averaged), in order to determine sentiment for the subject and/or a particular predefined domain-relevant category” (Leary at 0039).
IAB, Understanding Brand Safety & Brand Suitability in a Contemporary Media Landscape, dated December 2020, downloaded from https://www.iab.com/wp-content/uploads/2020/12/IAB_Brand_Safety_and_Suitability_Guide_2020-12.pdf on 7 March 2024, indicating that “issues of Brand Safety have begun to evolve beyond avoidance of malware, spam, and adult content, to include larger and more difficult-to-pin-down considerations of Brand Suitability” (at 6), the “IAB’s Programmatic+Data Center convened a working group of the leading ad verification and ad tech companies, as well as media agencies, to develop best practices based on what we learned during this unprecedented year” (Id.), that “Brand Safety and Brand Suitability are ever-evolving” (Id, at 7), and after identifying some “Relevant Groups” (Id. at 8) indicates that “TAG defines “Brand Safety” as the controls that companies in the digital advertising supply chain use to protect brands against negative impacts on consumer opinion associated with specific types of content and/or related loss of return on investment” (Id.), but nevertheless, under “Defining Brand Safety” (Id.)
Brand Safety solutions enable a brand to avoid content that is generally considered to be inappropriate for any advertising, and unfit for publisher monetization regardless of the advertisement or brand. This is where the 4A’s Brand Safety Floor categorization and IAB’s taxonomy classifications come into play.
For example, content that contains hate speech directed at a protected class would be inappropriate for any advertising. Likewise, content that promotes or glamorizes the consumption of illegal drugs would be inappropriate for any advertising.
(Id. at 8-9).
Array Basics, FSU, downloaded 4 November 2024 from https://www.cs.fsu.edu/~myers/c++/notes/arrays.html indicates that
An array is an indexed collection of data elements of the same type.
Indexed means that the array elements are numbered (starting at 0).
The restriction of the same type is an important one, because arrays are stored in consecutive memory cells. Every cell must be the same type (and therefore, the same size).
(at p. 1).
Everything you wanted to know about arrays, Microsoft PowerShell, dated 20 June 2024, downloaded 4 November 2024 from https://learn.microsoft.com/en-us/powershell/scripting/learn/deep-dives/everything-about-arrays?view=powershell-7.4, indicating (similar to the above) that “An array is a data structure that serves as a collection of multiple items. You can iterate over the array or access individual items using an index. The array is created as a sequential chunk of memory where each value is stored right next to the other.” (at p. 1) with further indexing information at p. 4 et seq.
Busbee et al., Arrays and Lists, Rebus Community, copyrighted 2018, downloaded 4 November 2024 from https://press.rebus.community/programmingfundamentals/chapter/arrays-and-lists/, indicating that “An array is a data structure consisting of a collection of elements (values or variables), each identified by at least one array index or key” (at p. 1).
Soroca et al. (U.S. Patent Application Publication No. 2010/0094878, hereinafter Soroca) describes that “In embodiments of the present invention improved capabilities are described for using a monetization platform server to associate sponsored content with contextual information relating to mobile content, and storing the sponsored content-contextual information association in a data facility for future use in optimizing the delivery of a sponsored content to a mobile communication facility based at least in part on a display datum associated with the mobile communication facility, wherein the display datum includes a contextual datum” (at Abstract), including content categorization (Soroca at 0826-0827, 0891), user (i.e., consumer) profiles and categorization of a consumer (Soroca at 1387-1388, 1399, 1401, 1465).
Merriam-Webster, “Related” definition, downloaded 5 February 2026 from https://www.merriam-webster.com/dictionary/related, indicating what “related to” would mean at the claims.
Merriam-Webster, “Correlation” definition, downloaded 5 February 2026 from https://www.merriam-webster.com/dictionary/correlation, indicating what “correlation” would mean at the claims.
Word Reference forum, Correlated and Related, downloaded 5 February 2026 from https://forum.wordreference.com/threads/correlated-and-related.3740959/, dated 12 September 2020, indicating what “correlation” and “related to” would mean at the claims.
Editage.com, Answer to the question: What is the difference between “related to”, “correlated to” and “associated with”?, dated 30 May 2022, downloaded 5 February 2026 from https://www.editage.com/insights/what-is-the-difference-between-related-to-correlated-to-and-associated-with, indicating what “correlation” and “related to” would mean at the claims.
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685