DETAILED ACTION
Status
This Office Action is in response to the application filed on 7 February 2025. Claims 1-20 are pending and presented for examination.
Continuation
This application is a continuation application of U.S. Application No. 16/113,447 filed on 27 August 2018. See MPEP §201.07. In accordance with MPEP §609.02(II)(A)(2) and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. In further accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application.
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 .
Examiner’s Note
The term “machine learning” is not described, defined, or exemplified by the specification. The only mention related to this is Applicant ¶ 0019, as submitted, indicating “Fig. 4B is a flowchart of an exemplary process for training a sentiment feature extraction model via machine learning”, that “a learning engine 540 is used in “the emotion-based ad filtering engine 340” and “the emotion-based ad filtering engine 340 includes a mechanism for learning emotion-based ad filtering models 405 based on instructions/inputs from humans” (at Applicant ¶ 0047), and “the user's filtering decision information is sent to the learning engine 540 so that the user specified input may be gathered to learn” (at Applicant ¶ 0050). This indicates that the light of Applicant’s specification is that “machine learning” is any method of “teaching” a general-purpose (see, e.g., Applicant ¶ 0056) or other computer to perform operations that would or have been otherwise performed by humans. See also Applicant ¶ 0043, indicating human personnel as performing the filtering. As such, “training … via machine learning” encompasses merely creating any modeling or filtering that follows or emulates human instructions.
The Examiner further notes that the “filtering automatically” as claimed is interpreted within the light of the specification as merely applying the user’s indication filtering instructions – i.e., automating the manual filtering provided by the user.
For clarity on the record, the Examiner notes that the term “sentiment feature” (or “sentiment features” as the plural) is NEVER actually described or defined by the specification. The closest description appears to be Applicant ¶ 0042 indicating that “if content is about death and injuries of people (including children) that occurred in a natural disaster with detected sentiment features related to sadness and sympathy, a selected advertisement on hosting fun birthday parties with sentiment features of happiness and fun may be specified as incompatible or even objectionable to each other”. Therefore, this is taken to mean that there is no specific feature – the sentiment feature is merely the sentiment associated with the content.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 3, 5, 10, 12, 17, and 19 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Independent claims 1, 8, and 15 recite the “candidate advertisements that match contextual features” are merely received – there is no claim indication of any determination regarding any determination of contextual features of online content, nor any determination regarding matching of advertisements to those contextual features. At the independent claims, the received candidate advertisements are required to already be matched to contextual features of online content (e.g., some outside entity, such as a third party, has apparently already performed the matching). As such, where dependent claims 3, 10, and 17 recite how the candidate advertisements are identified, there is no limitation at the parent independent claims that is limited by this – any basis for identifying the candidate advertisement(s) is outside the scope of the independent claims.
Similarly, where dependent claims 5, 12, and 19 also depend from claims 1, 8, and 15, and recite that “the contextual features of online content are determined based on a language model”. However, as indicated above, the candidate advertisements that match contextual features are merely received. The contextual features are not determined, nor identified, nor used the parent claims or other claims. As such, what the contextual features are and how they may have been determined are fairly immaterial to the claims – at best, some other entity or operation may (or may not) have determined them, but the contextual features were merely then used (e.g., by another entity or operation) to select candidate advertisements, and those candidate advertisements are sent or submitted so that the claims would receive them.
Based on the above, how either the candidate advertisements are identified, or how the contextual features are determined do not offer any limitation on any of the elements or operations of parent independent claims 1, 8, and 15. As such, claims 3, 5, 10, 12, 17, and 19 fail to further limit the subject matter of the claim upon which they depend.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 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 1-7), a non-transitory computer readable medium (claims 8-14), and a system (claims 15-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, the claim 1 recites a method for online advertising, comprising: receiving, by an engine, candidate advertisements that match contextual features of online content to be displayed; determining sentiment features for the online content and each of the candidate advertisements; if an emotion-based ad filtering model is available, filtering automatically the candidate advertisements using the emotion-based ad filtering model to select one or more of the candidate advertisements with sentiment features matching with the sentiment features of the online content; if the emotion-based ad filtering model is not yet available, presenting the sentiment features to a user to seek a filtering instruction, identifying, based on the filtering instruction, one or more of the candidate advertisements to be displayed together with the online content, and training, via machine learning, the emotion-based ad filtering model based on the filtering instruction and the identified one or more of the candidate advertisements; and providing the one or more of the candidate advertisements to be displayed together with the online content.
Independent claims 8 and 15 are analyzed in a similar manner as claim 1 above since claim 8 is directed to a non-transitory, computer-readable medium having information recorded thereon for online advertising, wherein the information, when read by a machine, causes the machine to perform operations comprising the same or similar activities as at claim 1 and claim 15 is directed to a system for online advertising, comprising: memory storing computer program instructions; and one or more processors that, in response to executing the computer program instructions, effectuate operations comprising the same or similar activities as at claim 1.
The underlined passage(s) of the claim indicate additional elements beyond the indication of the abstract idea.
The claim elements may therefore be summarized as the idea of providing advertisements based on the context and sentiment of content with which the advertisements are to be displayed. 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 since targeting content based on either or both of context and/or sentiment are at least included in advertising, marketing, sales, and/or business relations activities indicated by the grouping.
The Examiner notes that the Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) grouping also appears implicated since persons mentally process (i.e., evaluate, judge, or form opinions) regarding sentiment based on observations of the content or context.
It is also specifically noted that the light of Applicant’s specification does not exemplify or describe any types of models, or any methods of training the models, other than merely indicating that the “machine learning” is training the computer to perform the tasks (e.g., labeling data) that humans have previously done and/or would otherwise be doing. At least partly based on this, the training and/or use of the model for filtering is considered part of the abstract idea – the modeling can be performed by a person, such as supervised machine learning that uses human-labeled data to train a model. At least Applicant ¶ 0043 (as submitted) indicates that “emotion-based filtering may be initially performed by humans (e.g., personnel at the ad server 240 or at the publisher 230) and such filtering instructions may be used as training data to train a model” and at least Applicant ¶ 0045 (as submitted) indicates that the models used are based on “an offline mechanism” of labeled content, and “this offline portion receives … training data (labeled with sentiment features)” and processes it to obtain the models. This is to say, the specification specifically indicates that the selection process and modeling used is based on offline (i.e., human performed) labeling of data, whether performed mentally or merely manually, as a method of organizing human activity. It is noted that although modeling and machine learning would appear to be, by definition, the use of mathematics to model and predict, Applicant does not appear to describe it as such; therefore, although Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) may be implicated, the Examiner notes that the description of the modeling and machine learning specifically indicates (as above) that it is the emulation of human activities and/or evaluations or judgments, for example(s).
The dependent claims (claims 2-7, 9-14, and 16-20) appear encompassed by the abstract idea also since they merely indicate a request indicating the content for which the advertising is to be provided (at claims 2, 9, and 16), the basis for identifying or determining contextual features (claims 3, 5, 10, 12, 17, and 19), that the sentiment features reflect an emotion (claims 4, 11, and 18), that the filtering is based on advertisements for display being emotionally compatible with the content and/or filtered-out advertisements being incompatible with the content (at claims 6-7, 13-14, and 20).
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 the method activities being online advertising, receiving, by an engine, online content to be displayed, and filtering automatically (at claim 1), a non-transitory, computer-readable medium having information recorded thereon for online advertising, wherein the information, when read by a machine, causes the machine to perform operations (at claim 8), and a system for online advertising, comprising: memory storing computer program instructions; and one or more processors that, in response to executing the computer program instructions, effectuate operations (at claim 15). 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. Each of these additional elements appears to literally just be “[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” – see MPEP § 2106.05(I)(A) indicating that this is not considered significantly more than the abstract idea.
Therefore, 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. Therefore, the claims do not appear to be integrated into a practical application.
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 and when considered separately and in combination) are apparently 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.
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. Applicant ¶¶ 0055-0056 (as submitted) indicate the computer and/or components to be, or that they may be, a generic computer such as “a personal computer (PC)” (at 0055), and/or “a general-purpose computer” (at 0056).
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 all appear, as indicated above, to be encompassed by the abstract idea and therefore only limit the application of the idea, and 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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (U.S. Patent Application Publication No. 2015/0170218, hereinafter Rao) in view of Hicklin et al. (U.S. Patent Application Publication No. 2021/0042366, hereinafter Hicklin) .
Claim 1: Rao discloses a method for online advertising, comprising:
receiving, by an engine, candidate advertisements that match contextual features of online content to be displayed (see Rao at least at, e.g., ¶¶ 0057, “the server may search through the content of the article for specific keywords and compare the specific keywords with a keyword database to determine whether the context of the keywords is related to specific content or theme”, 0094, “the processor may associate the category of the article with the individual advertisement. For example, the processor may associate an ad of Samsung Galaxy.TM. with entire articles under a VAC category "Mobile Phones" of the VAC database. Further, the processor may associate the article with the ad by associating the VAC database with an ad database”; citation hereafter by number only) ;
determining sentiment features for the online content and each of the candidate advertisements (0070, “The sentiment analyzer may adopt a learning algorithm and weight every word in the article for positive and negative sentiment towards a subject matter mentioned in the article based on a test dataset, and then assign a corresponding sentiment to the article”, 0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article” – the Examiner noting that sentiment features of candidate advertisements must also have been determined in some manner in order to determine compatibility);
if an emotion-based ad filtering model is available, filtering automatically the candidate advertisements using the emotion-based ad filtering model to select one or more of the candidate advertisements with sentiment features matching with the sentiment features of the online content (0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article”); and
providing the one or more of the candidate advertisements to be displayed together with the online content (0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article”).
Rao, however, does not appear to explicitly disclose if the emotion-based ad filtering model is not yet available, presenting the sentiment features to a user to seek a filtering instruction, identifying, based on the filtering instruction, one or more of the candidate advertisements to be displayed together with the online content, and training, via machine learning, the emotion-based ad filtering model based on the filtering instruction and the identified one or more of the candidate advertisements. Where Rao indicates analyzing content using the sentiment analyzer that includes a learning algorithm based on test data to assign a sentiment, Hicklin, teaches “Certain aspects of this disclosure relate to a machine-learning query system for identifying a subset of digital content items. For instance, the machine-learning query system can service one or more queries for digital content items by extracting a result set of digital content, from the digital content items returned by a keyword query to a search engine. The result set of digital content can be extracted by filtering unwanted content, such as duplicative content, false positives, or content items lacking a specified sentiment” (Hicklin at 0012), where “the machine-learning algorithm can be a supervised classification algorithm that uses features such as lexical features, text statistics, and relative position of text blocks” (Hicklin at 0041). The Examiner notes that supervised models use labeled examples as training data for training the model (see, e.g., Romnani as below at pertinent prior). Therefore, the Examiner understands and finds that to build a model based on user-labeled data to create a sentiment analyzer (as in Rao) is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to create a/the sentiment analyzer in order to compare sentiments of the content and the advertisement.
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 emotion matching advertising of Rao with the supervised learning model of Hicklin in order to build a model based on user-labeled data to create a sentiment analyzer (as in Rao) so as to create a/the sentiment analyzer in order to compare sentiments of the content and the advertisement.
The rationale for combining in this manner is that to build a model based on user-labeled data to create a sentiment analyzer (as in Rao) is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to create a/the sentiment analyzer in order to compare sentiments of the content and the advertisement as explained above.
Claim 2: Rao in view of Hicklin discloses the method of claim 1, further comprising: receiving a request with an indication of the online content for selecting the one or more of the candidate advertisements to be displayed together with the online content (Rao at 0041, “The user device 124a, 124b may, for example, implement a web browser for viewing web pages and submitting user requests. A user operating the user device 124a, 124b may enter a search request and communicate the search request to the online information system 100. The search request may be processed by the search engine and search results may be returned to the user device”).
Claim 3: Rao in view of Hicklin discloses the method of claim 2, wherein the candidate advertisements that match contextual features of online content are identified based on at least some of:
one or more contextual features associated with the online content (Rao at 0030, “According to the example embodiments of the present disclosure, the search engine 106 may also provide to the user device 124a, 124b over the network 120 a web page with content including search results, information matching the context of a user inquiry”, 0057, “The analysis may be based on keywords comparison or based on context semantic analysis. The present disclosure intends to cover the broadest technologies in content analysis available before and after the time of the filing of the present disclosure. For example, the server may search through the content of the article for specific keywords and compare the specific keywords with a keyword database to determine whether the context of the keywords is related to specific content or theme”); and
one or more features associated with each piece of the candidate advertisements (Rao at 0030 and 0057, as above); and
information associated with a user to whom the online content and the one or more of the candidate advertisements are to be presented (Rao at 0061, “the server of the web page may employ demographic characteristics (e.g., age, income, sex, occupation, etc.) of the user for predicting user behavior, such as by group”, 0063, “The historical online activities, such as the demographic information and browsing path of the user, may be obtained through various methods. For example, the server may generate an entry to a cookie file in the user device 124a, 124b every time when the user visits the website. The entry may record the time and web page and/or content of the web page that the user browses in the website, so that the server may be able to collect the browsing history of the user in the website and generate a profile for the user”, 0104, “The personal information of the user may be his/her demographic information and/or his/her historical online activity information” and 0105, “the processor may select a plurality of VAC articles from the VAC database based on the personal information of the user. For example, the VAC article may be selected based on personal interest of the user that is reflected through his/her historical online activities“, where 0007 indicates “The at least one storage medium may include at least one set of instructions for generating a value added in-stream contents (VAC) database for ad display”).
Claim 4: Rao in view of Hicklin discloses the method of claim 1, wherein the sentiment features of the online content reflect an emotion expressed by the online content . (0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article”)
Claim 5: Rao in view of Hicklin discloses the method of claim 1, wherein the contextual features of online content are determined based on a language model. (Hicklin at 0041, “For example, the machine-learning algorithm can be a supervised classification algorithm that uses features such as lexical features, text statistics, and relative position of text blocks to classify portions of a digital content item as primary content or boilerplate content” – the text analysis indicating a language model is used, as combined above and using the rationale as at the combination above).
Claim 6: Rao in view of Hicklin discloses the method of claim 1, wherein the one or more of the candidate advertisements to be displayed together with the online content are emotionally compatible with the online content ((0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article”).
Claim 7: Rao in view of Hicklin discloses the method of claim 1, wherein the filtered-out candidate advertisements are emotionally incompatible with the online content (0084, “The content ingestion engine has determined that the article has a positive sentiment towards using smart phone. When the user clicks the link on slot 418a, the link may direct the user to a new web page with a full story of the article, and an ad 620 from Samsung, for example, may be displayed in conjunction with the content of the article. The content of the advertisement may be consistent and/or compatible with the sentiment of the article so that the user may feel the ad naturally fits into the content of the article” – since compatible content is displayed, the filtered out content would be considered incompatible).
Claims 8-20 are rejected on the same basis as claims 1-7 above since Rao discloses a non-transitory, computer-readable medium having information recorded thereon for online advertising, wherein the information, when read by a machine, causes the machine to perform operations comprising the same or similar activity as at claims 1-7 above (for claims 8-14) and a system for online advertising, comprising: memory storing computer program instructions; and one or more processors that, in response to executing the computer program instructions, effectuate operations comprising the same or similar activity as at claims 1-6 above (for claims 15-20) – see Rao at 0007, “a server system may include at least one non-transitory, processor-readable storage medium and at least one processor in communication with the at least one storage medium. The at least one storage medium may include at least one set of instructions for generating a value added in-stream contents (VAC) database for ad display”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ramnani et al. (U.S. Patent Application Publication No. 2014/0066044, hereinafter Ramnani) indicates that “Supervised machine learning methods take as input a set of labeled examples and produce as output a model” (Romnani at 0186).
Rottmann et al. (U.S. Patent Application Publication No. 2017/0177564, hereinafter Rottmann) indicates that “Embodiments for identifying and using a multi-media context during language processing are described. Multi-media context data can be obtained from one or more sources such as object, location, or person identification in multi-media items, multi-media characteristics, labels or characteristics provided by an author of the multi-media, or information about the author of the multi-media. This context data can be used as part of the input for a machine learning process that creates a model used in language processing. The resulting model can take multi-media context data or labeling for a content item as part of the input for computing results of the model. These models can be used as part of various language processing engines such as a translation engine, correction engine, tagging engine, etc.” (Rottmann at 0009).
Goodman et al. (U.S. Patent Application Publication No. 2006/0167747, hereinafter Goodman) indicates “According to one aspect of the invention, the user's action can be described as preparing or typing an outbound message or playing or obtaining a high score in a game environment. In either of these actions, content associated with the action can be analyzed. Thus, the content of the outbound message can be analyzed and an advertisement based in part on the content of such message can be delivered to the message sender. The advertisement can be presented while the action is being performed (e.g., as the user is inputting the message), at the sending of the message, or shortly after the message is sent” (Goodman at 0006), and “As a result of analyzing the content of an outbound message, calendar entry, and/or real-time outbound message, the system and/or method of the invention may determine that the content is not appropriate or does not warrant an advertisement. This can be particularly true when a topic of an outbound message or calendar entry denotes a negative subject matter such as death or some other failure (e.g., failed product, failed service, etc.)” (Goodman at 0009).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685