DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status
This Office Action is responsive to communications filed on 7 August 2024; Claim(s) 1-16 is/are pending in the application and have been presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7 November 2024 and 7 November 2024 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-16 is/are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Eligibility Step 1 analysis, it is determined that claims 1-16 are directed to a system and method.
Under Eligibility Step 2A, Prong 1 analysis, Claim 1 recites, "A method (300) for providing content to a user, the method (300) comprises: receiving (302), by a processor (201), a user input indicative of user intent, user preferences, and user interests, wherein the user input is utilized to create (303) one or more user profiles associated with one or more users; segmenting (304), by the processor (201), the one or more user profiles associated with the one or more users based on the received user input; receiving content from the one or more users, wherein the content corresponds to at least one of a post, poll, promotion, an advertisement, and an inquiry; automatically identifying (305), by the processor (201), one or more relevant profiles, from the one or more user profiles, associated with one or more relevant users, based on one or more machine learning techniques to provide content to the one or more relevant users; and providing (306), by the processor (201), the content to the one or more relevant users corresponding to the one or more relevant profiles, wherein the content is tailored based on the user input and the segmented profiles.”, the underlined limitations indicate additional elements that are to be further analyzed at Step 2A-2. Independent claim(s) 15 and 16 are similar to Claim 1, except for reciting, A system (100) for providing content to a user, the system comprising: a processor (201); and a memory (202) communicatively coupled to the processor (201), wherein the memory (202) stores processor executable instructions, which, on execution, causes the processor (201) (Claim 15), and A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps, based on one or more machine learning/algorithms/logic techniques to provide content to the one or more relevant users (Claim 16), therefore Claims 15 and 16 are analyzed similarly as Claim 1.
The claim(s) are found to be within the enumerated group(s) of Certain Methods of Organizing Human Activity, specifically as it relates to advertising/marketing or sales activities. For example, the claims limitations of - providing content to a user, user input indicative of user intent, user preferences, and user interests, wherein the user input is utilized to create one or more user profiles associated with one or more users; segmenting, the one or more user profiles associated with the one or more users based on the received user input; receiving content from the one or more users, wherein the content corresponds to at least one of a post, poll, promotion, an advertisement, and an inquiry; automatically identifying, one or more relevant profiles, from the one or more user profiles, associated with one or more relevant users and providing the content to the one or more relevant users corresponding to the one or more relevant profiles, wherein the content is tailored based on the user input and the segmented profiles – recites an abstract idea as it relates to advertising, marketing and sales.
Although the Examiner has provided this summary of the claims, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination).
Under Eligibility Step 2A, Prong 2 analysis, the limitations of – receiving, by a processor, receiving content from the one or more users, providing, by the processor (Claim 1, 15, 16), A system for providing content to a user, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor executable instructions (Claim 15), A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps (Claim 16), displaying the content on a user interface; delivering the content through different formats, including image, text, video and or audio output (Claim 7), wherein the inquiry corresponds to information to be received from the one or more relevant users, wherein upon providing the inquiry to the one or more relevant users, the one or more relevant users provides the information to a user, from the one or more users, who posted the inquiry (Claim 8) - does not integrate the judicial exception into practical application because the claims recite generic computer components performing generic computer functions which amounts to nothing more than mere instructions to implement the abstract idea in a computer environment.
The Examiner finds the limitations of - converting the categorical data into numerical vectors using one or more techniques comprising one-hot encoding, label encoding, or embedding representations; applying one or more clustering techniques to group one or more user profiles together based on the numerical vectors, wherein the clustering techniques comprise at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering (Claim 5), to be using common techniques for organizing and grouping data.
Furthermore, Claim 1, 5-6 and 15-16 indicates “using” a machine learning model/techniques (to perform various steps) as well as training the machine learning model (Claim 5-6), however the machine learning model is recited at a high level of generality and can be regarded as being nothing more than at an “apply it” level.
Dependent claims 2-4, and 7-14 are also considered to be encompassed by the abstract idea for indicating, the type of user (Claim 2), a user input selection (Claim 3), the type of data in the user profile (Claim 4), displaying ad content in various formats (Claim 7), sending and receiving data about the one or more users (Claim 8), performing data analytics to identify trends, observations and information about the one or more users (Claim 9), where the type of insights are for providing valuable feedback, recommendations and strategic guidance (Claim 10), selecting the type of information of the one or more users (Claim 11), creating a schedule for providing content to relevant users (Claim 12), providing content analytics such as impression views, clicks, click-through rates, comments, and shares (Claim 13), and receiving feedback on the provided content to refine future recommendations (Claim 14).
The limitations of the claim(s) does not appear to recite an improvement to another technology or technical field; does not provide any improvements to the functioning of the computer itself; does not apply the judicial exception with, or by use of, a particular machine; does not effect a transformation or reduction of a particular article to a different state or thing; it does not add a specific limitation, or add unconventional steps that confine the claim(s) to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Generic computer components performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. None of the limitations, considered alone or in an ordered combination provide eligibility, because taken as a whole, the claim(s) is/are merely instructions to implement the abstract idea in a computer environment.
Under Eligibility Step 2B analysis, the claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim elements, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim includes that, as stated above, it is implemented by a computer which employs a processor is nothing more than “apply it” with instruction to a generic computer. The claimed computer components are recited at a high level of generality and are merely invoked to perform the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-8, 11, and 14-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yan et al (US 2014/0012842 A1, hereinafter “Yan”).
Claim 1. Yan discloses, A method (300) for providing content to a user (0020, method or process, see also 0025, “configured to serve content or provide services to users over network(s)”), the method (300) comprises:
receiving (302), by a processor (201), a user input indicative of user intent, user preferences, and user interests, wherein the user input is utilized to create (303) one or more user profiles associated with one or more users (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
segmenting (304), by the processor (201), the one or more user profiles associated with the one or more users based on the received user input (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
receiving content from the one or more users, wherein the content corresponds to at least one of a post, poll, promotion, an advertisement, and an inquiry (0051-0052, “ user behavior data may be received from multiple sources including page view logs of web sites, query logs of search engines, external data aggregators or resellers, and/or other sources”, see also 0080, “the user comments on an online blog posting, product review, article, or the like”);
automatically identifying (305), by the processor (201), one or more relevant profiles, from the one or more user profiles, associated with one or more relevant users, based on one or more machine learning techniques to provide content to the one or more relevant users (0068, “a ranking model is developed through supervised machine learning employing training data”, 0069-0070, “…based on a determination that certain semantic queries are relevant to certain users”, 0074, “the rank represents a closeness or a relevancy measure between the semantic profiles and the search terms specified by the advertiser in the semantic query, e.g. more relevant semantic profiles are ranked higher in the search results”);
and providing (306), by the processor (201), the content to the one or more relevant users corresponding to the one or more relevant profiles, wherein the content is tailored based on the user input and the segmented profiles (0092, “user interface 800 may include further functionality to enable an advertiser to specify a user segment by selecting one or more of the displayed users. Such a user segment may then be targeted by the advertiser for one or more advertisements or advertising campaigns, e.g. the advertiser may select one or more particular advertisements to be displayed to users in the user segment”).
Claim 2. Yan discloses, The method (300) as claimed in claim 1, wherein the one or more users comprises at least one of a consultant, a manufacturer, an innovator, an expert, a supplier, or a service provider (Yan at 0099, “experienced advertiser…professional”).
Claim 3. Yan discloses, The method (300) as claimed in claim 1, wherein the user input comprises a selection of at least one of an industry type, a sub-industry type, an area of interest, categories, technologies, knowledge areas, and countries (Yan at 0084, “Domain control 708, intent control 710, and preference control 712 enable an advertiser to input search terms related to semantic elements domain, intent and preference as described herein”, 0085, “menu 720 displays a list of choices to the advertiser for specifying an INTENT search term (e.g., "buy," "rent," "sell," "repair" and so forth). Once the advertiser selects an option from the list, the search term may be added to the semantic query displayed in pane 730”).
Claim 4. Yan discloses, The method (300) as claimed in claim 1, wherein the one or more user profiles comprises detailed information on user intent/interests, historical interactions, engagement metrics, industry types, sub-industry types, areas of interest, and professional roles (Yan at 0056-0059, “Semantic profiles include data regarding domain, intent, preference, and/or habits that are inferred for the user based on the online behavior of the user. As used herein, DOMAIN is a particular category of product or service in which the user has indicated an interest through search queries, page views or other online behavior”).
Claim 7. Yan discloses, The method (300) as claimed in claim 1, wherein providing (306) the content comprises: displaying the content on a user interface (Yan at 0092, “one or more particular advertisements to be displayed to users in the user segment”); delivering the content through different formats, including image, text, video and or audio output (Yan at 0040, “Client device 200 may further include output device(s) 246 including but not limited to a display, printer, audio speakers, and the like”).
Claim 8. Yan discloses, The method (300) as claimed in claim 1, wherein the inquiry corresponds to information to be received from the one or more relevant users, wherein upon providing the inquiry to the one or more relevant users, the one or more relevant users provides the information to a user, from the one or more users, who posted the inquiry (Yan at 0027, Fig. 1).
Claim 11. Yan discloses, The method (300) as claimed in claim 1, wherein the method (300) enables selecting an industry type, sub-industry type, area of interest, categories, technologies, knowledge areas, engagement levels, functions, and demographic information of the users, by the user who posted the inquiry (Yan at 0085, “advertiser selects a control, in this case intent control 710. As shown, menu 720 displays a list of choices to the advertiser for specifying an INTENT search term (e.g., "buy," "rent," "sell," "repair" and so forth). Once the advertiser selects an option from the list, the search term may be added to the semantic query”, see also 0088).
Claim 14. Yan discloses, The method (300) as claimed in claim 1, comprises receiving feedback on the provided content, wherein the feedback is provided for the content analytics, to refine future recommendations for providing content (Yan at 0071, “this implicit feedback method employs data from an ad click-through log”, see also 0073, “explicit and implicit feedback methods”).
Claim 15. Yan discloses, A system (100) for providing content to a user (0020, method or process, see also 0025, “configured to serve content or provide services to users over network(s)”), the system comprising:
a processor (201); and a memory (202) communicatively coupled to the processor (201), wherein the memory (202) stores processor executable instructions, which, on execution, causes the processor (201) to (0041-0042):
receive a user input indicative of user intent, user preferences, and user interests, wherein the user input is utilized to create one or more user profiles associated with one or more users (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
segment the one or more user profiles associated with the one or more users based on the received user input (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
receive content from the one or more users, wherein the content corresponds to at least one of a post, poll, promotion, an advertisement, and an inquiry (0051-0052, “ user behavior data may be received from multiple sources including page view logs of web sites, query logs of search engines, external data aggregators or resellers, and/or other sources”, see also 0080, “the user comments on an online blog posting, product review, article, or the like”);
automatically identify one or more relevant profiles, from the one or more user profiles, associated with one or more relevant users, based on one or more machine learning/algorithms/logic techniques to provide content to the one or more relevant users (0068, “a ranking model is developed through supervised machine learning employing training data”, 0069-0070, “…based on a determination that certain semantic queries are relevant to certain users”, 0074, “the rank represents a closeness or a relevancy measure between the semantic profiles and the search terms specified by the advertiser in the semantic query, e.g. more relevant semantic profiles are ranked higher in the search results”);
and provide the content to the one or more relevant users corresponding to the one or more relevant profiles, wherein the content is tailored based on the user input and the segmented profiles (0092, “user interface 800 may include further functionality to enable an advertiser to specify a user segment by selecting one or more of the displayed users. Such a user segment may then be targeted by the advertiser for one or more advertisements or advertising campaigns, e.g. the advertiser may select one or more particular advertisements to be displayed to users in the user segment”).
Claim 16. Yan discloses, A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising (0041-0042):
receiving a user input indicative of user intent, user preferences, and user interests, wherein the user input is utilized to create (303) one or more user profiles associated with one or more users (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
segmenting the one or more user profiles associated with the one or more users based on the received user input (0018-0019, “a search may be performed by an advertiser or other entity seeking to define a particular user segment… the query is a semantic query employing terms (e.g., domain, intent, preference, and habit) that are substantially similar to the various categories of data that make up the semantic profile”, 0050-0051, “executed by one or more processors…generating and indexing semantic profiles”, see also 0056);
receiving content from the one or more users, wherein the content corresponds to at least one of a post, poll, promotion, an advertisement, and an inquiry (0051-0052, “ user behavior data may be received from multiple sources including page view logs of web sites, query logs of search engines, external data aggregators or resellers, and/or other sources”, see also 0080, “the user comments on an online blog posting, product review, article, or the like”);
automatically identifying one or more relevant profiles, from the one or more user profiles, associated with one or more relevant users, based on one or more machine learning techniques to provide content to the one or more relevant users (0068, “a ranking model is developed through supervised machine learning employing training data”, 0069-0070, “…based on a determination that certain semantic queries are relevant to certain users”, 0074, “the rank represents a closeness or a relevancy measure between the semantic profiles and the search terms specified by the advertiser in the semantic query, e.g. more relevant semantic profiles are ranked higher in the search results”);
and providing the content to the one or more relevant users corresponding to the one or more relevant profiles, wherein the content is tailored based on the user input and the segmented profiles (0092, “user interface 800 may include further functionality to enable an advertiser to specify a user segment by selecting one or more of the displayed users. Such a user segment may then be targeted by the advertiser for one or more advertisements or advertising campaigns, e.g. the advertiser may select one or more particular advertisements to be displayed to users in the user segment”, see also 0094).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior ‘’;4rart and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al (US 2014/0012842 A1, hereinafter “Yan”), further in view of Frazier et al (US 20220114186 A1, hereinafter “Frazier”).
Claim 5. Yan discloses, The method (300) as claimed in claim 1, wherein segmenting (304) further comprises: organizing the user input into one or more categorical data, wherein the categorical data comprises industry types, sub-industry types, areas of interest and professional roles (Yan at 0070-0074), however it appears that Yan may not explicitly disclose, converting the categorical into converting the categorical data into numerical vectors using one or more techniques comprising one-hot encoding, label encoding, or embedding representations; applying one or more clustering techniques to group one or more user profiles together based on the numerical vectors, wherein the clustering techniques comprise at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering; grouping the one or more user profiles into one or more distinct user groups with similar characteristics and user intents, wherein each of the one or more distinct user groups is assigned a label for easy identification.
Frazier, however teaches converting the categorical into converting the categorical data into numerical vectors using one or more techniques comprising one-hot encoding, label encoding, or embedding representations (Frazier at 0106, one-hot, 0109-0110, “In response to a determination by model creator 506 that the data profile attribute is categorical, in a next step 815, classifier 530 may initiate one-hot encoding for the token”); applying one or more clustering techniques to group one or more user profiles together based on the numerical vectors, wherein the clustering techniques comprise at least one of K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering (Frazier at 0098-0099, “model creator 506 may use one or more of connectivity models, centroid models, distribution models, density models, K-means model, and the like to perform clustering of data points”, see also 0089, “an attribute-based hierarchical clustering routine. In an embodiment, the bespoke hierarchical clustering routine may find natural densities within the data profiles to enable characterizing the makeup of the data profiles”); grouping the one or more user profiles into one or more distinct user groups with similar characteristics and user intents, wherein each of the one or more distinct user groups is assigned a label for easy identification (Frazier at 0096, “may determine whether two or more of the plurality of data profiles share similar features”, see also 0124-0125, corresponding identifications).
The Examiner finds and understands that it would have been readily apparent the benefits of combining the teachings of using known clustering techniques such as one-hot encoding, k-means clustering, and density models to identify similar profiles for segmenting users as taught by Frazier with the system and method disclosed by Yan in order to accomplish the result of, “accurately segmented data profiles achieve effective engagement of customers using a personalized approach, that is, identifying what is important for a particular segment and addressing those needs…clusters that result assist in better customer modeling and predictive analytics and are also used to target customers with offers and incentives personalized to their wants, needs and preferences”) – Frazier at 0024.
Claim 6. Yan in view of Frazier discloses, The method (300) as claimed in claim 5, wherein automatically identifying (305) one or more relevant profiles comprises: training a supervised machine learning model based on the user input and data present in each of the one or more user profiles (Yan at 0018, supervised machine learning, see also 0029, 0044, 0068-0069);
However it appears that Yan may not explicitly disclose, extracting one or more features from the content using NLP techniques, wherein the one or more features comprises a Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings and BERT embeddings, wherein the one or more features capture the semantic meaning of the content; determining a similarity index between the one or more features of the content and the one or more user profiles based on the trained supervised machine learning model using the labels assigned to each of the one or more distinct user groups; and predicting a probability of relevance for each user profile-content pair based on the similarity index.
Where Yan discloses a supervised machine learning model, Frazier further teaches extracting one or more features from the content using NLP techniques (Frazier at 0097, “feature generator 502 may use one or more methods utilizing natural language processing (NLP)”), wherein the one or more features comprises a Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings and BERT embeddings (Frazier at 0105, “the tokens may be vectorized by vectorizer 531 using techniques from the semantic embedding literature, e.g., word2vec, glove, or BERT embeddings”, 0157-0158, “ranking the n-gram terms, via a “term frequency inverse document frequency” (tf-idf) approach, wherein the most significant terms according to their relative prominence in the cluster may be identified in comparison to term prominence in a wider data universe”), wherein the one or more features capture the semantic meaning of the content (Frazier at 0105, “the tokens may be vectorized by vectorizer 531 using techniques from the semantic embedding literature, e.g., word2vec, glove, or BERT embeddings”, see also 0143, semantic clustering); determining a similarity index between the one or more features of the content and the one or more user profiles based on the trained supervised machine learning model using the labels assigned to each of the one or more distinct user groups (Frazier at 0096, “profile parser 508 may determine whether two or more of the plurality of data profiles share similar features. In an embodiment, feature generator 502 may extract features for each of the plurality of data profiles from the identified data points”, see also 0024, 0127-0128);
and predicting a probability of relevance for each user profile-content pair based on the similarity index (Frazier at 0127, “may calculate the correlation score using relative data profile attribute quantified importance through a trained prediction model”, see also 0136). As combined and under the same rationale ad Claim 5 above.
Claim 9-10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al, further in view of Ogawa et al (US 2013/0054349 A1, hereinafter “Ogawa”).
Claim 9. Yan discloses, The method (300) as claimed in claim 1, however it appears that Yan may not explicitly disclose, wherein the method (300) comprises performing data analytics on the user input from the one or more users and the content to generate one or more insights, wherein the one or more insights correspond to data-driven observations, trends, and actionable information derived from the analysis of user interactions, preferences, and engagement metrics.
Where Yan discloses advertiser input for defining and analyzing consumer profiles (see Yan at 0018-0019 and 0051-0052), Ogawa, further teaches performing data analytics on the user input from the one or more users and the content to generate one or more insights, wherein the one or more insights correspond to data-driven observations, trends, and actionable information derived from the analysis of user interactions, preferences, and engagement metrics (Ogawa at 0066, “providing advertisers with a holistic, data driven advertising picture and that operationalizes advertising strategies, insights, recommendations, incremental improvement changes, dollar spend recommendations, portfolio advertising management, and reporting, among other things. Systems are provided that aggregate data from a variety of sources and use methods to translate the data into meaningful and insightful business information, to present the information along with recommendations, to facilitate advertising purchases, and to provide holistic and insightful active advertising management, among other things”, see also 0070).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date the benefits of combining the teaching to analyze various advertiser criteria to generate meaningful and insightful business information as taught by Ogawa with the system and method of Yan in order to improve advertiser segmenting and campaigns.
Claim 10. Yan in view of Ogawa discloses, The method (300) as claimed in claim 9, however it appears that Yan may not explicitly disclose, wherein the one or more insights are aimed to provide valuable feedback, recommendations, and strategic guidance to the user who posted the inquiry, helping them make informed decisions regarding procurement of products, expertise in making products, services and receiving expert guidance to run their businesses more effectively. Ogawa, however teaches wherein the one or more insights are aimed to provide valuable feedback, recommendations, and strategic guidance to the user who posted the inquiry, helping them make informed decisions regarding procurement of products, expertise in making products, services and receiving expert guidance to run their businesses more effectively (Ogawa at 0119, 0066, 0070). As combined and under the same rationale as Claim 9 above.
Claim 13. Yan discloses, The method (300) as claimed in claim 1, however it appears that Yan may not explicitly disclose, wherein the method (300) comprises providing content analytics of at least one of direct results impressions, views, clicks, and click- through rates (CTR), reactions, comments, shares, reposts, connections, and a combination thereof, to facilitate the comparison of content in terms of performance metrics, audience insights, segment reach, engagement levels, and geographic distribution.
Ogawa, however teaches providing content analytics of at least one of direct results impressions, views, clicks, and click- through rates (CTR), reactions, comments, shares, reposts, connections, and a combination thereof, to facilitate the comparison of content in terms of performance metrics, audience insights, segment reach, engagement levels, and geographic distribution (Ogawa at 0167, “analyze existing or partial campaign performance and trends, obtain granular, visual displays and graphics to assess and compare various campaign performance breakdowns”, see also 0159, “example displays 1906 also include a geo-performance breakdown, which may include a display map with displayed regional performance information. The example displays 1906 also include an engagement demographics bar diagram, which may provide engagement performance information broken down by demographic segment or group. Furthermore, the example displays 1906 include a performance history graph, which may include segments indicating performance history over time for various aspects or channels of the campaign”).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date the benefits of combining the teaching to generate and compare various campaign performance metrics as taught by Ogawa with the system and method of Yan in order to improve future advertiser segmenting and campaigns.
Claim 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al, further in view of Coulbourne et al (US 2020/0145793 A1, hereinafter “Coulbourne”).
Claim 12. Yan discloses, The method (300) as claimed in claim 1, however it appears that Yan may not explicitly disclose, wherein the method (300) comprises creating a schedule for providing content to one or more relevant users, wherein the schedule comprises promotions to be provided at a future date or to provide immediately, with a default promotion duration of a specific number of days, or until served to the one or more relevant users.
Coulbourne, however teaches creating a schedule for providing content to one or more relevant users, wherein the schedule comprises promotions to be provided at a future date or to provide immediately, with a default promotion duration of a specific number of days, or until served to the one or more relevant users (Coulbourne at 0016, “users can interactively control parameters around scheduling the display of content immediately or in the future including, but not limited to using date, day and time parameters to control what date, days of the week and time of the day content is displayed; geographical and location parameters to control where content is displayed (including, but not limited to latitude/longitude, city, county, state, region, country) and whether content is displayed on a single or multiple display; and duration and frequency parameters to control the number of times the content is repeated and displayed on one or more displays”).
The Examiner finds and understands that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings to schedule the display of content at a future date and a specific duration as taught by Coulbourne with the system and method of Yan in order to optimize the date and time of when the content is displayed to the relevant users.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELINDA GIERINGER whose telephone number is (408)918-7593. The examiner can normally be reached Monday - Friday (11AM-6PM ET).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on (571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.G./Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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