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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. AU2023233186, filed on 09/22/2023.
Response to Amendment
This action is in response to applicant’s arguments and amendments filed 8/11/2026, which are in response to USPTO Office Action mailed 5/15/2026. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE NON-FINAL.
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) 1-2, 11-12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE et al. (US PGPUB No. 2022/0121660; Pub. Date: Apr. 21, 2022) in view of Mashiach et al. (US PGPUB No. 2016/0147893; Pub. Date; May 26, 2016)
Regarding independent claim 1,
GEORGE discloses computer implemented method including: retrieving, by one or more computer processing units, historical search data from a search query database, the historical search data including a plurality of historical search queries, each historical search query corresponding to a historical search for content items provided by a content delivery platform; See Paragraph [0028], (Disclosing a system for identifying content gaps based on relative user-selection rates between multiple discrete content sources. The system comprises content gap identification service 114 configured to store and access search query log data 112 generated by search engine 110.) See Paragraph [0030], (Search query log data 112 includes highly specific user-generated search queries 104 from which topics of interest and intents can be cleaned as well as user interaction data 108, i.e. retrieving, from a search query database, historical search data, the historical search data including a plurality of historical search queries (e.g. the search query log data includes information from prior search queries), each historical search query corresponding to a historical search for content items provided by a content delivery platform (e.g. search queries may retrieve content such as webpages responsive to search queries).)
processing, by the one or more computer processing units, the historical search data to determine a plurality of search topics, each search topic corresponding to a group of semantically similar historical search queries; See Paragraph [0030], (Search query log data 112 includes highly specific user-generated search queries 104 from which topics of interest and intents can be cleaned as well as user interaction data 108.) See Paragraph [0033], (Content gap identification service 114 may utilize a subgroup generator 120 configured to analyze search query log data 112 and content gap identification parameters 154 to identify a relevant subset 130 of user queries, i.e. processing the historical search data to determine a plurality of search topics, each search topic corresponding to a group of semantically similar historical search queries; (e.g. search query log data and content gap parameters are used to determine topics related to current search queries).)
selecting, by the one or more computer processing units, a first search topic from the plurality of search topics; See Paragraph [0033], (The content ga identification service 114 may determine a subset of search queries that include an indication of a topic of interest based on search query log data 112 and content gap identification parameters 154, i.e. selecting, by the one or more computer processing units, a first search topic from the plurality of search topics (e.g. content gap identification parameters 154 are used to determine a topic to which the subset of search queries are directed);)
based on the first set of search results, determining, by the one or more computer processing units, a first content score that provides a measure of how much content provided by the content delivery platform is relevant to the first search topic; See Paragraph [0029], (Search engine 110 returns search results 106 in response to a search query. Individual search results associated with user-selection rates and/or dwell times. User-selection rates and dwell times are used to indicate the relevance of a particular search result to the search query 104, i.e. determining, based on the first set of search results, a first content score that provides a measure of how much content provided by the content delivery platform is relevant to the first search topic; (e.g. user-selection rates and/or dwell times represent metrics related to the relevance of a search result).)
based on the first content score, automatically determining, by the one or more computer processing units, whether a content gap exists for the first search topic; See Paragraph [0033], (Content gap identification service 114 identifies search queries 104 from which search results 106 include an indication of the topic of interest, i.e. automatically determining, based on the first content score, whether a content gap exists for the first search topic (e.g. Note [0025] wherein the system is described as automatically identifying content gaps within a specified content source);)
and in response to determining that the content gap exists for the first search topic, generating, by the one or more computer processing units, a first content gap alert to one or more predefined users indicating that the content gap exists for the first search topic. See Paragraph [0012], (Content gaps are identified according to web-browsing behaviors in relation to discrete content sources form which search results are returned.) See FIG. 7 & Paragraph [0076], (A content gap notification may be generated in response to a user-selection rate associated with a specified content source satisfying a content gap threshold, i.e. in response determining that the content gap exists for the first search topic, generating a first content gap alert indicating that the content gap exists for the first search topic.) See FIG. 2 & Paragraph [0042], (FIG. 2 illustrates a dashboard GUI that allows an administrator to obtain insights in relation to user queries relevant to a topic and/or product of interest, i.e. generating, by the one or more computer processing units, a first content gap alert to one or more predefined user.)
GEORGE does not disclose the step of generating, by the one or more computer processing units, a first search topic descriptor corresponding to the first search topic;
performing, by the one or more computer processing units, a first search, wherein the first search is a search for content items provided by the content delivery platform that are relevant to the first search topic, and wherein performing the first search causes generation of a first set of search results;
Mashiach discloses the step of generating, by the one or more computer processing units, a first search topic descriptor corresponding to the first search topic; See FIG. 6 & Paragraph [0050], (Disclosing a system for processing queries received form suers of an online social network. FIG. 6 illustrates method 600 comprising step 610 of receiving search query associated with a first topic. Social networking system 160 may determine a topic that search query is associated with by analyzing the text of the search query, i.e. generating, by the one or more computer processing units (e.g. Note [0021] wherein social-networking system 160 is a network-addressable computing system), a first search topic descriptor corresponding to the first search topic (e.g. by determining a topic associated with a search query by identifying one or more topics related to a search query);)
performing, by the one or more computer processing units, a first search, wherein the first search is a search for content items provided by the content delivery platform that are relevant to the first search topic, and wherein performing the first search causes generation of a first set of search results; See FIG. 6 & Paragraph [0067], (Method 600 comprises step 630 of retrieving multiple objects from an online social network matching the search query, wherein the retrieved objects are associated with the topic determined as described in [0050]. At step 650, a search-results is sent to a client device of the user that submitted the search query at step 610, i.e. performing, by the one or more computer processing units, a first search, wherein the first search is a search for content items provided by the content delivery platform that are relevant to the first search topic (e.g. step 630 comprising retrieving results matching the search query that are associated with the identified topic(s)), and wherein performing the first search causes generation of a first set of search results (e.g. step 650);)
GEORGE and Mashiach are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE to include the method of identifying search topics for incoming search queries as disclosed by Mashiach. Paragraph [0050] of Mashiach discloses that the search query process may determine keywords, terms, etc. associated with or most relevant to topics associated with a search query. Paragraph [0074] of Mashiach describes the use of coefficient information used to rank and order content to provide users with information that is relevant to their interests and current circumstances thereby increasing the likelihood that the retrieved content is of interest.
Regarding dependent claim 2,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE further discloses the step wherein processing the historical search data to determine the plurality of search topics includes processing the historical search data according to a clustering technique to determine a plurality of historical search query clusters. See Paragraph [0033], (Search query log data 112 and content gap identification parameters 154 are utilized by subgroup generator 120 to identify a relevant subset 130 of search queries 104, i.e. processing the historical search data according to a clustering technique (e.g. via he subgroup generator 120) to determine a plurality of historical search query clusters (e.g. the subgroup generator 120 identifies search queries that include an indication of some topic of interest within the search string.)
Regarding independent claim 11,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer system and is rejected under similar rationale.
Regarding independent claim 20,
The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 2 above, and further in view of Misiewicz et al. (US PGPUB No. 2022/0138258; Pub. Date: May 5, 2022).
Regarding dependent claim 3,
As discussed above with claim 2, GEORGE-Mashiach discloses all of the limitations.
GEORGE further discloses the step wherein processing the historical search data according to the clustering technique includes: performing a first clustering operation on the historical search data to determine a set of first search query clusters, each first search query cluster associated with a set of one or more of the historical search queries; See Paragraph [0033], (Search query log data 112 and content gap identification parameters 154 are utilized by subgroup generator 120 to identify a relevant subset 130 of search queries 104, i.e. performing a first clustering operation on the historical search data to determine a set of first search query clusters, each first search query cluster associated with a set of one or more of the historical search queries (e.g. search query log data 112 is utilized to identify subgroups of search queries of interest).)
GEORGE-Mashiach does not disclose the step wherein for each first search query cluster, performing a second clustering operation to determine a set of second search query clusters, and wherein each second search query cluster corresponds to a search topic.
Misiewicz discloses the step wherein for each first search query cluster, performing a second clustering operation to determine a set of second search query clusters, and wherein each second search query cluster corresponds to a search topic. See FIG. 6 & Paragraph [0072], (Disclosing a system for generating and managing clusters of search terms. FIG. 6 illustrates method 600 comprising step 610 of executing a first cluster generating process to generate a cluster including a set of search terms. At step 630, a second cluster run is executed to determine one or more metrics of a cluster after a time interval following the first time associated with the execution of the first cluster run, i.e. and for each first search query cluster, performing a second clustering operation to determine a set of second search query clusters, and wherein each second search query cluster corresponds to a search topic.)
GEORGE, Mashiach and Misiewicz are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of iteratively clustering search term clusters as part of a search process as disclosed by Misiewicz. Paragraph [0022] of Misiewicz discloses that cluster data may be used to allow an entity to identify parameters associated with aggregated search data associated with multiple end user searches in order to allow the entity to analyze the type of knowledge or information about an entity to refine, adjust or adapt one or more knowledge search features and provide an improved searching experience.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer system and is rejected under similar rationale.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 1 above, and further in view of Ramachandran et al. (US PGPUB No. 2021/0150366; Pub. Date: May 20, 2021).
Regarding dependent claim 4,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE-Mashiach does not disclose the step wherein the first search topic descriptor is generated by processing the historical search queries in the group of semantically similar historical search queries that the first search topic corresponds to using a class-based term frequency - inverse document frequency process.
Ramachandran discloses the step wherein the first search topic descriptor is generated by processing the historical search queries in the group of semantically similar historical search queries that the first search topic corresponds to using a class-based term frequency - inverse document frequency process. See Paragraph [0087], (Disclosing a system for training a neural network with a high feature dimension. The system may utilize class-based TF-IDF by normalizing class-based TF-IDF word scores across an input sentence to determine a combined probability used to determine word importance in relation to each word of an input sentence.)
The examiner notes that while Ramachandran is directed to text generically, the system of GEORGE stores search query log data 112 as text. Therefore, the method of applying class-based TF-IDF to determine scores for terms for classification and labelling may be applied to a corpus of text such as the search query log data 112 of GEORGE.
GEORGE, Mashiach and Ramachandran are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of applying class-based TF-IDF word scores to determine importance probabilities of words as disclosed by Ramachandran. Paragraph [0087] of Ramachandran discloses that the system utilizes the determined probabilities to train a model to better recognize input sentences. TF-IDF is known in the art as a method of determining a uniqueness and relevance of individual terms in a corpus of text, therefore these benefits would also apply to the system of Ramachandran and any proposed combination.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 4 directed to a computer system and is rejected under similar rationale.
Claim(s) 5-6 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 1 above, and further in view of Chien et al. (US PGPUB No. 2023/0135293; Pub. Date: May 4, 2023).
Regarding dependent claim 5,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE-Mashiach does not disclose the method further including mapping the first search topic to content item metadata, the content item metadata being metadata associated with the content items provided by the content delivery platform.
Chien discloses a method including mapping the first search topic to content item metadata, the content item metadata being metadata associated with the content items provided by the content delivery platform. See Paragraph [0031], (Disclosing a system for identifying content relating to a human language input. The system comprises server 116 configured to identify content from content source(s) 119 that is relevant to the topic of primary content from a separate content source 114 by determining a semantic similarity such as via td-idf vectors and applying a cosine similarity calculation. A semantic enrichment/enhancement process may also be applied to both the primary content and/or secondary content such as by adding metadata terms to be included in a td-idf vector, i.e. mapping the first search topic to content item metadata, the content item metadata being metadata associated with the content items provided by the content delivery platform.)
GEORGE, Mashiach and Chien are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of performing semantic enrichment for content items as disclosed by Chien. Paragraph [0057] of Chien discloses that the system may perform semantic enrichment/enhancement, which allows the system to verify the accuracy or truthfulness of an assertion made by the user, which is determined according to similarity metrics.
Regarding dependent claim 6,
As discussed above with claim 5, GEORGE-Mashiach-Chien discloses all of the limitations.
Chien further discloses the step wherein the content item metadata includes a plurality of metadata attributes and mapping the first search topic to content item metadata includes: performing a semantic search to identify a first metadata attribute, the first metadata attribute being a metadata attribute that is sufficiently semantically similar to a first word of the first search topic descriptor; See Paragraph [0031], (Server is 116 configured to identify content from content source(s) 119 that is relevant to the topic of primary content from a separate content source 114 by determining a semantic similarity such as via td-idf vectors and applying a cosine similarity calculation. A semantic enrichment/enhancement process may also be applied to both the primary content and/or secondary content such as by adding metadata terms to be included in a td-idf vector, i.e. performing a semantic search to identify a first metadata attribute (e.g. server 116 may identify secondary content according to similarity calculations) , the first metadata attribute being a metadata attribute that is sufficiently semantically similar to a first word of the first search topic descriptor (e.g. similarity calculations are utilized to attach metadata terms to content).)
and mapping the first search topic to the first metadata attribute. See Paragraph [0031], (Server 116 may apply a semantic enrichment/enhancement process to primary and/or secondary content such as by adding metadata terms to be included in td-idf vector generation associated with the content. Note [0055] wherein the additional terms added to the td-idf vector are used to determine the td-idf metric or other similarity metrics, i.e. mapping the first search topic to the first metadata attribute (e.g. via the adding of terms such as metadata terms to the content).
GEORGE, Mashiach and Chien are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of performing semantic enrichment for content items as disclosed by Chien. Paragraph [0057] of Chien discloses that the system may perform semantic enrichment/enhancement, which allows the system to verify the accuracy or truthfulness of an assertion made by the user, which is determined according to similarity metrics.
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 5 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer system and is rejected under similar rationale.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 1 above, and further in view of Zhao et al. (US PGPUB No. 2021/0073224; Pub. Date: Mar. 11, 2021).
Regarding dependent claim 7,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE further discloses the method further including: calculating a first opportunity score for the first search topic, See FIG. 6 & Paragraph [0067], (FIG. 6 illustrates a routine 600 for determining user-selection rates for particular query string fragments included within a relevant subset of user-generated search queries, i.e. calculating a first opportunity score for the first search topic,
selecting the first search topic based on the first opportunity score. See Paragraph [0074], (The system may determine user-selection rates corresponding to resources associated with individual query string fragments and subsequently determine an ordered listing of the most selected resources that were clicked on from the plurality of search results that were returned in response to individual queries which included each individual query string fragment, i.e. selecting the first search topic based on the first opportunity score (e.g. the ordered list of search results is determined according to the user-selection rates which are associated with query string fragments comprising text such as a subject of a query, See [0043]).)
GEORGE-Mashiach does not disclose the step wherein the first opportunity score is calculated based on a number of historical search queries that in the group of semantically similar historical search queries that the first search topic corresponds to;
Zhao disclose the step wherein the first opportunity score is calculated based on a number of historical search queries that in the group of semantically similar historical search queries that the first search topic corresponds to; See Paragraph [0063], (Disclosing a system for managing a plurality of product query arrays during a user search session. The system may utilize a neural network to train an NLP system using similarity metrics such as a cosine function to determine a numerical score indicating how related two words/tokens are.) See Paragraph [0039], (A search session of a user may be directed to a particular topic via a plurality of queries that may be closely related to the context of the session. The system may determine rewrite candidates for a given user query based on previous queries submitted by other users. Semantic relations between queries may be determined according to similarity metrics such as a skip-gram model, i.e. wherein the first opportunity score is calculated based on a number of historical search queries that in the group of semantically similar historical search queries that the first search topic corresponds to;)
GEORGE, Mashiach and Zhao are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of determining similarity between topics as part of a search process as disclosed by Zhao. Paragraph [0043] of Zhao discloses that the query rewrite system may improve the similarity search process over time as the population of queries increases, therefore the system may be trained to determine similarity between queries from a wider corpus.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 7 directed to a computer system and is rejected under similar rationale.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach and Zhao as applied to claim 7 above, and further in view of Ngwije (US PGPUB No. 2010/0268712; Pub. Date: Oct. 21, 2010).
Regarding dependent claim 8,
As discussed above with claim 7, GEORGE-Mashiach-Zhao discloses all of the limitations.
GEORGE-Mashiach-Zhao does not disclose the step wherein calculating the first opportunity score includes: determining a search volume for the first search topic, the search volume providing a measure of a number of searches that were performed in a predefined period and that relate to the first search topic;
and calculating the first opportunity score based on the search volume.
Ngwije discloses the step wherein calculating the first opportunity score includes: determining a search volume for the first search topic, the search volume providing a measure of a number of searches that were performed in a predefined period and that relate to the first search topic; See Paragraph [0017], (Disclosing a system for automatically grouping keywords into ad groups. The system comprises a server 12 comprising search logs 20 which are linked to a keyword database and configured to retrieve a list of keywords an calculate a historical search volume associated with each keyword contained in the database. Search logs 20 indicate the total number of search requests conducted by a user for each keyword stored within keyword database 16 during a predetermined period of time, i.e. determining a search volume for the first search topic, the search volume providing a measure of a number of searches that were performed in a predefined period and that relate to the first search topic;)
and calculating the first opportunity score based on the search volume. See Paragraph [0017], (Disclosing a system for automatically grouping keywords into ad groups. The system comprises a server 12 comprising search logs 20 which are linked to a keyword database and configured to retrieve a list of keywords an calculate a historical search volume associated with each keyword contained in the database, i.e. calculating the first opportunity score based on the search volume (e.g. by determining the historical search volume of each keyword).)"
GEORGE, Mashiach, Zhao and Ngwije are analogous art because they are in the same field of endeavor, content delivery. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach-Zhao to include the method of calculating historical search volumes for search query terms stored in a database as disclosed by Ngwije. Paragraph [0018] of Nqwije discloses that the process of calculating a historical search volume allows for measuring particular traffic at a given website such as via a raw count of a number of events, which may be counted on a per-user basis in order to determine a suitable value range for presentation of search results or other processing.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 8 directed to a computer system and is rejected under similar rationale.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 1 above, and further in view of LUNDBAEK (US PGPUB No. 2022/0171873; Pub. Date: Jun. 2, 2022).
Regarding dependent claim 9,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE-Mashiach does not disclose the step wherein the plurality of historical search queries includes a plurality of internal historical search queries, each internal historical search query corresponding to a search performed using a search function of the content delivery platform.
LUNKBAEK discloses the step wherein the plurality of historical search queries includes a plurality of internal historical search queries, each internal historical search query corresponding to a search performed using a search function of the content delivery platform. See Paragraph [0233], (Disclosing a system for providing personalized search. The method includes maintaining a user history 1270 which includes previous search queries stored by the user apparatus in one or more local repositories.) See FIG. 8 & Paragraph [0184], (FIG. 8 illustrates method 800 describing a search process comprising step 810 of outputting personalized search result sets to a requesting user in response to a search query at step 802, i.e. wherein the plurality of historical search queries includes a plurality of internal historical search queries (e.g. a user search history comprises prior search queries), each internal historical search query corresponding to a search performed using a search function of the content delivery platform (e.g. search queries are used to retrieve a personalized search result set).)
GEORGE, Mashiach and LUNDBAEK are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of storing a user history as disclosed by LUNDBAEK. Paragraph [0058] of LUNKBAEK discloses that the system for privacy-preserving personalized data searching allows users to perform searches while improving the likelihood of providing a search result of interest and/or likely to be accessed by the user performing the search, which results in an optimization of computational resources, improving the user experience, etc.
Regarding dependent claim 19,
The claim is analogous to the subject matter of dependent claim 9 directed to a computer system and is rejected under similar rationale.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over GEORGE in view of Mashiach as applied to claim 1 above, and further in view of NGUYEN et al. (US PGPUB No. 2019/0050406; Pub. Date: Feb. 14, 2019).
Regarding dependent claim 10,
As discussed above with claim 1, GEORGE-Mashiach discloses all of the limitations.
GEORGE-Mashiach does not disclose the step wherein the plurality of historical search queries includes a plurality of external historical search queries, each external historical search query corresponding to a search performed using a search function provided by an entity other than the content delivery platform.
NGUYEN discloses the step wherein the plurality of historical search queries includes a plurality of external historical search queries, each external historical search query corresponding to a search performed using a search function provided by an entity other than the content delivery platform. See Paragraph [0034], (Disclosing a system for processing a message input for retrieving content. The system comprises database 220 configured to store queries directed to an external search engine 208. The system may construct a query from a user message input according to previously constructed queries stored in database 220. Note FIG.1 wherein users may interact with a plurality of servers 116, 118, 120 via the external search engine accessed via network 108.) See Paragraph [0025], (A user may interact with the system via a user device such as a mobile phone 102 which may utilize a local database 110 and access servers 116, 118, 120 via network 108 to process message data and provide an result set, i.e. wherein the plurality of historical search queries includes a plurality of external historical search queries, each external historical search query corresponding to a search performed using a search function provided by an entity other than the content delivery platform (e.g. the mobile device is part of the content delivery platform. The external search engine is a separate system accessed by the mobile device via network 108 that is not local to the user device(s)).)
GEORGE, Mashiach and NGUYEN are analogous art because they are in the same field of endeavor, content retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of GEORGE-Mashiach to include the method of storing queries made to an external search engine as disclosed by NGUYEN. Paragraph [0034] of NGUYEN discloses that the previous search queries may be used to generate future intelligent queries related to the requested content, in this case attachable entities. This represents an improvement in the search process by improving the quality of the generated queries.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 10 directed to a computer system and is rejected under similar rationale.
Response to Arguments
Applicant’s arguments, filed 8/11/2026, with respect to the rejection(s) of claim(s) 1 and 3 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the following:
Regarding independent claim 1,
Applicant’s remarks regarding the rejection of claim 1 under 35 USC 103 have been considered persuasive. New grounds of rejection have been made in view of Mashiach et al. (US PGPUB No. 2016/0147893; Pub. Date; May 26, 2016) as discussed above.
Regarding dependent claim 3,
Applicant’s remarks regarding the rejection of claim 3 under 35 USC 103 have been considered persuasive. New grounds of rejection have been made in view of Misiewicz et al. (US PGPUB No. 2022/0138258; Pub. Date: May 5, 2022) as discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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