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 .
Response to Amendments
This Office action is in response to Applicant's amendment filed on 8/8/2025.
Claim 1, 3-23 are pending. Claim 2 is cancelled. Claim 1, 4-23 are amended. Claim 1, 3-23 are rejected.
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 1, 3-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 18 is directed to statutory category process. The claim recites “extracting, ….. a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines … extracting……. a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories; based on the extracted first set of attributes….. determining, ….. a similarity score between the one or more first content items and the one or more breaking news headlines…….assigning…… the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines…….generating, …… a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items” and “–“map the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes”. The process of extracting attributes for breaking headline news and breaking news stories from news feeds, determining and assigning similarity score between headline news and breaking news headlines and generating a ordered recommendation list of news based similarity scores and mapping breaking news headlines with their associated stories based on extracted attributes involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “receiving, by one or more processors, one or more first content items from the plurality of first computing devices, the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form, wherein the one or more processors are operatively coupled to the plurality of first computing devices, the one or more processors coupled with a memory that stores instructions executed by the one or more processors; receiving, by the one or more processors, one or more second content items from the plurality of first computing devices, the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form”, “provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done”, “using a machine learning (ML) engine” and “one or more processors” above additional elements recite insignificant extra-solution activity of user query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Recitation of “processors” and “memory” are high-level recitations of a generic computer components and represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Where use of machine learning model may be characterized as using machine learning model as a tool or generally linking the abstract ideas identified above to the technology of machine learning, see MPEP 2106.05(f) and/or (h). This generic high-level recitation of machine learning is nothing more than mere instructions to apply on a computer with a possible field of use limitation to machine-learning field. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering. Further, receiving news feeds from server is insignificant extra-solution activity of data transmission, such is also well-understood, routine, and conventional (OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible. Accordingly, claim 18 is not patent eligible.
Claim 1 differs from claim 18 in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 1 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 1 is not patent eligible.
Claim 15 is directed to statutory category apparatus. The claim recites “extract..…. a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines; extract, …….a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories; ….. determine, …….., a similarity score between the one or more first content items and the one or more breaking news headlines …….assigning…… the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines ……. generate, …… a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items” and “map the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes”. The process of extracting attributes for breaking headline news and breaking news stories from news feeds, determining and assigning similarity score between headline news and breaking news headlines and generating a ordered recommendation list of news based similarity scores and mapping breaking news headlines with their associated stories based on extracted attributes involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “receive, by the receiver, one or more first content items from the plurality of first computing devices (104), the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form;
receive, by the receiver, one or more second content items from the plurality of first computing devices, the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form”, “provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done” and “using a machine learning (ML) engine” and “one or more processors” above additional elements recite insignificant extra-solution activity of user query specific mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Recitation of “processors” and “memory” are high-level recitations of a generic computer components and represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Where use of machine learning model may be characterized as using machine learning model as a tool or generally linking the abstract ideas identified above to the technology of machine learning, see MPEP 2106.05(f) and/or (h). This generic high-level recitation of machine learning is nothing more than mere instructions to apply on a computer with a possible field of use limitation to machine-learning field. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering. Further, receiving news feeds from server is insignificant extra-solution activity of data transmission, such is also well- understood, routine, and conventional (OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible. Accordingly, claim 16 is not patent eligible.
Dependent claim 16-17 are directed to the same abstract idea as the independent claim from which they depend and further At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “processor is associated with a source profiling module, wherein the source profiling module receives and establishes a set of trusted content providers” and “a user interface equipped in the UE is configured to display a combination of the recommended list and one or more second content items provided by the set of trusted content providers”, above mentioned additional element recite insignificant extra-solution activity of data gathering and outputting or displaying data. Receiving news related data from trusted source is mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Further, additional elements of displaying data is considered as insignificant extra solution activity of data output. Recitation of “processors” is high-level recitations of a generic computer component and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 16-17 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of mere data gathering and outputting data are well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f) and for applying the machine learning as a tool are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claims are ineligible. Accordingly, claim 16-17 are not patent eligible.
Claim 6-7 differ from claim 16-17 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 6-7 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 6-7 are not patent eligible.
Dependent claim 2-5 and 8-10 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations –“determine a best story associated with the one or more second content items based on the similarity scores associated with each of the mapped second content items with the ordered list of the one or more first content items present in the recommendation list”, “look up one or more entities, by a curated knowledge graph module ….. in any or a combination of the one or more breaking news and the plurality of new stories received; and, identify, by the curated knowledge graph module, the one or more entities mentioned in any language….”, “tag, the one or more second content items with the one or more breaking news headlines, for other news stories to compare to”, “update the recommended list by an entity matching module ……wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; re-rank the recommended list based on the updated recommended list”, and “determine, …….. a combined reranking score for the one or more new first and second content items received”. The process of mapping breaking news headlines with their associated stories based on extracted attributes, determining the best news story based on score, looking up entities in a curated knowledge graph and identifying mentioned entities in a knowledge graph in any language, tagging contents for mapping, updating recommended list, re-ranking recommended list and determining a combined score for contents involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “retrieve a plurality of new stories based on one or more cross lingually trained semantic models associated with the one or more processors”, “treat one or more second content items provided by the trusted content providers as a standard data” and “a user interface equipped in the UE is configured to display a combination of the recommended list and one or more second content items provided by the set of trusted content providers”, above mentioned additional element recite insignificant extra-solution activity of data gathering and outputting or displaying data. Receiving news related data from trusted source is mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Further, additional elements of providing clickable data is considered as insignificant extra solution activity of data output. Recitation of “processors” is high-level recitations of a generic computer component and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 2-5 and 8-10 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of mere data gathering and outputting data are well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f) and for applying the machine learning as a tool are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claims are ineligible. Accordingly, claim 2-5 and 8-10 are not patent eligible.
Dependent claim 11-14 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations –“iteratively add one or more new first content items to the recommended list in real time, wherein the one or more new first content items are extracted from a continuous incoming stream of first content items”, “trigger an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items” and “find out if a content provider publishes more than one first and second content item relating to a news event; determine a new version of the first content item with additional information added in the respective second content item; discard the previous version of the first content item from the recommended list; and, refresh the recommended list to include the new version of the first content item”. The process of iteratively adding contents to a recommended list, extracting contents from a feed, triggering event for re-refreshing a list, finding out availability of new contents, determining newer version of contents, discarding older version of contents and refreshing the recommended list with newer version of content items involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time” and “continuously refresh and keep, using a pruning module associated with the one or more processors (202), the most succinct one or more new first content items to the breaking news headline from the continuous incoming stream of first and second content items”, above mentioned additional element recite insignificant extra-solution activity of data gathering. Receiving news related data from trusted source is mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Recitation of “processors” is high-level recitations of a generic computer component and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 11-14 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of mere data gathering is well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claims are ineligible. Accordingly, claim 11-14 are not patent eligible.
Dependent claim 19-23 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations “determining, ……. a best story associated with the one or more second content items based on the similarity scores associated with each of the mapped second content items with the ordered list of the one or more first content items present in the recommendation list”, “Looking up one or more entities, by a curated knowledge graph module ……in any or a combination of the one or more breaking news and the plurality of new stories received; and, identifying, by the curated knowledge graph module, the one or more entities mentioned in any language in any of the plurality of first computing devices”, “wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; re-ranking, ……, the recommended list based on the updated recommended list”, “iteratively adding one or more new first content items to the recommended list in real time, wherein the one or more new first content items are extracted from a continuous incoming stream of first content items received”, “; triggering an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items” and “finding out if a content provider publishes more than one first and second content item relating to a news event; determining a new version of the first content item with additional information added in the respective second content item; discarding the previous version of the first content item from the recommended list; and, refreshing the recommended list to include the new version of the first content item”. The process of determining similarity scoring of contents for mapping, looking up entities in a curated knowledge graph and identifying mentioned entities in a knowledge graph in any language, updating recommendation list based on matching text, audio, video matching occurrences, re-ranking recommended list iteratively adding contents to a recommended list, extracting contents from a feed, triggering event for re-refreshing a list, finding out availability of new contents, determining newer version of contents, discarding older version of contents and refreshing the recommended list with newer version of content items involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “first content items received from the plurality of first computing devices, and wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time” and “continuously refreshing and keeping, using a pruning module associated with the one or more processors, a most succinct one or more new first content items to the breaking news headline from the continuous incoming stream of first and second content items”, above mentioned additional element recite insignificant extra-solution activity of data gathering and outputting or displaying data. Receiving news related data from trusted source is mere data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Further, additional elements of providing clickable data is considered as insignificant extra solution activity of data output. Recitation of “processors” is high-level recitations of a generic computer component and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), and do not provide integration into a practical application or significantly more. Further, recited use of machine learning model may be characterized as using machine learning model as a tool or generally linking the abstract ideas identified above to the technology of machine learning, see MPEP 2106.05(f) and/or (h). This generic high-level recitation of machine learning is nothing more than mere instructions to apply on a computer with a possible field of use limitation to machine-learning field. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 19-23 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of mere data gathering and outputting data are well-understood, routine or conventional activities. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f) and for applying the machine learning as a tool are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claims are ineligible. Accordingly, claim 19-23 are not patent eligible.
Claim Rejections - 35 USC § 103
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 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 1, 3-4, 12, 14-15, 18-19 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in further view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”.
Claim 1 (Currently Amended), Biru teaches A system for providing a breaking news headline across a plurality of domains, said system comprising; one or more processors operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes said system to(Biru, Fig. 1 discloses a system with computing hardware components for providing news):
receive one or more first content items from the plurality of first computing devices, the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news related content in plurality of languages “The retriever component 112 is configured to obtain (e.g., retrieve), over a network 114 (e.g., the Internet, intranet, etc.) a plurality of (computer-readable) news articles 116 (or portions of the plurality of news articles 116) from a plurality of electronic sources 118. It is to be understood that the plurality of news articles 116 may be written in different languages (e.g., English, Spanish, etc.)”; para 0021 further teaches receiving only the abstract or headlines of the news from the source “the retriever component 112 obtains titles and abstracts of the plurality of news articles 116 (without obtaining the full news articles themselves)”);
receive one or more second content items from the plurality of first computing devices, the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news stories or articles as well “According to embodiments, the retriever component 112 obtains the plurality of news articles 116 ……. that obtains the plurality of news articles 116”);
But Biru does not explicitly teach extract a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines; extract a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories;
based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines,
wherein the ML engine is associated with the one or more processors; assign the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines; generate a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items, map the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes; and provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of identifying breaking news Khasnis teaches extract a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines(Khasnis, para 0027 discloses tokenization of attributes related to breaking news headlines “The tokenizer 102a is configured to receive the breaking news headline from a content provider and tokenize the breaking news headline to identify a plurality of headline tokens in substantial real time………”);
extract a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories(Khasnis, para 0027 further discloses tokenization of attributes related to news stories “The tokenizer 102a is also configured to receive news stories from a plurality of content providers. As and when a news story is received from a content provider, the tokenizer 102a tokenizes the news story………”);
map the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes(Khasnis, para 0028 discloses establishing link between breaking new headline with news stories “Upon finding a match, the mapper module 102 c to link the related news story to the breaking news headline. The link may be by tagging the breaking news headline with an identifier associated with the news story”; where para 0007 discloses that tagged tokens are attributes of the headlines and stories which describes the headline and stories “the breaking news headline is dynamically tokenized to identify a plurality of headline tokens that substantially describe the breaking news headline” and “each of the news stories is tokenized in substantial real time to identify a plurality of story tokens. The headline tokens and story tokens are analyzed to determine if one or more of the plurality of news stories are related to the breaking news headline”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying attributes of breaking news headlines and stories of Khasnis into receiving of multi-lingual news headlines and story feeds of Biru to produce an expected result of linking breaking news headlines with its corresponding stories. The modification would be obvious because one of ordinary skill in the art would be motivated to keep users engaged with news providers website by presenting content from different sources related to breaking news headlines (Khasnis, para 0007).
But Biru and Khasnis don’t explicitly teach based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the one or more processors; assign the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines; generate a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items, and provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of scoring contents De Paolis teaches based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines(De Paolis, para 0079-0080 disclose similarity scoring of contents by comparing two sets of data using machine learning “The tasks that the one or more trained machine learning models may be trained to perform are as follows: [0080] a. List Matching Classifier—The list matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric) between the set and one or more additional sets of ranked items. The list matching classifier may identify relative similarities in similarities between sets of ranked items”),
wherein the ML engine is associated with the one or more processors; assign the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines(De Paolis, para 0080 discloses assignment of similarity metric to contents “matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric”);
generate a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items(De Paolis, para 0081 further teaches generating an ordered list most similar to least similar “a rank-based similarity comparison (e.g., cosine similarity) to obtain a set similarity metric between each list. Set similarity metrics may be aggregated for a given user comparison combination to determine a user to user similarity metric. The user profile matching classifier may return or otherwise output a list of user profiles ordered by the user to user similarity metric (e.g., most similar to least similar…” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of scoring contents by comparison using machine learning of De Paolis into receiving of multi-lingual news headlines and story feeds of Biru and Khasnis to produce an expected result of scoring contents for ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the accuracy of deep learning model by optimizing model parameters and validating the performance (De Paolis, para 0089).
But Biru and Khasnis and De Paolis don’t explicitly teach and provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of recommending news contents Liu teaches and provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done(Liu, element 500 of Fig. 9 and para 0117 discloses clickable link to contents “the recommended news list 500 is displayed….”; where Khasnis, para 0028 discloses establishing link between breaking new headline with news stories via attributes/tokens).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of recommending news of Liu into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis and De Paolis to recommend breaking news list. The modification would be obvious because one of ordinary skill in the art would be motivated to improve user experience by accurately determining user’s need and recommend contents accordingly(Liu, para 002 and 0010).
Regarding claim 3(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 and Khasnis further wherein the system is further configured to: determine a best story associated with the one or more second content items based on the similarity scores associated with each of the mapped second content items with the ordered list of the one or more first content items present in the recommendation list(Khasnis, para 0027 discloses that by setting the matching criteria in percentage(matching score) best match between breaking news headline to its story can be achieved “The matching criteria may specify the percentage of matching that may be required in order for the matcher module 102 b to declare that the news story is related to the breaking news headline”).
Regarding claim 4(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 and Khasnis further The system as claimed in claim 1, wherein the system is further configured to retrieve a plurality of new stories based on one or more cross lingually trained semantic models associated with the one or more processors(Khasnis, para 0030 discloses receiving new stories “subsequent to receiving and tokenizing the breaking news headline, the algorithm detects a news story, such as ‘CNN 1,’ received from a content provider, such as Cable News Network (CNN). In addition to detecting the news story”’; where prior art Biru in para 0022 discloses using of multi-lingual or language agnostic neural network encoder for processing news headlines and stories “According to other embodiments, the encoder component 120 is or includes a neural network-based encoder. According to some embodiments, the encoder component 120 includes a language agnostic encoder”).
Regarding claim 12(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 and Khasnis further teaches wherein the system is further configured to: continuously refresh and keep, using a pruning module associated with the one or more processors, the most succinct one or more new first content items to the breaking news headline from the continuous incoming stream of first and second content items (Khasnis, para0041 newer content items are getting continuously updated with the availability of new contents “one or more news story for the second breaking news headline may already have been received prior to the receipt of the second breaking news headline. It should be noted herein that when the algorithm initializes the cache memory, the algorithm initializes only the data related to the first breaking news headline. As a result, the one or more news stories that were not related to the first breaking news headline and the related news tokens are still in the cache memory and are now used in matching with the headline tokens of the second or subsequent breaking news headline. Thus, the algorithm continuously clears the cache memory of the breaking news headline and information related to the breaking news headline that are already reported/presented to the users and maintains news stories that have not yet been reported so that they can be mined for subsequent reporting”).
Regarding claim 14(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 and Khasnis further teaches wherein the system is further configured to: find out if a content provider publishes more than one first and second content item relating to a news event; determine a new version of the first content item with additional information added in the respective second content item(Khasnis, para 0041 discloses upon availability of newer version of same breaking news corresponding, stories get mapped and presented “the same subject matter that was reported in a first breaking news headlines may be received and reported as subsequent breaking news headlines. This can happen in cases where the breaking news headlines are related to developing stories that span over an extended period of time. In this embodiment, the newer breaking news headlines related to the same subject matter are received, tokenized and presented to the users with latest related news stories”); discard the previous version of the first content item from the recommended list; refresh the recommended list to include the new version of the first content item(Khasnis, para 0042 further discloses discarding previous version and refreshing the list with new version “Since the cache memory is initialized every time a newer breaking news headlines is received, the first breaking news headlines and the related news stories are flushed from the cache memory and the second breaking news headlines with the latest related news are tokenized, reported and stored in the cache memory till a subsequent breaking news headlines is received. This constant initialization of cache memory helps in managing the limited resources of the cache memory well while providing relevant news stories in a fast and efficient manner”).
Claim 15(Currently Amended), Biru teaches A user equipment (UE) for providing a breaking news headline across a plurality of domains, said UE comprising; a processor and a receiver, wherein the processor operatively coupled to a plurality of first computing devices, the processor coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes said UE to(Biru, Fig. 1 discloses a system with computing hardware components for providing news):
receive, by the receiver, one or more first content items from the plurality of first computing devices, the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news related content in plurality of languages “The retriever component 112 is configured to obtain (e.g., retrieve), over a network 114 (e.g., the Internet, intranet, etc.) a plurality of (computer-readable) news articles 116 (or portions of the plurality of news articles 116) from a plurality of electronic sources 118. It is to be understood that the plurality of news articles 116 may be written in different languages (e.g., English, Spanish, etc.)”; para 0021 further teaches receiving only the abstract or headlines of the news from the source “the retriever component 112 obtains titles and abstracts of the plurality of news articles 116 (without obtaining the full news articles themselves)”);
receive, by the receiver, one or more second content items from the plurality of first computing devices, the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news stories or articles as well “According to embodiments, the retriever component 112 obtains the plurality of news articles 116 ……. that obtains the plurality of news articles 116”);
But Biru does not explicitly teach extract, by the processor, a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines; extract, by the processor, a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories; based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the processors; assign, by the processor, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines; and generate, by the processor, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items; map, by the processor, the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes; and provide, by the processor, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of identifying breaking news Khasnis teaches extract, by the processor, a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines(Khasnis, para 0027 discloses tokenization of attributes related to breaking news headlines “The tokenizer 102a is configured to receive the breaking news headline from a content provider and tokenize the breaking news headline to identify a plurality of headline tokens in substantial real time………”);
extract, by the processor, a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories(Khasnis, para 0027 further discloses tokenization of attributes related to news stories “The tokenizer 102a is also configured to receive news stories from a plurality of content providers. As and when a news story is received from a content provider, the tokenizer 102a tokenizes the news story………”); map, by the processor, the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes(Khasnis, para 0028 discloses establishing link between breaking new headline with news stories “Upon finding a match, the mapper module 102 c to link the related news story to the breaking news headline. The link may be by tagging the breaking news headline with an identifier associated with the news story”; where para 0007 discloses that tagged tokens are attributes of the headlines and stories which describes the headline and stories “the breaking news headline is dynamically tokenized to identify a plurality of headline tokens that substantially describe the breaking news headline” and “each of the news stories is tokenized in substantial real time to identify a plurality of story tokens. The headline tokens and story tokens are analyzed to determine if one or more of the plurality of news stories are related to the breaking news headline”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying attributes of breaking news headlines and stories of Khasnis into receiving of multi-lingual news headlines and story feeds of Biru to produce an expected result of linking breaking news headlines with its corresponding stories. The modification would be obvious because one of ordinary skill in the art would be motivated to keep users engaged with news providers website by presenting content from different sources related to breaking news headlines (Khasnis, para 0007).
But Biru and Khasnis don’t explicitly teach based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the processors;
assign, by the processor, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines;
and generate, by the processor, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items; and provide, by the processor, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of scoring contents De Paolis teaches based on the extracted first set of attributes, determine, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines(De Paolis, para 0079-0080 disclose similarity scoring of contents by comparing two sets of data using machine learning “The tasks that the one or more trained machine learning models may be trained to perform are as follows: [0080] a. List Matching Classifier—The list matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric) between the set and one or more additional sets of ranked items. The list matching classifier may identify relative similarities in similarities between sets of ranked items”),
wherein the ML engine is associated with the processors; assign, by the processor, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines(De Paolis, para 0080 discloses assignment of similarity metric to contents “matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric”);
and generate, by the processor, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items(De Paolis, para 0081 further teaches generating an ordered list most similar to least similar “a rank-based similarity comparison (e.g., cosine similarity) to obtain a set similarity metric between each list. Set similarity metrics may be aggregated for a given user comparison combination to determine a user to user similarity metric. The user profile matching classifier may return or otherwise output a list of user profiles ordered by the user to user similarity metric (e.g., most similar to least similar…” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of scoring contents by comparison using machine learning of De Paolis into receiving of multi-lingual news headlines and story feeds of Biru and Khasnis to produce an expected result of scoring contents for ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the accuracy of deep learning model by optimizing model parameters and validating the performance (De Paolis, para 0089).
But Biru and Khasnis and De Paolis don’t explicitly teach and provide, by the processor, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of recommending news contents Liu teaches and provide, by the processor, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done (Liu, element 500 of Fig. 9 and para 0117 discloses clickable link to contents “the recommended news list 500 is displayed….”; where Khasnis, para 0028 discloses establishing link between breaking new headline with news stories via attributes/tokens).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of recommending news of Liu into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis and De Paolis to recommend breaking news list. The modification would be obvious because one of ordinary skill in the art would be motivated to improve user experience by accurately determining user’s need and recommend contents accordingly(Liu, para 002 and 0010).
Claim 18 (Currently Amended), Biru teaches A method for providing a breaking news headline across a plurality of domains, said method comprising; receiving, by one or more processors, one or more first content items from the plurality of first computing devices, the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news related content in plurality of languages “The retriever component 112 is configured to obtain (e.g., retrieve), over a network 114 (e.g., the Internet, intranet, etc.) a plurality of (computer-readable) news articles 116 (or portions of the plurality of news articles 116) from a plurality of electronic sources 118. It is to be understood that the plurality of news articles 116 may be written in different languages (e.g., English, Spanish, etc.)”; para 0021 further teaches receiving only the abstract or headlines of the news from the source “the retriever component 112 obtains titles and abstracts of the plurality of news articles 116 (without obtaining the full news articles themselves)”),
wherein the one or more processors are operatively coupled to the plurality of first computing devices, the one or more processors coupled with a memory that stores instructions executed by the one or more processors; receiving, by the one or more processors, one or more second content items from the plurality of first computing devices, the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form(Biru, para 0021 discloses retrieving/receiving news stories or articles as well “According to embodiments, the retriever component 112 obtains the plurality of news articles 116 ……. that obtains the plurality of news articles 116”);
But Biru does not explicitly teach extracting, by the one or more processors, a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines; extracting, by the one or more processors, a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories; based on the extracted first set of attributes, determining, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the one or more processors; assigning, by the ML engine, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines; generating, by the ML engine, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items; mapping, by the ML engine, the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes; and providing, by the ML engine, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of identifying breaking news Khasnis teaches extracting, by the one or more processors, a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines(Khasnis, para 0027 discloses tokenization of attributes related to breaking news headlines “The tokenizer 102a is configured to receive the breaking news headline from a content provider and tokenize the breaking news headline to identify a plurality of headline tokens in substantial real time………”);
extracting, by the one or more processors, a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories(Khasnis, para 0027 further discloses tokenization of attributes related to news stories “The tokenizer 102a is also configured to receive news stories from a plurality of content providers. As and when a news story is received from a content provider, the tokenizer 102a tokenizes the news story………”); mapping, by the ML engine, the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes(Khasnis, para 0028 discloses establishing link between breaking new headline with news stories “Upon finding a match, the mapper module 102 c to link the related news story to the breaking news headline. The link may be by tagging the breaking news headline with an identifier associated with the news story”; where para 0007 discloses that tagged tokens are attributes of the headlines and stories which describes the headline and stories “the breaking news headline is dynamically tokenized to identify a plurality of headline tokens that substantially describe the breaking news headline” and “each of the news stories is tokenized in substantial real time to identify a plurality of story tokens. The headline tokens and story tokens are analyzed to determine if one or more of the plurality of news stories are related to the breaking news headline”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying attributes of breaking news headlines and stories of Khasnis into receiving of multi-lingual news headlines and story feeds of Biru to produce an expected result of linking breaking news headlines with its corresponding stories. The modification would be obvious because one of ordinary skill in the art would be motivated to keep users engaged with news providers website by presenting content from different sources related to breaking news headlines (Khasnis, para 0007).
But Biru and Khasnis don’t explicitly teach based on the extracted first set of attributes, determining, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the one or more processors;
assigning, by the ML engine, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines;
generating, by the ML engine, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items; and providing, by the ML engine, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of scoring contents De Paolis teaches based on the extracted first set of attributes, determining, by using a machine learning (ML) engine, a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the one or more processors(De Paolis, para 0079-0080 disclose similarity scoring of contents by comparing two sets of data using machine learning “The tasks that the one or more trained machine learning models may be trained to perform are as follows: [0080] a. List Matching Classifier—The list matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric) between the set and one or more additional sets of ranked items. The list matching classifier may identify relative similarities in similarities between sets of ranked items”);
assigning, by the ML engine, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines(De Paolis, para 0080 discloses assignment of similarity metric to contents “matching classifier obtains a set of ranked items and determines a similarity (e.g., using a similarity metric”);
and generating, by the ML engine, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items(De Paolis, para 0081 further teaches generating an ordered list most similar to least similar “a rank-based similarity comparison (e.g., cosine similarity) to obtain a set similarity metric between each list. Set similarity metrics may be aggregated for a given user comparison combination to determine a user to user similarity metric. The user profile matching classifier may return or otherwise output a list of user profiles ordered by the user to user similarity metric (e.g., most similar to least similar…” ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of scoring contents by comparison using machine learning of De Paolis into receiving of multi-lingual news headlines and story feeds of Biru and Khasnis to produce an expected result of scoring contents for ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the accuracy of deep learning model by optimizing model parameters and validating the performance (De Paolis, para 0089).
But Biru and Khasnis and De Paolis don’t explicitly teach and providing, by the ML engine, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done.
However, in the same field of endeavor of recommending news contents Liu teaches and providing, by the ML engine, a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done (Liu, element 500 of Fig. 9 and para 0117 discloses clickable link to contents “the recommended news list 500 is displayed….”; where Khasnis, para 0028 discloses establishing link between breaking new headline with news stories via attributes/tokens).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of recommending news of Liu into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis and De Paolis to recommend breaking news list. The modification would be obvious because one of ordinary skill in the art would be motivated to improve user experience by accurately determining user’s need and recommend contents accordingly(Liu, para 002 and 0010).
Regarding claim 19(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 18 and Khasnis further determining, by the ML engine, a best story associated with the one or more second content items based on the similarity scores associated with each of the mapped second content items with the ordered list of the one or more first content items present in the recommendation list(Khasnis, para 0027 discloses that by setting the matching criteria in percentage(matching score) best match between breaking news headline to its story can be achieved “The matching criteria may specify the percentage of matching that may be required in order for the matcher module 102 b to declare that the news story is related to the breaking news headline”).
Regarding claim 23(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 18 and Khasnis further teaches wherein the method further comprises the step of: finding out if a content provider publishes more than one first and second content item relating to a news event; determining a new version of the first content item with additional information added in the respective second content item (Khasnis, para 0041 discloses upon availability of newer version of same breaking news corresponding, stories get mapped and presented “the same subject matter that was reported in a first breaking news headlines may be received and reported as subsequent breaking news headlines. This can happen in cases where the breaking news headlines are related to developing stories that span over an extended period of time. In this embodiment, the newer breaking news headlines related to the same subject matter are received, tokenized and presented to the users with latest related news stories”); discarding the previous version of the first content item from the recommended list; and refreshing the recommended list to include the new version of the first content item (Khasnis, para 0042 further discloses discarding pervious version and refreshing the list with new version “Since the cache memory is initialized every time a newer breaking news headlines is received, the first breaking news headlines and the related news stories are flushed from the cache memory and the second breaking news headlines with the latest related news are tokenized, reported and stored in the cache memory till a subsequent breaking news headlines is received. This constant initialization of cache memory helps in managing the limited resources of the cache memory well while providing relevant news stories in a fast and efficient manner”).
Claim 5 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in further view of Lipka, Nedim et al (PGPUB Document No. 20220253477), hereafter, referred to as “Lipka”.
Regarding claim 5(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 but don’t explicitly teach wherein the system is further configured to look up one or more entities, by a curated knowledge graph module associated with the machine learning (ML) engine, in any or a combination of the one or more breaking news and the plurality of new stories received; and identify, by the curated knowledge graph module, the one or more entities mentioned in any language in any of the plurality of first computing devices.
However, in the same field of endeavor of searching in knowledge graph Lipka teaches wherein the system is further configured to look up one or more entities, by a curated knowledge graph module associated with the machine learning (ML) engine, in any or a combination of the one or more breaking news and the plurality of new stories received(Lipka, para 0105 discloses searching entities in curated knowledge graph to find new entities which are linked “Entities of a knowledge graph are meaningful suggestions for user information because the entities are curated textual objects whose coverage is not limited based on word popularity. An entity linking algorithm is used to enable the use of a knowledge graph, which assigns a set of n entities from a knowledge graph to the target query and use these linked entities to find relevant entities to the initial query…”; where Khasnis teaches breaking news headlines and stories; and prior art De Paolis teaches generation of a news list by machine learning); and identify, by the curated knowledge graph module, the one or more entities mentioned in any language in any of the plurality of first computing devices (Lipka, para 0105 discloses query identifying entities in curated knowledge graph as a result “Entities of a knowledge graph are meaningful suggestions for user information because the entities are curated textual objects whose coverage is not limited based on word popularity. An entity linking algorithm is used to enable the use of a knowledge graph, which assigns a set of n entities from a knowledge graph to the target query and use these linked entities to find relevant entities to the initial query…”;).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of querying in the knowledge graph of Lipka into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend new breaking news from knowledge graph. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the news retrieval by using knowledge graph that includes surrogate entities related to query(Lipka, para 0020).
Regarding claim 20(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 18 but don’t explicitly teach wherein the method further comprises the step of: Looking up one or more entities, by a curated knowledge graph module associated with the machine learning (ML) engine, in any or a combination of the one or more breaking news and the plurality of new stories received; and identifying, by the curated knowledge graph module, the one or more entities mentioned in any language in any of the plurality of first computing devices.
However, in the same field of endeavor of searching in knowledge graph Lipka teaches wherein the method further comprises the step of: Looking up one or more entities, by a curated knowledge graph module associated with the machine learning (ML) engine, in any or a combination of the one or more breaking news and the plurality of new stories received (Lipka, para 0105 discloses searching entities in curated knowledge graph to find new entities which are linked “Entities of a knowledge graph are meaningful suggestions for user information because the entities are curated textual objects whose coverage is not limited based on word popularity. An entity linking algorithm is used to enable the use of a knowledge graph, which assigns a set of n entities from a knowledge graph to the target query and use these linked entities to find relevant entities to the initial query…”; where Khasnis teaches breaking news headlines and stories; and prior art De Paolis teaches generation of a news list by machine learning);
and identifying, by the curated knowledge graph module, the one or more entities mentioned in any language in any of the plurality of first computing devices (Lipka, para 0105 discloses query identifying entities in curated knowledge graph as a result “Entities of a knowledge graph are meaningful suggestions for user information because the entities are curated textual objects whose coverage is not limited based on word popularity. An entity linking algorithm is used to enable the use of a knowledge graph, which assigns a set of n entities from a knowledge graph to the target query and use these linked entities to find relevant entities to the initial query…”;).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of querying in the knowledge graph of Lipka into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend new breaking news from knowledge graph. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the news retrieval by using knowledge graph that includes surrogate entities related to query(Lipka, para 0020).
Claim 6-8, 11 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in further view of Wilson, Ashley Duane et al (PGPUB Document No. 20210289001), hereafter, referred to as “Wilson”.
Regarding claim 6(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 but don’t explicitly teach wherein the one or more processors are associated with a source profiling module, wherein the source profiling module receives and establishes a set of trusted content providers.
However, in the same field of endeavor of receiving contents for trusted news sources Wilson teaches wherein the one or more processors are associated with a source profiling module, wherein the source profiling module receives and establishes a set of trusted content providers (Wilson, para 0293 discloses receiving news from trusted source “a first news outlet might syndicate stories from a list of trusted sources, such as other news outlets……”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of receiving news from trusted source of Wilson into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news list from trusted source. The modification would be obvious because one of ordinary skill in the art would be motivated to ensure delivering trusted news by authenticating news sources(Wilson, para 0293).
Regarding claim 7(Currently Amended), Biru, Khasnis, De Paolis, Liu and Wilson teach all the limitations of claim 6 and Khasnis further teaches wherein a user interface at one or more computing devices (104) is configured to display a combination of the recommended list and one or more second content items provided by the set of trusted content providers(Khasnis, Fig. 4 discloses displaying breaking news article; where Wilson in para 0293 discloses receiving news from trusted sources).
Regarding claim 8(Currently Amended), Biru, Khasnis, De Paolis, Liu and Wilson teach all the limitations of claim 6 and Wilson further teaches the system is configured to treat one or more second content items provided by the trusted content providers as a standard data (Wilson, para 0293 discloses receiving news from trusted source “a first news outlet might syndicate stories from a list of trusted sources, such as other news outlets……”).
Khasnis further teaches tag, the one or more second content items with the one or more breaking news headlines, for other news stories to compare to(Khasnis, para 0028 discloses tagging of breaking news headlines and stories for comparison in linking process “Upon finding a match, the mapper module 102 c to link the related news story to the breaking news headline. The link may be by tagging the breaking news headline with an identifier associated with the news story, such as a universally unique identifier (UUID)”).
Regarding claim 11(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 and Khasnis further teaches wherein the system is further configured to: iteratively add one or more new first content items to the recommended list in real time, wherein the one or more new first content items are extracted from a continuous incoming stream of first content items received from the plurality of first computing devices(Khasnis, para0041 newer content items are getting continuously updated with the availability of new contents “one or more news story for the second breaking news headline may already have been received prior to the receipt of the second breaking news headline. It should be noted herein that when the algorithm initializes the cache memory, the algorithm initializes only the data related to the first breaking news headline. As a result, the one or more news stories that were not related to the first breaking news headline and the related news tokens are still in the cache memory and are now used in matching with the headline tokens of the second or subsequent breaking news headline. Thus, the algorithm continuously clears the cache memory of the breaking news headline and information related to the breaking news headline that are already reported/presented to the users and maintains news stories that have not yet been reported so that they can be mined for subsequent reporting”),
But Biru, Khasnis, De Paolis and Liu don’t explicitly teach and wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time.
However, in the same field of endeavor of receiving contents for trusted news sources Wilson teaches and wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time (Wilson, para 0293 discloses receiving news from trusted source “a first news outlet might syndicate stories from a list of trusted sources, such as other news outlets……”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of receiving news from trusted source of Wilson into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news list from trusted source. The modification would be obvious because one of ordinary skill in the art would be motivated to ensure delivering trusted news by authenticating news sources(Wilson, para 0293).
Regarding claim 16(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 15 but don’t explicitly teach wherein the processor is associated with a source profiling module, wherein the source profiling module receives and establishes a set of trusted content providers.
However, in the same field of endeavor of receiving contents for trusted news sources Wilson teaches wherein the processor is associated with a source profiling module, wherein the source profiling module receives and establishes a set of trusted content providers (Wilson, para 0293 discloses receiving news from trusted source “a first news outlet might syndicate stories from a list of trusted sources, such as other news outlets……”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of receiving news from trusted source of Wilson into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news list from trusted source. The modification would be obvious because one of ordinary skill in the art would be motivated to ensure delivering trusted news by authenticating news sources(Wilson, para 0293).
Regarding claim 17(Currently Amended), Biru, Khasnis, De Paolis, Liu, Wilson teach all the limitations of claim 16 and Khasnis further teaches wherein a user interface equipped in the UE is configured to display a combination of the recommended list and one or more second content items provided by the set of trusted content providers (Khasnis, Fig. 4 discloses displaying breaking news article; where Wilson in para 0293 discloses receiving news from trusted sources).
Claim 9 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in further view of Inamdar, Vijay et al (PGPUB Document No. 20210097471), hereafter, referred to as “Inamdar”.
Regarding claim 9(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 but don’t explicitly teach wherein the system is further configured to: update the recommended list by an entity matching module associated with the one or more processors, wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; and re-rank the recommended list based on the updated recommended list.
However, in the same field of endeavor of ranking contents Inamdar teaches wherein the system is further configured to: update the recommended list by an entity matching module associated with the one or more processors, wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; and re-rank the recommended list based on the updated recommended list(Inamdar, abstract discloses re-ranking the updated list based updated scores “re-ranking the set of candidates based on updated assigned scores for the set of candidates and a candidate re-ranking parameter to provide an updated ranked candidate list corresponding to the candidate list”; where Khasnis in para 0028 discloses breaking news headline and stories).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of re-ranking contents based on updated scores of Inamdar into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to update recommendation of breaking news list based on updated scores. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the content ranking using machine learning by train the machine learning model with additional training data(Inamdar, para 0048).
Regarding claim 21(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 18 but don’t explicitly teach wherein the method further comprises the step of: updating, by the ML engine, the recommended list by an entity matching module associated with the one or more processors, wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; re-ranking, by the ML engine, the recommended list based on the updated recommended list.
However, in the same field of endeavor of ranking contents Inamdar teaches wherein the method further comprises the step of: updating, by the ML engine, the recommended list by an entity matching module associated with the one or more processors, wherein the update of the recommended list is based on a text, an audio or a video based matching occurrence of one or more entities in any or a combination of one or more breaking news headlines, incoming headlines and one or more second content items comprising new stories; re-ranking, by the ML engine, the recommended list based on the updated recommended list(Inamdar, abstract discloses re-ranking the updated list based updated scores “re-ranking the set of candidates based on updated assigned scores for the set of candidates and a candidate re-ranking parameter to provide an updated ranked candidate list corresponding to the candidate list”; where Khasnis in para 0028 discloses breaking news headline and stories).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of re-ranking contents based on updated scores of Inamdar into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to update recommendation of breaking news list based on updated scores. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the content ranking using machine learning by train the machine learning model with additional training data(Inamdar, para 0048).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in further view of Tiwari, Nidhi et al (PGPUB Document No. 20230156063), hereafter, referred to as “Tiwari”.
Regarding claim 10(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 but don’t explicitly teach wherein the system is further configured to: determine, by a combiner module associated with the one or more processors, a combined reranking score for the one or more new first and second content items received.
However, in the same field of endeavor of ranking contents Tiwari teaches wherein the system is further configured to: determine, by a combiner module associated with the one or more processors, a combined reranking score for the one or more new first and second content items received (Tiwari, para 0058 discloses combining ranking score “Based on the correlation analysis 440, certain audio scores 418, text scores 428, and/or video scores 438 may be combined into a combined model 442. The combined model 342 may include the audio scores 418, the text scores 428, and/or the video scores 438 for the clip combined together ……”; where Khasnis in para 0032 discloses ranking news headlines and stories “breaking news headline based only on the percentage/number of respective token matches but may use other attributes associated with the breaking news headlines and news stories to rank and prioritize the news stories with respect to the breaking news headlines”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of combining ranking scores of Tiwari into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news list based on ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the machine learning engine by improving the training accuracy for content generation(Tiwari, para 0060).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in further view of Gupta, Rahul et al (US Patent Document No. 11531940), hereafter, referred to as “Gupta”.
Regarding claim 13(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 1 but don’t explicitly wherein the system is configured to trigger an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items.
However, in the same field of endeavor of receiving contents based on event activation Gupta teaches wherein the system is configured to trigger an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items(Gupta, claim 1 discloses updating a recommendation list based on a triggering event “in response to the triggering event, cause a notification to be provided via a visual output component of the user device to automatically update the task list to indicate a state of the user task ……”; where Khasnis in para 0041 discloses continuous incoming streams).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of updating list on user device based on a triggering event of Gupta into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news automatically based on a configured event. The modification would be obvious because one of ordinary skill in the art would be motivated to automatically update a list upon a desired event occurrence(Gupta, claim 1).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Biru, Tadesse et al (PGPUB Document No. 20230029058), hereafter referred as to “Biru”, in view of Khasnis, Abhijit et al (PGPUB Document No. 20120259853), hereafter, referred to as “Khasnis”, in further view of De Paolis, Alessandro et al (PGPUB Document No. 20220156334), hereafter, referred to as “De Paolis”, in view of Liu, Xiaoli et al (PGPUB Document No. 20180275838 ), hereafter, referred to as “Liu”, in view of Wilson, Ashley Duane et al (PGPUB Document No. 20210289001), hereafter, referred to as “Wilson”, in further view of Gupta, Rahul et al (US Patent Document No. 11531940), hereafter, referred to as “Gupta”.
Regarding claim 22(Currently Amended), Biru, Khasnis, De Paolis and Liu teach all the limitations of claim 18 and Khasnis further teaches wherein the method further comprises the step of: iteratively adding one or more new first content items to the recommended list in real time, wherein the one or more new first content items are extracted from a continuous incoming stream of first content items received from the plurality of first computing devices(Khasnis, para0041 newer content items are getting continuously updated with the availability of new contents “one or more news story for the second breaking news headline may already have been received prior to the receipt of the second breaking news headline. It should be noted herein that when the algorithm initializes the cache memory, the algorithm initializes only the data related to the first breaking news headline. As a result, the one or more news stories that were not related to the first breaking news headline and the related news tokens are still in the cache memory and are now used in matching with the headline tokens of the second or subsequent breaking news headline. Thus, the algorithm continuously clears the cache memory of the breaking news headline and information related to the breaking news headline that are already reported/presented to the users and maintains news stories that have not yet been reported so that they can be mined for subsequent reporting”),
continuously refreshing and keeping, using a pruning module associated with the one or more processors, a most succinct one or more new first content items to the breaking news headline from the continuous incoming stream of first and second content items(Khasnis, para0041 newer content items are getting continuously updated with the availability of new contents “one or more news story for the second breaking news headline may already have been received prior to the receipt of the second breaking news headline. It should be noted herein that when the algorithm initializes the cache memory, the algorithm initializes only the data related to the first breaking news headline. As a result, the one or more news stories that were not related to the first breaking news headline and the related news tokens are still in the cache memory and are now used in matching with the headline tokens of the second or subsequent breaking news headline. Thus, the algorithm continuously clears the cache memory of the breaking news headline and information related to the breaking news headline that are already reported/presented to the users and maintains news stories that have not yet been reported so that they can be mined for subsequent reporting”);
Biru, Khasnis, De Paolis and Liu don’t explicitly teach and wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time; triggering an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items.
However, in the same field of endeavor of receiving contents for trusted news sources Wilson teaches and wherein the one or more new first content items and respective one or more new second content items associated with the one or more new first content items are published and distributed by the trusted content providers in real time (Wilson, para 0293 discloses receiving news from trusted source “a first news outlet might syndicate stories from a list of trusted sources, such as other news outlets……”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of receiving news from trusted source of Wilson into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis and Liu to recommend breaking news list from trusted source. The modification would be obvious because one of ordinary skill in the art would be motivated to ensure delivering trusted news by authenticating news sources(Wilson, para 0293).
But Biru, Khasnis, De Paolis, Liu and Wilson don’t explicitly teach triggering an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items.
However, in the same field of endeavor of receiving contents based on event activation Gupta teaches triggering an event for refreshing one or more suggestions to a plurality of users based on the continuous incoming stream of first and second content items(Gupta, claim 1 discloses updating a recommendation list based on a triggering event “in response to the triggering event, cause a notification to be provided via a visual output component of the user device to automatically update the task list to indicate a state of the user task ……”; where Khasnis in para 0041 discloses continuous incoming streams).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of updating list on user device based on a triggering event of Gupta into receiving of multi-lingual news headlines and story feeds of Biru, Khasnis, De Paolis, Liu and Wilson to recommend breaking news automatically based on a configured event. The modification would be obvious because one of ordinary skill in the art would be motivated to automate the process of updating a list upon a desired event occurrence instead of manually requesting for the update(Gupta, claim 1).
Response to Arguments
I. 35 U.S.C §112 (b)
Rejection to claim 15-18 are withdrawn in light of amendments to claim 15.
II. 35 U.S.C §103
The crux of applicant’s arguments present on last paragraph of page 20 through paragraph 3 of page 25 filed on 8/8/2025 is “These headline and story tokens are analyzed using a predefined matching algorithm to determine whether any of the news stories are related to the breaking news headline. This matching process is based on text similarity rather than structured data or attribute based correlation” .
Applicant’s above mentioned arguments have been have been fully considered but the examiner respectfully disagrees as Khasnis in 0007 discloses that tagged tokens are attributes of the headlines and stories that describes the headline and stories as following “the breaking news headline is dynamically tokenized to identify a plurality of headline tokens that substantially describe the breaking news headline” and “each of the news stories is tokenized in substantial real time to identify a plurality of story tokens. The headline tokens and story tokens are analyzed to determine if one or more of the plurality of news stories are related to the breaking news headline”. Therefore, disclosed tag matching of Khasnis is not merely based on similarity rather by the attributes which describes news headlines and their associated stories.
Further, Prior art Liu at element 500 of Fig. 9 and para 0117 discloses clickable link to contents “the recommended news list 500 is displayed….”; where Khasnis, para 0028 discloses establishing link or mapping between breaking new headline with news stories via attributes/tokens. Therefore, Khasnis in view of Liu teaches the amended limitation “map the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on a mapping of the extracted first and second set of attributes; and provide a clickable link of the ordered list of the one or more first content items present in the recommendation list with the one or more second content items based on the mapping done”.
III. 35 U.S.C §101 abstract idea rejection
An updated §101 abstract idea rejection to Claim 1, 3-23 has been presented in the “Claim Rejections - 35 USC § 101” section of this office action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH A DAUD whose telephone number is (469)295-9283. The examiner can normally be reached M~F: 9:30 am~6:30 pm.
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, Amy Ng can be reached at 571-270-1698. 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.
/ABDULLAH A DAUD/
Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
Prosecution Insights