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 Amendment
This Office action is in response to Applicant's amendment filed on 11/25/2025.
Claim 1-14 and 16-20 are pending. Claim 1, 6, 13 and 20 are amended. Claim 15 is cancelled. Claim 1-14 and 16-20 are rejected.
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, 5, 7-14, 16-17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jain, Arvind et al (PGPUB Document No. 20240256582), hereafter referred as to “Jain”, in further view of Hattangady, Poonam et al (PGPUB Document No. 20240296276), hereafter, referred to as “Hattangady”, in view of Krishnan, Aparna et al (PGPUB Document No. 20250005050), hereafter, referred to as “Krishnan”, in further view of Kocienda, Luke et al (PGPUB Document No. 20240310900), hereafter, referred to as “Kocienda”.
Regarding Claim 1(Currently Amended), Jain teaches A method for providing personalized recommendations from a generative artificial intelligence (AI) system integrated within a platform, comprising: receiving, at a platform comprising a search engine and a generative Al system bridging communications between the search engine and a client device, one or more input submissions from a user engaging in a conversation via a conversational interface of the platform(Jain, Fig. 2A and para 0041 teaches Generative AI system connected via an user interface for receiving questions through conversational agent by a client device “The query and response path 246 may receive a search query from a user computing device……..The query and response path 246 may also include or interface with an automated digital assistant that may interact with a user of the user computing device in a conversational manner in which answers are outputted in response to messages or questions provided to the automated digital assistant”),
Using the broadest reasonable interpretation consistent with the specification (para 0052) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “a generative Al system bridging communications between the search engine and a client device” to mean presence or use of a generative system between user interface and the search engine.
the conversation comprising a conversational context, the one or more input submissions comprising one or more user preferences(Jain, para 0058 discloses context is being considered by conversational agent “an artificial intelligence powered digital assistant is the knowledge assistant 214 that may automatically output answers to messages or questions provided to the digital assistant….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”; para 0054 further discloses user preferences (access rights) are being considered “The automated digital assistant may comprise a computer-implemented assistant that may access and display only information that a user's access rights permit”), wherein the conversational interface comprises a screen layout including: a conversation portion that displays text generated by the generative Al system; a user interface input control that receives conversational text from the user(Jain, Fig. 3A-C disclose a user interface for taking input as search into a generative AI system (element 312));
and a results portion that depicts search results generated from the search engine of the platform where the search results have been ordered by the generative Al system according to a priority score(Jain, element 327, 333, 328 and 324 of Fig. 3C disclose the result portion of the user interface to display generative AI results; para 0056 further teaches scoring and ranking documents “the set of relevant documents may be provided to the ranking modification pipeline 222 to be scored and ranked for relevance to the search query”);
generating, by the generative Al system using a large language model (LLM) to process the conversational context and the one or more input submissions, a generative search query designed to interface with the search engine of the platform(Jain, para 0079 and element 402, 406 of Fig. 4A and Fig. 3C after input submission “Gleanbot custom emoji” a natural language search query “Does Gleanbot provide custom emojis?” is getting generated by generative AI system “The search query may be acquired from a search bar, …. In step 406, a natural language phrase is generated based on the search query ….. The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C”; where para 0015 discloses use Large Language Model “A GPT model may comprise a type of large language model (LLM) that uses deep learning to generate human-like text” and Jain in para 0058 further discloses that search is being performed considering context “….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”);
sending, by the platform, the generative search query generated by the generative Al system to the search engine(Jain, para 0079 further discloses acquiring the query sent by the user interface and using GenAI transforming the received query into natural query phrases for search result retrieval “The search query may be acquired from a search bar, such as the search bar 312 in FIG. 3C….. In step 406, a natural language phrase is generated based on the search query and the user identifier. The natural language phrase may be generated using a generative AI model and a prompt” );
But Jain does not explicitly teach generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system,
the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results,
each annotation in the set of annotations comprising one or more pieces of information relating to items from sorted list of initial search results from the search engine;
generating a set of initial personalized recommendations for the user by utilizing the generative Al system to process the bridge prompt together with
presenting, via the conversational interface
the conversational interface incorporating media content representing at least a portion of the items from the sorted list of initial search results comprising images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score; and while conversing with the user by the generative Al system, iteratively performing the operations of:
receiving subsequent conversational context from the user; generating, by the generative Al system, a subsequent search query with subsequent conversational context; sending the subsequent search query generated by the generative Al system to the search engine of the platform and receiving subsequent search results; generating refined personalized recommendations for the user by the generative Al system based on the subsequent search results;
presenting, via the conversational interface, the refined personalized recommendations; modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Hattangady teaches generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system(Hattangady, para 0076 discloses search query is being generated by GenAI “where the subsequent prompt is included in a subsequent query provided to the generative AI model 108. For instance, results from the subsequent query are included in a next suggested draft reply 233 that is presented to the user in the application UI 106”),
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 generation of subsequent query of Hattangady into personalization of contents of Jain to produce an expected result of presenting relevant results. The modification would be obvious because one of ordinary skill in the art would be motivated to generate relevant results produced by GenAI model by reducing the latency using prompt (Hattangady, abstract).
But Jain and Hattangady don’t explicitly teach the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results,
each annotation in the set of annotations comprising one or more pieces of information relating to items from sorted list of initial search results from the search engine;
generating a set of initial personalized recommendations for the user by utilizing the generative Al system to process the bridge prompt together with
presenting, via the conversational interface
the conversational interface incorporating media content representing at least a portion of the items from the sorted list of initial search results comprising images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score; and while conversing with the user by the generative Al system, iteratively performing the operations of:
receiving subsequent conversational context from the user; generating, by the generative Al system, a subsequent search query with subsequent conversational context; sending the subsequent search query generated by the generative Al system to the search engine of the platform and receiving subsequent search results; generating refined personalized recommendations for the user by the generative Al system based on the subsequent search results;
presenting, via the conversational interface, the refined personalized recommendations; modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Krishnan teaches the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results(Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion…………. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles……….”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”), each annotation in the set of annotations comprising one or more pieces of information relating to items from sorted list of initial search results from the search engine(Krishnan, element 330 of Fig. 3B discloses result item list with annotations);generating a set of initial personalized recommendations for the user by utilizing the generative Al system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context and the one or more user preferences(Krishnan, para 0152 disclose selection of contents based dialog context “The hyperlinks are generated based on dialog context data obtained”; where user preference is depicted in the question), presenting, via the conversational interface (Krishnan, Fig. 3B and para 0152 disclose initial content presentation on user device (element 330)), the conversational interface incorporating media content representing at least a portion of the items from the sorted list of initial search results comprising images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score(Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion, which has been generated using portions of the technologies described herein including, for example, one or more large language models. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles. For example, the natural language portion 330 includes hyperlinks to online courses 332, 340, an article 338, and hyperlinks to profile pages of the associated course instructors and/or authors 334, 336”; para 0075 discloses output of dialogs can be images and texts “in some examples, a multimodal neural network implemented in the generative summarization dialog-based information retrieval system is capable of outputting digital content that includes a combination of two or more of text, images, video or audio”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”); and while conversing with the user by the generative Al system, iteratively performing the operations of: receiving subsequent conversational context from the user; generating, by the generative Al system, a subsequent search query with subsequent conversational context; sending the subsequent search query generated by the generative Al system to the search engine of the platform and receiving subsequent search results (Krishnan, para 0135 discloses iteratively receiving user input/context in subsequent user dialog and executing those queries subsequently for target result set by generative model “one or more subsequent rounds of dialog, for example, at a time instance N, where N is greater than 1, after the system receives subsequent input from the user in response to a request for clarification, the dialog history is updated to include the subsequent round(s) of dialog at dialog history N. The search prompt generator 202 generates and outputs search prompt N based on dialog history N. The first generative model 204 generates and outputs search query N based on the search prompt N” ); generating refined personalized recommendations for the user by the generative Al system based on the subsequent search results; presenting, via the conversational interface, the refined personalized recommendations; modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query(Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog and generating a refined list of recommendation).
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 generation of contents using subsequent user input of Krishnan into personalization of contents of Jain and Hattangady to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the user feedback via dialogs to improve the prompt generation or engineering (Krishnan, para 0150).
But Jain, Hattangady and Krishnan don’t explicitly teach wherein the generative Al system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences;
However, in the same field of endeavor of ranking contents Kocienda teaches wherein the generative Al system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences(Kocienda, para 0256 disclose priority to recommended contents are given based on annotation/summary and user preferences “the wearable multimedia device can use machine learning adapt to the user's preferences over time. For example, based on the user's feedback, the wearable multimedia device can determine the types of information that the user would like to prioritize in a summary, and continuously adjust its operations to account for changes in the user's preferences over time”; where para 0027 teaches generation of a stored/ranked list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”);
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 prioritizing contents based on annotations/summary and user preference of Kocienda into personalization of contents of Jain, Hattangady and Krishnan to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the feature of object identification using polygon and sending the poly segmentation piece rather than the whole image for computation to improve the privacy, security and speed (Kocienda, para 0169).
Regarding claim 2 (Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Jain further teaches wherein the generative AI system is further configured to continue the conversation with the user to determine a refinement of the one or more user preferences (Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog (preference) and generating a refined list of recommendation).
Regarding claim 3(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Jain further teaches wherein the generative Al system is further configured to iteratively generate the subsequent search query and present the refined personalized recommendations after receiving subsequent conversational context from the user while conversing with the user (Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog or conversation and generating a refined list of recommendation).
Regarding claim 5(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Krishnan further teaches wherein the generative AI system (Krishnan, para 0038 disclose selection of contents based dialog context “embodiments of the disclosed technologies use one or more contextual resources to formulate, disambiguate, expand, or interpret a search query and/or to curate a set of search results before the search results are presented to the user. For example, if a user inputs a question such as “how do I get promoted,” embodiments can generate a summary of the user's dialog history and/or one or more other contextual resources, and use the summary to disambiguate the user's question and generate a concise search query”; where user preference is depicted in the question).
Regarding claim 7(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Kocienda further teaches wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations by considering the initial search result rankings, the set of annotations, and the one or more user preferences (Kocienda, para 0256 disclose priority to recommended contents are given based on annotation/summary and user preferences “the wearable multimedia device can use machine learning adapt to the user's preferences over time. For example, based on the user's feedback, the wearable multimedia device can determine the types of information that the user would like to prioritize in a summary, and continuously adjust its operations to account for changes in the user's preferences over time”; where Jane para 0056 teaches scoring by generative AI system).
Regarding claim 8(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Krishnan further teaches wherein the media content integrated into the conversational interface includes images, audio clips, video snippets, or a combination thereof (Krishnan, para 0075 discloses output of dialogs can be images and texts “in some examples, a multimodal neural network implemented in the generative summarization dialog-based information retrieval system is capable of outputting digital content that includes a combination of two or more of text, images, video or audio”).
Regarding claim 9(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Jain further teaches wherein the generative AI system (Krishnan, Fig. 3B-E and para 0152 disclose stating with an initial result and iteratively receiving user input/context in subsequent user dialog and generating a refined list of recommendation; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”; where Kocienda, para 0256 disclose priority to recommended contents are given based on annotation/summary, user preferences and context “the wearable multimedia device can use machine learning adapt to the user's preferences over time. For example, based on the user's feedback, the wearable multimedia device can determine the types of information that the user would like to prioritize in a summary, and continuously adjust its operations to account for changes in the user's preferences over time”).
Regarding claim 10(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Krishnan further teaches wherein the generative AI system employs machine learning techniques to generate the refined personalized recommendations based on one or more user interactions and user feedback (Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input and interaction in subsequent user dialog and generating a refined list of recommendation).
Regarding claim 11 (Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Kocienda further teaches wherein the personalized recommendations presented to the user are influenced by geographic location data and other contextual factors (Kocienda, para 0254-0255 disclose recommending contents to users based on user’s location and contextual information “the wearable multimedia device can present and prioritize information to the user in future based on these determinations ……contexts include the types of messages that were received by the user, the contents of those messages, and/or the sender of those messages. Further example contexts include the time of day, day of week, and/or date. Further example contexts include the past, present, and/or planned future locations of the user. Further example contexts include the weather at the user's past, present, and/or future locations ….”).
Regarding claim 12 (Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Krishnan further teaches wherein the generative AI system prompts the user for additional information to further tailor the generative search query, the subsequent search query, the initial personalized recommendations, or the refined personalized commendations (Krishnan, para 0135 discloses iteratively receiving user input/context in subsequent user dialog and executing those queries subsequently for target result set by generative model “one or more subsequent rounds of dialog, for example, at a time instance N, where N is greater than 1, after the system receives subsequent input from the user in response to a request for clarification, the dialog history is updated to include the subsequent round(s) of dialog at dialog history N. The search prompt generator 202 generates and outputs search prompt N based on dialog history N. The first generative model 204 generates and outputs search query N based on the search prompt N”).
Regarding Claim 13 (Currently Amended), Jain teaches A system for providing personalized recommendations from a generative artificial intelligence (AI) system integrated within a platform, comprising one or more processors configured to perform the operations of(Jain, Fig. 1 and para 0030 discloses a system with processors, memory and storages “Processor 126 allows the search and knowledge management system 120 to execute computer readable instructions stored in memory 127 in order to perform processes described herein…”):
receiving, at a platform comprising a search engine and a generative AI system bridging communications between the search engine and a client device, one or more input submissions from a user engaging in a conversation via a conversational interface of the platform(Jain, Fig. 2A and para 0041 teaches Generative AI system connected via an user interface for receiving questions through conversational agent by a client device “The query and response path 246 may receive a search query from a user computing device……..The query and response path 246 may also include or interface with an automated digital assistant that may interact with a user of the user computing device in a conversational manner in which answers are outputted in response to messages or questions provided to the automated digital assistant”),
Using the broadest reasonable interpretation consistent with the specification (para 0052) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “a generative Al system bridging communications between the search engine and a client device” to mean presence or use of a generative system between user interface and the search engine.
the conversation comprising a conversational context, the one or more input submissions comprising one or more user preferences(Jain, para 0058 discloses context is being considered by conversational agent “an artificial intelligence powered digital assistant is the knowledge assistant 214 that may automatically output answers to messages or questions provided to the digital assistant….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”; para 0054 further discloses user preferences (access rights) are being considered “The automated digital assistant may comprise a computer-implemented assistant that may access and display only information that a user's access rights permit”), wherein the conversational interface comprises a screen layout including: a conversation portion that displays text generated by the generative AI system; a user interface input control that receives conversational text from the user(Jain, Fig. 3A-C disclose a user interface for taking input as search into a generative AI system (element 312));
and a results portion that depicts search results generated from the search engine of the platform where the search results have been ordered by the generative AI system according to a priority score(Jain, element 327, 333, 328 and 324 of Fig. 3C disclose the result portion of the user interface to display generative AI results; para 0056 further teaches scoring and ranking documents “the set of relevant documents may be provided to the ranking modification pipeline 222 to be scored and ranked for relevance to the search query”); generating, by the generative AI system using a large language model (LLM) to process the conversational context and the one or more input submissions, a generative search query designed to interface with the search engine of the platform(Jain, para 0079 and element 402, 406 of Fig. 4A and Fig. 3C after input submission “Gleanbot custom emoji” a natural language search query “Does Gleanbot provide custom emojis?” is getting generated by generative AI system “The search query may be acquired from a search bar, …. In step 406, a natural language phrase is generated based on the search query ….. The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C”; where para 0015 discloses use Large Language Model “A GPT model may comprise a type of large language model (LLM) that uses deep learning to generate human-like text” and Jain in para 0058 further discloses that search is being performed considering context “….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”);
sending, by the platform, the generative search query to the search engine(Jain, para 0079 further discloses acquiring the query sent by the user interface and using GenAI transforming the received query into natural query phrases for search result retrieval “The search query may be acquired from a search bar, such as the search bar 312 in FIG. 3C….. In step 406, a natural language phrase is generated based on the search query and the user identifier. The natural language phrase may be generated using a generative AI model and a prompt” );
But Jain does not explicitly teach generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system, the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results,
each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine; generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences, wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences; presenting, via the conversational interface the subsequent search results; presenting, via the conversational interface, the refined personalized recommendations; and modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Hattangady teaches generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system (Hattangady, para 0076 discloses search query is being generated by GenAI “where the subsequent prompt is included in a subsequent query provided to the generative AI model 108. For instance, results from the subsequent query are included in a next suggested draft reply 233 that is presented to the user in the application UI 106”),
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 generation of subsequent query of Hattangady into personalization of contents of Jain to produce an expected result of presenting relevant results. The modification would be obvious because one of ordinary skill in the art would be motivated to generate relevant results produced by GenAI model by reducing the latency using prompt (Hattangady, abstract).
But Jain and Hattangady don’t explicitly teach a bridge prompt as input back to the generative AI system, the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results,
each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine; generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences, wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences; presenting, via the conversational interface the subsequent search results; presenting, via the conversational interface, the refined personalized recommendations; and modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Krishnan teaches the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results(Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion…………. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles……….”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”), each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine(Krishnan, element 330 of Fig. 3B discloses result item list with annotations); generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences(Krishnan, para 0152 disclose selection of contents based dialog context “The hyperlinks are generated based on dialog context data obtained”; where user preference is depicted in the question), presenting, via the conversational interface presented at the client device, the set of initial personalized recommendations for the user(Krishnan, Fig. 3B and para 0152 disclose initial content presentation on user device (element 330)), the conversational interface incorporating media content representing at least a portion of the items from the sorted list of initial search results comprising images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score(Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion…………. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles……….”; para 0075 discloses output of dialogs can be images and texts “in some examples, a multimodal neural network implemented in the generative summarization dialog-based information retrieval system is capable of outputting digital content that includes a combination of two or more of text, images, video or audio”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”);
and while conversing with the user by the generative AI system, iteratively performing the operations of: receiving subsequent conversational context from the user; generating, by the generative AI system, a subsequent search query with the subsequent conversational context; sending the subsequent search query generated by the generative AI system to the search engine (Krishnan, para 0135 discloses iteratively receiving user input/context in subsequent user dialog and executing those queries subsequently for target result set by generative model “one or more subsequent rounds of dialog, for example, at a time instance N, where N is greater than 1, after the system receives subsequent input from the user in response to a request for clarification, the dialog history is updated to include the subsequent round(s) of dialog at dialog history N. The search prompt generator 202 generates and outputs search prompt N based on dialog history N. The first generative model 204 generates and outputs search query N based on the search prompt N” ); presenting, via the conversational interface, the refined personalized recommendations; and modifying the order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query(Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog and generating a refined list of recommendation).
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 generation of contents using subsequent user input of Krishnan into personalization of contents of Jain and Hattangady to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the user feedback via dialogs to improve the prompt generation or engineering (Krishnan, para 0150).
But Jain, Hattangady and Krishnan don’t explicitly teach wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences;
However, in the same field of endeavor of ranking contents Kocienda teaches wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences(Kocienda, para 0256 disclose priority to recommended contents are given based on annotation/summary and user preferences “the wearable multimedia device can use machine learning adapt to the user's preferences over time. For example, based on the user's feedback, the wearable multimedia device can determine the types of information that the user would like to prioritize in a summary, and continuously adjust its operations to account for changes in the user's preferences over time”; where para 0027 teaches generation of a stored/ranked list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”);
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 prioritizing contents based on annotations/summary and user preference of Kocienda into personalization of contents of Jain, Hattangady and Krishnan to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the feature of object identification using polygon and sending the poly segmentation piece rather than the whole image for computation to improve the privacy, security and speed (Kocienda, para 0169).
Regarding claim 14(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 13 and Krishnan further teaches wherein the generative AI system employs natural language processing techniques to understand and respond to conversational input from the user(Krishnan, Fig. 3B and para 0152 disclose natural language process of user input and generating response using GenAI “in response to the user's dialog input 328, …….. for example, one or more large language models. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles” ).
Claim 15, Cancelled.
Regarding claim 16 (Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 13 and Krishnan teaches wherein the generative AI system uses historical interactions with the user and the one or more user preferences to adjust how the generative AI system generates the personalized recommendations(Krishnan, para 0038 disclose generation of contents based on dialog context and history “embodiments of the disclosed technologies use one or more contextual resources to formulate, disambiguate, expand, or interpret a search query and/or to curate a set of search results before the search results are presented to the user. For example, if a user inputs a question such as “how do I get promoted,” embodiments can generate a summary of the user's dialog history and/or one or more other contextual resources, and use the summary to disambiguate the user's question and generate a concise search query”; where user preference is depicted in the question/dialog).
Regarding claim 17(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 13 and Krishnan teaches wherein the generative AI system incorporates user-provided ratings or user-provided feedback on recommended items to enhance subsequent search results and refined personalized recommendations (Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/feedback and interaction in subsequent user dialog and generating a refined list of recommendation).
Regarding claim 19(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 13 and Jain further teaches wherein the generative AI system is further configured to continue conversing with the user to refine the one or more user preferences(Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/feedback through conversation in subsequent user dialog and generating a refined list of recommendation; where user preference is depicted in the question/dialog).
Regarding claim 20(Currently Amended), Jain teaches A non-transitory computer-readable medium for providing personalized recommendations from a generative artificial intelligence (AI) system integrated within a platform, comprising(Jain, Fig. 1 and para 0030 disclosed storage media for storing instructions to be executed by processors and memory “Processor 126 allows the search and knowledge management system 120 to execute computer readable instructions stored in memory 127 in order to perform processes described herein….”):
instructions for receiving, at a platform comprising a search engine and a generative AI system bridging communications between the search engine and a client device, one or more input submissions from a user engaging in a conversation via a conversational interface of the platform(Jain, Fig. 2A and para 0041 teaches Generative AI system connected via an user interface for receiving questions through conversational agent by a client device “The query and response path 246 may receive a search query from a user computing device……..The query and response path 246 may also include or interface with an automated digital assistant that may interact with a user of the user computing device in a conversational manner in which answers are outputted in response to messages or questions provided to the automated digital assistant”),
Using the broadest reasonable interpretation consistent with the specification (para 0052) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “a generative Al system bridging communications between the search engine and a client device” to mean presence or use of a generative system between user interface and the search engine.
the conversation comprising a conversational context, the one or more input submissions comprising one or more user preferences(Jain, para 0058 discloses context is being considered by conversational agent “an artificial intelligence powered digital assistant is the knowledge assistant 214 that may automatically output answers to messages or questions provided to the digital assistant….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”; para 0054 further discloses user preferences (access rights) are being considered “The automated digital assistant may comprise a computer-implemented assistant that may access and display only information that a user's access rights permit”), wherein the conversational interface comprises a screen layout including: a conversation portion that displays text generated by the generative AI system; a user interface input control that receives conversational text from the user(Jain, Fig. 3A-C disclose a user interface for taking input as search into a generative AI system (element 312));
and a results portion that depicts search results generated from the search engine of the platform where the search results have been ordered by the generative AI system according to a priority score(Jain, element 327, 333, 328 and 324 of Fig. 3C disclose the result portion of the user interface to display generative AI results; para 0056 further teaches scoring and ranking documents “the set of relevant documents may be provided to the ranking modification pipeline 222 to be scored and ranked for relevance to the search query”);
instructions for generating, by the generative AI system using a large language model (LLM) to process the conversational context and the one or more input submissions, a generative search query for the search engine of the platfor(Jain, para 0079 and element 402, 406 of Fig. 4A and Fig. 3C after input submission “Gleanbot custom emoji” a natural language search query “Does Gleanbot provide custom emojis?” is getting generated by generative AI system “The search query may be acquired from a search bar, …. In step 406, a natural language phrase is generated based on the search query ….. The natural language phrase may be generated using a generative AI model and a prompt, such as the prompt 327 in FIG. 3C”; where para 0015 discloses use Large Language Model “A GPT model may comprise a type of large language model (LLM) that uses deep learning to generate human-like text” and Jain in para 0058 further discloses that search is being performed considering context “….. question and answer pair may also be stored within the search index 204 to provide context for the searchable content”),
But Jain does not explicitly teach instructions for generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system, the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results, each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine; instructions for generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences, wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences; instructions for presenting, via the conversational interface images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score; and instructions for, while conversing with the user by the generative AI system, iteratively performing the operations of:
receiving subsequent conversational context from the user; generating, by the generative AI system, a subsequent search query with the subsequent conversational context; sending the subsequent search query generated by the generative AI system to the search engine of the platform and receiving subsequent search results; generating refined personalized recommendations for the user by the generative AI system based on the subsequent search results;order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Hattangady teaches instructions for generating, by using the search engine of the platform to process the generative search query from the generative AI system, a bridge prompt as input back to the generative AI system(Hattangady, para 0076 discloses search query is being generated by GenAI “where the subsequent prompt is included in a subsequent query provided to the generative AI model 108. For instance, results from the subsequent query are included in a next suggested draft reply 233 that is presented to the user in the application UI 106”),
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 generation of subsequent query of Hattangady into personalization of contents of Jain to produce an expected result of presenting relevant results. The modification would be obvious because one of ordinary skill in the art would be motivated to generate relevant results produced by GenAI model by reducing the latency using prompt (Hattangady, abstract).
But Jain and Hattangady don’t explicitly teach the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results, each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine; instructions for generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences, wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences; instructions for presenting, via the conversational interface images and text, wherein the set of initial personalized recommendations are depicted in the results portion of the conversational interface in an order according to the priority score; and instructions for, while conversing with the user by the generative AI system, iteratively performing the operations of:
receiving subsequent conversational context from the user; generating, by the generative AI system, a subsequent search query with the subsequent conversational context; sending the subsequent search query generated by the generative AI system to the search engine of the platform and receiving subsequent search results; generating refined personalized recommendations for the user by the generative AI system based on the subsequent search results;order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query.
However, in the same field of endeavor of content generation by GenAi Krishnan teaches the generative search query generated by the generative AI system to the search engine; instructions for generating, by the search engine of the platform, a bridge prompt as input to the generative AI system, the bridge prompt comprising a sorted list of initial search results from the search engine and a set of annotations associated with the sorted list of initial search results(Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion…………. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles……….”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”), each annotation in the set of annotations comprising one or more pieces of information relating to items from the sorted list of initial search results from the search engine(Krishnan, element 330 of Fig. 3B discloses result item list with annotations); instructions for generating a set of initial personalized recommendations for the user by utilizing the generative AI system to process the bridge prompt together with the sorted list of initial search results from the search engine, the set of annotations, the conversational context, and the one or more user preferences(Krishnan, para 0152 disclose selection of contents based dialog context “The hyperlinks are generated based on dialog context data obtained”; where user preference is depicted in the question),
instructions for presenting, via the conversational interface (Krishnan, Fig. 3B and para 0152 disclose initial content presentation on user device (element 330)), the conversational interface incorporating media content representing at least a portion of the items from the sorted list of initial search results (Krishnan, Fig. 3B & para 0152 disclose generating an initial search result in response to user question (element 328) where each item is accompanied with its respective annotation “in response to the user's dialog input 328, another system-generated dialog portion, which has been generated using portions of the technologies described herein including, for example, one or more large language models. The system-generated dialog portion of user interface 324 includes natural language portion 330 and structured element portion 342. Natural language portion 330 includes machine-generated natural language text including embedded hyperlinks to recommended online content items and user profiles. For example, the natural language portion 330 includes hyperlinks to online courses 332, 340, an article 338, and hyperlinks to profile pages of the associated course instructors and/or authors 334, 336”; para 0075 discloses output of dialogs can be images and texts “in some examples, a multimodal neural network implemented in the generative summarization dialog-based information retrieval system is capable of outputting digital content that includes a combination of two or more of text, images, video or audio”; where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”); and instructions for, while conversing with the user by the generative AI system, iteratively performing the operations of: receiving subsequent conversational context from the user; generating, by the generative AI system, a subsequent search query with the subsequent conversational context; sending the subsequent search query generated by the generative AI system to the search engine of the platform and receiving subsequent search results Krishnan, para 0135 discloses iteratively receiving user input/context in subsequent user dialog and executing those queries subsequently for target result set by generative model “one or more subsequent rounds of dialog, for example, at a time instance N, where N is greater than 1, after the system receives subsequent input from the user in response to a request for clarification, the dialog history is updated to include the subsequent round(s) of dialog at dialog history N. The search prompt generator 202 generates and outputs search prompt N based on dialog history N. The first generative model 204 generates and outputs search query N based on the search prompt N” ); generating refined personalized recommendations for the user by the generative AI system based on the subsequent search results;order of the set of initial personalized recommendations to reflect the subsequent conversational context utilized to generate the subsequent search query(Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog and generating a refined list of recommendation).
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 generation of contents using subsequent user input of Krishnan into personalization of contents of Jain and Hattangady to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the user feedback via dialogs to improve the prompt generation or engineering (Krishnan, para 0150).
But Jain, Hattangady and Krishnan don’t explicitly teach wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences;
However, in the same field of endeavor of ranking contents Kocienda teaches wherein the generative AI system assigns the priority score to each of the personalized recommendations in the set of initial personalized recommendations based on a combination of initial search result rankings, the set of annotations, and the one or more user preferences (Kocienda, para 0256 disclose priority to recommended contents are given based on annotation/summary and user preferences “the wearable multimedia device can use machine learning adapt to the user's preferences over time. For example, based on the user's feedback, the wearable multimedia device can determine the types of information that the user would like to prioritize in a summary, and continuously adjust its operations to account for changes in the user's preferences over time”; where para 0027 teaches generation of a stored/ranked list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”);
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 prioritizing contents based on annotations/summary and user preference of Kocienda into personalization of contents of Jain, Hattangady and Krishnan to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the feature of object identification using polygon and sending the poly segmentation piece rather than the whole image for computation to improve the privacy, security and speed (Kocienda, para 0169).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Jain, Arvind et al (PGPUB Document No. 20240256582), hereafter referred as to “Jain”, in further view of Hattangady, Poonam et al (PGPUB Document No. 20240296276), hereafter, referred to as “Hattangady”, in view of Krishnan, Aparna et al (PGPUB Document No. 20250005050), hereafter, referred to as “Krishnan”, in view of Kocienda, Luke et al (PGPUB Document No. 20240310900), hereafter, referred to as “Kocienda”. in further view of Hariramasamy, Kumar et al (US Patent No. 11954167), hereafter, referred to as “Hariramasamy”.
Regarding claim 4 (Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 but don’t explicitly teach further comprising: detecting whether one or more paid promotions materially affected how the generative AI system sorts or selects content the personalized recommendations; and presenting a disclosure of the one or more paid promotions that materially affected how the generative AI system sorted or selected content of the
Using the broadest reasonable interpretation consistent with the specification (para 0079-0081) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “paid promotions materially affected how the generative AI system sorts or selects content the personalized recommendations” to mean at least to the affect/impact of promotional contents in the display order of recommended list.
However, in the same field of endeavor of content recommendation Hariramasamy teaches paid promotions materially affected how the generative AI system sorts or selects content the personalized recommendations (Hariramasamy, Fig. 2 and col 7:5-10 disclose that presenting/recommending paid contents to users which are affectioning sorting order of the presented contents “a user can request a keyword search, and the content serving system can determine that the sponsored content is ranked in the top 20 of the search results. Given that the sponsored content is ranked higher than a predetermined rank number (e.g., top 20), then the content serving system can include the sponsored content in the search results” ); and presenting a disclosure of the one or more paid promotions that materially affected how the generative AI system sorted or selected content of the (Hariramasamy, element 214 of Fig. 2 and col 7: 29-31 discloses recommendation to promotional contents with an indication “in some instances, the sponsored content can be labeled as ‘promoted’ or ‘sponsored’ content in the search results”).
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 providing promotional contents in the recommendation list of Hariramasamy into personalization of contents of Jain, Hattangady, Krishnan and Kocienda to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to provide additional information such as image and name of the organization for promotional contents to improve the user experience(Hariramasamy, col 6:15-20).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jain, Arvind et al (PGPUB Document No. 20240256582), hereafter referred as to “Jain”, in further view of Hattangady, Poonam et al (PGPUB Document No. 20240296276), hereafter, referred to as “Hattangady”, in view of Krishnan, Aparna et al (PGPUB Document No. 20250005050), hereafter, referred to as “Krishnan”, in view of Kocienda, Luke et al (PGPUB Document No. 20240310900), hereafter, referred to as “Kocienda”, in further view of Elson, David et al ( PGPUB Document No. 20180232436), hereafter, referred to as “Elson”.
Regarding claim 6(Currently Amened), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 1 and Hattangady further teaches further comprising: processing, utilizing the generative AI system, the bridge prompt by (Hattangady, para 0076 discloses search query is being generated by GenAI “where the subsequent prompt is included in a subsequent query provided to the generative AI model 108. For instance, results from the subsequent query are included in a next suggested draft reply 233 that is presented to the user in the application UI 106”):
Jain teaches and generating, based on the one or more features and the one or more user preferences(Jain, para 0054 further discloses user preferences (access rights) are being considered “The automated digital assistant may comprise a computer-implemented assistant that may access and display only information that a user's access rights permit”),
Krishnan teaches the priority score for the search results in the sorted list of initial search results(Krishnan, where para 0027 teaches generation of a stored list of content items by score ”The generative model can sort the task description-output pairs by score and output only the pair or pairs with the top scores. For example, the generative model could discard the lower-scoring pairs and only output the top-scoring pair as its final output”); and generating the set of initial personalized recommendations by reordering the search results according to the priority score(Krishnan, Fig. 3B-E and para 0152 disclose iteratively receiving user input/context in subsequent user dialog and generating a refined list of recommendation).
But Jain, Hattangady, Krishnan and Kocienda don’t explicitly teach analyzing metadata associated with the set of annotations to extract one or more features relevant to the initial search result rankings of the search results in the sorted list of initial search results;
However, in the same field of endeavor of ranking contents Elson teaches analyzing metadata associated with the set of annotations to extract one or more features relevant to the initial search result rankings of the search results in the sorted list of initial search results(Elson, para 0046 discloses using metadata/annotation for content ranking for result set candidates “Each potential candidate may also have respective annotations or metadata that may be used by the system for ranking and pruning potential candidates”).
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 using annotation data for content ranking of Elson into personalization of contents of Jain, Hattangady, Krishnan and Kocienda to produce an expected result of presenting relevant contents to users. The modification would be obvious because one of ordinary skill in the art would be motivated to use the bi-directional user feedback or dialog to improve the predictive feature of the system(Elson, para 0003).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Jain, Arvind et al (PGPUB Document No. 20240256582), hereafter referred as to “Jain”, in further view of Hattangady, Poonam et al (PGPUB Document No. 20240296276), hereafter, referred to as “Hattangady”, in view of Krishnan, Aparna et al (PGPUB Document No. 20250005050), hereafter, referred to as “Krishnan”, in view of Kocienda, Luke et al (PGPUB Document No. 20240310900), hereafter, referred to as “Kocienda”. in further view of Ali, Asif et al (PGPUB Document No. 20080249833), hereafter, referred to as “Ali”.
Regarding claim 18(Previously Presented), Jain, Hattangady, Krishnan and Kocienda teach all the limitations of claim 13 but don’t explicitly teach wherein the one or more processors are further configured to perform the operation of: adjusting
However, in the same field of endeavor of displaying contents to different type of user devices Ali teaches wherein the one or more processors are further configured to perform the operation of: adjusting (Ali, para 0037 discloses adjustment of recommended contents based on screen size and user preferences for a device “advertisements sent to two different phone models are modified, based on screen size. In an embodiment, the preferences of the user of mobile terminal 108 may also be used to customize the advertisement…..”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of adjusting contents based on recipient devices of Ali into presenting recommended contents of Jain, Hattangady, Krishnan and Kocienda to produce an expected result of presenting relevant contents to users based user’s device. The modification would be obvious because one of ordinary skill in the art would be motivated to use the feature of providing contents to users’ device in a way so that users can users can conveniently view and respond to provided contents(Ali, para 0005).
Response to Arguments
I. 35 U.S.C §103
Applicant’s arguments filed on 11/25/2025 have been fully considered but are
moot because the independent claim 1, 13 and 20 have been amended with newly added features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
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.
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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164