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 .
Election/Restrictions
Applicant’s election without traverse of Group I including generic claims (1-4, 26 and 27) in the reply filed on May 28, 2026 is acknowledged.
All elected claims 1-4, 26 and 27 filed May 28, 2026 are examined in this non-final office action. Claims 5-25 are withdrawn.
Priority
The effective priority date for this instant application is September 1, 2023, the filing date for provisional application #63/536245. Parent application #18/585212 incorporates in its entirety provisional application #63/536245 as does this instant application. None of the provisional applications filed from February 24, 2023 to July 4, 2023 support the following claimed subject matter: “posting, in the livestream chat, the answer to the question, if the engagement metric is above a threshold value.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 26 and 27 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 1 is directed to a process. Analysis of claim 1 applies to analysis of claims 2-4, 26 and 27.
Step 2A Revised (First Prong)
Determine whether claim 1 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 1 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 1 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
In Claim 1:
(Original) A computer-implemented method for video analysis comprising:
accessing a livestream, wherein the livestream includes a livestream chat and one or more products for sale from a catalog of products, and wherein the livestream includes at least one host and a plurality of users;
“monitoring, by a large language model (LLM), the livestream chat, wherein the monitoring detects a question from a user within the plurality of users;”
“determining, by the LLM, an answer to the question that was detected;”
calculating an engagement metric, for the answer, wherein the engagement metric is predictive of a future engagement in the livestream, by one or more users within the plurality of users, if the answer was posted in the livestream chat; and
posting, in the livestream chat, the answer to the question, if the engagement metric is above a threshold value.
In Step 1, claim 1 recites at least one step or act, including calculating an engagement metric, and is therefore a claim to a process.
In Step 2A (first prong), claim 1 is directed to a judicial exception.
In Step 2A (second prong) claim 1 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements.
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device.
In claim 1:
“monitoring, by a large language model (LLM), the livestream chat, wherein the monitoring detects a question from a user within the plurality of users;”
“determining, by the LLM, an answer to the question that was detected;”
is the equivalent of “apply the LLM” to the judicial exception.
In Step 2B, claim 1 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-4, 26 and 27 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 26 and 27 are rejected under 35 USC 103 as being unpatentable over Yoon et al., US 2024/0370652 “Yoon,” in view of Chen et al., US 2025/0014607 “Chen,” further in view of Popat et al., US 2023/0342411 “Popat.”
In Yoon see at least (underlined text is for emphasis):
Regarding claim 1: (Original) A computer-implemented method for video analysis comprising:
accessing a livestream, wherein the livestream includes a livestream chat and one or more products for sale from a catalog of products, and wherein the livestream includes at least one host and a plurality of users;
[Yoon: Abstract] A method, a computer device, and a computer program for a real-time inspector in a live commerce platform may categorize chat messages received during live broadcasting of a host in real time by using a function of a live commerce tool for the host, analyze viewer messages in real time to visualize and provide analysis results, and provide, to users, automatic answers to inquiry messages about a broadcasting item of the host.
[Yoon: 0043] Each of the servers 150 and 160 may be configured as a computer device or a plurality of computer devices that provides an instruction, a code, a file, content, a service, etc., through communication with the plurality of electronic devices 110, 120, 130, 140 over the network 170. For example, the server 150 may be a system that provides a first service to the plurality of electronic devices 110, 120, 130, 140 connected through the network 170 and the server 160 may be a system that provides a second service to the plurality of electronic devices 110, 120, 130, 140 connected through the network 170. As a specific example, the server 150 may provide a service (e.g., live commerce service) desired by a corresponding application to the plurality of electronic devices 110, 120, 130, 140 through the application of the computer program that is installed and executed on the plurality of electronic devices 110, 120, 130, 140. As another example, the server 160 may provide a service that distributes a file for installing and executing the application to the plurality of electronic devices 110, 120, 130, 140 as the second service.
[Yoon: 0046] The memory 210 may include a permanent mass storage device, such as a random access memory (RAM), a read only memory (ROM), and a disk drive, as a computer-readable recording medium. The permanent mass storage device, such as ROM and a disk drive, may be included in the computer device 200 as a permanent storage device separate from the memory 210. Also, an operating system (OS) and at least one program code may be stored in the memory 210. Such software components may be loaded to the memory 210 from another computer-readable recording medium separate from the memory 210. The separate computer-readable recording medium may include, for example, a floppy drive, a disk, a tape, a DVD/CD-ROM drive, a memory card, etc. According to other example embodiments, software components may be loaded to the memory 210 through the communication interface 230, instead of the computer-readable recording medium. For example, the software components may be loaded to the memory 210 of the computer device 200 based on a computer program installed by files received over the network 170.
[Yoon: 0050] Also, according to other example embodiments, the computer device 200 may include greater or less number of components than those shown in FIG. 2. For example, the computer device 200 may include at least a portion of the I/O device 250, or may further include other components, for example, a transceiver, a database, etc.
[Yoon: 0054] Herein, a live commerce service represents an online channel that sells products through real-time video streaming and may represent a streaming broadcast service that combines chat and shopping and introduces products while communicating with viewers of the products in real time through chat. Please note: The examiner interprets the claim language to read on the reference because information of one or more products for sale is stored in a database.
monitoring, by a large language model (LLM), the livestream chat, wherein the monitoring detects a question from a user within the plurality of users;
Rejection is based in part upon the teachings applied to claim 1 by Yoon and further upon the combination of Yoon-Chen.
In Yoon see at least:
[Yoon: 0062] In operation S430, the automatic responder 330 may provide an automatic response to the viewer's chat message. The automatic responder 330 may provide an answer to a message classified into an inquiry category among the chat messages of the viewers. For example, the automatic responder 330 may provide an automatic response to an inquiry message of a viewer based on a dataset (pair of expected inquiry and answer for each inquiry) provided in advance by the host. As another example, the automatic responder 330 may provide an automatic response to an inquiry message of a viewer based on product information (e.g., product specifications, inventory, etc.) of the host in conjunction with a shopping platform related to live commerce. As another example, the automatic responder 330 may provide an automatic response to an inquiry message of a viewer based on a dataset that converts the host's voice to text through speech to text (STT) during a live broadcast. As another example, the automatic responder 330 may provide an automatic response to an inquiry message of a viewer based on a dataset (inquires and answers for the respective inquiries in previous broadcast) acquired during previous live broadcasts of the same product or similar products. That is, if there is a sufficient dataset accumulated from previous broadcasts for a product introduced by the host, the automatic responder 330 may provide an automatic response to a more general and broader inquiry. Here, the automatic responder 330 may save a corresponding inquiry and answer for a viewer inquiry to which an automatic response was successfully generated and may automatically post the same to an inquiry bulletin board related to the product of the host. The inquiry bulletin board may be provided through a user interface screen of the viewer. Also, the automatic responder 330 may separately collect an inquiry to which an automatic response has failed among the inquiry messages of viewers and may provide the same through a separate interface on which the host may focus.
[Yoon: 0074] The processor 220 may use the language model to generate an automatic response to an inquiry in addition to the message classification. That is, the processor 220 may provide question-and-answer data related to a product of the host as an example and may generate an answer to content of a target inquiry according to a pattern of the example. Therefore, the processor 220 may generate and provide the automatic response to the inquiry using the language model.
Although Yoon’s language model is used to generate an automatic response to an inquiry, Yoon does not expressly mention techniques for using a large language model (LLM) to respond to a question/inquiry. Chen on the other hand would have taught Yoon such techniques.
In Chen see at least:
[Chen: 0014] Various machine learning techniques, such as deep learning models, can be applied. In some implementations, the media content editing architecture includes a large language model (LLM) for parsing and interpreting user input to predict one or more editing actions to be performed. The inference prediction can be performed based on conversational text interactions with the user through receiving user textual input and responding with dialog replies. The media content editing architecture can further include a prompt manager for providing a prompt in response to an editing request. The prompt can be retrieved from a prompt database. The prompt manager then fills the user's request into the provided prompt and feeds it to an LLM agent that utilizes the LLM to perform inference prediction, resulting in a list of instructions or actions corresponding to edits to be performed. The LLM agent can be further configured to perform said actions to edit the media content. To perform the edits, the LLM agent utilizes a register database of available editing tools to which the agent has access. The database can be linked to available editing tools and their associated application programming interfaces (APIs) that the LLM agent can utilize to perform editing actions.
[Chen: 0020] The LLM agent 120 includes an LLM prediction module 122 that utilizes an LLM 124 for performing an inference prediction on the received input. The LLM 124 can be implemented as a language model formed from a trained neural network with a large number of parameters. The LLM 124 can be trained as a general-purpose model or for a limited range of tasks. For example, a media content editing architecture can be implemented with a single general-purpose trained LLM or with multiple LLMs that are each trained for different tasks. In some implementations, a set of LLMs, each trained for a specific range of tasks, are provided, and the LLM agent 120 selects the LLM to use based on the received prompt 118. The use of a prompt 118 along with the user's editing request 112 provides structure and context to the input to the LLM 124. As such, the input can be somewhat predictable in terms of structure, enabling the LLM 124 to provide more accurate inference predictions. The LLM agent 120 can be configured to provide an interactive text conversation with the user 108, where a dialog reply 126 is generated using the LLM 124 and provided back to the user 108 through the dialog interface 106. The user 108 can then provide new text input to advance the conversation. The conversation continues until the LLM agent 120 determines to terminate the conversation, which can be based on the new text input and/or the current number of rounds in the conversation. Upon termination of the conversation, the LLM prediction module 122 produces an inference prediction using the LLM 124 based on the received textual input(s).
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Chen, which use a large language model (LLM) as the language model to response to user questions/inquiries, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Chen to the teachings of Yoon would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
determining, by the LLM, an answer to the question that was detected;
Rejection is based upon the teachings and rationale applied to claim 1 by Yoon-Chen and further upon the combination of Yoon-Chen:
[Yoon: 0074] The processor 220 may use the language model to generate an automatic response to an inquiry in addition to the message classification. That is, the processor 220 may provide question-and-answer data related to a product of the host as an example and may generate an answer to content of a target inquiry according to a pattern of the example. Therefore, the processor 220 may generate and provide the automatic response to the inquiry using the language model. Please note: Yoon-Chen teach and suggest the use of a large language model as the language model.
calculating an engagement metric, for the answer, wherein the engagement metric is predictive of a future engagement in the livestream, by one or more users within the plurality of users, if the answer was posted in the livestream chat; and posting, in the livestream chat, the answer to the question, if the engagement metric is above a threshold value.
Rejection is based upon the teachings and rationale applied to claim 1 by Yoon-Chen and further upon the combination of Yoon-Chen-Popat.
In Yoon-Chen see at least:
[Chen: 0035] A “successful” editing process can be defined in various ways. In some implementations, an editing process is considered successful upon publication of the edited media content. At that point, information, such as the conversation history 308 and the editing draft history 304, related to the editing process is recorded. In other implementations, every editing process interaction with users is recorded. However, this may produce vast amounts of unwanted data with little influence on whether the prompts and tool suggestions were effective. In yet other implementations, editing processes of published edited media content reaching a predetermined threshold of viewer engagement are considered successful.
[Chen: 0023] … Different reward functions can be used to determine the amount of influence of a given refinement iteration. In the illustrated example of FIG. 1, the computing system 100 includes a platform viewer engagement aggregation module 148 for providing information on viewer engagement indicators with respect to a published media content 144. Example indicators include the number of views/listens, comments, shares, likes, etc. Higher viewer engagement indicators imply a more “successful” edited media content. As such, greater weight can be given to information used in the refinement process related to published media content with higher viewer engagement indicators. For example, upon reaching a predetermined threshold of viewer engagement indicators (e.g., a predetermined number of video views within a predetermined time frame), the refinement process can be performed with respect to the published media content that reached said threshold.
[Chen: 0055] At step 706, the method 700 includes storing contextual information relating to the editing of the media content. Examples of contextual information include conversational history, editing context, and editing draft history. In some implementations, the contextual information includes asset descriptions of the edited media content. In some implementations, the contextual information is stored in a contextual memory. The contextual information can be used for various purposes. During the editing process, the contextual information is aware of historic action of the editing process, which can influence the dialog replies of the media content editing architecture. For example, if the contextual information includes conversational history where a user has rejected a given proposed edits, the media content editing architecture can be configured to not suggest said edit for the given editing process. Another use of the contextual information includes refinement of the media content editing architecture.
Although Yoon-Chen’s system a) uses a language model/large language model to respond to real time chat queries during a livestream session, b) calculates an engagement metric and metric threshold, and c) may suggest conditional posting of an answer, Yoon-Chen do not expressly mention techniques that determine conditional posting of an answer to a query based upon a threshold value. Popat on the other hand would have taught Yoon-Chen such techniques.
In Popat see at least:
[Popat: 0003] … The determination of whether to display a particular short answer depends on an accuracy score provided by an accuracy score prediction engine; the short answer is displayed or not displayed based on whether the accuracy score is greater than or less than an accuracy score threshold, respectively. The accuracy score may be determined by an accuracy score prediction engine that predicts an accuracy score for a passage from a top-ranked search result based on a consensus with other passages from other search results.
[Popat: 0020] In accordance with the implementations described herein, a technical solution to the above-described technical problem includes an improved scoring engine (accuracy score prediction engine) for determining whether to display a short answer. The improved scoring engine uses multiple passages from multiple different respective search results. The determination of whether to display a particular short answer depends on an accuracy score provided by an accuracy score prediction engine; the short answer is displayed or not displayed based on whether the accuracy score is greater than or less than an accuracy score threshold, respectively. The accuracy score may be determined by an accuracy score prediction engine that predicts an accuracy score for a passage from a top-ranked search result based on a consensus with other passages from other search results. The accuracy score prediction engine is trained using passages from search engine results, manually scored by raters based on consensus with context passages from other search results.
[Popat: 0022] FIG. 1A depicts an example environment 100 in which users can interact with one or more computer-implemented search services. Example computer-implemented search services can include a search service for an electronic mail service, a chat service, a document sharing service, a calendar sharing service, a photo sharing service, a video sharing service, a shopping service, a blogging service, a micro-blogging service, a social networking service, a location (location-aware) service, a check-in service and/or a ratings and review service.
[Popat: 0048] In some implementations, the decision regarding whether a short answer is displayed or not is based on whether the accuracy score 242(1) is greater than or less than a specified accuracy score threshold 244. In some implementations, the accuracy scores provided by raters is between −1.0 and 3.0 in steps of 0.5, with a −1.0 indicating a short answer that is demonstrably wrong in context of other passages (or in the personal knowledge of the rater) and a 3.0 indicating a short answer in complete agreement in context with other passages. In this case, the prediction engine manager 250 may be trained to output a score between −1.0 and 3.0 and the search system may use an accuracy score threshold 244 of 0.5; that is, short answers with a rating of −1.0, −0.5, and 0.0 may not be displayed, while short answers having an accuracy score of 0.5, 1.0, 1.5, 2.0, 2.5, or 2.0 may be displayed.
[Popat: 0060] The encoder 251 is configured to encode input into the prediction engine to a form appropriate for input into a machine learning engine such as a neural network. For example, in some implementations the inputs into the accuracy score prediction engine include a query (e.g., query data 231), a top-rated passage (e.g., passage 235(1)), a title of the top-rated passage, and additional passages. In some implementations, the inputs may include additional titles (e.g., corresponding to the additional passages). The additional passages are referred to as context passages. The encoder 251 is configured to convert these inputs into an input token embedding vector. The decoder 252 is configured to decode an output layer of the accuracy score prediction engine to produce predicted accuracy scores. The actions of the encoder 251 and decoder 252 are shown in FIG. 4.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Popat, which determine conditional posting of an answer to a query based upon a threshold value, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Popat to the teachings of Yoon-Chen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claims 2 and 3: Rejections are based upon the teachings and rationales applied to claim 1 by Yoon-Chen-Popat and further upon the combination of Yoon-Chen-Popat regarding a plurality of potential answers to the question:
[Popat: 0003] Implementations described herein are related to generating short answers for display based on multiple passages resulting from a query provided to a search engine. A short answer is an answer to a factual query prominently displayed in a browser window running a search engine; such an answer may be derived from—or is—a passage in a top-ranked search result.
[Popat: 0032] FIG. 1B is a diagram that illustrates a user interface 150 in which an example short answer 190 appears. In the example of FIG. 1B, the short answer 190 includes an extracted answer 170. The extracted answer 170 is extracted from the short answer 190. The short answer 190 is displayed in a browser window on a display, resulting from a search query 160. In this case, the short answer 190 is based on a passage from a top-ranked search result 180. It is noted that the short answer 190 (and more specifically the extracted answer 170) correctly answers the question. In some implementations, the short answer 190 may be displayed without the extracted answer 170.
[Popat: 0071] FIG. 3 is a diagram that illustrates example flow of data 300 from multiple, top-scoring passages from search results into a callout. As shown in FIG. 3, a query 310 input into a search engine produces search results in the form of documents 320(1-4). As shown in FIG. 3, each of the documents 320(1-4) has M passages (although some may have fewer) 330(1-4)(1-M). The passages 330(1)(1), 330(1)(2), and 330(1,3) are input into the accuracy score prediction engine to produce callout 340.
Regarding claims 26 and 27: Rejections are based upon the teachings and rationales applied to claim 1 by Yoon-Chen-Popat and further upon the combination of Yoon-Chen-Popat regarding system computing elements, e.g. processor(s), memory etc., see [Yoon: Fig. 1; Fig. 2 (210, 220, 230, 240); 0047].
Claim 4 is rejected under 35 USC 103 as being unpatentable over Yoon, US 2024/0370652, Chen, US 2025/0014607, and Popat, US 2023/0342411, as applied to claim 3 further in view of Boshy et al., US 10,521,824 “Boshy.”
Rejection is based upon the teachings and rationales applied to claim 3 by Yoon-Chen-Popat and further upon the combination of Yoon-Chen-Popat-Boshy regarding the potential answer with a highest engagement metric.
In Yoon-Chen-Popat see at least:
[Chen: 0023] … For example, upon reaching a predetermined threshold of viewer engagement indicators (e.g., a predetermined number of video views within a predetermined time frame), the refinement process can be performed with respect to the published media content that reached said threshold.
Although Yoon-Chen-Popat’s LLM chooses from a plurality of potential determined answers, Yoon-Chen-Popat do not expressly mention techniques that choose the potential answer with a highest engagement metric. Boshy on the other hand would have taught Yoon-Chen-Popat such techniques.
In Boshy see at least:
(Boshy: D9: col. 2, lines 15-27) According to implementations, user data may include any data relating to a user's electronic or web-based content consumption history, including user action data, user non-action data, and user property data. User action data may include any data associated with a user's electronic actions or activity including, but not limited to, page visits, clicks on a widget or application, scrolling of webpage sections, pointing device (e.g., mouse or keyboard) movements, clicks or other indications, a time of activity on a webpage and/or web site, a listing of web sites visited by a user, languages a user reads, special interest indicators such as “like” indications or “dislike” indications, user explicit data such as categories, etc.
(Boshy: D58: col. 11, lines 14-44) According to implementations of the present disclosure, any suitable grading methodology may be employed in grading the candidate recommendations for a given cluster. In an implementation, a grading methodology may be employed wherein a grade is calculated based on one or more grade components determined for the candidate recommendations. In an implementation, a first exemplary grade component may include a measurement of a key performance indicator (KPI) associated with user engagement (referred to as a “user engagement indicator”), such as, for example, click-through rate (CTR), a revenue per thousand impressions (RPM), time on a web page, user browsing on a web page, a number of returning users, etc.). The user engagement indicator grade component may be employed to identify candidate recommendations having the highest user engagement indicator (e.g., highest CTR) in a cluster over a period of time. In an implementation, the candidate recommendation having the highest user engagement indicator value may be identified by determining a highest absolute user engagement indicator value of the multiple candidate recommendations for the users in a particular cluster. In an implementation, the candidate recommendation having the highest user engagement indicator value may be identified by determining a user engagement indicator value for a candidate recommendation based on the users in a particular cluster relative to a “global” user engagement indicator value measurement of the candidate recommendation over all clusters (e.g., by dividing a user engagement indicator value for the users in a particular cluster by the “global” user engagement indicator value across all clusters for the candidate recommendation).
(Boshy: D68: col. 13, lines 27-40) In an implementation, another type of personalized content recommendation may be generated by the recommendation module based on a matching a user's interest and particular content (referred to as “content-based recommendations”). Given a set of user interests/features identified for a target user (e.g., User A likes arts, sports, long documents, does not like video, likes English and Spanish, etc.), the recommendation module may generate a content-based recommendation including these features having a highest user engagement value (e.g., a highest CTR value, a highest RPM value, etc.). For example, a user having an interest in content categories X and Y are provided with content-based recommendations having a highest user engagement value (e.g., a highest CTR) for category X and category Y. Please note: A recommendation is a response to an action taken by a user.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Boshy, which choose the potential recommendation with a highest engagement value would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Boshy to the teachings of Yoon-Chen-Popat would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2025/0047919 (HSU et al.) “Method, Computing Device, and Computer-Readable Storage Medium for Facilitating Streamer Interaction with Viewer,” discloses: [0007] In some embodiments, generating the topic suggestion based on the historical topic and the score corresponding to the historical topic may include calculating a threshold of the streamer based on the level of the streamer; calculating a real-time engagement score of the viewer in real time based on at least one real-time parameter; comparing the real-time engagement score with the streamer's threshold; generating a topic suggestion based on the historical topic and the score corresponding to the historical topic when the real-time engagement score is less than the streamer's threshold; and generating a current topic as the topic suggestion or no topic suggestion based on the current interactive content of the streamer when the real-time engagement score is greater than or equal to the streamer's threshold.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 June 24, 2026
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