DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/13/2026 has been entered.
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 .
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.
Claims 1-2, 5-10, 12, 13 and 16-24 are rejected under 35 U.S.C. 103 as being unpatentable over Mancuso et al. (US 20240403366 A1, published 12/05/2024), hereinafter Mancuso, in view of Heere et al. (US 20210089860 A1, published 03/25/2021), hereinafter Heere.
Regarding claim 1, Mancuso teaches the claim comprising:
An apparatus comprising: at least one processing platform comprising at least one processor coupled to at least one memory, the at least one processing platform, when executing program code, is configured to (Mancuso Figs. 1-10; [0103], Embodiments of the present disclosure may comprise or utilize a special purpose or general purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., memory) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein):
manage an interface between a user and an information processing system, wherein, when managing the interface, the at least one processing platform is further configured to (Mancuso Figs. 1-10; [0023], the content stack generation system can condense many functions into a single application and a single interface. For example, the content stack generation system can embed multiple external applications directly within a single user interface, thereby reducing the navigational burden of prior systems that require many navigational operations across different interfaces and applications; [0068], FIG. 5 illustrates the content stack generation system 102 providing a content stack for display via a graphical user interface in accordance with one or more embodiments; [0069], FIG. 5 shows a graphical user interface of a client device 500 displaying a calendar event 502 for “Team Meeting.” In some implementations, the content stack generation system 102 utilizes the large language model 118 to analyze the calendar event 502 and define a topic prompt for the calendar event 502; [0070], Based on the topic prompt, the content stack generation system 102 can generate a content stack 504 to suggest to the user account. For instance, FIG. 5 shows the content stack generation system 102 suggesting the content stack 504 containing a digital document, a digital video, and a webpage):
generate a data structure comprising data, the data representing one or more previous interactions between the user and the information processing system, wherein generation of the data structure comprises executing one or more machine learning models (Mancuso Figs. 1-10; [0049], As illustrated in FIG. 3, in some implementations, the content stack generation system 102 generates and utilizes a stack formulation graph 304 (e.g., the stack formulation graph 204 or similar) for a user account 302. In some embodiments, the content stack generation system 102 generates a user-account-specific stack formulation graph 304 for the user account 302, where the stack formulation graph 304 defines relationships associated with the user account 302, including relationships with content items and with other user accounts. In certain embodiments, the content stack generation system 102 generates a system-wide stack formulation graph that includes a node for the user account 302 and that includes nodes for content items and other user accounts; [0050], the content stack generation system 102 generates the stack formulation graph 304 using nodes to represent user accounts and content items, and using edges to represent relationships between the nodes (e.g., where shorter distances represent stronger or closer relationships than longer distances). To generate the stack formulation graph 304, the content stack generation system 102 monitors or detects user account behavior over time. For example, the content stack generation system 102 monitors user account accesses, shares, comments, edits, receipts, clips (e.g., generating content items from other content items), and/or other user interactions over time to determine frequencies, recencies, and/or overall numbers of user interactions (of the user account 302, of collaborating user accounts with the user account 302, and/or of similar user accounts) with content items and/or with other user accounts. In some cases, the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items. Indeed, in some implementations, the content stack generation system 102 generates, modifies, and maintains the stack formulation graph 304 using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts. For example, the content stack generation system 102 generates the stack formulation graph 304 by utilizing a machine-learning model to embed the content items into a latent vector space (e.g., indicating topic features of the various content items); [0052-0053], the content stack generation system 102 determines one or more access patterns of the user account 302 with the content items. To illustrate, the content stack generation system 102 determines that the user account 302 has recently and/or frequently opened particular content items. Additionally, or alternatively, the content stack generation system 102 determines that the user account 302 created, edited, shared, and/or viewed particular content items);
and utilize the data structure to respond to one or more subsequent interactions between the user and the information processing system (Mancuso Figs. 1-10; [0058], To generate or identify the content item 308, in some embodiments, the content stack generation system 102 determines an input intent (e.g., a topic prompt) from the user interaction. To elaborate, the content stack generation system 102 utilizes the large language model 306 to process the user interaction, such as a selection of an interface element for performing one or more predefined processes or an entered text query, to determine the input intent. For example, the content stack generation system 102 utilizes the large language model 306 to generate a set of input intent predictions using model parameters learned during model training (e.g., training on large sets of user interactions and corresponding ground truth input intents). In some cases, the content stack generation system 102 selects an input intent prediction with a highest probability as the input intent for a user interaction; [0062], in some implementations, the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0064], Over a period of time, the content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account. To illustrate, additional documents and other files, such as a calendar event, may become relevant to the project. Accordingly, the content stack generation system 102 can modify the content stack for the project and present the modified content stack; [0066], the content stack generation system 102 can monitor changes in user account activity and/or the corpus of content items, and manage content stacks accordingly. For example, the content stack generation system 102 identifies one or more changes in the composition of the content items, changes in account data, and/or new content interaction data. Based on these changes and/or new data, the content stack generation system 102 determines an update to the content-based signals for the content items and/or the account-based signals for the user account. Then, the content stack generation system 102 can generate an updated stack formulation graph, which may include some or all of the nodes of the original (or most recent) stack formulation graph, as well as additional nodes for new content items (e.g., newly created content items or newly relevant content items). The content stack generation system 102 can then modify the content stack to reflect the updates to the stack formulation graph);
wherein the interface comprises a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting, wherein the one or more machine learning models include one or more of a large language model, a classification model, a regression model, and a reinforcement model (Mancuso Figs. 1-10; [0019], the content stack generation system can select a set of content items that are pertinent to a task, project, question, meeting, workflow, or other need of the user account; [0020], the content stack generation system can provide content stacks containing content items that assist a user account with a variety of activities, such as prioritizing work, sharing projects, authoring documents, retrieving answers and other content, summarizing communications, and orchestrating computing applications. To illustrate, in some implementations, the content stack generation system ingests content items from various sources (e.g., a database of files, an email application, a calendar, a messaging application, the Internet, etc.); [0022], the content stack generation system can utilize a stack formulation graph and a large language model to adaptively access and provide content items from a wide range of locations, including locally stored content items and content items hosted on a server; the content stack generation system is able to provide access to content items from web locations and other server locations (by utilizing a large language model to analyze a stack formulation graph) without requiring entirely separate applications to access web-based content items than those for accessing stored content items (stored locally or on the cloud). Moreover, in cases where user accounts engage in communication regarding one or more content items and/or engage in editing (or otherwise interacting with) the content items, the content stack generation system integrates external applications such as web browsers, chat applications, and email clients directly within a common user interface to facilitate interaction with (or about) the content items; [0023], the content stack generation system can embed multiple external applications directly within a single user interface; [0024], the content stack generation system can provide content stacks as a set of links to content items, which allows a user to interact with the content items in a content stack as if they were stored together, when the actual content items may be stored in various locations within a content management system; [0025], A content item can include a file or a folder such as a digital text file, a digital image file, a digital audio file, a webpage, a website, a digital video file, a web file, a link, a hyperlinked video file streamable from a webpage, a calendar event, a contact card, a text message thread, a direct message thread, a chat group thread, a social media feed, a social media post, a news article, a headline, a technical support ticket, a digital document file, or some other type of file or digital object; [0027], a machine-learning model can include, but is not limited to, a neural network (e.g., a convolutional neural network, recurrent neural network, or other deep learning network), a decision tree (e.g., a gradient boosted decision tree), association rule learning, inductive logic programming, support vector learning, Bayesian networks, a regression-based model (e.g., censored regression), principal component analysis, or a combination thereof. In some embodiments, the content stack generation system utilizes a large language machine-learning model in the form of a neural network; [0028], the term “neural network” refers to a machine-learning model that can be trained and/or tuned based on inputs to determine classifications and/or scores; [0041], the content stack generation system 102 can identify recent activity data such as web browser history, file accesses, and communications (e.g., email, text message, group chat) with other user accounts to determine current interests, tasks, workflows, projects, and/or data needs of the user account 202; [0050], using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts; [0058], the content stack generation system 102 determines an input intent to access a particular content item, to gather relevant documents for a team meeting, to generate an email to a particular recipient, to summarize a document, to answer a question about a particular topic, to identify user accounts associated with a project, or to schedule a meeting; [0086], the content stack generation system 102 can interface with one or more of document processing applications, spreadsheet applications, slide presentation applications, email applications, messaging applications, calendar applications, image editing applications, video editing applications, webpage creation applications, text editing applications, task tracking applications, and/or code development applications, etc)
wherein, when managing the interface to generate the data structure, the at least one processing platform is further configured to: classify at least a portion of the data representing the one or more previous interactions between the user and the information processing system into one or more domains executing at least one of the one or more machine learning models (Mancuso Figs. 1-10; [0050], the content stack generation system 102 generates the stack formulation graph 304 using nodes to represent user accounts and content items, and using edges to represent relationships between the nodes (e.g., where shorter distances represent stronger or closer relationships than longer distances). To generate the stack formulation graph 304, the content stack generation system 102 monitors or detects user account behavior over time. For example, the content stack generation system 102 monitors user account accesses, shares, comments, edits, receipts, clips (e.g., generating content items from other content items), and/or other user interactions over time to determine frequencies, recencies, and/or overall numbers of user interactions (of the user account 302, of collaborating user accounts with the user account 302, and/or of similar user accounts) with content items and/or with other user accounts. In some cases, the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items. Indeed, in some implementations, the content stack generation system 102 generates, modifies, and maintains the stack formulation graph 304 using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts. For example, the content stack generation system 102 generates the stack formulation graph 304 by utilizing a machine-learning model to embed the content items into a latent vector space (e.g., indicating topic features of the various content items); [0052], The content stack generation system 102 utilizes these types of content-based signals to classify the content items and generate nodes of the stack formulation graph 304 representing the content items, and edges between the nodes representing relationships between content items; [0053], the content stack generation system 102 determines one or more access patterns of the user account 302 with the content items. To illustrate, the content stack generation system 102 determines that the user account 302 has recently and/or frequently opened particular content items. Additionally, or alternatively, the content stack generation system 102 determines that the user account 302 created, edited, shared, and/or viewed particular content items)
derive one or more actions from at least a portion of the data representing the one or more previous interactions between the user and the information processing system, and to associate the one or more derived actions with the one or more domains to which the one or more derived actions correspond (Mancuso Figs. 1-10; [0019], Using comparison metrics, the content stack generation system can select a set of content items that are pertinent to a task, project, question, meeting, workflow, or other need of the user account; [0034], the server device(s) 106 may receive data from the client device 108 in the form of a topic prompt to perform a particular task or to generate or retrieve a particular content item. In addition, the server device(s) 106 can transmit data to the client device 108 in the form of an interface that includes a content item related to performing the requested task; [0062], in some implementations, the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0050], the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items. Indeed, in some implementations, the content stack generation system 102 generates, modifies, and maintains the stack formulation graph 304 using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts. For example, the content stack generation system 102 generates the stack formulation graph 304 by utilizing a machine-learning model to embed the content items into a latent vector space (e.g., indicating topic features of the various content items); [0064], Over a period of time, the content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account; [0066], the content stack generation system 102 can monitor changes in user account activity and/or the corpus of content items, and manage content stacks accordingly. For example, the content stack generation system 102 identifies one or more changes in the composition of the content items, changes in account data, and/or new content interaction data; [0075], the content stack generation system 102 provides a content stack (or adds a content item to a content stack) that includes an action item, decision, or task determined during a team meeting. For example, the content stack generation system 102 utilizes a transcript of a videoconference or audio recording to identify an action item from the conference, and scrapes the action item (e.g., a paragraph describing the action item) from the transcript to generate a new content item for the action item. The content stack generation system 102 can generate a new node for the new content item within one or more stack formulation graphs and update a content stack (e.g., content stack 504) for the user account; [0078], FIG. 6 illustrates the content stack generation system 102 providing a content stack for display via a graphical user interface with selection elements to save, open, and edit the content stack)
However, Mancuso fails to expressly disclose wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules and the one or more machine learning models, wherein the plurality of modules include modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting, wherein the one or more machine learning models include one or more of a large language model, a classification model, a regression model, and a reinforcement model. In the same field of endeavor, Heere teaches:
wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules and the one or more machine learning models, wherein the plurality of modules include modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting, wherein the one or more machine learning models include one or more of a large language model, a classification model, a regression model, and a reinforcement model (Heere Figs. 1-20; [0039], The digital assistant system 300 may employ such technologies as artificial intelligence (AI), in addition to and beyond natural language processing and speech recognition. Among these are supervised and unsupervised machine learning methods, predictive analytics and prescriptive analytics; [0044], the digital assistant system 300 may be configured to provide is context awareness. A central challenge of the proactive user notification pattern is how to manage, and in some cases limit, the number and timing of push notifications, in order to avoid information overload to the user and the user's computing device. In some example embodiments, the digital assistant system 300 is configured to, instead of directly pushing every event directly to the user, analyze the content and metadata (e.g., influenced semantic identifiers) of a notification together with context information of the user and situation to predict relevance and importance of the notification; [0045], The relevance prediction and classification models of the digital assistant system 300 can be enriched by using explicitly user defined rules, as well as explicit and implicit user feedback that is used to retrain machine learning models in a supervised manner. To realize this context-aware behavior, the digital assistant system 300 may utilize application context, business context, data state/context, device context, external context, geographic context, domain context, user context, user tracing, topic storage, semantics, ranking, contexts management, common user knowledge, topic extraction, long-term memory, and machine learning; [0055], fundamentals for the prescriptive capabilities of the digital assistant system 300 are provided by the machine learning manager 380, which orchestrates and administrates analytical models for different combinations of semantic entities and analytical tasks; [0057], insights provided by the digital assistant system 300; [0058], the digital assistant system 300 is configured to increase user productivity by taking over administrative tasks and automating simple, repetitive activities. This administrative capability may be triggered by explicit user request or proactively from analysis of recently requested and viewed data in combination with the user's context that is run in the background and leads the digital assistant system 300 to suggest one-time or repetitive, automated execution of certain activities; [0086], the projection manager 340 regularly calls the machine learning manager 380 to update its internal ranking mechanisms as well as its pattern detection mechanisms based on explicit and implicit user feedback data. Furthermore, the plans of the projection manager 340 can be updated(e.g., leveraging the machine learning manager 380) based on explicit and implicit feedback data, as well as based on updates to the semantic knowledge graphs, which may be managed by the semantics manager 370. In some example embodiments, the projection manager 340 is configured to act as a bridge between user interaction on the one hand and system actions (e.g., data processing) on the other hand. The projection manager 340 may build on pre-configured data processing pipelines (e.g., flows) and may be deeply integrated with tools that help identify and execute actions in given situations; [0087], The action manager 350 is configured to handle the execution of identified tasks, also referred to as action plans, as well as to trigger workflows; [0106], the machine learning manager 380 comprises machine learning and analytics models, which may be executed directly within the machine learning manager 380; [0125], a linear regression model that generates a prediction; [0131], reported issue with the monitored KPI)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules and the one or more machine learning models, wherein the plurality of modules include modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting, wherein the one or more machine learning models include one or more of a large language model, a classification model, a regression model, and a reinforcement model as suggested in Bulusu into Mancuso in view of Heere. Doing so would be desirable because current digital assistants rely on explicit user instruction to perform certain tasks, such as tasks related to data analysis, notifications related to data events, and other complicated processes. Furthermore, current digital assistants lack the ability to predict future events, to proactively generate notifications of predicted future events, and to recommend actions that address predicted future events. Additionally, current digital assistants do not employ adequate contextual awareness when presenting content to users. For example, the technical characteristics of a computing device, such as the device type (e.g., smartphone, laptop) and the screen size, are not factored in determining the presentation of content, thereby resulting in inefficient use of screen space and poor performance of the computing device (e.g., due to excessive content). The present disclosure addresses these and other technical problems that plague the computer functionality of digital assistant systems (see Heere [0003]). The digital assistant system of the present disclosure provides features such as proactiveness, context awareness, personalization, improved conversational interaction, an improved knowledge base, aggregation of data, recommendations of prescriptive actions, simulations of data in general, as well as simulations of prescriptive actions in particular, and explanations of data, as well as explanations of simulations and recommendations, thereby improving the functioning of the underlying computer system (see Heere [0019]). The digital assistant system 300 significantly reduces complexity of user interfaces up to the degree of automating most parts of the user interaction and enables users to keep track of multiple tasks simultaneously (see Heere [0039]).
Regarding claim 12, claim 12 contains substantially similar limitations to those found in claim 1, except for as part of an interface between a user and an information processing system (Mancuso Figs. 1-10; [0049], As illustrated in FIG. 3, in some implementations, the content stack generation system 102 generates and utilizes a stack formulation graph 304 (e.g., the stack formulation graph 204 or similar) for a user account 302. In some embodiments, the content stack generation system 102 generates a user-account-specific stack formulation graph 304 for the user account 302, where the stack formulation graph 304 defines relationships associated with the user account 302, including relationships with content items and with other user accounts. In certain embodiments, the content stack generation system 102 generates a system-wide stack formulation graph that includes a node for the user account 302 and that includes nodes for content items and other user accounts; [0050], the content stack generation system 102 generates the stack formulation graph 304 using nodes to represent user accounts and content items, and using edges to represent relationships between the nodes (e.g., where shorter distances represent stronger or closer relationships than longer distances). To generate the stack formulation graph 304, the content stack generation system 102 monitors or detects user account behavior over time. For example, the content stack generation system 102 monitors user account accesses, shares, comments, edits, receipts, clips (e.g., generating content items from other content items), and/or other user interactions over time to determine frequencies, recencies, and/or overall numbers of user interactions (of the user account 302, of collaborating user accounts with the user account 302, and/or of similar user accounts) with content items and/or with other user accounts. In some cases, the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items. Indeed, in some implementations, the content stack generation system 102 generates, modifies, and maintains the stack formulation graph 304 using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts. For example, the content stack generation system 102 generates the stack formulation graph 304 by utilizing a machine-learning model to embed the content items into a latent vector space (e.g., indicating topic features of the various content items); [0052-0053], the content stack generation system 102 determines one or more access patterns of the user account 302 with the content items. To illustrate, the content stack generation system 102 determines that the user account 302 has recently and/or frequently opened particular content items. Additionally, or alternatively, the content stack generation system 102 determines that the user account 302 created, edited, shared, and/or viewed particular content items). Consequently, claim 12 is rejected for the same reasons.
Regarding claim 20, claim 20 contains substantially similar limitations to those found in claim 1. Consequently, claim 20 is rejected for the same reasons.
Regarding claim 2, Mancuso in view of Heere teaches all the limitations of claim 1, further comprising:
wherein, when managing the interface, the at least one processing platform is further configured to update the data structure based the one or more subsequent interactions between the user and the information processing system (Mancuso Figs. 1-10; [0062], in some implementations, the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0064], Over a period of time, the content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account. To illustrate, additional documents and other files, such as a calendar event, may become relevant to the project. Accordingly, the content stack generation system 102 can modify the content stack for the project and present the modified content stack; [0066], the content stack generation system 102 can monitor changes in user account activity and/or the corpus of content items, and manage content stacks accordingly. For example, the content stack generation system 102 identifies one or more changes in the composition of the content items, changes in account data, and/or new content interaction data. Based on these changes and/or new data, the content stack generation system 102 determines an update to the content-based signals for the content items and/or the account-based signals for the user account. Then, the content stack generation system 102 can generate an updated stack formulation graph, which may include some or all of the nodes of the original (or most recent) stack formulation graph, as well as additional nodes for new content items (e.g., newly created content items or newly relevant content items). The content stack generation system 102 can then modify the content stack to reflect the updates to the stack formulation graph)
Regarding claim 13, claim 13 contains substantially similar limitations to those found in claim 2. Consequently, claim 13 is rejected for the same reasons.
Regarding claim 5, Mancuso in view of Heere teaches all the limitations of claim 1, further comprising:
wherein the one or more derived actions are associated with one or more rules (Mancuso Figs. 1-10; [0027], a machine-learning model can include association rule learning; [0019], Using comparison metrics, the content stack generation system can select a set of content items that are pertinent to a task, project, question, meeting, workflow, or other need of the user account; [0034], the server device(s) 106 may receive data from the client device 108 in the form of a topic prompt to perform a particular task or to generate or retrieve a particular content item. In addition, the server device(s) 106 can transmit data to the client device 108 in the form of an interface that includes a content item related to performing the requested task; [0050], the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items. Indeed, in some implementations, the content stack generation system 102 generates, modifies, and maintains the stack formulation graph 304 using one or more machine-learning models (e.g., neural networks) to predict or determine relationships among content items and user accounts. For example, the content stack generation system 102 generates the stack formulation graph 304 by utilizing a machine-learning model to embed the content items into a latent vector space (e.g., indicating topic features of the various content items); [0062], in some implementations, the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0066], the content stack generation system 102 determines comparison metrics between nodes of the updated stack formulation graph and the topic prompt for the user account. Utilizing these comparison metrics, the content stack generation system 102 determines a new set of relevant content items to include in the updated content stack; [0075], the content stack generation system 102 provides a content stack (or adds a content item to a content stack) that includes an action item, decision, or task determined during a team meeting. For example, the content stack generation system 102 utilizes a transcript of a videoconference or audio recording to identify an action item from the conference, and scrapes the action item (e.g., a paragraph describing the action item) from the transcript to generate a new content item for the action item. The content stack generation system 102 can generate a new node for the new content item within one or more stack formulation graphs and update a content stack (e.g., content stack 504) for the user account; [0078], FIG. 6 illustrates the content stack generation system 102 providing a content stack for display via a graphical user interface with selection elements to save, open, and edit the content stack)
Regarding claims 16 and 21, claims 16 and 21 contain substantially similar limitations to those found in claim 5. Consequently, claims 16 and 21 are rejected for the same reasons.
Regarding claim 9, Mancuso in view of Heere teaches all the limitations of claim 7, further comprising:
when managing the interface to utilize the data structure to respond to the one or more subsequent interactions between the user and the information processing system, the at least one processing platform is further configured to: search the one or more user-specific context documents executing at least another one of the one or more machine learning models, generate one or more responses to the one or more subsequent interactions, and cause presentation of the one or more responses on the interface to the user (Mancuso Figs. 1-10; [0025], A content item can include a digital document; [0058], To generate or identify the content item 308, in some embodiments, the content stack generation system 102 determines an input intent (e.g., a topic prompt) from the user interaction. To elaborate, the content stack generation system 102 utilizes the large language model 306 to process the user interaction, such as a selection of an interface element for performing one or more predefined processes or an entered text query, to determine the input intent; [0062], the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0064], Over a period of time, the content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account. To illustrate, additional documents and other files, such as a calendar event, may become relevant to the project. Accordingly, the content stack generation system 102 can modify the content stack for the project and present the modified content stack; [0066], the content stack generation system 102 can monitor changes in user account activity and/or the corpus of content items, and manage content stacks accordingly. For example, the content stack generation system 102 identifies one or more changes in the composition of the content items, changes in account data, and/or new content interaction data. Based on these changes and/or new data, the content stack generation system 102 determines an update to the content-based signals for the content items and/or the account-based signals for the user account. Then, the content stack generation system 102 can generate an updated stack formulation graph, which may include some or all of the nodes of the original (or most recent) stack formulation graph, as well as additional nodes for new content items (e.g., newly created content items or newly relevant content items). The content stack generation system 102 can then modify the content stack to reflect the updates to the stack formulation graph; [0073], the content stack generation system 102 determines comparison metrics between nodes of the stack formulation graph and the topic prompt. For instance, the content stack generation system 102 determines a cosine similarity between topic features for the content items and the topic prompt. To illustrate, in some implementations, the content stack generation system 102 generates topic feature vectors representing nodes of the stack formulation graph. For example, the content stack generation system 102 utilizes the large language model to embed content items in a feature vector space that represents topics or descriptions of the content items. In some cases, the topic features are in the same vector space as the topic prompt. The content stack generation system 102 can determine a distance between a topic feature for a node and the topic prompt. For instance, the content stack generation system 102 determines a cosine distance between the topic prompt and the topic feature vector for a node)
Regarding claims 18 and 23, claims 18 and 23 contain substantially similar limitations to those found in claim 9. Consequently, claims 18 and 23 are rejected for the same reasons.
Regarding claim 10, Mancuso in view of Heere teaches all the limitations of claim 9, further comprising:
wherein at least one of the one or more responses are proactively presented to the user on the interface prior to at least one of the one or more subsequent interactions (Mancuso Figs. 1-10; [0058], To generate or identify the content item 308, in some embodiments, the content stack generation system 102 determines an input intent (e.g., a topic prompt) from the user interaction. To elaborate, the content stack generation system 102 utilizes the large language model 306 to process the user interaction, such as a selection of an interface element for performing one or more predefined processes or an entered text query, to determine the input intent. For example, the content stack generation system 102 utilizes the large language model 306 to generate a set of input intent predictions using model parameters learned during model training (e.g., training on large sets of user interactions and corresponding ground truth input intents). In some cases, the content stack generation system 102 selects an input intent prediction with a highest probability as the input intent for a user interaction; [0062], in some implementations, the content stack generation system 102 adapts content stacks based on user account activity. For instance, FIG. 4 illustrates the content stack generation system 102 updating a content stack over a period of time in accordance with one or more embodiments; [0064], Over a period of time, the content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account. To illustrate, additional documents and other files, such as a calendar event, may become relevant to the project. Accordingly, the content stack generation system 102 can modify the content stack for the project and present the modified content stack; [0066], the content stack generation system 102 can monitor changes in user account activity and/or the corpus of content items, and manage content stacks accordingly. For example, the content stack generation system 102 identifies one or more changes in the composition of the content items, changes in account data, and/or new content interaction data. Based on these changes and/or new data, the content stack generation system 102 determines an update to the content-based signals for the content items and/or the account-based signals for the user account. Then, the content stack generation system 102 can generate an updated stack formulation graph, which may include some or all of the nodes of the original (or most recent) stack formulation graph, as well as additional nodes for new content items (e.g., newly created content items or newly relevant content items). The content stack generation system 102 can then modify the content stack to reflect the updates to the stack formulation graph; [0069], FIG. 5 shows a graphical user interface of a client device 500 displaying a calendar event 502 for “Team Meeting.” In some implementations, the content stack generation system 102 utilizes the large language model 118 to analyze the calendar event 502 and define a topic prompt for the calendar event 502. For instance, the content stack generation system 102 determines the topic prompt based on a description for the calendar event 502 and/or account data of invited participants (e.g., other user accounts) to the calendar event 502; [0070], Based on the topic prompt, the content stack generation system 102 can generate a content stack 504 to suggest to the user account; [0079], FIG. 6 shows a graphical user interface of a client device 600 displaying a task item 602 for “Task 7.” In some implementations, the content stack generation system 102 utilizes the large language model 118 to analyze the task item 602 and define a topic prompt for the task item 602)
Regarding claims 19 and 24, claims 19 and 24 contain substantially similar limitations to those found in claim 10. Consequently, claims 19 and 24 are rejected for the same reasons.
Regarding claim 17, Mancuso in view of Heere teaches all the limitations of claim 15, further comprising:
wherein the data structure comprises a hierarchical data structure comprising one or more first nodes representing the one or more domains and one or more second nodes representing the one or more derived actions, wherein the one or more second nodes are connected to the one or more first nodes, further wherein the one or more second nodes are mapped to one or more user-specific context documents, and still further wherein the one or more user-specific context documents are indexed in a vector database operatively coupled between the one or more user-specific context documents and the hierarchical data structure (Mancuso Figs. 1-10; [0020], In particular, the content stack generation system can provide content stacks containing content items that assist a user account with a variety of activities, such as prioritizing work, sharing projects, authoring documents, retrieving answers and other content, summarizing communications, and orchestrating computing applications. To illustrate, in some implementations, the content stack generation system ingests content items from various sources (e.g., a database of files, an email application, a calendar, a messaging application, the Internet, etc.). The content stack generation system can extract the content items into unary features and/or binary relationships, and embed the unary features and/or binary relationships into a latent vector space, thereby generating feature vectors for the content items (e.g., topic features as described below); [0025], A content item can include a digital document; [0044], the content stack generation system 102 can utilize the large language model 206 to generate embedded representations of content items in a latent feature vector space representing various content topics; [0046], the content stack 210 can include several content items of a variety of content types, such as calendar items, digital documents; [0050], the content stack generation system 102 monitors user account accesses, shares, comments, edits, receipts, clips (e.g., generating content items from other content items), and/or other user interactions over time to determine frequencies, recencies, and/or overall numbers of user interactions (of the user account 302, of collaborating user accounts with the user account 302, and/or of similar user accounts) with content items and/or with other user accounts; the content stack generation system 102 generates the stack formulation graph 304 using nodes to represent user accounts and content items, and using edges to represent relationships between the nodes; the content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items; [0051], the content stack generation system 102 generates the nodes and edges of the stack formulation graph 304 as vectors in three-dimensional space that can be represented visually in a graphical user interface. In certain embodiments, the content stack generation system 102 utilizes higher-order dimensions to represent the stack formulation graph 304. For instance, the content stack generation system 102 can generate the nodes and edges of the stack formulation graph 304 in an n-dimensional vector space; [0052], The content stack generation system 102 utilizes these types of content-based signals to classify the content items and generate nodes of the stack formulation graph 304 representing the content items, and edges between the nodes representing relationships between content items; [0053], the content stack generation system 102 determines that the user account 302 has recently and/or frequently opened particular content items. Additionally, or alternatively, the content stack generation system 102 determines that the user account 302 created, edited, shared, and/or viewed particular content items; [0055], the content stack generation system 102 generates larger nodes for higher frequencies of interaction of the user account 302 with respective content items and user accounts; [0075], the content stack generation system 102 provides a content stack (or adds a content item to a content stack) that includes an action item, decision, or task determined during a team meeting. For example, the content stack generation system 102 utilizes a transcript of a videoconference or audio recording to identify an action item from the conference, and scrapes the action item (e.g., a paragraph describing the action item) from the transcript to generate a new content item for the action item. The content stack generation system 102 can generate a new node for the new content item within one or more stack formulation graphs and update a content stack (e.g., content stack 504) for the user account; [0078], FIG. 6 illustrates the content stack generation system 102 providing a content stack for display via a graphical user interface with selection elements to save, open, and edit the content stack)
Regarding claims 6-8 and 22, claims 6-8 and 22 contain substantially similar limitations to those found in claim 17. Consequently, claims 6-8 and 22 are rejected for the same reasons.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mancuso in view of Heere in further view of Bulusu et al. (US 10853867 B1, published 12/01/2020), hereinafter Bulusu.
Regarding claim 11, Mancuso in view of Heere teaches all the limitations of claim 11. However, Mancuso in view of Heere fails to expressly disclose wherein the information processing system comprises a digital commerce system. In the same field of endeavor, Bulusu teaches:
wherein the information processing system comprises a digital commerce system (Bulusu Figs. 1-8; abs. The sequence of actions is then assessed to determine a gateway action within the sequence of actions that is likely to be performed by the user and has a high likelihood of resulting in subsequent performance of the high-value action. The gateway action may then be presented to the user; col. 2 [line 23], consider the scenario in which the system is operated by an online retailer having an online marketplace. In this scenario, consider that the online retailer may wish to make a recommendation to a target user of the online marketplace; col. 3 [line 43], the system may comprise an online marketplace that includes an electronic catalog (e.g., a data repository that includes information associated with a number of offered products). At least some of the actions included in the historic data may comprise actions performed with respect to products listed in the electronic catalog; col. 11 [line 10], FIG. 4 depicts an action node mapping that may be generated as prediction model data. In some embodiments, an action node map 400 may be generated using a process that builds a knowledge repository of all actions and at least a portion of their attributes. In some embodiments, this repository is automatically built using information extracted from one or more data sources. In some embodiments, the data sources may be unrelated to each other. For example, actions related to users' online interactions may be obtained from server logs as clickstream data. In another example, users' television viewing history may be obtained from television service providers. In some embodiments, actions may be associated with a pre-identified sequence of actions; col. 15 [line 13], by retrieving a user's clickstream data from an electronic marketplace server and by retrieving that user's viewing history from an online media presentation site, the service provider may determine that the user purchased Product A shortly after watching Movie B. In this example, the service provider may increment a recorded correlation between the action “watch Movie B” and “purchase Product A”;)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the information processing system comprises a digital commerce system as suggested in Bulusu into Mancuso in view of Heere. Doing so would be desirable because with the drastic growth of online commerce in recent years and the corresponding growth in competition, electronic retailers are finding it increasingly difficult to maximize customer spending on their sites. One way in which an electronic retailer is able to distinguish itself from its competition is through the use of an effective recommendation engine. However, recommendations provided by these recommendation engines are often simply aligned with the user's already known interests. For example, if the recommendation engine is provided information that a target user likes a particular type of good or service, the recommendation engine may simply provide a recommendation to purchase that good or service. Other recommendation engines may identify similarities between a target user and other users and may provide recommendations based on what users similar to the target user are interested in. However, recommendations personalized in this manner only reinforce what the user already knows he or she likes and prevents exposure to new things that the user would likely enjoy. For example, in conventional systems, users are only exposed to recommendations within their existing realm of interests and their tendency towards specific behaviors are reinforced by creating self-referential loops. This is often referred to as the serendipity problem, which stems from the fact that the goal of such systems is to find items that best match a user's preferences in order to improve accuracy, irrespective of their actual usefulness and future value to the system. Due to the serendipity problem, users are often left unaware of alternatives that they may enjoy (see Bulusu col. 1 [line 6]). Embodiments of the disclosure provide a number of advantages over current systems. For example, embodiments of the disclosure enable a recommendation to be provided for an action that may eventually lead to performance of a high-value action. Conventional systems may identify high-value actions, but those conventional systems then simply provide a recommendation to the user to simply perform the high-value action. This is less than ideal as a user may not be comfortable with the high-value action (e.g., the customer may not be familiar with the action or may not have the means to perform it). By identifying actions likely to lead to the performance of the high-value action, the current disclosure enables a service provider to provide recommendations for actions that the user is more likely to engage in than the high-value action. This has the advantage of building the comfort level of the user and indirectly encourages the performance of the high-value action (see Bulusu col. 17 [line 33]).
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to claims 1, 5, 6, 12, 16, 17, and 20, the cancelation of claims 3, 4, 14, and 15, and the addition of claims 21-24. The rejection of the claims under 35 U.S.C. 101 is respectfully withdrawn. The rejection of the claims under 35 U.S.C. 112(b) is respectfully withdrawn.
Regarding independent claim 1, the Applicant alleges that Mancuso as described in the previous Office action, does not explicitly teach amended claim 1. Examiner has therefore rejected independent claim 1 under 35 U.S.C § 103 as unpatentable over Mancuso in view of Heere.
Specifically applicant alleges that Mancuso fails to expressly disclose wherein managing the interface to generate the data structure further comprises: classifying at least a portion of the data representing the one or more previous interactions between the user and the information processing system into one or more domains executing at least one of the one or more machine learning models; and deriving one or more actions from at least a portion of the data representing the one or more previous interactions between the user and the information processing system, and to associate the one or more derived actions with the one or more domains to which the one or more derived actions correspond. Examiner respectfully disagrees.
As discussed in the rejection above, Mancuso teaches wherein, when managing the interface to generate the data structure, the program code when executed further causes the at least one processing platform to: classify at least a portion of the data representing the one or more previous interactions between the user and the information processing system into one or more domains executing at least one of the one or more machine learning models (Mancuso Figs. 1-10; [0050], [0052-0053]), derive one or more actions from at least a portion of the data representing the one or more previous interactions between the user and the information processing system, and to associate the one or more derived actions with the one or more domains to which the one or more derived actions correspond (Mancuso Figs. 1-10; [0019], [0034], [0062], [0050], [0066], [0075], [0078]).
As discussed in the rejection above, Mancuso discloses the content stack generation system 102 monitors or detects user account behavior over time ([0050]). The content stack generation system 102 further utilizes a large language model 306 (e.g., the large language model 206 or another neural network) to determine topic features associated with content items ([0050]). The content stack generation system 102 utilizes these types of content-based signals to classify the content items and generate nodes of the stack formulation graph 304 representing the content items, and edges between the nodes representing relationships between content items ([0052-0053]). The content stack generation system 102 monitors user account activity of the user account to determine potential changes to the content stack 402. For instance, FIG. 4 shows the content stack generation system 102 providing an updated content stack 404 on February 1 as a suggested content stack relevant to “Project 3” based on updates associated with the user account and progress through the project. For example, as a user account undertakes a project (or any other workflow or task), additional content items may become relevant to the user account ([0064]). The content stack generation system 102 provides a content stack (or adds a content item to a content stack) that includes an action item, decision, or task determined during a team meeting. For example, the content stack generation system 102 utilizes a transcript of a videoconference or audio recording to identify an action item from the conference, and scrapes the action item (e.g., a paragraph describing the action item) from the transcript to generate a new content item for the action item ([0075]).
Thus, Mancuso’s disclosure of wherein, when managing the interface to generate the data structure, the program code when executed further causes the at least one processing platform to: classify at least a portion of the data representing the one or more previous interactions between the user and the information processing system into one or more domains executing at least one of the one or more machine learning models (Mancuso Figs. 1-10; [0050], [0052-0053]), derive one or more actions from at least a portion of the data representing the one or more previous interactions between the user and the information processing system, and to associate the one or more derived actions with the one or more domains to which the one or more derived actions correspond (Mancuso Figs. 1-10; [0019], [0034], [0062], [0050], [0066], [0075], [0078]) is considered within the broadest reasonable interpretation of the claimed limitations.
Similar arguments have been presented for claims 12 and 20 and thus, Applicant' s arguments are not persuasive for the same reasons.
Applicant states that the dependent claims recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding the independent claims. However, as discussed above, Mancuso in view of Heere is considered to teach the independent claims, and consequently, the dependent claims are rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Makhija (US 20250272652 A1) see Figs. 1-12 and [0118], [0135].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T REPSHER III whose telephone number is (571)272-7487. The examiner can normally be reached Monday - Friday, 8AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at (571) 272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN T REPSHER III/ Primary Examiner, Art Unit 2143
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