DETAILED ACTION
Remarks
Claims 1-20 have been examined and rejected. This Office action is responsive to the amendment filed on 05/11/2026, which has been entered in the above identified application.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, 12, and 20, the claims recite “wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model”. It is unclear whether “with the one or more machine learning models” is intended to modify the reporting. It is unclear whether the modules are integrated together with the one or more machine learning models. It is unclear whether the one or more machine learning models are used, by the data pipeline architecture, to perform the integration. For the purposes of examination, this limitation is interpreted as: wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules and one or more machine learning models, wherein the plurality of modules include are 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
Regarding claims 2-11 and 13-19, claims 2-11 and 13-19 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on an indefinite parent claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12, and 20
Step 1: Claims 1, 12 and 20 recite an apparatus, a method and a medium; therefore, they are directed to the statutory categories of a machine, a method, and a manufacture.
Step 2A Prong 1: The claims recite, inter alia:
generate a data structure comprising data, the data representing one or more previous interactions between the user and the information processing system; utilize the data structure to respond to one or more subsequent interactions between the user and the information processing system; Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating data representing interactions and using the data to respond to subsequent interactions, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of “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”, “A method comprising”, “A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing platform causes the at least one processing platform to”, “the at least one processing platform is further configured to”, “wherein generation of the data structure comprises executing one or more machine learning models”, and “wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). The claimed computer components are recited at a high level of generality and are merely invoked as tool to perform the abstract idea. The additional elements of “manage an interface between a user and an information processing system“ amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)). Even when viewed in combination, these additional element do not integrate the abstract idea into a practical application and the claims are thus directed to the abstract idea.
Step 2B: The claims do not contain significantly more than the judicial exception. “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”, “A method comprising”, “A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing platform causes the at least one processing platform to”, “the at least one processing platform is further configured to”, “wherein generation of the data structure comprises executing one or more machine learning models”, and “wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements of “manage an interface between a user and an information processing system” amounts to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”). Nothing in the claims provides significantly more than that abstract idea. As such, the claims are ineligible.
Claims 2-11 and 13-19
Step 1: Claims 2-11 and 13-19 recite an apparatus, a method and a medium; therefore, they are directed to the statutory categories of a machine, a method, and a manufacture.
Step 2: claims 2-11 and 13-19 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1, 12, and 20, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Claims 2 and 13 further recite the additional element of “wherein, when managing the interface, update the data structure based the one or more subsequent interactions between the user and the information processing system”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of updating a data structure, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “the at least one processing platform is further configured to” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 3 and 14 further recite the additional element of “when managing the interface to generate the data structure, 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”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of classifying data into domains, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “the at least one processing platform is further configured to” and “executing at least one of the one or more machine learning models” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 4 and 15 further recite the additional element of “when managing the interface to generate the data structure, 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”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of deriving actions and associating the actions to domains, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “the at least one processing platform is further configured to” and “using at least one of the one or more machine learning models” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 5 and 16 further recite the additional element of “wherein the one or more derived actions are associated with one or more rules”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of associating actions with rules, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Claims 6-8 and 17 further recite the additional element of “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”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of graphing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “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” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claims 9 and 18 further recite the additional element of “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: search the one or more user-specific context documents, generate one or more responses to the one or more subsequent interactions”. Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of searching data and generating responses, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. The additional elements of “the at least one processing platform is further configured to” and “executing at least another one of the one or more machine learning models” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The additional element of “cause presentation of the one or more responses on the interface to the user” amounts to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)).
Claims 10 and 19 further recite the additional elements of “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”. These elements amount to insignificant extra-solution activity in the form of mere data gathering and output (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”).
Claim 11 further recites the additional element of “wherein the information processing system comprises a digital commerce system”. These elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 and 12-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mancuso et al. (US 20240403366 A1, published 12/05/2024), hereinafter Mancuso.
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 data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which 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)
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 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 3, Mancuso teaches all the limitations of claim 1, further comprising:
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)
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 3. Consequently, claim 14 is rejected for the same reasons.
Regarding claim 4, Mancuso teaches all the limitations of claim 3, further comprising:
when managing the interface to generate the data structure, the at least one processing platform is further configured to 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)
Regarding claim 15, claim 15 contains substantially similar limitations to those found in claim 4. Consequently, claim 15 is rejected for the same reasons.
Regarding claim 5, Mancuso teaches all the limitations of claim 4, 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 claim 16, claim 16 contains substantially similar limitations to those found in claim 5. Consequently, claim 16 is rejected for the same reasons.
Regarding claim 9, Mancuso 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 claim 18, claim 18 contains substantially similar limitations to those found in claim 9. Consequently, claim 18 is rejected for the same reasons.
Regarding claim 10, Mancuso 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 claim 19, claim 19 contains substantially similar limitations to those found in claim 10. Consequently, claim 19 is rejected for the same reasons.
Regarding claim 17, Mancuso 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, claims 6-8 contain substantially similar limitations to those found in claim 17. Consequently, claims 6-8 are rejected for the same reasons.
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.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mancuso in view of Bulusu et al. (US 10853867 B1, published 12/01/2020), hereinafter Bulusu.
Regarding claim 11, Mancuso teaches all the limitations of claim 11. However, Mancuso 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. 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, 3, 6, 9, 10, 12, 14, and 17-20. The previous objections to claims 6 and 17 are respectfully withdrawn. The corrections to claims 1 and 20 to address the previous rejection under 35 U.S.C. 112(b) are approved; however, the claims stand rejected under 35 U.S.C. 112(b), as necessitated by the amendments.
Independent claims 1, 12 and 20 have been amended to recite "the interface comprises data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model." The applicant alleges that based on such claim amendments, which introduce further structural architecture into the claims to further clarify that the recited steps/operations cannot be performed as mental steps or pen and paper operations, it is therefore respectfully submitted that the §101 rejection is improper and should be withdrawn (see remarks pp. 8-9). Examiner respectfully disagrees.
As discussed in the rejection above, the claim is directed to an abstract idea that encompasses mental processes including evaluations or observations that are practically capable of being performed in the human mind with the assistance of pen and paper. The claim places no limits on how the generating and utilizing steps are performed. That is, nothing in the claim elements preclude the steps from practically being performed in the mind. As discussed above, the pipeline architecture limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Thus, the broadest reasonable interpretation of the steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04.
The Applicant further alleges that the Mancuso and Bulusu references, alone or in any combination, fail to disclose, teach or suggest at least such structural architecture and, thus, the § 102 and § 103 rejections are improper and should be withdrawn (see remarks p. 9). Examiner respectfully disagrees.
As discussed above, Mancuso is considered to teach an apparatus to 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], [0068-0070]); generate a data structure comprising data, the 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; [0049-0050], [0052-0053]); 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], [0062], [0064], [0066]); wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model (Mancuso Figs. 1-10; [0019-0028], [0041], [0050], [0058], [0086]).
Specifically Mancuso teaches a system that proactively assists a user with respect to a task, project, question, meeting, workflow, or other need of the user account ([0019]). Mancuso discloses the modules of the system are used for multiple tasks of workflow, automation, context data ingestion, proactive assistance, insight, and reporting, such as assisting 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 ([0020]). The system comprises a data pipeline architecture (see Figs. 1-3) that ingests content items from various sources (e.g., a database of files, an email application, a calendar, a messaging application, the Internet, etc.) ([0020]). The system uses a large language model to adaptively access and provide content items from a wide range of locations ([0022]), which enables the system to embed multiple external applications directly within a single user interface ([0023]). Mancuso further discloses the data pipeline architecture integrates a plurality of modules ([0025], [0086]) with a plurality of machine learning models including a large language model, a classification model, and a regression model ([0027-0028]).
Examiner notes the claims place no limitations on what the data pipeline architecture or modules must comprise. Thus, Mancuso’s disclosure of wherein the interface comprises a data pipeline architecture that is configured to integrate a plurality of modules for one or more of workflow, automation, context data ingestion, proactive assistance, insight, and reporting with the one or more machine learning models which include one or more of a large language model, a classification model, a regression model, and a reinforcement model (Mancuso Figs. 1-10; [0019-0028], [0041], [0050], [0058], [0086]) is considered to teach the limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Todirel (US 20220222047 A1) see Figs. 1-7 and [0005], [0049-0053].
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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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