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 .
DETAILED ACTION
Claims 1-20 are pending and are considered in this Non-Final Office action.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/5/2025 is acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Continuation-in-Part
This application is a continuation-in-part (“CIP”) application of U.S. application no. 18/407,415 filed on 1/8/2024 (“Parent Application”). See MPEP §201.08. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al. (United States Patent Application Publication, 2023/0077130, hereinafter referred to as Matsuoka) in view of Wachell et al. (United States Patent Application Publication, 2022/0028001, hereinafter referred to as Wachell).
As per Claim 1, Matsuoka discloses a system for task scheduling and financial planning, comprising:
a computer system comprising at least one memory and at least one processor (Fig. 21); a machine learning engine comprising a first plurality of programming instructions stored in the at least one memory and executable by the at least one processor (Embodiment 706), wherein the first plurality of programming instructions, when executed, cause the computer system to:
generate scheduling optimization outputs based on temporal dependencies between scheduled events (Matsuoka: ¶0175: The machine learning model may output a scheduled task dependent on the data encompassed in the task specification including particular time intervals.); and
implement incremental learning techniques that preserve previously learned patterns while adapting to new user preferences derived from modifications to scheduled items (Matsuoka: ¶0243-0244 and 0248-0249: A machine learning model may be generated, trained and updated as new information, including new member preferences, is received. Scheduled tasks are then generated from the machine learning model.); and
a smart scheduling application comprising a second plurality of programming instructions stored in the at least one memory and executable by the at least one processor, wherein the second plurality of programming instructions, when executed, cause the computer system to: maintain user profile data in a datastore with end-to-end encryption and client-controlled access permissions (Matsuoka: See ¶0075 where the member profile is stored in the user datastore. See encryption and client-controlled access permissions in ¶0270. See embodiment 708 for task management engine or smart scheduling application. See ¶0260 for smart scheduling service providing calendaring application.);
receive scheduling data from a plurality of heterogeneous data sources including unstructured communications and documents (Matsuoka: ¶0259-0261 and 0270-0272: The application may request for data from a user to maintain and update their profile by receiving data from third-party applications with the task facilitation service. See ¶0270-0272 where external sources include finance accounts, market providers, etc. See ¶0247 where a router [data fusion suite] is used to transmit and receive data between the task facilitation service system and/or subsystems.);
receive the scheduling optimization outputs generated by the machine learning engine (Matsuoka: ¶0279-0280 and 0336: Optimized scheduling tasks are outputted by the machine learning model as recommendations.);
organize events, obligations, tasks, and notifications in a calendar-based schedule using the scheduling optimization outputs with compartmentalized project workspaces; and render the calendar-based schedule to a user device while maintaining comprehensive audit trails of all data access activities (Matsuoka: ¶0260-0262: Data related to tasks, events and obligations is organized and exported for entry in a calendar of a computing device where the member can interact with the calendar. See ¶0281 where real-time updates of user activities track a comprehensive trail of user tasks.).
Matsuoka does not explicitly disclose; Wachell discloses:
implement a recurrent neural network comprising feedback connections between layers, wherein the recurrent neural network maintains stateful information across multiple scheduling iterations to identify temporal patterns that stateless algorithms cannot detect (Wachell: ¶0200: In effort to characterize scheduled patterns and temporal patterns of user behavior data sets, a recurrent neural network (RNN) is implemented. See example of RNN implementation in ¶0341-0349.);
apply genetic or evolutionary programming to optimize neural network parameters through successive generations of models (Wachell: See ¶0336-0341 for the application of state evolution programming to input parameters through the recurrent neural network.);
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s machine learning implementation of identifying user behavior because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s machine learning implementation of identifying user behavior in Matsuoka would have served Matsuoka’s pursuit of implementing machine learning models based on user data, performance and tasks (See Matsuoka, ¶0244); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 2, Matsuoka in view of Wachell discloses the system of claim 1, wherein the smart scheduling application further comprises a data fusion suite configured to: format outgoing data requests to external sources according to heterogeneous protocols; normalize and aggregate incoming data for processing by the scheduling application (Matsuoka: Fig. 9 and ¶0277: In general, APis provide a connection between task facilitation service and external applications over which data may be exchanged. In addition to supporting methods and functions for facilitating such communication, APis may also support functionality that translates task and user data from a first format as used or stored by applications into a second format supported by task facilitation service.); process unstructured data sources including emails, text messages, and documents using optical character recognition and natural language processing to automatically extract scheduling-relevant information (Matsuoka: ¶0083 The task recommendation system utilizes natural language processing and semantic analysis to extract schedule related intent information on unstructured data sources such as chat, messages, etc.); and implement privacy protection mechanisms including data minimization protocols and automated retention policies to ensure compliance with applicable privacy regulations (Matsuoka: See ¶0320 for privacy functionality mechanisms in compliance with the task facilitation service.).
As per Claim 3, Matsuoka in view of Wachell discloses the system of claim 1, wherein the smart scheduling application is further configured to: detect scheduling conflicts and dependency chains among tasks; and visually render such conflicts and dependencies within the calendar-based schedule on the user device (Matsuoka: ¶0343-0345: The task facilitation service may determine a calendar that conflicts with dependent tasks that visually rendered on an electronic calendar.) …
Matsuoka does not explicitly disclose; however, Wachell discloses implement role-based access controls for multiple authorized stakeholders including financial advisors, attorneys, certified public accountants, and family members (Wachell: ¶0071: The service and operation layer provides a tool for team members in different roles, such as a financial advisor given access controls on their devices.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 4, Matsuoka in view of Wachell discloses the system of claim 1, wherein the machine learning engine is further configured to: receive user modifications to scheduled items from the smart scheduling application; and retrain the recurrent neural network using incremental updates based on the user modifications; and analyze multi-party collaboration patterns to optimize stakeholder coordination and communication effectiveness (Matsuoka: See ¶0344-0346 where user modifications to scheduled calendar items provide a feature vector for task recommendation. See ¶0279-0280 where the task recommendation system accesses one or more machine learning models configured to receive feature vector. See ¶0150 where the machine learning algorithm is retrained.).
As per Claim 5, Matsuoka in view of Wachell discloses the system of claim 1.
Matsuoka does not explicitly disclose; however, Wachell discloses further comprising a stakeholder management engine comprising a third plurality of programming instructions stored in the at least one memory and executable by the at least one processor, wherein the third plurality of programming instructions, when executed, cause the computer system to: implement comprehensive role-based access control systems for authorized parties including financial advisors, certified public accountants (CPAs), attorneys, estate planners, and family members involved in collaborative financial planning activities; maintain compartmentalized project workspaces that prevent cross-contamination of client information while enabling seamless collaboration among authorized stakeholders working on the same client engagement; provide real-time communication transcription services that convert multi-party discussions into searchable text records integrated into client project files with appropriate access controls (Wachell: See ¶0065 where various parties can access the collaborative client application through their API. See ¶0080-0084 where the intelligence and governance layer present visualization workspaces to a data user depending on their authorized role. See ¶0088-0089 where the system enables the API to ingest file resources into query-able representations.);
enable client oversight mechanisms that provide complete transparency into stakeholder collaboration activities while maintaining professional communication effectiveness (Wachell: ¶0098-0122: The software provides an overview of stakeholder collaboration and communication to provide insights and transparency.); and generate comprehensive audit trails of all stakeholder interactions, permission changes, and collaborative decisions for regulatory compliance and client accountability (Wachell: ¶0375-0376:The system creates a comprehensive auditable flow of insights for stakeholder interactions with inputs and outputs.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 6, Matsuoka in view of Wachell discloses the system of claim 1, wherein the smart scheduling application employs a transaction processing protocol configured to: permit concurrent schedule modifications from multiple sources; and maintain schedule integrity and temporal consistency across such modifications while preventing unauthorized cross-contamination of client information between different projects (Matsuoka: See ¶0343-0345 where the modification to calendar items can be concurrent to multiple users, where modifications can be recommended pursuant to authorized approval.).
As per Claim 7 Matsuoka in view of Wachell discloses the system of claim 1, wherein the smart scheduling application applies user-defined time budgeting constraints to scheduled items, the constraints being stored in the user profile data (Matsuoka: See ¶0075 where the task recommendation utilizes a machine learning algorithm to generate a schedule task according to budget constraints.) …
Matsuoka does not explicitly disclose; however, Wachell discloses … enforced during schedule generation and further implements automated compliance frameworks configured to meet regulatory requirement including GDPR, CPRA, and financial industry privacy standards (Wachell: ¶0379: Data compliance of client data is configured to be regulated by regimes such as GDPR and other privacy standards.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s processing of data for managing client data because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s processing of data for managing client data in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 8, Matsuoka in view of Wachell discloses the system of claim 1, further comprising a natural language processing interface configured to: receive user input in text or voice form describing desired schedule changes; convert the input into structured modifications to the calendar-based schedule; and provide real-time transcription services for multi-party communications with authorized stakeholders (Matsuoka: See ¶0183-0185 where a natural language processor my parse semantic meaning of voice and text corresponding to an intent of scheduled task, including the location, date and/or time location, etc. See ¶0283 where the users interacting with the system may be updated in real-time about modifications through a chat interface.).
As per Claim 9, Matsuoka in view of Wachell discloses the system of claim 1,
Matsuoka does not explicitly disclose; however, Wachell discloses wherein communications with external data sources and between authorized stakeholders are conducted over a secure communication channel comprising: an adaptive protocol selection mechanism for optimizing data transfer; an encrypted data pipeline configured to protect schedule and profile data; and compartmentalized communication channels that prevent unauthorized access to confidential client information (Wachell: See ¶0259-0265 where the system establishes privacy zones for protected user profile data through de-identification and encryption. See example of communication channels that maintain confidential client information in ¶0411.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 10, Matsuoka in view of Wachell discloses the system of claim 1, further comprising a health analysis engine comprising a fourth plurality of programming instructions stored in the at least one memory and executable by the at least one processor, wherein the fourth plurality of programming instructions, when executed, cause the computer system to: receive biometric data from one or more wearable devices associated with the user; and modify scheduling outputs to avoid task overload or excessive stress indicators based on the biometric data (Matsuoka: ¶0082-0083 and 0085: The task recommendation system may receive data from a member’s personal fitness or biometric devices to generate recommendations of a set of tasks to schedule for completion for the user. The data retrieved from member’s sources (i.e., fitness or biometric devices, etc.) used by machine learning or artificial intelligence algorithm to develop the recommendations for scheduling tasks.).
As per Claim 11, discloses Matsuoka discloses a method for task scheduling and financial planning, comprising the steps of:
generating scheduling optimization outputs based on temporal dependencies between scheduled events (Matsuoka: ¶0175: The machine learning model may output a scheduled task dependent on the data encompassed in the task specification including particular time intervals.); and
implementing incremental learning techniques that preserve previously learned patterns while adapting to new user preferences derived from modifications to scheduled items (Matsuoka: ¶0243-0244 and 0248-0249: A machine learning model may be generated, trained and updated as new information, including new member preferences, is received. Scheduled tasks are then generated from the machine learning model.); and
maintaining user profile data in a datastore with end-to-end encryption and client-controlled access permissions (Matsuoka: See ¶0075 where the member profile is stored in the user datastore. See encryption and client-controlled access permissions in ¶0270. See embodiment 708 for task management engine or smart scheduling application.);
receiving scheduling data from a plurality of heterogeneous data sources including unstructured communications and documents (Matsuoka: ¶0259-0261 and 0270-0272: The application may request for data from a user to maintain and update their profile by receiving data from third-party applications with the task facilitation service. See ¶0270-0272 where external sources include finance accounts, market providers, etc. See ¶0247 where a router [data fusion suite] is used to transmit and receive data between the task facilitation service system and/or subsystems.);
transmitting the scheduling optimization outputs generated by the machine learning engine to a smart scheduling application (Matsuoka: ¶0279-0280 and 0336: Optimized scheduling tasks are outputted by the machine learning model as recommendations. See ¶0260 for smart scheduling service providing calendaring application.);
organizing events, obligations, tasks, and notifications in a calendar-based schedule with compartmentalized project workspaces within the smart scheduling application based on the scheduling optimization outputs; and rendering the calendar-based schedule to a user device while maintaining comprehensive audit trails of all data access activities (Matsuoka: ¶0260-0262: Data related to tasks, events and obligations is organized and exported for entry in a calendar of a computing device where the member can interact with the calendar. See ¶0281 where real-time updates of user activities track a comprehensive trail of user tasks.).
Matsuoka does not explicitly disclose; Wachell discloses:
implement a recurrent neural network comprising feedback connections between layers, wherein the recurrent neural network maintains stateful information across multiple scheduling iterations to identify temporal patterns that stateless algorithms cannot detect (Wachell: ¶0200: In effort to characterize scheduled patterns and temporal patterns of user behavior data sets, a recurrent neural network (RNN) is implemented. See example of RNN implementation in ¶0341-0349.);
applying genetic or evolutionary programming to optimize neural network parameters through successive generations of models, wherein the recurrent neural network maintains stateful information across multiple scheduling iterations to identify temporal patterns that stateless algorithms cannot detect (Wachell: See ¶0336-0341 for the application of state evolution programming to input parameters through the recurrent neural network.)
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s machine learning implementation of identifying user behavior because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s machine learning implementation of identifying user behavior in Matsuoka would have served Matsuoka’s pursuit of implementing machine learning models based on user data, performance and tasks (See Matsuoka, ¶0244); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 12, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the step of: normalizing data received from the plurality of heterogeneous data sources and formatting outgoing data requests to external systems according to their respective communication protocols (Matsuoka: Fig. 9 and ¶0277: In general, APis provide a connection between task facilitation service and external applications over which data may be exchanged. In addition to supporting methods and functions for facilitating such communication, APis may also support functionality that translates task and user data from a first format as used or stored by applications into a second format supported by task facilitation service.); processing unstructured data using optical character recognition and natural language processing to automatically identify and extract scheduling-relevant information from emails, text messages, and documents (Matsuoka: ¶0083 The task recommendation system utilizes natural language processing and semantic analysis to extract schedule related intent information on unstructured data sources such as chat, messages, etc.); and
Matsuoka does not explicitly disclose; however, Wachell discloses implementing automated data retention and privacy protection policies with data minimization protocols to ensure compliance with applicable regulations including GDPR, CPRA, and financial industry standards (Wachell: ¶0379: Data compliance of client data is configured to be regulated by regimes such as GDPR and other privacy standards.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s processing of data for managing client data because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s processing of data for managing client data in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 13, Matsuoka in view of Wachell discloses the method of claim 11.
Matsuoka does not explicitly disclose; however, Wachell discloses further comprising the steps of: implementing comprehensive role-based access control for authorized stakeholders including financial advisors, certified public accountants, attorneys, estate planners, and family members involved in collaborative financial planning activities; maintaining compartmentalized project workspaces that prevent cross-contamination of client information while enabling seamless collaboration among authorized parties; providing real-time communication transcription services that convert multi-party discussions into searchable text records (Wachell: See ¶0065 where various parties can access the collaborative client application through their API. See ¶0080-0084 where the intelligence and governance layer present visualization workspaces to a data user depending on their authorized role. See ¶0088-0089 where the system enables the API to ingest file resources into query-able representations.); and enabling client oversight mechanisms that provide complete transparency into stakeholder collaboration activities (Wachell: ¶0098-0122: The software provides an overview of stakeholder collaboration and communication to provide insights and transparency.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 14, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the steps of: receiving user modifications to scheduled items via the smart scheduling application from multiple authorized stakeholders; updating the recurrent neural network based on the modifications to improve future scheduling outputs (Matsuoka: See ¶0344-0346 where user modifications to scheduled calendar items provide a feature vector for task recommendation. See ¶0279-0280 where the task recommendation system accesses one or more machine learning models configured to receive feature vector. See ¶0150 where the machine learning algorithm is retrained.);
Matsuoka does not explicitly disclose; however, Wachell discloses analyzing stakeholder collaboration patterns and communication effectiveness to optimize multi-party coordination; and generating comprehensive audit trails of all stakeholder interactions, permission changes, and collaborative decisions (Wachell: See ¶0098-0122: The software provides an overview of stakeholder collaboration and communication to provide insights and transparency.)¶0375-0376: The system creates a comprehensive auditable flow of insights for stakeholder interactions with inputs and outputs.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 15, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the steps of: detecting scheduling conflicts and task dependency chains among multiple authorized stakeholders; implementing automated conflict resolution protocols that prioritize changes based on predefined hierarchy rules and stakeholder authority levels; and rendering visual representations of conflicts and dependencies… (Matsuoka: ¶0343-0345: The task facilitation service may determine a calendar conflicts with dependent tasks that visually rendered on an electronic calendar. The task facilitation service may implement conflict resolution through modifications)
Matsuoka does not explicitly disclose; however, Wachell discloses rendering visual representations… within compartmentalized project workspaces accessible to authorized parties (Wachell: See ¶0065 where various parties can access the collaborative client application through their API. See ¶0080-0084 where the intelligence and governance layer present visualization workspaces to a data user depending on their authorized role. See ¶0088-0089 where the system enables the API to ingest file resources into query-able representations.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 16, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the step of: permitting concurrent modifications to the calendar-based schedule from authorized stakeholders and maintaining schedule consistency by applying transaction processing protocols that resolve conflicts, preserve temporal integrity, and prevent unauthorized cross-contamination of client information between different projects while enabling seamless multi-party collaboration (Matsuoka: See ¶0343-0345 where the modification to calendar items can be concurrent to multiple users, where modifications can be recommended pursuant to authorized approval.).
As per Claim 17, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the steps of: receiving time budgeting constraints from user profiles and applying the constraints to limit time allocation for scheduled items based on task categories and stakeholder-specific preferences (Matsuoka: See ¶0075 where the task recommendation utilizes a machine learning algorithm to generate a schedule task according to budget constraints.);… providing clients with dynamic control over data sharing permissions that can be modified or revoked in real-time; and maintaining end-to-end encryption for all multi-party communications and document sharing activities (Matsuoka: See ¶0075 where the member profile is stored in the user datastore. See encryption and client-controlled access permissions in ¶0270. See embodiment 708 for task management engine or smart scheduling application.).
Matsuoka does not explicitly disclose; however, Wachell discloses implementing automated compliance frameworks that enforce regulatory requirements including financial industry standards and privacy regulations; (Wachell: ¶0379: Data compliance of client data is configured to be regulated by regimes such as GDPR and other privacy standards.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s processing of data for managing client data because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s processing of data for managing client data in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 18, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the steps of: receiving natural language input describing desired schedule changes from multiple authorized stakeholders; parsing the input using natural language processing and modifying the calendar-based schedule based on parsed intent while respecting role-based access permissions; providing real-time transcription services for multi-party communications including conference calls, video meetings, and collaborative planning sessions; and integrating transcribed communications into appropriate client project files with proper access controls and audit trail documentation (Matsuoka: See ¶0183-0185 where a natural language processor my parse semantic meaning of voice and text corresponding to an intent of scheduled task, including the location, date and/or time location, etc. See ¶0283 where the users interacting with the system may be updated in real-time about modifications through a chat interface.).
As per Claim 19, Matsuoka in view of Wachell discloses the method of claim 11.
Matsuoka does not explicitly disclose; however, Wachell discloses wherein communications with external data sources and between authorized stakeholders are conducted over secure channels that include: an adaptive protocol selection mechanism for optimizing data transmission; encrypted data pipelines configured to prevent unauthorized access to schedule, profile, or project information; compartmentalized communication channels that maintain strict separation between different client projects; and automated breach detection and notification systems that alert relevant parties and regulatory authorities in the event of unauthorized data access (Wachell: See ¶0259-0265 where the system establishes privacy zones for protected user profile data through de-identification and encryption. See example of communication channels that maintain confidential client information in ¶0411. See ¶0458-0459 for alert system that sends notifications to the relevant parties for the necessary alert.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claim 20, Matsuoka in view of Wachell discloses the method of claim 11, further comprising the steps of: receiving biometric or physiological data from a wearable device associated with users; modifying the calendar-based schedule in response to health indicators while maintaining HIPAA-compliant data handling procedures (Matsuoka: ¶0085: Evaluation data is received from different member sources including biometric devices. This data is then used to identify and modify scheduled tasks based on external factors.);
Matsuoka does not explicitly disclose; however, Wachell discloses coordinating health-related scheduling adjustments with authorized healthcare providers and family members through secure, role-based communication channels; and integrating health analysis outcomes with multi-party stakeholder coordination to ensure collaborative planning activities consider user wellness factors and time management constraints (Wachell: See ¶0313-0314 where health analysis is integrated in the collaboration and optimization of activities and applied as constraint factors.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Matsuoka with Wachell’s authorization of stakeholder access and collaboration because the references are analogous/compatible since each is directed toward features for managing operational tasks collaborating with others, and because incorporating Wachell’s authorization of stakeholder access and collaboration in Matsuoka would have served Matsuoka’s pursuit of collaborating in real-time dynamically amongst user interaction in the system (See Matsuoka, ¶0249); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Neal et al. (US 2022/0358240): The present disclosure describes an adaptive data privacy platform that facilitates compliance with privacy laws and regulations, and compliance with organizational requirements within an organizational context. Other embodiments and implementations may be described and/or claimed.
Crabtree et al. (US 2025/0259042): A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.
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ALLISON MICHELLE NEAL
Examiner
Art Unit 3625
/ALLISON M NEAL/Primary Examiner, Art Unit 3625