DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Effective Priority
The effective priority of the instant application and claims is the filing date of provisional application #63/241,253 filed September 7, 2021.
Preliminary Amendment
All pending claims 2-22 filed August 19, 2025 are examined in this non-final office action.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over parent patent claims 1, 10 and 17 of Matsuoka et al., US 12,354,150.
Although the claims at issue are not identical, they are not patentably distinct from each other because the rejected claims achieve a non-distinct outcome using the same computing structures.
Rejected claim 2 (represents claims 9 & 16)
Matsuoka claim 1
A computer-implemented method comprising:
A computer-implemented method comprising:
receiving interaction data associated with a web browser executed on a user computing device associated with a user, wherein the interaction data includes one or more user interactions with a multimedia object associated with a website displayed on the web browser;
receiving website data for a website from a browser executed on a user computing device associated with a user;
extracting multimedia content based on the interaction data;
extracting multimedia content from the website data, wherein the multimedia content is extracted by performing a scraping or parsing operation on the website data;
processing the multimedia content to generate a task recommendation for a task of the user, wherein processing the multimedia content includes applying a task prediction model to the multimedia content to generate the task recommendation; and
processing the multimedia content to generate a task recommendation for a task of the user, wherein processing the multimedia content includes applying the task prediction model to the multimedia content to generate the task recommendation;
transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by the user computing device, the user computing device dynamically displays a graphical user interface element on the web browser, and
wherein the graphical user interface element includes an option whether to approve the task recommendation.
transmitting an indication corresponding to the task recommendation, wherein, when the indication is received by a computing device, the computing device is enabled to approve the task recommendation to generate a task corresponding to the task recommendation in a task facilitation service;
receiving interaction data indicating an approval or a rejection of the task recommendation;
35 USC § 101-Subject Matter Eligibility
Independent claim 2 (representing independent claims 9 & 16) reduces the processing load by managing the implementation of tasks and projects (e.g., a set of tasks that execute to implement a larger goal). The task facilitation service generates task recommendations that can be presented to the user for execution authorization.
Under Step 1, claim 2 is a process. Under Step 2A (first prong) “receiving interaction data …” and “extracting multimedia content …” are directed to a judicial exception such that the claim as a whole executes processes that, under its broadest reasonable interpretation, are directed to abstract ideas related to commercial interactions and therefore fall under the grouping of Certain Methods of Organizing Human Activity.
Under Step 2A (second prong) guidelines, the following provides additional elements that integrate the claim into a practical application:
“processing the multimedia content to generate a task recommendation for a task of the user, wherein processing the multimedia content includes applying a task prediction model to the multimedia content to generate the task recommendation;”
Independent claims 2, 9, 16 and respective dependents are subject matter eligible.
Closest US Patent/US Pre-Grant Publication
Rezaeian et al., US 2020/0125586 IDS filed May 6, 2025 and recited in parent patent US 12,354,150, is the closest prior art. Forward citations of Rezaeian failed to reveal closer prior art. Forward/backward citations of Rezaeian US 11,263,241 failed to reveal closer prior art. AI tools, More Like This Document (MLTD) and Similarity, failed to reveal closer prior art. Rezaeian alone or in combination with cited prior art fails to teach and/or suggest the methods as claimed.
Closest Non-Patent Literature
Myers et al., IDS filed May 6, 2025 Cite No. 46 and recited in parent patent US 12,354,150, is the closest non-patent literature. Myers discloses:
The role of the Task Manager is to organize, filter, prioritize, and oversee execution of tasks on behalf of the user. Tasks may be posed explicitly by the user or another PExA agent or adopted proactively by the Task Manager in anticipation of user needs. The Task Manager can both perform tasks itself and draw on other problem-solving entities (including the user) to support task execution.
The Task Manager is built on top of a Belief-Desire-Intention (BDI) agent framework called SPARK (SRI Procedural Agent Realization Kit) (Morley and Myers 2004). SPARK embraces a procedural reasoning model of problem solving, in the spirit of earlier agent systems such as PRS (Georgeff and Ingrand 1989) and RAPS (Firby 1994). Central to SPARK's operation is a body of process models that encode knowledge of how activities can be undertaken to achieve objectives. The process models are represented in a procedural language that is similar to the hierarchical task network (HTN) representations used in many practical AI planning systems (Erol, Hendler, and Nau 1994). However, the SPARK language extends standard HTN languages through its use of a rich set of task types (for example, achievement, performance, waiting) and advanced control constructs (for example, conditionals, iteration).
The Task Manager includes a library of process models (alternatively, plans or procedures) that provide a range of capabilities in the areas of visitor planning, meeting scheduling, expense reimbursement, and communication and coordination. The processes were designed primarily to automate capabilities but include explicit interaction points where the Task Manager solicits inputs from the user.
Myers alone or in combination with cited prior art fails to teach and/or suggest the methods as claimed.
Pertinent Prior Art
US 2017/0032275 (Lytkin et al.) “Entity Matching for Ingested Profile Data,” discloses:
[0016] Turning now to FIG. 1, a method 1000 of suggesting a member profile attribute to a member is shown according to some examples of the present disclosure. At operation 1010 the social networking service may ingest information from network-based data sources. Example network-based data sources include publication databases, patent databases, citation databases, university databases, library databases, or any other network-based source of data that may relate to a potential attribute of a member such as a member's professional achievement. The data sources may be preselected by an administrator. Information may be ingested by extraction engines which may be custom modules which are designed for each data source based upon the specific content and format of the network-based data source. The extraction engines may scrape a web page of the data source, such as by extracting information from Hyper Text Markup Language (HTML), an eXtensible Markup Language (XML), JavaScript, or other web components. For example, the extraction engines parse the HTML or XML describing a public user interface of the network-based data source looking for particular, predefined text and then extract that text. Text may be found based upon contextual cues (e.g., the page may say “Author: Ted Jones”) whereby the extraction engine searches for the particular contextual cues (e.g., the string “Author”) and then extracts the text that follows until another contextual cue (e.g., a period or other punctuation marking the end of the author list). Contextual cues may also be structural cues, such as particular markup tags or location based cues (e.g., the desired text may be at a particular location in the document).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 July 11, 2026
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