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 .
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.
Claim 14 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.
The term “high priority level” in claim 14 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purpose of examination, examiner interprets any priority as high priority level.
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. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method.
Step 2A, prong one: the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components:
“determining, (Mental processes- concept of observation and evaluation of collecting, identifying and organizing tasks which is known that it can be done in the mind or using pen and paper).
“generating, from the set of tasks performable by the user account, a task initialization prompt to provide to a large language model;”: (Mental processes- concept of observation and evaluation of generating prompts which is known that it can be done in the mind or using pen and paper).
“generating, Mental processes- concept of using prompts to obtain combined recommendation and presenting recommendations, which is known that it can be done in the mind or using pen and paper)
This falls within the mental process grouping of abstract ideas that can be performed in the human mind, or by a human with pencil and paper. Thus, Claim 1 recites an abstract idea.
Step 2A, prong two: the judicial exception is not integrated into a practical application. The claim includes the additional elements:
“utilizing connectors to collect data from software tools used by a user account of a content management system;” involves the mere gathering of data or transmitting data, which is insignificant extra-solution activity. See MPEP § 2106.05(g).
“utilizing the large language model to process the task initialization prompt”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“providing an indication of the composite action for display via the client device, wherein the indication is selectable to access the set of content items relevant to the set of tasks”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Therefore, the above additional elements do not integrate the judicial exception into a practical application.
Step 2B: The additional elements do not amount to significantly more because:
“utilizing connectors to collect data from software tools used by a user account of a content management system;” involves the mere gathering of data, which is well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II).
“utilizing the large language model to process the task initialization prompt”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“providing an indication of the composite action for display via the client device, wherein the indication is selectable to access the set of content items relevant to the set of tasks”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Regarding Claim 2,
Claim 2 is dependent on claim 1, “wherein utilizing the connectors to collect the data from the software tools used by the user account comprises collecting at least one of audio data from a video conference recording associated with the user account, text data from written communications associated with the user account, schedule data from a calendar associated with the user account, or project data from a project management application associated with the user account.”.
only includes additional elements directed to recitation of the mere gathering of data or transmitting data, which is insignificant extra-solution activity. See MPEP § 2106.05(g) wherein the mere gathering of data, which is well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II).
Regarding Claim 3,
Claim 3 is dependent on claim 1, “wherein generating the task initialization prompt to provide to the large language model comprises prioritizing the set of tasks performable by the user account by: determining, for each task within the set of tasks, a hierarchy of requesting user accounts of the content management system; determining due dates associated with each task within the set of tasks; determining, based on signals of a knowledge graph associated with the set of tasks, an importance metric for each task within the set of tasks; or determining a task creation date for each task within the set of tasks.”.
includes additional elements directed to additional mental processes of prioritization.
Regarding Claim 4,
Claim 4 is dependent on claim 3, “wherein generating the task initialization prompt to provide to the large language model further comprises comparing the set of tasks with related tasks performable by one or more additional user accounts of the content management system”.
includes additional elements directed to additional mental processes of comparing tasks with users.
Regarding Claim 5,
Claim 5 is dependent on claim 1, “wherein generating the composite action comprises: determining account-based signals between the user account and additional user accounts of the content management system; generating a knowledge graph comprising: nodes representing the user account and the additional user accounts; and edges representing relationships between the user account and the additional user accounts; and identifying, based on edge lengths of the edges, clusters of prioritized tasks performable by the user account.”.
includes additional elements directed to additional mental processes of using a graph as a tool to analyze relationships.
Regarding Claim 6,
Claim 6 is dependent on claim 1, “wherein providing the indication of the composite action for display via the client device comprises: utilizing the large language model to analyze the hybridized combination of the set of tasks; and generating a summary message comprising an explanation of the hybridized combination of the set of tasks.”.
only includes using LLM to generate a summery message, which is additional elements directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
Regarding Claim 7,
Claim 7 is dependent on claim 1, “receiving a notification intended for the user account; determining a priority level for the notification; and based on determining that the priority level exceeds a predetermined threshold priority, providing the notification for display via the client device.”.
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claim 8 is directed to system performing a process that has limitations similar to the limitations of claim 1. Thus, claim 8 is rejected with the same rationale applied against claim 1, as performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 8 remains subject matter ineligible.
Regarding Claim 9,
Claim 9 is dependent on claim 8, “wherein utilizing the connectors to collect the data from the software tools used by the user account comprises collecting at least one of audio data from a videoconference recording, text data from written communications, schedule data from a calendar, or project data from a project management application.”.
only includes additional elements directed to recitation of the mere gathering of data or transmitting data, which is insignificant extra-solution activity. See MPEP § 2106.05(g) wherein the mere gathering of data, which is well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II).
Regarding Claim 10,
Claim 10 is dependent on claim 8, “wherein generating the task initialization prompt to provide to the large language model comprises: comparing the set of tasks with related tasks performable by one or more additional user accounts of the content management system; and prioritizing the set of tasks performable by the user account utilizing one or more prioritization algorithms”.
includes additional elements directed to additional mental processes of comparing tasks with users.
Regarding Claim 11,
Claim 11 is dependent on claim 8, “wherein generating the composite action comprises: generating an index mapped from a knowledge graph of user accounts and content items of the content management system; and determining the content items relevant to the set of tasks based on relationships, shown in the index, between the set of tasks and a plurality of content items within the content management system”.
includes additional elements directed to additional mental processes of using a graph as a tool to analyze relationships.
Regarding Claim 12,
Claim 12 is dependent on claim 8, “wherein the instructions, when executed by the at least one processor, further cause the system to: determine a classification for the composite action; if the classification indicates that the composite action is a new composite action, provide relevant content items from the content management system for display via the client device; and if the classification indicates that the composite action is a continued composite action, generate a continuation summary based on previous tasks in the composite action and provide the continuation summary for display via the client device”.
includes additional elements directed to additional mental processes of classification.
Regarding Claim 13,
Claim 13 is dependent on claim 8, “provide, for display via the client device, the set of content items relevant to the set of tasks; and suppress at least one user interface element associated with a different composite action.”.
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed.
Regarding Claim 14,
Claim 14 is dependent on claim 8, “determine that a user interface element has a high priority level; and provide the user interface element for display via the client device”.
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed.
Claim 15 is directed to non-transitory computer-readable storage medium performing a process that has limitations similar to the limitations of claim 1. Thus, claim 15 is rejected with the same rationale applied against claim 1, as performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 15 remains subject matter ineligible.
Regarding Claim 16,
Claim 16 is dependent on claim 15, “wherein generating the composite action comprises: generating a knowledge graph of user accounts and content items of the content management system; and generating the composite action for the set of tasks based on relationships, represented in the knowledge graph, between the set of tasks and a plurality of content items within the content management system”.
includes additional elements directed to additional mental processes of using a graph as a tool to analyze relationships.
Regarding Claim 17,
Claim 17 is dependent on claim 15, “determine a classification for the composite action; if the classification indicates that the composite action is a new composite action, provide relevant content items from the content management system for display via the client device; and if the classification indicates that the composite action is a continued composite action, provide a continuation summary of previous tasks in the composite action for display via the client device.”.
includes additional elements directed to additional mental processes of classification.
Regarding Claim 18,
Claim 18 is dependent on claim 15, “receive a user interface element intended for the user account; determine a priority level for the user interface element; and based on determining that the priority level exceeds a predetermined threshold priority, provide the user interface element for display via the client device”.
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed.
Regarding Claim 19,
Claim 19 is dependent on claim 15, “determine that a composite action is complete; generate, for additional user accounts of the content management system, an update notification indicating a completion status for the composite action; and provide a selection option to the user account to publish the update notification to one or more client devices of the additional user accounts”.
additional elements directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
Regarding Claim 20,
Claim 20 is dependent on claim 15, “generate a suggested calendar item for a task of the composite action; and provide, via the client device, a selection option for the user account to accept, change, or reject the suggested calendar item”.
additional elements directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over VANGALA et al. (US 20240419918 A1) in view of Koukoumidis et al. (US 20230186618 A1).
Regarding claim 1.
VANGALA teaches a computer-implemented method comprising: determining, see ¶ 29, “the graph data source 110 and/or graph data processor 120 correspond to a user application or user service for the enterprise organization, where the user application facilitates the use and/or storage of data for, or about, entities of the enterprise organization. Example user applications include email applications (e.g., Microsoft Outlook), messaging applications (e.g., Microsoft Teams), social networks or professional networks (e.g., LinkedIn), search applications (e.g., Bing), document repository applications (e.g., Microsoft SharePoint), or other suitable applications and services. The user applications may be referred to as graph data generators in that they generate data about entities (e.g., documents, emails, contacts) and relationships among those entities.”, also see ¶ 59, “the system 400 further comprises an application 411, such as a user application (e.g., a document processing application such as Microsoft Word, an email client such as Microsoft Outlook, etc.), server-based application (e.g., Microsoft Exchange), or other suitable application.”);
generating, from the set of tasks performable by the user account, a task initialization prompt to provide to a large language model (see ¶ 21, “a data graph is used to provide an input to a neural network model, such as a large language model (LLM). A data graph input may be a data structure that represents a portion of the data graph or data that is generated based on a query result from the data graph, in various examples. The data graph inputs may be in the form of a prompt to the neural network model, a training set for the neural network model, or a combination thereof. By training the LLM with the data graph inputs, the LLM may be configured to access information from the data graph, for example, to identify relevant documents for a user, extract portions of the data graph for further processing, etc. Moreover, the LLM may be configured to process data based on prioritizations represented within the data graph.”, also see ¶ 22);
generating, utilizing the large language model to process the task initialization prompt, see ¶ 22, “the target user may request a meeting preparation summary for their next meeting, where relevant documents and emails for the meeting, along with an entity for the meeting itself, are represented in the data graph. A graph data query is generated with a large language model (LLM) using the natural language request as a first input to the LLM. The graph data query is based on the graph schema so that the graph data query may be performed against the data graph, resulting in a graph data output that represents a sub-portion of the data graph. The sub-portion of the data graph may include a node for an agenda of the meeting, nodes for users expected to attend the meeting, a node for recent documents discussed at a previous meeting, or other suitable node. The output data is generated with the LLM using the graph data output as a second input to the LLM. For example, the LLM may generate a summary of the agenda and recent documents using information from the sub-portion of the data graph.”, also see ¶ 64, “The text data 470 is provided to the LLM 468 (shown at 468B as a second instance of invoking the LLM 468), which generates output data 480 based on the text data 470. In some examples, the LLM 468 also receives entities from a content data store 465 (corresponding to content data store 165) with the text data 470 (or as part of a follow-up request by the LLM 468) to generate the output data 480. For example, the LLM 468B may generate a summary of changes to the agenda for the meeting, tasks that have been completed based on a comparison of the Agenda-2023.pptx and the Agenda-2022.pptx, etc.”);
and providing an indication of the composite action for display via the client device, wherein the indication is selectable to access the set of content items relevant to the set of tasks (see ¶ 66, “ FIG. 5 depicts an example of a graphical user interface 500 for providing output data from an LLM using graph data, according to an embodiment. Generally, the node processor 112 (or node processor 122, 162, 444) may be configured to identify nodes that are similar, related, or adjacent to a given node or to a search query. In some examples, the node processor 112 identifies the nodes based on a graph data query generated by the LLM, as described above. The graph data query may be generated in response to an NL request from a user or automatically based on a suitable trigger (e.g., opening a user interface menu item, receiving an email, saving a document) such as the application request 413, in various examples.”, also see ¶ 67, “FIG. 5, the graphical user interface 500 includes a meeting insights “tile” or pop-up for an email node corresponding to an emailed invite to a quarterly sprint status meeting. The graphical user interface 500 may include suggested e-mails 510, suggested files 520, and/or suggested users 530, for example, corresponding to the graph data output 448 and/or text data 470. The graphical user interface 500 also comprises a summary 540. In one example, the summary 540 corresponds to the output data 480 and includes a summary of the documents Agenda-2023.pptx and Agenda-2022.pptx generated by the LLM 468.”, also see ¶ 91, “The output device(s) 814 such as a display, speakers, a printer, etc. may also be included.”).
VANGALA do not specifically teach determining, utilizing connectors to collect data from software tools used by a user account of a content management system, a set of tasks performable by the user account using the software tools on a client device; generating, utilizing the large language model to process the task initialization prompt, a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks.
Koukoumidis teaches determining, utilizing connectors to collect data from software tools used by a user account of a content management system, a set of tasks performable by the user account using the software tools on a client device; generating, utilizing the large language model to process the task initialization prompt, a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks (see ¶ 72-73, “the multi-perspective response 430 may be based on one or more modalities. The one or more modalities may comprise one or more of text, audio, image, video, any suitable modality, or any combination thereof. As an example and not by way of limitation, the CU composer 270 may stitch text and some pictures together to generate a multi-perspective response 430. As another example and not by way of limitation, the CU composer 270 may generate a slide show as a multi-perspective response 430 based on multiple pictures. As another example and not by way of limitation, the CU composer 270 may generate a multi-perspective response 430 comprising a frame and a caption for a picture.”, also see ¶ 76, “generating a multi-perspective response 430. The method may begin at step 710, where the assistant system 140 may receive, from a client system 130 associated with a first user, a user query 405. At step 720, the assistant system 140 may determine, based on the user query 405, a plurality of dialog-intents 410, wherein each dialog-intent 410 is associated with a particular agent of a plurality of agents. At step 730, the assistant system 140 may execute, via the plurality of agents corresponding to the plurality of dialog-intents 410, a plurality of tasks corresponding to the user query 405. At step 740, the assistant system 140 may receive, from the plurality of agents, a plurality of execution results 420 corresponding to the plurality of tasks, respectively. At step 750, the assistant system 140 may select two or more of the plurality of execution results 420 for combination. At step 760, the assistant system 140 may generate, by a stitching model, a multi-perspective response 430 based on the selected execution results 425, wherein the multi-perspective response 430 comprises a natural-language response combining the selected execution results 425. In alternative embodiments, the multi-perspective response 430 may comprise a multi-modal response (e.g., natural language, pictures, videos, etc.) combining the selected execution results 425. At step 770, the assistant system 140 may send, to the client system 130 in response to the user query 405, instructions for presenting the multi-perspective response 430 to the first user.”, i.e. assistant executing multiple actions via software agents or tools then passing results to language model).
Both VANGALA and Koukoumidis pertain to the problem of language model knowledge graphs, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine VANGALA and Koukoumidis to teach the above limitations. The motivation for doing so would be “receiving a user query inputted on a head-mounted device from the head-mounted device, wherein the user query corresponds to multiple dialog-intents, executing multiple tasks corresponding to the multiple dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the multiple tasks, wherein the stitching model combines the two or more of the execution results based on natural language processing, and wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and sending instructions to the head-mounted device for presenting the multi-perspective response on the head-mounted device.” (see Koukoumidis abstract).
Regarding claim 2.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 1,
VANGALA further teaches wherein utilizing the connectors to collect the data from the software tools used by the user account comprises collecting at least one of audio data from a video conference recording associated with the user account, text data from written communications associated with the user account, schedule data from a calendar associated with the user account, or project data from a project management application associated with the user account (see ¶ 19, “Data graphs may be generated where nodes represent entities associated with an enterprise organization and edges between the nodes represent relationships among the entities. The nodes may represent entities such as users, documents, emails, meetings, and conversations, while the edges represent relationships, such as document authorship, document modification, document sharing, meeting invites, linked data between documents, email sending, email replying, etc. Data graphs often contain information that improves searches, predictions, recommendations, entity-entity lookups, clustering, and other processing scenarios, but efficient searching and consumption of data from a data graph encompassing an entire organization is challenging.”, also see ¶ 22, and 25).
Koukoumidis further teaches collecting at least one of audio data from a video conference recording associated with the user account, text data from written communications associated with the user account ( see ¶ 73, “the multi-perspective response 430 may be based on one or more modalities. The one or more modalities may comprise one or more of text, audio, image, video, any suitable modality, or any combination thereof.”, also see ¶ 86, “an object comprising audio data may be mapped to a vector based on features such as a spectral slope, a tonality coefficient, an audio spectrum centroid, an audio spectrum envelope, a Mel-frequency cepstrum, or any other suitable information. In particular embodiments, when an object has data that is either too large to be efficiently processed or comprises redundant data, a function π may map the object to a vector using a transformed reduced set of features (e.g., feature selection).”)
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 2.
Regarding claim 3.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 1,
VANGALA further teaches wherein generating the task initialization prompt to provide to the large language model comprises prioritizing the set of tasks performable by the user account by: determining, for each task within the set of tasks, a hierarchy of requesting user accounts of the content management system; determining due dates associated with each task within the set of tasks; determining, based on signals of a knowledge graph associated with the set of tasks, an importance metric for each task within the set of tasks; or determining a task creation date for each task within the set of tasks (see ¶ 42, “a prioritization prompt comprises syntax examples for the LLM 168 to extract weights for nodes of the data graph 200 based on edges of the nodes. The prioritization prompt may include examples on how to extract embeddings, metadata, or fields associated with nodes. In some examples, the prioritization prompt comprises examples calls to an API for the node processor 162 to rank or prioritize a group of nodes according to relevance to a user based on how recently a corresponding entity has been used or modified by the user (e.g., higher priority for more recent access), to whom an entity (e.g., document) was sent (e.g., higher priority for files sent to a manager than to an external contact), how many times an entity (e.g., user) was called, etc.”, also see ¶ 21).
Koukoumidis further teaches prioritizing the set of tasks performable by the user account by: determining, for each task within the set of tasks, a hierarchy of requesting user accounts of the content management system (see ¶ 72, “the multi-perspective response 430 in the following way. In particular embodiments, the CU composer 270 may determine an order of the selected execution results 425. The CU composer 270 may then combine, by the stitching model, the selected execution results 425 based on the determined order.”, also see ¶ 78, 82).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 3.
Regarding claim 4.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 3,
VANGALA further teaches wherein generating the task initialization prompt to provide to the large language model further comprises comparing the set of tasks with related tasks performable by one or more additional user accounts of the content management system (see ¶ 47, “The user node 265 may represent a fourth employee that has authored a message (message node 260), where the message was viewed by the first employee (user node 202). The fourth employee may also have shared the text document (text document node 270). Accordingly, the fourth employee (user node 265) has shared the text document (node 270) that was authored by the first employee (node 202). In some examples, the node processor 162 is configured to combine data graphs from different graph data stores (e.g., graph data stores 114, 124) into the data graph 200, combining the corresponding nodes and relationships and providing additional insights to the entities of the enterprise organization.”, also see ¶ 30).
Regarding claim 5.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 1,
VANGALA further teaches wherein generating the composite action comprises: determining account-based signals between the user account and additional user accounts of the content management system; generating a knowledge graph comprising: nodes representing the user account and the additional user accounts; and edges representing relationships between the user account and the additional user accounts; and identifying, based on edge lengths of the edges, clusters of prioritized tasks performable by the user account (see ¶ 42, “a prioritization prompt comprises syntax examples for the LLM 168 to extract weights for nodes of the data graph 200 based on edges of the nodes. The prioritization prompt may include examples on how to extract embeddings, metadata, or fields associated with nodes. In some examples, the prioritization prompt comprises examples calls to an API for the node processor 162 to rank or prioritize a group of nodes according to relevance to a user based on how recently a corresponding entity has been used or modified by the user (e.g., higher priority for more recent access), to whom an entity (e.g., document) was sent (e.g., higher priority for files sent to a manager than to an external contact), how many times an entity (e.g., user) was called, etc.”, also see ¶ 50, “embeddings are generated for data graphs where the embeddings represent semantics of entities within an enterprise organization. The embeddings may be implemented in a relatively low dimension vector space or feature space, for example, as a vector having ten, twenty, one hundred elements, or another suitable number of elements to allow for more efficient processing as compared to graph walks. Moreover, in contrast to systems that use embeddings based only on the content (e.g., text) for an entity, examples described herein utilize a node processor that generates embeddings based on content, relationships, and/or both content and relationships among the entities.”, also see ¶ 19).
Koukoumidis further teaches mapping objects into d-dimensional vectors (see ¶ 85, “an object or an n-gram may be represented in a d-dimensional vector space, where d denotes any suitable number of dimensions. Although the vector space 900 is illustrated as a three-dimensional space, this is for illustrative purposes only, as the vector space 900 may be of any suitable dimension.”, also see ¶ 87, “the social-networking system 160 may calculate a similarity metric of vectors in vector space 900. A similarity metric may be a cosine similarity, a Minkowski distance, a Mahalanobis distance, a Jaccard similarity coefficient, or any suitable similarity metric.”)
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 5.
Regarding claim 6.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 1,
VANGALA further teaches wherein providing the indication of the composite action for display via the client device comprises: utilizing the large language model to analyze the hybridized combination of the set of tasks; and generating a summary message comprising an explanation of the hybridized combination of the set of tasks (see ¶ 67, “shown in FIG. 5, the graphical user interface 500 includes a meeting insights “tile” or pop-up for an email node corresponding to an emailed invite to a quarterly sprint status meeting. The graphical user interface 500 may include suggested e-mails 510, suggested files 520, and/or suggested users 530, for example, corresponding to the graph data output 448 and/or text data 470. The graphical user interface 500 also comprises a summary 540. In one example, the summary 540 corresponds to the output data 480 and includes a summary of the documents Agenda-2023.pptx and Agenda-2022.pptx generated by the LLM 468. As described above, the summary 540 may be tailored to a particular user. In the example shown in FIG. 5, the summary 540 is generated by the LLM to focus more on software-related features (e.g., as might be relevant for a software developer) with only a small mention of budget at the end. In contrast, an alternate instance of the summary 540 for a project manager may provide further details on the budget, such as a remaining budget available, line items waiting to be approved before the meeting, etc.”, also see ¶ 21).
Koukoumidis further teaches utilizing the large language model to analyze the hybridized combination of the set of tasks; and generating a summary message comprising an explanation of the hybridized combination of the set of tasks (see ¶ 72 and ¶ 75, “the intent/rule-based QA mechanism may enable the assistant system 140 to classify questions into different categories based on predefined intents/rules and the agents may then provide answers to those questions according to their respective categories. In particular embodiments, the answer augmentation mechanism may function as a way to augment answers already generated by other agents by adding more text to make these answers more natural sounding and interesting to users. For example, the chitchat agent may handle the user query 405 based on the intent/rule-based QA mechanism with an execution result 420 of “I am doing great!” The assistant system 140 may further generate a multi-perspective response 430 as “I am doing great!” As another example and not by way of limitation, a user query 405 may be “what would you do if a meteorite hits the earth?” The chitchat agent may handle this query based on the unstructured open-ended QA mechanism with an execution result 420 of “I don’t even want to think about it.” The assistant system 140 may further generate a multi-perspective response 430 as “I don’t even want to think about it.” As can be seen, each of the aforementioned user queries 405 is handled by one agent. In particular embodiments, a user query 405 may be handled by multiple agents”)
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 6.
Regarding claim 7.
VANGALA and Koukoumidis teaches the computer-implemented method of claim 1,
VANGALA further teaches further comprising: receiving a notification intended for the user account; determining a priority level for the notification; and based on determining that the priority level exceeds a predetermined threshold priority, providing the notification for display via the client device (see ¶ 98, “The visual indicator 920 may be used to provide visual notifications, and/or an audio interface 974 may be used for producing audible notifications via an audio transducer (not shown). In the illustrated embodiment, the visual indicator 920 is a light emitting diode (LED) and the audio transducer may be a speaker. These devices may be directly coupled to the power supply 970 so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor 960 and other components might shut down for conserving battery power.”).
Koukoumidis further teach determining a priority level for the notification; and based on determining that the priority level exceeds a predetermined threshold priority (see ¶ 71, “information gain may be referred as relative entropy which is a measure of how one probability distribution is different from a second reference probability distribution. In particular embodiments, selecting the two or more of the plurality of execution results 420 for combination may comprise selecting two or more execution results 420 based on their respective information-gain values. As an example and not by way of limitation, the assistant system 140 may have determined to stitch the first and second execution results 420. A third execution result 420 may be also returned from an agent and the assistant system 140 may determine whether to stitch it into the multi-perspective response which already includes the first and second execution results 420. However, stitching the third execution result 420 into the multi-perspective response may lead to no information gain, e.g., the third execution result 420 is substantially redundant. As a result, the assistant system 140 may leave the third execution result 420 out of the multi-perspective response. Selecting execution results 420 based on information gain may be an effective solution for addressing the technical challenge of determining which execution results 420 to stitch together since information gain may well evaluate whether additionally stitched execution result(s) 420 can increase the informative cues for the multi-perspective response.”).
The motivation utilized in the combination of claim 1, super, applies equally as well to claim 7.
Claim 8 recites a system to perform the method recited in claim 1. Therefore the rejection of claim 1 above applies equally here. Koukoumidis also teaches the addition elements of claim 8 not recited in claim 1 comprising: at least one processor; and at least one non-transitory computer-readable storage medium (see figure 11, and ¶ 115).
Claim 9 recites a system to perform the method recited in claim 2. Therefore the rejection of claim 2 above applies equally here.
Regarding claim 10.
VANGALA and Koukoumidis teaches the system of claim 8,
VANGALA further teaches wherein generating the task initialization prompt to provide to the large language model comprises: comparing the set of tasks with related tasks performable by one or more additional user accounts of the content management system; and prioritizing the set of tasks performable by the user account utilizing one or more prioritization algorithms (see ¶ 42, “a prioritization prompt comprises syntax examples for the LLM 168 to extract weights for nodes of the data graph 200 based on edges of the nodes. The prioritization prompt may include examples on how to extract embeddings, metadata, or fields associated with nodes. In some examples, the prioritization prompt comprises examples calls to an API for the node processor 162 to rank or prioritize a group of nodes according to relevance to a user based on how recently a corresponding entity has been used or modified by the user (e.g., higher priority for more recent access), to whom an entity (e.g., document) was sent (e.g., higher priority for files sent to a manager than to an external contact), how many times an entity (e.g., user) was called, etc.”, also see ¶ 21).
Koukoumidis further teaches prioritizing the set of tasks performable by the user account utilizing one or more prioritization algorithms (see ¶ 72, “the multi-perspective response 430 in the following way. In particular embodiments, the CU composer 270 may determine an order of the selected execution results 425. The CU composer 270 may then combine, by the stitching model, the selected execution results 425 based on the determined order.”, also see ¶ 78, 82).
The motivation utilized in the combination of claim 8, super, applies equally as well to claim 10.
Regarding claim 11.
VANGALA and Koukoumidis teaches the system of claim 8,
VANGALA further teaches wherein generating the composite action comprises: generating an index mapped from a knowledge graph of user accounts and content items of the content management system; and determining the content items relevant to the set of tasks based on relationships, shown in the index, between the set of tasks and a plurality of content items within the content management system (see ¶ 42, “a prioritization prompt comprises syntax examples for the LLM 168 to extract weights for nodes of the data graph 200 based on edges of the nodes. The prioritization prompt may include examples on how to extract embeddings, metadata, or fields associated with nodes. In some examples, the prioritization prompt comprises examples calls to an API for the node processor 162 to rank or prioritize a group of nodes according to relevance to a user based on how recently a corresponding entity has been used or modified by the user (e.g., higher priority for more recent access), to whom an entity (e.g., document) was sent (e.g., higher priority for files sent to a manager than to an external contact), how many times an entity (e.g., user) was called, etc.”, also see ¶ 50, “embeddings are generated for data graphs where the embeddings represent semantics of entities within an enterprise organization. The embeddings may be implemented in a relatively low dimension vector space or feature space, for example, as a vector having ten, twenty, one hundred elements, or another suitable number of elements to allow for more efficient processing as compared to graph walks. Moreover, in contrast to systems that use embeddings based only on the content (e.g., text) for an entity, examples described herein utilize a node processor that generates embeddings based on content, relationships, and/or both content and relationships among the entities.”, also see ¶ 19).
Koukoumidis further teaches mapping objects into d-dimensional vectors (see ¶ 85, “an object or an n-gram may be represented in a d-dimensional vector space, where d denotes any suitable number of dimensions. Although the vector space 900 is illustrated as a three-dimensional space, this is for illustrative purposes only, as the vector space 900 may be of any suitable dimension.”, also see ¶ 87, “the social-networking system 160 may calculate a similarity metric of vectors in vector space 900. A similarity metric may be a cosine similarity, a Minkowski distance, a Mahalanobis distance, a Jaccard similarity coefficient, or any suitable similarity metric.”)
The motivation utilized in the combination of claim 8, super, applies equally as well to claim 11.
Regarding claim 12.
VANGALA and Koukoumidis teaches the system of claim 8,
VANGALA further teaches wherein the instructions, when executed by the at least one processor, further cause the system to: determine a classification for the composite action; if the classification indicates that the composite action is a new composite action, provide relevant content items from the content management system for display via the client device; and if the classification indicates that the composite action is a continued composite action, generate a continuation summary based on previous tasks in the composite action and provide the continuation summary for display via the client device tasks (see ¶ 67, “shown in FIG. 5, the graphical user interface 500 includes a meeting insights “tile” or pop-up for an email node corresponding to an emailed invite to a quarterly sprint status meeting. The graphical user interface 500 may include suggested e-mails 510, suggested files 520, and/or suggested users 530, for example, corresponding to the graph data output 448 and/or text data 470. The graphical user interface 500 also comprises a summary 540. In one example, the summary 540 corresponds to the output data 480 and includes a summary of the documents Agenda-2023.pptx and Agenda-2022.pptx generated by the LLM 468. As described above, the summary 540 may be tailored to a particular user. In the example shown in FIG. 5, the summary 540 is generated by the LLM to focus more on software-related features (e.g., as might be relevant for a software developer) with only a small mention of budget at the end. In contrast, an alternate instance of the summary 540 for a project manager may provide further details on the budget, such as a remaining budget available, line items waiting to be approved before the meeting, etc.”, also see ¶ 21, see ¶ 66, “ FIG. 5 depicts an example of a graphical user interface 500 for providing output data from an LLM using graph data, according to an embodiment. Generally, the node processor 112 (or node processor 122, 162, 444) may be configured to identify nodes that are similar, related, or adjacent to a given node or to a search query. In some examples, the node processor 112 identifies the nodes based on a graph data query generated by the LLM, as described above. The graph data query may be generated in response to an NL request from a user or automatically based on a suitable trigger (e.g., opening a user interface menu item, receiving an email, saving a document) such as the application request 413, in various examples.”, also see ¶ 91, “The output device(s) 814 such as a display, speakers, a printer, etc. may also be included.”).
Koukoumidis further teaches generate a continuation summary based on previous tasks in the composite action and provide the continuation summary (see ¶ 72 and ¶ 75, “the intent/rule-based QA mechanism may enable the assistant system 140 to classify questions into different categories based on predefined intents/rules and the agents may then provide answers to those questions according to their respective categories. In particular embodiments, the answer augmentation mechanism may function as a way to augment answers already generated by other agents by adding more text to make these answers more natural sounding and interesting to users. For example, the chitchat agent may handle the user query 405 based on the intent/rule-based QA mechanism with an execution result 420 of “I am doing great!” The assistant system 140 may further generate a multi-perspective response 430 as “I am doing great!” As another example and not by way of limitation, a user query 405 may be “what would you do if a meteorite hits the earth?” The chitchat agent may handle this query based on the unstructured open-ended QA mechanism with an execution result 420 of “I don’t even want to think about it.” The assistant system 140 may further generate a multi-perspective response 430 as “I don’t even want to think about it.” As can be seen, each of the aforementioned user queries 405 is handled by one agent. In particular embodiments, a user query 405 may be handled by multiple agents”)
The motivation utilized in the combination of claim 8, super, applies equally as well to claim 12.
Regarding claim 13.
VANGALA and Koukoumidis teaches the system of claim 8,
VANGALA further teaches wherein the instructions, when executed by the at least one processor, further cause the system to: provide, for display via the client device, the set of content items relevant to the set of tasks; and suppress at least one user interface element associated with a different composite action (see ¶ 66, “ FIG. 5 depicts an example of a graphical user interface 500 for providing output data from an LLM using graph data, according to an embodiment. Generally, the node processor 112 (or node processor 122, 162, 444) may be configured to identify nodes that are similar, related, or adjacent to a given node or to a search query. In some examples, the node processor 112 identifies the nodes based on a graph data query generated by the LLM, as described above. The graph data query may be generated in response to an NL request from a user or automatically based on a suitable trigger (e.g., opening a user interface menu item, receiving an email, saving a document) such as the application request 413, in various examples.”, also see ¶ 67, “FIG. 5, the graphical user interface 500 includes a meeting insights “tile” or pop-up for an email node corresponding to an emailed invite to a quarterly sprint status meeting. The graphical user interface 500 may include suggested e-mails 510, suggested files 520, and/or suggested users 530, for example, corresponding to the graph data output 448 and/or text data 470. The graphical user interface 500 also comprises a summary 540. In one example, the summary 540 corresponds to the output data 480 and includes a summary of the documents Agenda-2023.pptx and Agenda-2022.pptx generated by the LLM 468.”, also see ¶ 91, “The output device(s) 814 such as a display, speakers, a printer, etc. may also be included.”).
Regarding claim 14.
VANGALA and Koukoumidis teaches the system of claim 8,
VANGALA further teaches wherein the instructions, when executed by the at least one processor, further cause the system to: determine that a user interface element has a high priority level; and provide the user interface element for display via the client device (see ¶ 98, “The visual indicator 920 may be used to provide visual notifications, and/or an audio interface 974 may be used for producing audible notifications via an audio transducer (not shown). In the illustrated embodiment, the visual indicator 920 is a light emitting diode (LED) and the audio transducer may be a speaker. These devices may be directly coupled to the power supply 970 so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor 960 and other components might shut down for conserving battery power.”).
Koukoumidis further teach determine that a user interface element has a high priority level; and provide the user interface element for display via the client device. (see ¶ 71, “information gain may be referred as relative entropy which is a measure of how one probability distribution is different from a second reference probability distribution. In particular embodiments, selecting the two or more of the plurality of execution results 420 for combination may comprise selecting two or more execution results 420 based on their respective information-gain values. As an example and not by way of limitation, the assistant system 140 may have determined to stitch the first and second execution results 420. A third execution result 420 may be also returned from an agent and the assistant system 140 may determine whether to stitch it into the multi-perspective response which already includes the first and second execution results 420. However, stitching the third execution result 420 into the multi-perspective response may lead to no information gain, e.g., the third execution result 420 is substantially redundant. As a result, the assistant system 140 may leave the third execution result 420 out of the multi-perspective response. Selecting execution results 420 based on information gain may be an effective solution for addressing the technical challenge of determining which execution results 420 to stitch together since information gain may well evaluate whether additionally stitched execution result(s) 420 can increase the informative cues for the multi-perspective response.”).
The motivation utilized in the combination of claim 8, super, applies equally as well to claim 14.
Claim 15 recites a non-transitory computer-readable storage medium to perform the method recited in claim 1. Therefore the rejection of claim 1 above applies equally here. Koukoumidis also teaches the addition elements of claim 15 not recited in claim 1 comprising non-transitory computer-readable storage medium comprising instructions that, when executed by at least one processor (see figure 11, and ¶ 115).
Regarding claim 16.
VANGALA and Koukoumidis teaches the non-transitory computer-readable storage medium of claim 15,
VANGALA further teaches wherein generating the composite action comprises: generating a knowledge graph of user accounts and content items of the content management system; and generating the composite action for the set of tasks based on relationships, represented in the knowledge graph, between the set of tasks and a plurality of content items within the content management system account (see ¶ 42, “a prioritization prompt comprises syntax examples for the LLM 168 to extract weights for nodes of the data graph 200 based on edges of the nodes. The prioritization prompt may include examples on how to extract embeddings, metadata, or fields associated with nodes. In some examples, the prioritization prompt comprises examples calls to an API for the node processor 162 to rank or prioritize a group of nodes according to relevance to a user based on how recently a corresponding entity has been used or modified by the user (e.g., higher priority for more recent access), to whom an entity (e.g., document) was sent (e.g., higher priority for files sent to a manager than to an external contact), how many times an entity (e.g., user) was called, etc.”, also see ¶ 50, “embeddings are generated for data graphs where the embeddings represent semantics of entities within an enterprise organization. The embeddings may be implemented in a relatively low dimension vector space or feature space, for example, as a vector having ten, twenty, one hundred elements, or another suitable number of elements to allow for more efficient processing as compared to graph walks. Moreover, in contrast to systems that use embeddings based only on the content (e.g., text) for an entity, examples described herein utilize a node processor that generates embeddings based on content, relationships, and/or both content and relationships among the entities.”, also see ¶ 19).
Koukoumidis further teaches mapping objects into d-dimensional vectors (see ¶ 85, “an object or an n-gram may be represented in a d-dimensional vector space, where d denotes any suitable number of dimensions. Although the vector space 900 is illustrated as a three-dimensional space, this is for illustrative purposes only, as the vector space 900 may be of any suitable dimension.”, also see ¶ 87, “the social-networking system 160 may calculate a similarity metric of vectors in vector space 900. A similarity metric may be a cosine similarity, a Minkowski distance, a Mahalanobis distance, a Jaccard similarity coefficient, or any suitable similarity metric.”)
The motivation utilized in the combination of claim 15, super, applies equally as well to claim 16.
Claim 17 recites a non-transitory computer-readable storage medium to perform the system recited in claim 12. Therefore the rejection of claim 12 above applies equally here.
Claim 18 recites a non-transitory computer-readable storage medium to perform the method recited in claim 7. Therefore the rejection of claim 7 above applies equally here.
Regarding claim 19.
VANGALA and Koukoumidis teaches the non-transitory computer-readable storage medium of claim 15,
VANGALA further teaches wherein the instructions, when executed by the at least one processor, further cause the computing device to: determine that a composite action is complete; generate, for additional user accounts of the content management system, an update notification indicating a completion status for the composite action; and provide a selection option to the user account to publish the update notification to one or more client devices of the additional user accounts (see ¶ 30 and 59, “the system 400 further comprises an application 411, such as a user application (e.g., a document processing application such as Microsoft Word, an email client such as Microsoft Outlook, etc.), server-based application (e.g., Microsoft Exchange), or other suitable application. The application 411 provides an application request 413 to the LLM 468, where the application request 413 generally corresponds to the NL request 412, but is instead generated by the application 411. The application request 413 may correspond to a user context for the target user, or a task at hand for the target user, such as an upcoming meeting to prepare for, an upcoming task to be completed, as a follow-up notice after a meeting (e.g., a to-do list), or other suitable scenario. In some examples, the user interface 410 is omitted and only the application 411 is used to generate the output request.”).
Koukoumidis teaches provide a selection option to the user account (see ¶ 74 and 81, “a concept node 804 may represent a third-party web interface or resource hosted by a third-party system 170. The third-party web interface or resource may include, among other elements, content, a selectable or other icon, or other inter-actable object (which may be implemented, for example, in JavaScript, AJAX, or PHP codes) representing an action or activity. As an example and not by way of limitation, a third-party web interface may include a selectable icon such as “like,” “check-in,” “eat,” “recommend,” or another suitable action or activity. A user viewing the third-party web interface may perform an action by selecting one of the icons (e.g., “check-in”), causing a client system 130 to send to the social-networking system 160 a message indicating the user’s action.”)
The motivation utilized in the combination of claim 15, super, applies equally as well to claim 19.
Regarding claim 20.
VANGALA and Koukoumidis teaches the non-transitory computer-readable storage medium of claim 15,
VANGALA further teaches wherein the instructions, when executed by the at least one processor, further cause the computing device to: generate a suggested calendar item for a task of the composite action; and provide, via the client device, a selection option for the user account to accept, change, or reject the suggested calendar item (see ¶ 22, “the target user may request a meeting preparation summary for their next meeting, where relevant documents and emails for the meeting, along with an entity for the meeting itself, are represented in the data graph. A graph data query is generated with a large language model (LLM) using the natural language request as a first input to the LLM. The graph data query is based on the graph schema so that the graph data query may be performed against the data graph, resulting in a graph data output that represents a sub-portion of the data graph. The sub-portion of the data graph may include a node for an agenda of the meeting, nodes for users expected to attend the meeting, a node for recent documents discussed at a previous meeting, or other suitable node.” and 30, 48, 94, “The system 902 may also include an optional keypad 935 (analogous to keypad 935) and one or more peripheral device ports 930, such as input and/or output ports for audio, video, control signals, or other suitable signals.”).
Koukoumidis teaches provide a selection option to the user account (see ¶ 72 and 74, 82, “a first user may indicate that a second user is a “friend” of the first user. In response to this indication, the social-networking system 160 may send a “friend request” to the second user. If the second user confirms the “friend request,” the social-networking system 160 may create an edge 806 connecting the first user’s user node 802 to the second user’s user node 802 in the social graph 800 and store edge 806 as social-graph information in one or more of data stores 168.”)
The motivation utilized in the combination of claim 15, super, applies equally as well to claim 20.
Related prior arts:
George et al. (US 20210256047 A1) teaches a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.
MEYERZON et al. (US 20210110278 A1) teaches an entity record within the knowledge graph for a mined entity name based on an entity schema and the source documents. The entity record includes attributes aggregated from the source documents. The computer system receives a curation action on the entity record from a first user. The computer system updates the entity record based on the curation action. The computer system displays an entity page including at least a portion of the attributes to a second user based on permissions of the second user to view the source documents.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD M KASSIM whose telephone number is (571)272-2958. The examiner can normally be reached 10:30AM-5:30PM, M-F (E.S.T.).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J. Huntley can be reached at (303) 297 - 4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IMAD KASSIM/ Primary Examiner, Art Unit 2129
Prosecution Insights