DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of February 19, 2026, Applicant, on May 18, 2026, amended claims 1, 2, 6, 9, 10, 14, 17, & 18 and canceled claims 3 & 11. Claims 1, 2, 4-10, & 12-20 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action for being directed to an abstract idea. Therefore, these rejections for being directed to an abstract idea are maintained below.
Applicant's amendments render moot the 35 USC 103 rejections set forth in the previous action in view of new grounds for rejection necessitated by Applicant’s amendments. Therefore, the rejections have been updated and the new grounds for rejection necessitated by Applicant’s amendments are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections for being directed to an abstract idea have been fully considered, but they are not persuasive.
Applicant argues that the claims do not recite an abstract idea under Prong 1 of Step 2A because the emphasized portions of “aggregating, by a computer, a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform different from the first collaborative messaging platform, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel; capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks; … receiving, via the first user-interface, a selection of a first candidate from the list of ranked candidates; and sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed in a second user-interface in an inbox of the first collaborative messaging platform" cannot be categorized as mathematical concepts, methods of organizing human activity, or mental processes, and for example, the required elements of "aggregating” and "capturing " cannot be categorized as a mental process, since the vast quantity of data that would need to be parsed through cannot be practically performed in the human mind as a mental process. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
While, other than the messaging platforms, the user-interface, the database, and the functions being performed by a computer, as recited in the argued limitations, the aggregating of messages between users and candidates including tasks to be performed, capturing snapshots of messages including the tasks, receiving a selection of candidates, and sending a notification to a candidate of the tasks to be completed are mental processes and a certain method of organizing human activity because these limitations can be performed mentally and also manage personal human behavior for the reasons set forth below, even if these elements could not be performed mentally nor manage human behavior, that not would result in the claims failing to recite an abstract idea under Prong 1 of Step 2A since the standard for this prong of Step 2A set forth in the guidance requires a determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes) and there are several other limitations in the claims than those referred to by Applicant that also recite abstract ideas including mental processes that can be performed mentally and certain methods of organizing human activity by managing relationships between people and providing rules to follow to manage human behavior.
Specifically with respect to the argued features, aside from the recited generic computer components of “messaging platforms,” “database,” “user-interface,” and “by the computer,” the remaining elements of the argued limitations of “aggregating … a first plurality of collaborative messaging channels of the first collaborative messaging … and a second plurality of collaborative messaging channels on a second collaborative messaging … of a different format from the first collaborative messaging …, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel; capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks; … receiving … a selection of a first candidate from the list of ranked candidates; and sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed … in an inbox of the first collaborative messaging” recited in the claims can be mentally can be performed mentally by a human observing information regarding tasks, including aggregating and capturing messages exchanged between parties collaborating on tasks in different communication channels mentally and/or with a pen and paper, a human performing an evaluation and using judgment based on the observed and evaluated information to identify a candidate pool and determine a likelihood and list of candidates to complete the tasks, a human using judgment to select one of the list of candidates, and a human notifying selected candidate manually and/or with a pen and paper; therefore, these argued features do indeed recite a mental process. In addition, each of the above limitations provide instructions or rules to follow to manage the human behavior and relationships of candidates and people communicating regarding tasks based on the human behavior and relationships of candidates’ histories performing task and communicating with each other to identify tasks to be completed; thus, the claims, including the limitations referred to by Applicant, recite certain methods of organizing human activity.
Regarding Applicant’s assertion that elements of "aggregating” and "capturing " cannot be categorized as a mental process, since the vast quantity of data that would need to be parsed through cannot be practically performed in the human mind as a mental process, a human can observe and evaluate messages to aggregate and capture the messages as discussed above regardless of the quantity and the time it takes to do so. Further, as discussed above, aggregating and capturing the messages including tasks also manages human behavior of candidates performing tasks and humans communicating with candidates to communicate tasks to be performed, and thus, these elements are also directed to a certain method of organizing human activity. Simply performing the aggregating and capturing on a vast quantity of data more efficiently or quickly with a computer rather than manually is nothing more than the improved speed or efficiency inherent with applying the abstract idea on a computer, which does not transform an abstract idea into a patent eligible invention. MPEP 2106.05(f).
With respect to the recitations of “messaging platforms,” “database,” “user-interface,” and “by the computer,” these elements are additional elements beyond the recited abstract idea; however, these are nothing more than recitations of generic computer components applying the recited abstract idea, which is not sufficient to integrate an abstract idea into a practical application nor amount to significantly more than an abstract idea.
With respect to the remaining elements of the claims, these elements recite an abstract idea under Prong 1 of Step 2A because Claim 1, and similarly claims 2, 4-10, & 12-20, recites “receiving … in a first collaborative messaging channel of a first collaborative messaging …, a plurality of tasks to be completed; aggregating … a first plurality of collaborative messaging channels of the first collaborative messaging … and a second plurality of collaborative messaging channels on a second collaborative messaging … of a different format from the first collaborative messaging …, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel; capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks; storing, in … historical task completion, the plurality of snapshots that include the tasks related to the plurality of tasks; based on a semantic match between a task criteria for the plurality of tasks and … historical task completion, identifying, by the computer, a candidate pool with a plurality of candidates for completing the plurality of tasks, wherein the plurality of candidates participated in the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels; determining … a likelihood of each candidate in the candidate pool completing the plurality of tasks; generating … a list of ranked candidates for completing the plurality of tasks based on the likelihood of each candidate in the candidate pool completing the plurality of tasks; receiving … a selection of a first candidate from the list of ranked candidates; and sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed … in an inbox of the first collaborative messaging ….” Claims 1, 2, 4-10, & 12-20, in view of the claim limitations, recite the abstract idea of assigning tasks to message participants by receiving tasks to be completed in a first collaborative messaging channel, aggregating first and second different collaborative messaging channels, capturing snapshots of conversation data from the first and second collaborative messaging channels including tasks, storing the snapshots in historical task completion, identifying a candidate pool for completing the tasks based on a semantic match between the task and the stored historical task completion, determining a likelihood of each candidate completing the of tasks, generating a list of ranked candidates for the tasks based on the likelihood, receiving a selection of a candidate from the list of candidates to complete the tasks, and notifying the selected candidate of the tasks to be completed.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the receiving tasks to be completed in a first collaborative messaging channel, aggregating first and second different collaborative messaging channels, capturing snapshots of conversation data from the first and second collaborative messaging channels including tasks, storing the snapshots in historical task completion, identifying a candidate pool for completing the tasks based on a semantic match between the task and the stored historical task completion, determining a likelihood of each candidate completing the of tasks, generating a list of ranked candidates for the tasks based on the likelihood, receiving a selection of a candidate from the list of candidates to complete the tasks, and notifying the selected candidate of the tasks to be completed could all be reasonably interpreted as a human observing information regarding tasks, including receiving, aggregating, capturing, and storing paper messages exchanged between parties collaborating on tasks in different communication channels mentally and/or with a pen and paper, a human performing an evaluation and using judgment based on the observed and evaluated information to identify a candidate pool and determine a likelihood for a candidate to complete the tasks, a human performing an evaluation and comparison based on the likelihood to generate ranked list of candidates, a human using judgment to select one of the list of candidates, and a human notifying selected candidate manually and/or with a pen and paper; therefore, the claims recite mental processes. In addition, each of the above limitations provide instructions or rules to follow to manage the human behavior and relationships of candidates and people communicating regarding tasks based on the human behavior and relationships of candidates’ histories performing task and communicating with each other to identify tasks to be completed; thus, the claims recite certain methods of organizing human activity. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2, 4-8, 10, 12-16, & 18-20 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and the sales and marketing activity. Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
Applicant argues that even if the claims recite a judicial exception, which Applicant is again not conceding, the claims integrate the alleged judicial exception into a practical application since the claims recite an improvement to technology directed towards "improv[ing] the technical field of contextual collaboration and interaction” in the recited portions of “aggregating, by a computer, a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform different from the first collaborative messaging platform, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel; capturing a plurality off snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks; … receiving, via the first user-interface, a selection of a first candidate from the list of ranked candidates; and sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed in a second user-interface in an inbox of the first collaborative messaging platform.” Examiner respectfully disagrees.
As noted above, but for the limitations referred to by Applicant being performed “by a computer,” using messaging “platforms,” and via a “user interface”, the limitations referred to by Applicant recite mental processes and certain method of organizing human activity. However, mere automation of manual processes is not an improvement to computer technology. MPEP 2106.05(a). Further, the alleged improvement in “contextual collaboration and interaction” is directed to improving managing relationships and human behavior of people, and thus, is directed toward a certain method of human activity, which is an abstract idea. Yet, Examiner notes, “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” MPEP 2106.05(a). Thus, the mere recitations of the generic computer components of “messaging platforms,” “user-interface,” “by the computer,” to perform the recited abstract mental processes and certain method of organizing human activity is not an improvement to computer technology, but rather, these additional elements beyond the recited abstract idea amount to nothing more than generic computer components to apply the recited abstract idea.
As in the claims at issue in Electric Power Group, the present claims are not focused on a specific improvement in computers or any other technology, but instead on certain independently abstract ideas that simply invokes computers as tools to implement the abstract idea. Electric Power Group, LLC v. Alstom S.A., et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Under the second prong of Step 2A, the claims recite the additional elements beyond the recited abstract idea of “[a] method,” “training a natural language processing model,” “storage records in a database,” “using a natural language processing model,” “database,” “platforms,” and “by a computer” in claim 1, and similarly claims 9 and 17; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components.
Under Step 2B, as in Prong 2 of Step 2A above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0094]-[0095] (describing the present invention can be implemented by a computer readable program instructions provided to a processor of a general purpose computer). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself.
Response to Arguments - 35 USC § 103
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are moot and not persuasive.
Applicant submits that Matsuoka combined with Ranjan and Latzina does not disclose, teach, or suggest, “aggregating, by a computer, a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform of a different format from the first collaborative messaging platform, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel,” “capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks,” and “storing, in a database of historical task completion, the plurality of snapshots that include the tasks related to the plurality of tasks,” as required by Applicant's amended claim 1, and similarly claims 9 and 17. Examiner respectfully disagrees.
Contrary to Applicant’s assertion Matsuoka, et al. (US 20230060753 A1), hereinafter Matsuoka, discloses the argued features as follows.
Matsuoka discloses “aggregating, by a computer, a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform of a different format from the first collaborative messaging platform” in paragraphs [0047], wherein as a member 118 interacts with a representative 106 over a chat session or stream, messages exchanged over the chat session or stream may be recorded in the user datastore 108, [0089], the task coordination system 114 may monitor, in real-time, any communications between the representative 106 and the member 118 regarding the representative's performance of the task, and the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed. These messages, chat sessions, or streams aggregated by being recorded in a user datastore and stored in a database in association with the tasks being performed are “a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform of a different format from the first collaborative messaging platform” because Matsuoka discloses, in paragraphs [0061], the task facilitation service 102 may generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.) (i.e., first and second), corresponding to the task, the communications session corresponding to the task may be distinct from the chat session previously established between the member 118 and the representative, [0164], communications and/or interactions between the representative 106 and any of the members may include text (e.g., via alphanumeric text, etc.), audio, video, programming instructions (e.g., via a programming language code and/or the like). Moreover, Matsuoka discloses “wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel” in paragraphs [0052]-[0053], the representative assignment system 104 may establish a chat session or other communications session between the member 118 and the assigned representative to facilitate communications between the member 118 and representative 106, e.g., via an application provided by the task-facilitation service 102, a web portal provided by the task facilitation service 102, and/or the like, the member 118 may exchange messages with the assigned representative 106 over the chat session or other communication session.
In addition, similarly Matsuoka discloses “capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks;
storing, in a database of historical task completion, the plurality of snapshots that include the tasks related to the plurality of tasks” in paragraphs [0089], if the task is to be performed by the representative 106, the task coordination system 114 can monitor performance of the task by the representative 106, the task coordination system 114 may monitor, in real-time, any communications between the representative 106 and the member 118 regarding the representative's performance of the task, the communications may include messages from the representative 106 indicating any status updates with regard to performance of the task, any purchases or expenses incurred by the representative 106 in performing the task, the timeframe for completion of the task, and the like, and the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed, [0061], the task facilitation service 102 may generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.) (i.e., first and second), corresponding to the task between the member 118 and the representative, [0164], communications and/or interactions between the representative 106 and any of the members may include text (e.g., via alphanumeric text, etc.), audio, video, programming instructions (e.g., via a programming language code and/or the like).
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, 2, 4-10, & 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1, and similarly claims 2, 4-10, & 12-20, recites “receiving … in a first collaborative messaging channel of a first collaborative messaging …, a plurality of tasks to be completed; aggregating … a first plurality of collaborative messaging channels of the first collaborative messaging … and a second plurality of collaborative messaging channels on a second collaborative messaging … of a different format from the first collaborative messaging …, wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel; capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks; storing, in … historical task completion, the plurality of snapshots that include the tasks related to the plurality of tasks; based on a semantic match between a task criteria for the plurality of tasks and … historical task completion, identifying, by the computer, a candidate pool with a plurality of candidates for completing the plurality of tasks, wherein the plurality of candidates participated in the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels; determining … a likelihood of each candidate in the candidate pool completing the plurality of tasks; generating … a list of ranked candidates for completing the plurality of tasks based on the likelihood of each candidate in the candidate pool completing the plurality of tasks; receiving … a selection of a first candidate from the list of ranked candidates; and sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed … in an inbox of the first collaborative messaging ….” Claims 1, 2, 4-10, & 12-20, in view of the claim limitations, recite the abstract idea of assigning tasks to message participants by receiving tasks to be completed in a first collaborative messaging channel, aggregating first and second different collaborative messaging channels, capturing snapshots of conversation data from the first and second collaborative messaging channels including tasks, storing the snapshots in historical task completion, identifying a candidate pool for completing the tasks based on a semantic match between the task and the stored historical task completion, determining a likelihood of each candidate completing the of tasks, generating a list of ranked candidates for the tasks based on the likelihood, receiving a selection of a candidate from the list of candidates to complete the tasks, and notifying the selected candidate of the tasks to be completed.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the receiving tasks to be completed in a first collaborative messaging channel, aggregating first and second different collaborative messaging channels, capturing snapshots of conversation data from the first and second collaborative messaging channels including tasks, storing the snapshots in historical task completion, identifying a candidate pool for completing the tasks based on a semantic match between the task and the stored historical task completion, determining a likelihood of each candidate completing the of tasks, generating a list of ranked candidates for the tasks based on the likelihood, receiving a selection of a candidate from the list of candidates to complete the tasks, and notifying the selected candidate of the tasks to be completed could all be reasonably interpreted as a human observing information regarding tasks, including receiving, aggregating, capturing and storing paper messages exchanged between parties collaborating on tasks in different communication channels mentally and/or with a pen and paper, a human performing an evaluation and using judgment based on the observed and evaluated information to identify a candidate pool and determine a likelihood for a candidate to complete the tasks, a human performing an evaluation and comparison based on the likelihood to generate ranked list of candidates, a human using judgment to select one of the list of candidates, and a human notifying selected candidate manually and/or with a pen and paper; therefore, the claims recite mental processes. In addition, each of the above limitations provide instructions or rules to follow to manage the human behavior and relationships of candidates and people communicating regarding tasks based on the human behavior and relationships of candidates’ histories performing task and communicating with each other to identify tasks to be completed; thus, the claims recite certain methods of organizing human activity. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2, 4-8, 10, 12-16, & 18-20 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and the sales and marketing activity. Accordingly, since the claims recite a certain method of organizing human activity and mental processes, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] method,” via a “user interface,” “platform,” “by a computer,” and “database” in claim 1, “[a] computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising,” “by a computer,” and “database” in claim 9, “[a] computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising,” via a “user interface,” “platform,” “by a computer,” and “database” in claim 17; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 4-8, 10, 12-16, & 18-20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0094]-[0095] (describing the present invention can be implemented by a computer readable program instructions provided to a processor of a general purpose computer). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 4-8, 10, 12-16, & 18-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 2, 4-10, & 12-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-10, & 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka, et al. (US 20230060753 A1), hereinafter Matsuoka, in view of Latzina, et al. (US 20140258882 A1), hereinafter Latzina.
Regarding claim 1, Matsuoka discloses a method comprising ([0003]-[0005]):
receiving, via a first user-interface, in a first collaborative messaging channel of a first collaborative messaging platform, a plurality of tasks to be completed ([0052]-[0053], the member 118 can submit a message to the representative to indicate that the member 118 would like assistance with a particular task, and the representative may presented with the submitted message via an interface provided by the task-facilitation service 102, the representative assignment system 104 may establish a chat session or other communications session between the member 118 and the assigned representative to facilitate communications between the member 118 and representative 106, e.g., via an application provided by the task-facilitation service 102, a web portal provided by the task facilitation service 102, and/or the like, the member 118 may exchange messages with the assigned representative 106 over the chat session or other communication session);
aggregating, by a computer ([0047], as a member 118 interacts with a representative 106 over a chat session or stream, messages exchanged over the chat session or stream may be recorded in the user datastore 108, [0089], the task coordination system 114 may monitor, in real-time, any communications between the representative 106 and the member 118 regarding the representative's performance of the task, and the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed), a first plurality of collaborative messaging channels of the first collaborative messaging platform and a second plurality of collaborative messaging channels on a second collaborative messaging platform of a different format from the first collaborative messaging platform ([0061], the task facilitation service 102 may generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.) (i.e., first and second), corresponding to the task, the communications session corresponding to the task may be distinct from the chat session previously established between the member 118 and the representative, [0164], communications and/or interactions between the representative 106 and any of the members may include text (e.g., via alphanumeric text, etc.), audio, video, programming instructions (e.g., via a programming language code and/or the like)), wherein the first plurality of collaborative messaging channels includes the first collaborative messaging channel ([0052]-[0053], the representative assignment system 104 may establish a chat session or other communications session between the member 118 and the assigned representative to facilitate communications between the member 118 and representative 106, e.g., via an application provided by the task-facilitation service 102, a web portal provided by the task facilitation service 102, and/or the like, the member 118 may exchange messages with the assigned representative 106 over the chat session or other communication session);
capturing a plurality of snapshots of conversation data from the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels, wherein the plurality of snapshots include tasks related to the plurality of tasks;
storing, in a database of historical task completion, the plurality of snapshots that include the tasks related to the plurality of tasks ([0089], if the task is to be performed by the representative 106, the task coordination system 114 can monitor performance of the task by the representative 106, the task coordination system 114 may monitor, in real-time, any communications between the representative 106 and the member 118 regarding the representative's performance of the task, the communications may include messages from the representative 106 indicating any status updates with regard to performance of the task, any purchases or expenses incurred by the representative 106 in performing the task, the timeframe for completion of the task, and the like, and the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed, [0061], the task facilitation service 102 may generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.) (i.e., first and second), corresponding to the task between the member 118 and the representative, [0164], communications and/or interactions between the representative 106 and any of the members may include text (e.g., via alphanumeric text, etc.), audio, video, programming instructions (e.g., via a programming language code and/or the like));
based on a semantic match between a task criteria for the plurality of tasks and the database of historical task completion ([0067], an intent can be determined, for example, based on a semantic analysis of a communication (e.g., by identifying keywords, sentence structures, repeated words, punctuation characters, tone, and/or non-article words), and the intent may be used by the NLP algorithm or other artificial intelligence to identify possible tasks that may be recommended to the member 118, the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks, and if the task recommendation system 112 identifies one or more possible tasks that may be recommended to the member 118, the task recommendation system 112 may present these possible tasks to the representative 106, which may select tasks that can be shared with the member 118 over the chat session, [0211], [0219], the task specification 604 may include output from the machine-learning models 612, wherein the machine learning models 612 include the natural language processors may include a first layer that parses the input to derive a structure and semantic meaning of the input. The natural language processors may include a second layer that classifies the structure and semantic meaning according to a particular intent, interest, task type, category, location, timestamp (e.g., date and/or time), event), identifying, by the computer, a candidate pool with a plurality of candidates for completing the plurality of tasks ([0040], representatives 106 may be profiled based on various criteria, including (but not limited to) demographics and other identifying information, geographic location, experience in handling different categories of tasks, experience in communicating with different categories of members, and the like, wherein using the classification or clustering algorithm, the representative assignment system 104 may identify a set of representatives 106 that may be more likely to develop a positive, long-term relationship with the member 118 while addressing any tasks that may need to be addressed for the benefit of the member 118, [0205]-[0206], after a predetermined time interval and/or occurrence of one or more events, the matchmaking system 500 may reevaluate the matching of the member 118 to the representative 106, wherein events can include a quantity of tasks generated for the member 118, a quantity of task recommendations generated for the member 118, and the matchmaking system 500 may then execute to match the member 118 to a new representative from the set of available representatives), wherein the plurality of candidates participated in the first plurality of collaborative messaging channels and the second plurality of collaborative messaging channels ([0061], the task facilitation service 102 may generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.) (i.e., first and second), corresponding to the task between the member 118 and the representative, [0164], communications and/or interactions between the representative 106 and any of the members may include text (e.g., via alphanumeric text, etc.), audio, video, programming instructions (e.g., via a programming language code and/or the like), and [0089], the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed);
determining, by the computer, a likelihood of each candidate in the candidate pool completing the plurality of tasks ([0041]-[0042], once a set of representatives 106 has been identified that may be assigned to the member 118, the representative assignment system 104 may evaluate data corresponding to each representative of the set of representatives 106, e.g., the representative assignment system 104 may rank each representative of the set of representatives 106 according to degrees or vectors of similarity between the member's and representative's demographic information, similar background (e.g., attended university in the same city, are from the same hometown, share particular interests, etc.) geographic proximity to one another, and each factor may be weighted based on the impact of the factor on the creation of a positive, long-term relationship between members and representatives based on historical data corresponding to member interactions with representatives identifying correlations between different factors and the polarities of these interactions (e.g., positive, negative, etc.), the possible score may be multiplied by a weighting factor, and the scores determined for the various factors may be aggregated to obtain a composite score for each representative of the set of representatives 106, [0194], the matchmaking 516 may include one or more machine-learning models configured to predict a likelihood that a particular match of a member to a representative will result in an effective, positive connection (indicated by a matching quality metric)); and
generating, by the computer, a list of ranked candidates for completing the plurality of tasks based on the likelihood of each candidate in the candidate pool completing the plurality of tasks ([0041]-[0043], the representative assignment system 104 may rank each representative of the set of representatives 106 according to degrees or vectors of similarity between the member's and representative's demographic information, similar background (e.g., attended university in the same city, are from the same hometown, share particular interests, etc.) geographic proximity to one another, the representative assignment system 104 may use the ranking of the set of representatives 106 to select a representative that may be assigned to the member 118, wherein the representative assignment system 104 may select a highest ranked representative and determine the representative's availability to engage the member 118 in identifying and recommending tasks, coordinating resolution of tasks, and otherwise communicating with the member 118 to assure that their needs are addressed, and if the selected representative is unavailable (e.g., the representative is already engaged with one or more other members, etc.), the representative assignment system 104 may select another representative according to the aforementioned ranking, and this process may be repeated until a representative is identified from the set of representatives 106 that is available to engage the member 118);
receiving, …, a selection of a first candidate from the list of ranked candidates ([0044], the representative assignment system 104 may select a representative from the set of representatives 106 based on information associated with the availability of each representative, e.g., the representative assignment system 104 may automatically select the first available representative from the set of representatives 106. In some instances, the representative assignment system 104 may automatically select the first available representative that satisfies one or more criteria corresponding to the member's identifying information (e.g., a representative associated with a representative profile that best matches the member profile, etc.)); and
sending, based on the selection, a notification to the first candidate with the plurality of tasks to be completed, wherein the notification is displayed in a second user-interface in an inbox of the first collaborative messaging platform ([0052], once the representative assignment system 104 has assigned a particular representative to the member 118, the representative assignment system 104 notifies the member 118 and the particular representative of the pairing, the representative assignment system 104 may establish a chat session or other communications session between the member 118 and the assigned representative to facilitate communications between the member 118 and representative, e.g., via an application provided by the task-facilitation service 102 installed on the computing device 120, a web portal provided, and/or the like the member 118 may exchange messages with the assigned representative over the chat session or other communication session, and similarity, the representative may be provided with an interface through which the representative may exchange messages with the member 118, [0064], any actions taken by the representative on behalf of the member 118 for completion of the task may be recorded in an entry corresponding to the task in the task datastore 110).
While Matsuoka discloses all of the above, including receiving, …, a selection of a first candidate from the list of ranked candidates (as above), Matsuoka does not expressly disclose the following remaining elements, which however, are taught by further teachings in Latzina.
Latzina teaches receiving, via the first user-interface, a selection of a first candidate from the list of ranked candidates ([0038]-[0039], the manager may choose task worker Ian Burgess from decision aid 410 even though he is available for only 50% of the time because he has a higher level of knowledge (50%) about Task A than the other available candidate--Clara Hanson, and the display of decision aid 410 as a configurator component may be arranged so that the manager can select or indicate his or her choice of a particular candidate task worker to replace task worker 301, for example, by clicking on candidate icon or name listed in decision aid 410 with a mouse cursor or pointer).
Matsuoka and Latzina are analogous fields of invention because both address the problem of assigning tasks to workers. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Matsuoka the ability to receive a selection from the task leader including at least one candidate for completing the plurality of tasks, as taught by Latzina, 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 combination would produce the predictable results of receiving a selection from the task leader including at least one candidate for completing the plurality of tasks. Further, it would have been obvious to one of ordinary skill in the art to have modified Matsuoka with the aforementioned teachings of Latzina in order to produce the added benefit of aiding to help managers facilitate the assignment of tasks and select a suitable task worker. [0041]-[0042].
Regarding claim 2, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising: monitoring, by the computer, a plurality of message exchanges on at least the first collaborative messaging platform to determine when the plurality of tasks are completed by the first candidate; determining, based on the monitoring of the plurality of message exchanges on at least the first collaborative messaging platform, that the plurality of tasks are completed by the first candidate; receiving, by the computer, feedback on task completion; and responsive to receiving the feedback, updating, by the computer, the database of historical task completion ([0089], if the task is to be performed by the representative 106, the task coordination system 114 can monitor performance of the task by the representative 106, the task coordination system 114 may monitor, in real-time, any communications between the representative 106 and the member 118 regarding the representative's performance of the task, the communications may include messages from the representative 106 indicating any status updates with regard to performance of the task, any purchases or expenses incurred by the representative 106 in performing the task, the timeframe for completion of the task, and the like, and the task coordination system 114 may associate these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed, [0064], any actions taken by the representative on behalf of the member 118 for completion of the task may be recorded in an entry corresponding to the task in the task datastore 110, [0262], the task monitoring sub-system 704 may associate these status updates with a data record corresponding to the task being performed within the task datastore 110)
Regarding claim 4, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising: identifying, by the computer, a task ambiguity associated with one or more outstanding tasks not being assigned to a candidate in the candidate pool ([0040], the representative assignment system 104 may identify a set of representatives 106 that may be more likely to develop a positive, long-term relationship with the member 118 while addressing any tasks that may need to be addressed for the benefit of the member 118, [0067], if the task recommendation system 112 identifies one or more possible tasks that may be recommended to the member 118, the task recommendation system 112 may present these possible tasks to the representative 106, which may select tasks that can be shared with the member 118 over the chat session, and the task recommendation system 112 may assign a likelihood (e.g., a probability that the member 118 will select or approve the task) or rank to each task and, if the likelihood or rank is greater than a threshold, share the select the task to be shared with the member 118 (e.g., in the chat session or outside a chat session such as via push notifications, a task dashboard, and/or the like)).
Regarding claim 5, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses wherein determining the likelihood further comprises: computing, by the computer, a likelihood function from a weighted score using, at least in part, features associated with each candidate including a task relevancy ([0041]-[0043], once a set of representatives 106 has been identified that may be assigned to the member 118, the representative assignment system 104 may evaluate data corresponding to each representative of the set of representatives 106, e.g., the representative assignment system 104 may rank each representative of the set of representatives 106 according to degrees or vectors of similarity between the member's and representative's demographic information, similar background (e.g., attended university in the same city, are from the same hometown, share particular interests, etc.) geographic proximity to one another, and each factor may be weighted based on the impact of the factor on the creation of a positive, long-term relationship between members and representatives based on historical data corresponding to member interactions with representatives identifying correlations between different factors and the polarities of these interactions (e.g., positive, negative, etc.), the possible score may be multiplied by a weighting factor, and the scores determined for the various factors may be aggregated to obtain a composite score for each representative of the set of representatives 106), a working pace ([0117], the task recommendation system 112 may utilize computer vision, NLP, and/or other machine-learning algorithms or artificial intelligence to process user recordings 206 to identify possible tasks and parameters associated with these identified possible tasks, [0142], the parameters related to these tasks may specify a level of urgency for completion of these tasks (e.g., timing requirements, deadlines, date corresponding to upcoming events, etc.)), and an availability ([0043], the representative assignment system 104 may select a highest ranked representative and determine the representative's availability to engage the member 118 in identifying and recommending tasks).
Regarding claim 6, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising:
using, by the computer, a sliding window of sentiment analysis for monitoring a performance of the first candidate on a related task; and determining, by the computer, a sentiment analysis ([0067], the data collected from a member 118 over a chat session with the representative may be evaluated by the task recommendation system 112 to identify one or more tasks that may be presented to the member 118 for completion, the task recommendation system 112 may utilize natural language processing (NLP) or other artificial intelligence to evaluate received messages or other communications from the member 118 to identify an intent corresponding to an issue that a member 118 wants resolved, wherein intents can include sentiments, and if the task recommendation system 112 identifies one or more possible tasks that may be recommended to the member 118, the task recommendation system 112 may present these possible tasks to the representative 106, [0074], the task recommendation system 112 may process messages corresponding to tasks presented to the member 118 by the representative over the chat session, as well as any interactions with the task-specific interfaces corresponding to these tasks (e.g., any task-specific communications sessions, member creation of discussions related to particular tasks, etc.) to determine a polarity or sentiment corresponding to each task, and the task recommendation system 112 can use these responses to tasks recommended to the member 118 to further train or reinforce the machine-learning algorithm or artificial intelligence utilized to generate task recommendations) score across related tasks to rank candidates in the candidate pool for completing the plurality of tasks ([0041]-[0042], once a set of representatives 106 has been identified that may be assigned to the member 118, the representative assignment system 104 may evaluate each factor may be weighted based on the correlations between different factors and the polarities of these interactions (e.g., positive, negative, etc.), the possible score may be multiplied by a weighting factor, and the scores determined for the various factors may be aggregated to obtain a composite score for each representative of the set of representatives 106).
Regarding claim 7, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses wherein the task criteria is selected from the group consisting of an overall task description, a completion criteria, a deadline for the completion, and a potential manual owner proposal ([0142], the parameters related to these tasks may specify the nature of these tasks (e.g., gutter cleaning, installation of carbon monoxide detectors, party planning, etc.), a level of urgency for completion of these tasks (e.g., timing requirements, deadlines, date corresponding to upcoming events, etc.), any member preferences for completion of these tasks, and the like, [0094], the member can manually enter one or more tasks that the member would like to delegate to the representative 106 for performance).
Regarding claim 8, the combined teachings of Matsuoka and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses wherein the plurality of tasks are tagged with additional metadata including a task category to enable accurate candidate matches ([0040], to identify representatives that may be well-suited to interact and communicate with the member 118 in a productive manner, representatives 106 may be profiled based on various criteria, including (but not limited to) demographics and other identifying information, geographic location, experience in handling different categories of tasks, [0127], the task-facilitation service 102 utilizes a machine-learning algorithm or artificial intelligence to generate recommendations for the representative 106 regarding data fields that may be presented to the member in a proposal, e.g., the task-facilitation service 102 may use information corresponding to the task for which a proposal is being generated (e.g., a task type or category, etc.), [0218]-[0219], machine-learning models in the machine-learning models 612 to parse natural language input from the member to identify data corresponding to a possible task for the member includes a machine-learning model (e.g., another classifier, or the like) may be used to categorize the output as corresponding to a particular task, task type).
Regarding claims 9, 10, & 12-16, these claims are substantially similar to claims 1, 2, & 4-8, and are, therefore, rejected on the same basis as claims 1, 2, & 4-8. While claims 9, 10, & 12-16 is directed to a system comprising program instructions stored on computer-readable storage media to cause a processor set to perform, Matsuoka discloses a system, as claimed. [0003]-[0005].
Regarding claims 17, 18, & 20, these claims are substantially similar to claims 1, 2, & 5, and are, therefore, rejected on the same basis as claims 1, 2, & 5. While claims 17, 18, & 20 is directed to a computer program product comprising program instructions stored on computer-readable storage media to perform operations, Matsuoka discloses a computer program product, as claimed. [0003]-[0005].
Regarding claim 19, the combined teachings of Matsuoka and Latzina teach the computer program product of claim 17 (as above). Further, Matsuoka discloses wherein the operations further comprise: aggregating, multiple channels and formats of information for capturing the data snapshots ([0061], the task facilitation service 102 may automatically generate a communications session (e.g., chat communication session such as via instant messaging or text messaging, audio-based communication sessions via a telephone or the like, a video-based communication session, etc.), corresponding to the task, and he task-specific communications session, the member 118 and the representative may exchange communications related to the particular task), the data snapshots comprising time-tagged data ([0123], the task-facilitation service 102, via the data model, can dictate that all published data is to include metadata that specifies the time of data generation, this may include, as metadata, a timestamp).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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