DETAILED ACTION
Status of the Application
The following is a non-Final Office Action. In response to Examiner's communication of October 21, 2025, Applicant, on January 20, 2026, amended claims 1, 6, 9, 14, & 17. Claims 1-20 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 20, 2026 has been entered.
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 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 "training a natural language processing model based on a time frame window for timestamps of storage records in a database of historical task completion; processing, using the natural language processing model, structured task data from labeled data in a tabular data form from a problem ticket database for feature extraction of tasks; processing, using the natural language processing model, unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging platforms for identifying task and task criteria; … and generating, by the computer, a list of ranked candidates for completing the plurality of tasks based on relevance scores" cannot be practically performed in the human mind and the claims do not recite any method of organizing human activity such a fundament economic concept or managing interactions between people. 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, natural language processing, database, and the functions performed by a computer, as recited in the argued limitations, the processing of the structured and unstructured data recited in the claims can be mentally for the reasons set forth below, even if these elements could not be performed mentally, 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,” “natural language processing model,” and “by the computer,” the remaining elements of the argued limitations of ““a time frame window for timestamps … of historical task completion; processing, … structured task data from labeled data in a tabular data form from a problem ticket … for feature extraction of tasks; processing … unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging … for identifying task and task criteria;” and “generating … a list of ranked candidates for completing the plurality of tasks based on relevance scores” recited in the claims can be mentally can be performed mentally by a human observing information regarding tasks, including structured historic records of tasks, such as text in paper tables or forms, and unstructured data, such as free form text in paper messages exchanged between parties collaborating on tasks, a human mentally evaluating the observed information regarding tasks and using judgment to identify features of tasks, tasks, and task criteria, and task leader, and a human performing an evaluation and comparison based on a relevancy score to generate ranked list of candidates manually and/or with a pen and paper, and thus, these argued features do indeed recite a mental process. Further, these elements of the argued limitations manage the human behavior and relationships of human communicating and collaborating on the tasks by identifying tasks that will be managed by the human task leader and completed by the selected human candidate; therefore, these limitations referred to by Applicant also recite a certain method of organizing human activity. With respect to the recitations of “messaging platforms,” “database,” “natural language processing model,” 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.
Unlike the claims at issue in Example 39, the claims do not recite an improvement in technology. “[A]n improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” MPEP 2106.05(a). Mere automation of manual processes is not an improvement to computer technology. Id. Thus, the mere recitations of the generic computer components of “messaging platforms,” “database,” “by the computer,” and “natural language processing model” to perform the recited abstract mental processes and certain method of organizing human activity is not an improvement to computer technology.
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).
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 2-20, recites “a time frame window for timestamps … of historical task completion; processing, … structured task data from labeled data in a tabular data form from a problem ticket … for feature extraction of tasks; processing … unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging … for identifying task and task criteria; based on the processing of the structured task data and the processing of the unstructured text data, identifying … a plurality of tasks requiring completion, a task leader among one or more participants of the collaborative message exchanges generating the plurality of tasks, and a task criteria; based on a semantic match between the task criteria and … historical task completion, identifying … a candidate pool for completing the plurality of tasks; determining … a likelihood of each candidate in the candidate pool completing the plurality of tasks; assigning a relevancy score based on the likelihood; and generating … a list of ranked candidates for completing the plurality of tasks based on relevance scores.” Claims 1-20, in view of the claim limitations, recite the abstract idea of assigning tasks to message participants by processing structured data regarding historic task completion and unstructured data in messages exchanged between participants for identifying tasks and task criteria, identifying tasks, a task leader among the participants, and a task criteria based on the processing of the data, identifying a candidate pool for completing the tasks based on a semantic match between the task and historical task completion, determining a likelihood of each candidate completing the plurality of tasks and assigning a relevancy score based on the likelihood, and generating a list of ranked candidates for the tasks based on the relevancy score.
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 processing structured data regarding historic task completion and unstructured data in messages exchanged between participants for identifying tasks and task criteria, identifying tasks, a task leader among the participants, and a task criteria based on the processing of the data, identifying a candidate pool for completing the tasks based on a semantic match between the task and historical task completion, determining a likelihood of each candidate completing the plurality of tasks and assigning a relevancy score based on the likelihood, and generating a list of ranked candidates for the tasks based on the relevancy score could all be reasonably interpreted as a human observing information regarding tasks, including structured historic records of tasks, such as text in paper tables or forms, and unstructured data, such as free form text in paper messages exchanged between parties collaborating on tasks, a human mentally evaluating the observed information regarding tasks and using judgment to identify features of tasks, tasks, task criteria, and task leader, 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 relevancy score for a candidate to complete the tasks, and a human performing an evaluation and comparison based on the relevancy score to generate ranked list of candidates 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 leaders completing 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 2-8, 10-16, & 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.
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 now moot in view of new grounds for rejection necessitated by Applicant’s amendments.
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. Claim 1, and similarly claims 2-20, recites “a time frame window for timestamps … of historical task completion; processing, … structured task data from labeled data in a tabular data form from a problem ticket … for feature extraction of tasks; processing … unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging … for identifying task and task criteria; based on the processing of the structured task data and the processing of the unstructured text data, identifying … a plurality of tasks requiring completion, a task leader among one or more participants of the collaborative message exchanges generating the plurality of tasks, and a task criteria; based on a semantic match between the task criteria and … historical task completion, identifying … a candidate pool for completing the plurality of tasks; determining … a likelihood of each candidate in the candidate pool completing the plurality of tasks; assigning a relevancy score based on the likelihood; and generating … a list of ranked candidates for completing the plurality of tasks based on relevance scores.” Claims 1-20, in view of the claim limitations, recite the abstract idea of assigning tasks to message participants by processing structured data regarding historic task completion and unstructured data in messages exchanged between participants for identifying tasks and task criteria, identifying tasks, a task leader among the participants, and a task criteria based on the processing of the data, identifying a candidate pool for completing the tasks based on a semantic match between the task and historical task completion, determining a likelihood of each candidate completing the plurality of tasks and assigning a relevancy score based on the likelihood, and generating a list of ranked candidates for the tasks based on the relevancy score.
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 processing structured data regarding historic task completion and unstructured data in messages exchanged between participants for identifying tasks and task criteria, identifying tasks, a task leader among the participants, and a task criteria based on the processing of the data, identifying a candidate pool for completing the tasks based on a semantic match between the task and historical task completion, determining a likelihood of each candidate completing the plurality of tasks and assigning a relevancy score based on the likelihood, and generating a list of ranked candidates for the tasks based on the relevancy score could all be reasonably interpreted as a human observing information regarding tasks, including structured historic records of tasks, such as text in paper tables or forms, and unstructured data, such as free form text in paper messages exchanged between parties collaborating on tasks, a human mentally evaluating the observed information regarding tasks and using judgment to identify features of tasks, tasks, task criteria, and task leader, 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 relevancy score for a candidate to complete the tasks, and a human performing an evaluation and comparison based on the relevancy score to generate ranked list of candidates 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 leaders completing 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 2-8, 10-16, & 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,” “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, “[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,” “training a natural language processing model,” “storage records in a database,” “using a natural language processing model,” “database,” and “platforms” 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,” “training a natural language processing model,” “storage records in a database,” “using a natural language processing model,” “database,” and “platforms” 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 2-8, 10-16, & 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 2-8, 10-16, & 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 1-20 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka, et al. (US 20230060753 A1), hereinafter Matsuoka, in view of Ranjan, et al. (US 20200380623 A1), hereinafter Ranjan, in further view of Latzina, et al. (US 20140258882 A1), hereinafter Latzina.
Regarding claim 1, Matsuoka discloses a method comprising ([0003]-[0005]):
training a natural language processing model ([0067], [0149], the task creation sub-system 302 may utilize historical task data from the task datastore 110 and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks. If the task creation sub-system 302 identifies one or more possible tasks that may be recommended to the member 118, the task creation sub-system 302 may present these possible tasks to the representative 106) based on a time frame window for timestamps of storage records in a database of historical task completion ([0219], natural language processors include a second layer that classifies the structure and semantic meaning according to an intent, task type, timestamp (e.g., date and/or time), [0089], the task coordination system 114 can monitor performance of the task by the representative 106 e.g., communications regarding the representative's performance of the task including messages from the representative 106 indicating any status updates with regard to timeframe for completion of the task, and the task coordination system 114 associates these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed);
processing, using the natural language processing model, … from … data form from a problem ticket database for feature extraction of tasks ([0067], [0149], the task creation sub-system 302 may utilize historical task data from the task datastore 110 and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks. If the task creation sub-system 302 identifies one or more possible tasks that may be recommended to the member 118, the task creation sub-system 302 may present these possible tasks to the representative 106, [0089], the task coordination system 114 can monitor performance of the task by the representative 106 e.g., communications regarding the representative's performance of the task including messages from the representative 106 indicating any status updates with regard to timeframe for completion of the task, and the task coordination system 114 associates these messages from the representative 106 with the data record in the task datastore 110 corresponding to the task being performed);
processing, using a natural language processing model, unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging platforms for identifying task and task criteria ([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, [0067], 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, [0102], the task recommendation system 112 may utilize NLP or other artificial intelligence to evaluate exchanged messages or other communications, e.g., the task recommendation system 112 may process any incoming messages from the member using NLP or other artificial intelligence);
based on the processing of the structured task data ([0099], if the member submits a task template corresponding to a task that is to be performed for the benefit to the member, the representative 106 assigned to the member may obtain and evaluate the completed task template to determine how best to perform the task for the benefit of the member) and the processing of the unstructured text data, identifying, by the computer, a plurality of tasks requiring completion, a task leader among one or more participants of the collaborative message exchanges generating the plurality of tasks, and a task criteria ([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, but are not limited to, topics, sentiments, complexities, and urgencies, and a topic can include, but is not limited to, a subject, a product, a service, a technical issue, a use question, a complaint, a purchase request, etc., 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, [0102], the task recommendation system 112 may utilize NLP or other artificial intelligence to evaluate exchanged messages or other communications from the member to identify possible tasks that may be recommended to the member or other issue that the member would like to have resolved, [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, [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 and 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);
based on a semantic match between the task criteria 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 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);
determining, by the computer, a likelihood of each candidate in the candidate pool completing the plurality of tasks;
assigning a relevancy score based on the likelihood ([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 relevance scores ([0043], 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);
While Matsuoka discloses all of the above, including processing, using the natural language processing model, … from … data form from a problem ticket database for feature extraction of tasks;
processing, using a natural language processing model, unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging platforms for identifying task and task criteria;
based on the processing of the structured task data and the processing of the unstructured text data, identifying, by the computer, a plurality of tasks requiring completion, a task leader among one or more participants of the collaborative message exchanges generating the plurality of tasks, and a task criteria (as above), Matsuoka does not expressly disclose the following remaining elements, which however, are taught by further teachings in Ranjan.
Ranjan teaches processing, using the natural language processing model, structured task data from labeled data in a tabular data form from a problem ticket database for feature extraction of tasks ([0156]-[0160], NLP module 1616 extract and identifies parameters including keywords, intents, entities, and contexts of text segments as parameters identified as matching a corresponding component in incident database 1624 for incident identifier 1618 to use to determine if a template or incident is satisfied; to determine the unrecognized entity referenced in a text segment, NLP module 1616 may compare the letters of the word associated with the identified unrecognized entity to the incident data (including the BRICK data structure) in incident database 1624; NLP module 1616 extracts keywords from text segments by comparing the words of the text segment to a table of incident database 1624 storing a table indicating keywords that have been identified (e.g., labeled) as being associated with one or more incidents that have previously occurred or that have otherwise been labeled in incident database 1624, and responsive to identifying a match within the table, NLP module 1616 may determine the matched word is a keyword and is a parameter and identify or extract the keyword).
processing, using the natural language processing model, unstructured text data from data snapshots of collaborative message exchanges across multiple collaborative messaging platforms for identifying task and task criteria ([0155]-[0156], NLP module 1616 may be configured to identify entities, intents, keywords, and/or contexts of text segments provided to incident management system 1604 using natural language processing techniques, wherein inputs may be a transcription of one or more utterances made in a phone call and/or a description in an incident ticket received via an application, NLP module 1616 can identify or extract parameters from a transcription or an incident ticket comprising keywords, intents, entities, or contexts that NLP module 1616 identified as matching a corresponding component in incident database 1624, and the incident identifier 1618 may use the extracted parameters to determine if a template of an incident is satisfied and consequently the incident that is the subject of the text segment, [0165], NLP module 1616 may receive incident tickets through a graphical user interface that is displayed to a user reporting an incident to describe the incident and photos the user has of the incident, and the NLP module 1616 may parse the description of the incident to identify keywords, intents, entities, and/or contexts that are associated with incidents of incident database 1624);
based on the processing of the structured task data and the processing of the unstructured text data, identifying, by a computer, a plurality of tasks requiring completion ([0156], NLP module 1616 can identify or extract parameters from a transcription or an incident ticket comprising keywords, intents, entities, or contexts that NLP module 1616 identified as matching a corresponding component in incident database 1624, and the incident identifier 1618 may use the extracted parameters to determine if a template of an incident is satisfied and the incident that is the subject of the text segment), a task leader among one or more participants [based on] the collaborative message exchanges generating the plurality of tasks ([0175], entity matcher 1622 can be configured to identify second entities (e.g., a technician) that have the most experience with a type of incident that incident identifier 1618 identifies from text segments in phone call transcription and/or an incident ticket, wherein incident identifier 1618 can identify the type of incident and aspects of the incident (e.g., space, device, system, issue, etc.), and entity matcher 1622 can identify the second entity (e.g., a technician) that has the most experience with the identified type and aspects of the incident (e.g., that has fixed the most incidents of that type)), and a task criteria ([0156], NLP module 1616 can identify or extract parameters from a transcription or an incident ticket comprising keywords, intents, entities, or contexts that NLP module 1616 identified as matching a corresponding component in incident database 1624, and the incident identifier 1618 may use the extracted parameters to determine if a template of an incident is satisfied and the incident that is the subject of the text segment).
Matsuoka and Ranjan are analogous fields of invention because both address the problem of evaluating communication sessions to identify tasks to assign 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 Ranjan the ability to process structured task data and unstructured task data, using a natural language processing model, from data snapshots of collaborative message exchanges across the multiple collaborative messaging platforms for identifying task and task criteria, as taught by Ranjan, 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 processing structured task data and unstructured task data, using a natural language processing model, from data snapshots of collaborative message exchanges across the multiple collaborative messaging platforms for identifying task and task criteria. Further, it would have been obvious to one of ordinary skill in the art to have modified Matsuoka with the aforementioned teachings of Ranjan in order to produce the added benefit of helping users report issues and reduce delays. [0057].
Further, while Matsuoka discloses all of the above, including generating, by the computer, a list of ranked candidates for completing the plurality of tasks … based on relevance scores (as above), Matsuoka does not expressly disclose the following remaining elements, which however, are taught by further teachings in Latzina.
Latzina teaches generating, by the computer, a list of ranked candidates for completing the plurality of tasks based on relevance scores ([0037], CTM 210 may include decision aid generator 224 to provide information and guidance to manager 305 for changing the workflow tasks assignments, e.g., decision aid generator 224 may process information in organization databases and identify candidate task workers to whom Task A may be reassigned by manager 305, and decision aid generator 224 presents the candidate task workers and their attributes in a decision aid list (e.g., decision aid 410, FIG. 4) on user interface 222/display 15 to enable the manager to visually weigh different criteria for selecting a substitute or replacement task worker for Task A, e.g., decision aid 410 may include a shortlist of candidate task workers names (e.g., Tim Idle, Ian Burgess and Clara Hanson) and attributes such as skill or knowledge level and availability for Task A).
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 generate a list of ranked candidates for completing the plurality of tasks and presenting the generated list to the task leader, and 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 generating a list of ranked candidates for completing the plurality of tasks and presenting the generated list to the task leader, and 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, Ranjan, and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising: monitoring, by the computer, the plurality of tasks for the completion; responsive to the plurality of tasks being completed, receiving, by the computer a feedback on task completion; and responsive to receiving the feedback, updating, by the computer, the database of historical task completion ([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 3, the combined teachings of Matsuoka, Ranjan, and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising: aggregating, by the computer, 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).
Regarding claim 4, the combined teachings of Matsuoka, Ranjan, 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, Ranjan, 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, Ranjan, and Latzina teach the method of claim 1 (as above). Further, Matsuoka discloses further comprising:
receiving, by the computer, a selection … including at least one candidate from the list of ranked candidates for completing the plurality of tasks ([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
automatically notifying the at least one candidate of one or more tasks to be completed ([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, and further, 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, [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);
using, by the computer, a sliding window of sentiment analysis for monitoring a performance of the at least one 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).
While Matsuoka discloses all of the above, including receiving, by the computer, a selection … including at least one candidate from the list of ranked candidates for completing the plurality of tasks (as above), Matsuoka does not expressly disclose the following remaining elements, which however, are taught by further teachings in Latzina.
Latzina teaches receiving, by the computer, a selection from the task leader including at least one candidate from the list of ranked candidates for completing the plurality of tasks ([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 7, the combined teachings of Matsuoka, Ranjan, 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, Ranjan, 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 9-16, these claims are substantially similar to claims 1-8, and are, therefore, rejected on the same basis as claims 1-8. While claims 9-16 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 17-20, these claims are substantially similar to claims 1-3 & 5, and are, therefore, rejected on the same basis as claims 1-3 & 5. While claims 17-20 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].
Conclusion
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623