DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The response was received on 3/13/2026. Claims 1-8 and 11-20 are pending where claims 1-8 and 11-20 were previously presented and claims 9 and 10 were cancelled.
Claim Objections
Claim 3 is objected to because of the following informalities: Claim 3 was amended to “generating an alarm to the user identifying in response to the detected outlier;” where the word “identifying” may need to be deleted like the other amendments since there is nothing immediately after the identifying indicating what is or isn’t being identified. Appropriate correction is required.
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 1-8 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to s without significantly more.
With regard to claim 1:
Step 2A, Prong One:
The claim recites the following limitations which are drawn towards an abstract idea:
A method, comprising: iteratively, with an interactive chatbot, until at least one change document is populated: generating, based on the least one change document and a query history comprised by a non-transitory computer-readable memory, a query, prompting a user with the query, obtaining, from the user, a response to the query, parsing the response to the query, comprising: transforming the response to an expected data type of the at least one change document; updating the at least one change document with the response; and determining, using a natural language processing technique, a user sentiment of the response (recites certain methods of organizing human activity associated with an assistant or consultant or other type of professional interacting with a user to fill out complicated or specialized forms, similar to a tax preparer interacting with a client to fill out their tax forms or a consultant or auditor interviewing/interacting with employees to fill out a form of compliance/evaluation and even recommendations),
determining, based on the at least one change document, one or more derived quantities (recites mental process steps of evaluating and determining data from analysis of observed/obtained data);
generating,
determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history (recites mental process steps of evaluating data and forming a decision/judgment including an order or sequence for tasks in a schedule as well as weight/priority and how long/duration the respective activities/tasks should occur for);
and determining a key performance indicator comprised by the change manage plan (recites mental process steps of evaluating data and forming a decision/judgment including a type of indicator to measure key performance of the respective tasks/activities of the plan);
tracking an implementation of the change management plan, comprising (recites mental process steps of evaluating data over a time period):
determining a key performance indicator score based on the value (recites mental process steps of evaluating information to determine a score/value);
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule (recites mental process steps of forming a decision/judgement/alarm that the observed/tracked data over time is not on schedule).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
and storing the user sentiment and respective query in the query history (recites insignificant extrasolution activity of storing information in memory, see MPEP 2106.05(g));
“…with an interactive chatbot…” (recites generic computer functionality such as software as a tool to implement the abstract idea, see MPEP 2106.05(f)),
“with an artificial intelligence (Al) engine” (recites the usage of generic computer elements at a high-level of functionality to indicate usage of a computer as a tool to implement the abstract idea, see MPEP 2106.05(f))
transmitting a learning resource to the user based on the change management plan (recites insignificant extrasolution activity of transmitting information, see MPEP 2106.05(g));
obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster (recites insignificant extrasolution activity of receiving information over a network, see MPEP 2106.05(g)),
As seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). This judicial exception is not integrated into a practical application because the additional elements recites generic computer elements at a high-level of generality such as software tools to perform various high-level functions such as storing information and receiving/transmitting information.
Step 2B:
Below is the analysis of the claims:
and storing the user sentiment and respective query in the query history (recites well-understood, routine, and conventional activity of storing information in memory, see MPEP 2106.05(g));
“…with an interactive chatbot…” (recites generic computer functionality such as software as a tool to implement the abstract idea, see MPEP 2106.05(f)),
“with an artificial intelligence (Al) engine” (recites the usage of generic computer elements at a high-level of functionality to indicate usage of a computer as a tool to implement the abstract idea, see MPEP 2106.05(f))
transmitting a learning resource to the user based on the change management plan (recites well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d));
obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster (recites well-understood, routine, and conventional activity of receiving information over a network, see MPEP 2106.05(d)),
As seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements recite generic computer elements at a high-level of generality such as software tools to perform various high-level functions such as storing information and receiving/transmitting information.
With regard to claim 2, this claim recites generating one or more pre-populated change documents wherein the pre-populated change documents are pre-populated using at least one response (recites creation of a document based on obtained data which amounts to using the abstract idea in the context of change documents and recites a result-oriented solution that recites mere instructions to apply an exception, see MPEP 2106.05(f));
transmitting the one or more pre-populated change documents to the user (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d));
and completing, through interaction of the chatbot and the user, the one or more pre-populated change documents (recites filling out a document which amounts to a result-oriented solution that recites mere instructions to apply an exception, see MPEP 2106.05(f)).
With regard to claim 3, this claim recites predicting, using the Al engine, a performance of the change management plan based on the key performance indicator score; detecting that the value for performing key performance indicator is an outlier using the Al engine; generating an alarm to the user identifying in response to the detected outlier (recites mental process steps of predicting data based on the obtained data variables of key performance indicators, evaluation steps to determine outliers, and forming a decision/judgement/alarm when the outlier is of concern; the “using the AI engine” limitation relates to apply it type limitations of using the computer as a tool to implement the abstract idea, see MPEP 2106.05(f));
performing a root cause analysis, by the Al engine, to determine the cause of the outlier; recommending an intervention to correct the outlier (recites mental process steps where particular mental process techniques, such as root cause analysis, can also be utilized to evaluate and determine the reason or root cause for the discrepancy/issue; the “using the AI engine” limitation relates to apply it type limitations of using the computer as a tool to implement the abstract idea, see MPEP 2106.05(f));
and generating a report detailing the root cause analysis (recites mental process steps of evaluating and determining the overall outcome of the analysis/judgement that was performed) and transmitting the report to the user (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 4, this claim recites comparing the derived quantities to documents in a knowledge database, wherein the change management plan is further based on a comparison of the derived quantities and the documents in the knowledge database (recites mental process steps of comparing/evaluating data between two data sets).
With regard to claim 5, this claim recites receiving a score for the change management plan, wherein the score is provided by one or more subject matter experts (recites insignificant extrasolution activity of receiving information which amounts to well-understood, routine, and conventional activity of receiving information, see MPEP 2106.05(d));
and updating the change management plan, with the Al engine, based on the score (recites updating of a document based on obtained data which is similar to using the abstract idea in the context of dynamic documents and recites a result-oriented solution that recites mere instructions to apply an exception, see MPEP 2106.05(f))).
With regard to claim 6, this claim recites determining a gap assessment of the user based on the change management plan; identifying another learning resource from a knowledge database based on the gap assessment (recites mental process steps of evaluating/analyzing skills needed and what is missing and be able to identify what resources can be used to help someone learn those skills);
transmitting another learning resources to the user (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d));
and administering a test to the user based on the gap assessment and another learning resource (recites method of organizing human activity associated with managing interactions between people including teach or administrating a test).
With regard to claim 7, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 8, this claim recites a resource server that transmits, at least, the change management plan to the user (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 11, this claim recites a skills center, wherein the skills center continually identifies, using the Al engine, gaps in one or more skills of the user (recites mental process steps of evaluating/analyzing skills needed and what is missing and be able to identify what resources can be used to help someone learn those skills)
and provides another learning resource to the user (recites insignificant extrasolution activity of transmitting information which amounts to well-understood, routine, and conventional activity of transmitting information, see MPEP 2106.05(d)).
With regard to claim 12, this claim recites wherein the computer system is further configured to detect that the value for the key performance indicator is an outlier and perform a root cause analysis to determine a cause of the outlier (recites mental process steps of predicting data based on the obtained data variables of key performance indicators, evaluation steps to determine outliers, and forming a decision/judgement/alarm when the outlier is of concern where particular mental process techniques, such as root cause analysis, can also be utilized to evaluate and determine the reason or root cause for the discrepancy/issue).
With regard to claim 13, this claim recites wherein further in response to the detection of the outlier the computer system is further configured to generate an alarm to the user comprising a recommendation to remedy the outlier (recites mental process steps of forming a decision/judgement/alarm that the observed/tracked data over time is not on schedule as well as decisions/judgements on how to correct it).
With regard to claim 14, this claim recites wherein the interactive chatbot comprises a graphical user interface (recites technological environment limitation describing the user interface as a graphical user interface, see MPEP 2106.05(h)).
With regard to claims 15-20, these claims are substantially similar to claims 1-6 respectively and are rejected for similar reasons as discussed above. The main difference between claims 1-6 and 15-20 is that claims 15-20 recites a non-transitory computer-readable memory (recites generic computer hardware as an apply-it type limitation to implement the abstract idea on a computer, see MPEP 2106.05(f)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4, 6-8, 11, 14-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al [US 2022/0292393 A1] (from IDS) in view of Matsuoka et al [US 2022/03432328 A1], Roy et al [US 2020/005801 A1], Vora et al [US 2020/0042515 A1], Zhang et al [US 2014/0067544 A1], Albero et al [US 2023/0306329 A1], and Celano et al [US 2022/0406207 A1].
With regard to claim 1, Srivastava teaches a method, comprising: iteratively, with an interactive chatbot, until at least one document is populated: generating, based on the least one change document computer-readable memory, query, prompting a user with the query, obtaining, from the user, a response to the query (see paragraphs [0045] and [0047]; the system has means to create a change document or initiative plan that comprises presenting information to the user and receiving a response/feedback from the user);
updating the at least one change document with the response (see paragraphs [0045] and [0047]; the system can receive the feedback from the user and be able to update/modify the document/plan accordingly);
determining, based on the at least one change document, one or more derived quantities (see paragraph [0028]; the system can utilize the received/obtained data to be able to derive additional data/quantities such as future state of the client);
generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities
Srivastava teaches a roadmap but does not appear to explicitly teach:
generating, based on the least one change document and a query history …, a query,
parsing the response to the query, comprising: transforming the response to an expected data type of the at least one change document; and determining, using a natural language processing technique, a user sentiment of the response,
and storing the user sentiment and respective query in the query history;
generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities and the query history, comprising: determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history,
and determining a key performance indicator comprised by the change management plan;
transmitting a learning resource to the user based on the change management plan;
tracking an implementation of the change management plan, comprising: obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster, and
determining a key performance indicator score based on the value;
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule.
Matsuoka teaches generating, based on the least one change document and a query history …, a query (see paragraph [0124]; analysis of previous conversations can be used to determine what information needs to be solicited from the user).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava by being able to keep track of user interactions with the system such as query/conversation history as taught by Matsuoka in order to be able to analyze the information to determine what information needs to be prompted/solicited from the user and what information the system needs but already has received previously thus reducing interactions with the user.
Srivastava in view of Matsuoka do not appear to explicitly teach:
parsing the response to the query, comprising: transforming the response to an expected data type of the at least one change document; and determining, using a natural language processing technique, a user sentiment of the response,
and storing the user sentiment and respective query in the query history;
generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities and the query history, comprising: determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history,
and determining a key performance indicator comprised by the change management plan;
transmitting a learning resource to the user based on the change management plan;
tracking an implementation of the change management plan, comprising: obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster, and
determining a key performance indicator score based on the value;
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule.
Roy teaches parsing the response to the query, comprising: transforming the response to an expected data type of the at least one change document (see paragraphs [0021]-[0025]; the system has means to analyze/parse input and be able to map/transform the data into the appropriate fields of the document automatically).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka by providing means to parse and map user input automatically to various different document fields as taught by Roy in order to utilize machine learning means to dynamically map/translate input to the particular fields and data types without manual intervention thus helping the robustness of the system to handle various different formats of input including in different orders while still allowing for correct determining and mapping of that input to the appropriate field versus a static template approach that limits how input is received by users.
Srivastava in view of Matsuoka and Roy teach sentiment score associated with the document (see paragraph [0023] of Srivastava) but do not appear to explicitly teach:
determining, using a natural language processing technique, a user sentiment of the response,
and storing the user sentiment and respective query in the query history;
generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities and the query history, comprising: determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history,
and determining a key performance indicator comprised by the change management plan;
transmitting a learning resource to the user based on the change management plan;
tracking an implementation of the change management plan, comprising: obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster, and
determining a key performance indicator score based on the value;
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule.
Vora teaches determining, using a natural language processing technique, a user sentiment of the response (see paragraph [0034]; the system can determine the sentiment of the user).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka and Roy by using natural language processing means to analyze and determine sentiment of the user as taught by Vora in order to allow the system to be able to determine how the user of the system is feeling with regards to their interaction and be able to take additional actions if necessary to try to improve user sentiment.
Srivastava in view of Matsuoka, Roy, and Vora teach storing the user sentiment and respective query in the query history (see Vora, paragraph [0034]; Srivastava, paragraph [0023]; see Matsuoka, paragraph [0124]; the system can store the determined user sentiment as well as the chat/conversation/query history for later usage/analysis).
Srivastava in view of Matsuoka, Roy, and Vora do not appear to explicitly teach:
generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities and the query history, comprising: determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history,
and determining a key performance indicator comprised by the change management plan;
transmitting a learning resource to the user based on the change management plan;
tracking an implementation of the change management plan, comprising: obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster, and
determining a key performance indicator score based on the value;
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule.
Zhang teaches determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan (see paragraphs [0016] and [0039]; the system can determine various pieces of information for activities including sequence or ordering of activities; weight/importance of a task/activity; and duration of a task).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka, Roy, and Vora by being able to utilize various pieces of information of tasks such as their ordering/sequence, importance, and duration to plan a schedule of activities for the initiative/project as taught by Zhang in order to allow the system’s roadmap to provide a schedule of activities that including a sequence in which activities need to occur to help reduce any scheduling conflicts that can occur later if attempting to work on a task that can’t be completed until another task is performed first.
Srivastava in view of Matsuoka, Roy, Vora, and Zhang generating, with an artificial intelligence (Al) engine, a change management plan based on the derived quantities and the query history, comprising: determining a sequence, weight, and duration for each task of at least one task forming a schedule comprised by the change management plan, wherein the sequence, weight, and duration for each task is based on at least one user sentiment comprised by the query history (see Zhang, paragraphs [0016] and [0039]; see Vora, paragraph [0034]; Srivastava, paragraphs [0023] and [0038]; see Matsuoka, paragraph [0124]; the system can store the determined user sentiment as well as the chat/conversation/query history for later usage/analysis and be able to determine/adjust the schedule or plan of activities based on various factors including determined/predicted user sentiment of the task(s)),
and determining a key performance indicator comprised by the change management plan (see Srivastava, paragraph [0052]; the system can determine KPI for the respective plans);
tracking an implementation of the change management plan (see Zhang, paragraph [0039]; see Srivastava, paragraphs [0046]-[0047]; the system can keep track of the various scheduled tasks/activities of the initiative/change management plan), comprising: obtaining, over a network and in real time, a value for the key performance indicator from at least one field device of an internet of things cluster, and determining a key performance indicator score based on the value (see Srivastava, paragraphs [0027], [0037], [0074], [0076]-[0078], [0084], and [0086]; see Zhang, paragraphs [0016] and [0053]; the system may comprise multiple devices that communicate and work together to gather data in order to implement the plan/initiative including being able to identify current state data that includes a KPI where the determination of current state data can be done by a plurality of IoT devices),
Srivastava in view of Matsuoka, Roy, Vora, and Zhang do not appear to explicitly teach:
transmitting a learning resource to the user based on the change management plan;
and generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule.
Albero teaches generating an alarm when the implementation of the change management plan,
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka, Roy, Vora, and Zhang by being able to provide notifications about observed events as taught by Albero in order to allow the system to be able to identify and indicate including a notification/alarm to designed individuals about the issue/event that is of concern so that the respective individuals can learn of the issue/event and address it as soon as possible including tasks/activities of the project/initiative being behind schedule thereby allowing for an earlier notification/identification of issues.
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero teach generating an alarm when the implementation of the change management plan, based on the key performance indicator score, does not align with the schedule (see Albero, paragraph [0065]; Srivastava, paragraphs [0027], [0037], [0074], [0076]-[0078], [0084], and [0086]; see Zhang, paragraphs [0016] and [0053]; the system can utilize a schedule and determine when the respective plan is behind schedule based on analysis of various parameters).
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero do not appear to explicitly teach:
transmitting a learning resource to the user based on the change management plan.
Celano teaches transmitting a learning resource to the user based on the change management plan (see paragraphs [0125]-[0127], [0117], [0120], and [0096]-[0097]; the system has means to provide/transmit learning resources to the user based on their skills, or lack thereof, with respect to the tasks of the project).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero by being to identify skills of users that can be improved based on the project/task/activities to be performed as taught by Celano in order to have means to identify what skills the plan/project needs and whether the personnel have the required skills for the plan/project while having the ability to determine what training courses can be provided to the personnel so that they can improve/acquire those needed skills.
With regard to claim 2, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach generating one or more pre-populated change documents wherein the pre-populated change documents are pre-populated using at least one response (see Srivastava, paragraphs [0019] and [0039]-[0041]; the system can generate the documents for the changes/initiatives based on the client data that was received);
transmitting the one or more pre-populated change documents to the user; and completing, through interaction of the chatbot and the user, the one or more pre-populated change documents (see Srivastava, paragraphs [0041]-[0045]; the system can present the change documents/initiative plan to the user and allow the user, via the chatbot/virtual assistant, to provide modifications to complete/modify the respective change document/initiative plan).
With regard to claim 4, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach comparing derived quantities to documents in a knowledge database, wherein the change management plan is further based on a comparison of the derived quantities and the documents in the knowledge database (see Srivastava, paragraphs [0025], [0036]-[0037] and [0040]; the system can utilize information from databases that store related information including other entities that have performed similar initiatives in order to compare the data in order to modify any derived/determined values associated with the respective initiative plan/change management plan).
With regard to claim 6, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach determining a gap assessment of the user based on the change management plan(see Celano, paragraphs [0049]-[0050] and [0125]-[0127]; the system can determine the skill level of users via an assessment as well as what skills employees have, or don’t have, with respect to an initiative plan/change management plan);
identifying another learning resource from a knowledge database based on the gap assessment; transmitting the another learning resource to the user; and administering a test to the user based on the gap assessment and another learning resource (see Celano, paragraphs [0125]-[0127], [0117], [0120], [0077], and [0096]-[0097]; the system has means to provide/transmit learning resources to the user based on their skills, or lack thereof, with respect to the tasks of the project as well as having tests/assessments for the user to complete).
With regard to claim 7, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claim 8, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach a resource server that transmits, at least, the change management plan to the user ((see Srivastava, paragraphs [0041]-[0045]; the system can present the change documents/initiative plan to the user and allow the user, via the chatbot/virtual assistant, to provide modifications to complete/modify the respective change document/initiative plan).
With regard to claim 11, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach a skills center, wherein the skills center continually identifies, using the Al engine, gaps in one or more skills of the user and provide another learning resource to the user (see Celano, paragraphs [0125]-[0127], [0117], [0120], [0077], and [0096]-[0097]; the system has means to provide/transmit learning resources to the user based on their skills, or lack thereof, with respect to the tasks of the project as well as having tests/assessments for the user to complete).
With regard to claim 14, Srivastava in view of Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach wherein interactive chatbot comprises a graphical user interface (see Srivastava, paragraphs [0015] and [0021]; see Zhang, Figure 6; the user interface can be a graphical user interface).
With regard to claim 15, this claim is substantially similar to claim 1 and is rejected for similar reasons as discussed above.
With regard to claims 16, 18, and 20, these claims are substantially similar to claims 2, 4, and 6 and are rejected for similar reasons as discussed above.
Claims 3, 12, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al [US 2022/0292393 A1] (from IDS) in view of Matsuoka et al [US 2022/03432328 A1], Roy et al [US 2020/005801 A1], Vora et al [US 2020/0042515 A1], Zhang et al [US 2014/0067544 A1], Albero et al [US 2023/0306329 A1], and Celano et al [US 2022/0406207 A1] in further view of Heere et al [US 2021/0089860 A1].
With regard to claim 3, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach all the claim limitations of claim 1 as discussed above.
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach one or more key performance indicators of the change management plan (see Srivastava, paragraph [0027]; the system can include KPIs of the client data).
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano do not appear to explicitly teach:
predicting, using the Al engine, a performance of the change management plan based on the key performance indicator score;
detecting that the value of the performing key performance indicator is an outlier using the Al engine;
generating an alarm to the user identifying in response to the detected outlier;
performing a root cause analysis, by the Al engine, to determine the cause of the outlier;
recommending an intervention to correct the outlier;
and generating a report detailing the root cause analysis and transmitting the report to the user.
Heere teaches predicting, using the Al engine, a performance of the
detecting that the value of the performing key performance indicator is an outlier using the Al engine; generating an alarm to the user identifying in response to the detected outlier (see paragraphs [0020] and [0084]-[0085] and [0087]; the system can determine when a KPI is an outlier based on some observed deviation or threshold and be able to generate an alarm or notification to a user);
performing a root cause analysis, by the Al engine, to determine the cause of the outlier; recommending an intervention to correct the outlier; and generating a report detailing the root cause analysis and transmitting the report to the user (see paragraphs [0020], [0055]-[0056], and [0135]; the system can perform a root cause analysis of the issue affecting the KPI as well as being able to provide explanation to the user and recommendations to correct the issue).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano by being able to monitor various pieces of information including KPIs to determine any issues or deviations as taught by Heere in order to automate repeated human tasks of regularly checking the data to determine any issues in the project/task and be able to automatically analyze the respective underlying data to determine a root cause and any recommended corrective actions thus helping the users of the system by being able to quickly identify issues as they arise and be able to save user time and effort by providing a likely explanation and any associated corrective actions thus enabling for the users to be able to ascertain and verify what the problem is and how to fix it sooner.
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano in further view of Heere teach predicting, using the Al engine, a performance of the change management plan based on the key performance indicator score (see Heere, paragraphs [0020], [0084]-[0085] and [0121]; see Srivastava, paragraphs [0019]-[0020], [0027], [0033]-[0034], and [0045]-[0046]; the system can monitor data that is utilized to indicate a performance of the object/plan and utilize that data as feedback to determine the current state and predict the future state).
With regard to claim 12, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
With regard to claim 13, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
With regard to claim 17, this claim is substantially similar to claim 3 and is rejected for similar reasons as discussed above.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al [US 2022/0292393 A1] (from IDS) in view of Matsuoka et al [US 2022/03432328 A1], Roy et al [US 2020/005801 A1], Vora et al [US 2020/0042515 A1], Zhang et al [US 2014/0067544 A1], Albero et al [US 2023/0306329 A1], and Celano et al [US 2022/0406207 A1] in further view of Lee et al [US 2020/0133964 A1].
With regard to claim 5, Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teach all the claim limitations of claim 1 as discussed above.
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano teaches updating the change management plan, with the Al engine, based on the [feedback] (see Srivastava, paragraphs [0045] and [0050]; the plan can receive feedback and be updated accordingly).
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano do not appear to explicitly teach receiving a score for the change management plan, wherein the score is provided by one or more subject matter experts; and updating the change management plan, with the Al engine, based on the score.
Lee teaches receiving a score for the change management plan, wherein the score is provided by one or more subject matter experts (see paragraphs [0042] and [0093]; the system can utilize active-supervised learning from experts based on their review/score of the output).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the planning system of Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano by allowing for active supervised learning from experts as taught by Lee in order to help improve the confidence and accuracy of the output of the model by continuously leveraging the knowledge of experts by being able to discern the accuracy and confidence score of the model’s output and be able to take that experts’ feedback to adjust the respective output and also refine the model so that future usage of the model will be more accurate and would require less feedback.
Srivastava in view of Matsuoka, Roy, Vora, Zhang, and Albero, and Celano in further view of Lee teach updating the change management plan, with the Al engine, based on the score (see Lee, paragraphs [0042] and [0093]; see Srivastava, paragraphs [0045] and [0050]; the plan can receive feedback and be updated accordingly).
With regard to claim 19, this claim is substantially similar to claim 5 and is rejected for similar reasons as discussed above.
Response to Arguments
Applicant's arguments (see the second to last paragraph on page 11 through the top of page 14) have been fully considered but they are not persuasive. The applicant argues the claims do not recite a judicial exception because, when considered as a whole, the claims contain limitations that cannot be performed by the human mind. The Examiner respectfully disagrees. In particular the applicant argues that usage of natural language processing to determine user sentiment; obtain a KPI from a field device; and generate/track implementation of a plan based on user sentiment and value cannot be performed by the human mind. As discussed in the 35 USC 101 rejections, the obtaining step is an additional element; however, with respect to the other various limitations discussed above, the analysis/evaluation of a response is can be either mental process since its evaluation/determination of how someone feels such as reading a letter or hearing someone speak to you and determining if their feelings/emotions as well as human activity since it relates to interactions between people. Therefore, since the claims do recite a judicial exception in at least the above noted limitations (plus other limitations too as noted in the 35 USC 101 rejections), the applicant’s arguments are not persuasive.
Applicant's arguments (see the first whole paragraph on page 14 through the second paragraph on page 17) have been fully considered but they are not persuasive. The applicant argues that the claims are rooted in a practical application, specifically limitations i.b-i.e and iii.a from claim 1 (as labeled in applicant’s remarks). With regards to step 2A, prong 2; this step evaluates the additional elements where, as noted in the 35 USC 101 rejections, the majority of the recited limitations recite the judicial exceptions. The lone limitation identified by applicant was recited as being insignificant extrasolution activity with applicant providing no further details elaborating on that limitation (i.e). The applicant argues that the combination as a whole reflect an improvement; however, per MPEP 2106.05(a), the judicial exception alone cannot provide the improvement. As such, applicant’s arguments are not persuasive.
Applicant's arguments (see the second to last paragraph on page 17 through the second paragraph on page 20) have been fully considered but they are not persuasive. The applicant argues that the claims recite an unconventional combination of elements including chatbot, change document, memory with query history and AI engine where the combination provides a technological solution that solves technical problems such as “lack of a universal plan and/or paradigm” for organizational transformation. The Examiner respectfully disagrees. With regard to applicant’s arguments regarding an improvement to the functioning of a computer or to any other technology or technical field, the Examiner notes that, per MPEP 2106.05(a), that “[a]n important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.” (emphasis added). Additionally, it is important to note that “the judicial exception alone cannot provide the improvement” and that “the claim reflects the asserted improvement”. As shown in the 35 USC 101 rejections, the judicial exception itself is being relied upon by the applicant to show the improvement, which according to the MPEP, cannot provide the improvement. Although the claims recite some computer-related elements such as chatbot, AI engine, and non-transitory computer-readable memory; those respective elements are recited at a high-level of generality and at most, are used to merely apply the judicial exception on a computer (see MPEP 2106.05(f)). As such, applicant’s arguments are not persuasive.
Applicant’s arguments (see the second to last paragraph on page 20 through the first paragraph on page 21) with respect to the 35 USC 101 rejections of claim 7 and respective dependent claims have been fully considered and are persuasive. The 35 USC 101 rejection for software per se of claim 7 and respective dependent claims have been withdrawn.
Applicant’s arguments (see the second to last paragraph on page 21 through the last paragraph on page 27) with respect to the rejection(s) of claim(s) under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Matsuoka, Roy, and Vora. The applicant amended the claim limitations which required further search that resulted in new references being found that, when combined, would teach or fairly suggest the claim limitations as recited.
The applicant also broadly asserts there was no motivation for the references to supply the missing elements without benefit of applicant’s disclosure. The Examiner notes that there is multiple avenues when supporting a rejection under 35 USC 103 where, as noted in MPEP 2144(IV), “[t]he reason or motivation to modify the reference may often suggest what the inventor has done, but for a different purpose or to solve a different problem. It is not necessary that the prior art suggest the combination to achieve the same advantage or result discovered by applicant.” As noted in the 35 USC 103 rejections, an articulated rationale supporting the rejection was provided.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST.
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 5/5/2026
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