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 .
DETAILED ACTION
This office action is in response to communication filed 6/1/2026. Claims 1-7 and 9-21 are currently pending and claim 8 is cancelled. Claims 1, 10, and 17 are the independent claims.
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 1-7 and 9-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1, it recites “A system comprising: a memory that stores instructions; and one or more processors coupled to the memory and configured to execute the instructions to perform operations comprising: providing a support ticket for a software application to a first trained machine learning model as input; receiving, from the first trained machine learning model, a first probability that the support ticket is addressed by modification of source code of the software application; based on the first probability and a predetermined threshold: determining that the first trained machine learning model was unable to determine which of a plurality of support groups to send the support ticket to; providing the support ticket to a second trained machine learning model as input; and receiving, from the second trained machine learning model, a second probability that the support ticket is addressed by modification of source code of the software application; based on the second probability and the predetermined threshold, selecting a support group to send the support ticket to; and sending the support ticket to the selected support group.” The limitation “based on the first probability and a predetermined threshold: determining that the first trained machine learning model was unable to determine which of a plurality of support groups to send the support ticket to” and “based on the second probability and the predetermined threshold, selecting a support group to send the support ticket to”, as drafted, recites a function that, under its broadest reasonable interpretation, covers a function that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. As such, the limitation, as drafted, is a function that, under its broadest reasonable interpretation, recites the abstract idea of a mental process. The limitation encompasses a human mind carrying out the function through observation, evaluation, judgment, and /or opinion, or even with the aid of pen and paper. For example, a human may mentally judge/decide/determine/observe/recognize/etc. that a decision/determination/selection/etc. of a group/support group cannot be made based on a criteria/requirements/probability and threshold/etc. that the decision/selection/determination/judgement is to be based on, and may mentally judge/ select/decide/etc. a group based on data/information/probability and threshold/etc.. Accordingly, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claim recites the following additional elements/limitations “A system comprising: a memory that stores instructions; and one or more processors coupled to the memory and configured to execute the instructions to perform operations comprising:”, “providing a support ticket for a software application to a first trained machine learning model as input”, “receiving, from the first trained machine learning model, a first probability that the support ticket is addressed by modification of source code of the software application”, “providing the support ticket to a second trained machine learning model as input”, “receiving, from the second trained machine learning model, a second probability that the support ticket is addressed by modification of source code of the software application”, and “sending the support ticket to the selected support group.” The additional elements “A system comprising: a memory that stores instructions; and one or more processors coupled to the memory and configured to execute the instructions to perform operations comprising:”, “first trained machine learning model”, and “second trained machine learning model” recite that high-level/generic computer/computer components are used to implement/perform the abstract idea/mental process and as such amounts to no more than mere instructions to apply the exception using generic computer, and/or mere computer components. The additional elements “providing a support ticket for a software application to a first trained machine learning model as input”, “receiving, from the first trained machine learning model, a first probability that the support ticket is addressed by modification of source code of the software application”, “providing the support ticket to a second trained machine learning model as input”, “receiving, from the second trained machine learning model, a second probability that the support ticket is addressed by modification of source code of the software application”, and “sending the support ticket to the selected support group” do nothing more than add insignificant extra solution activities to the judicial exception of merely transmitting/gathering data/information and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f), 2106.05(g), etc.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer which is not significantly more than the abstract idea/mental process/judicial exception, and/or mere computer components, and mere insignificant extra solution activities to the judicial exception of merely transmitting/gathering data/information and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). Accordingly, the claims are not patent eligible under 35 USC 101.
As per claim 2, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the operations further comprise: generating the first trained machine learning model by providing a training set comprising a set of historical support tickets, each of the historical support tickets labeled with a class of a set of classes comprising a minority class and a majority class, more of the historical support tickets having majority class labels than minority class labels” which, conceptually, with broadest reasonable interpretation, further recites updating/modifying/training/ generating/etc. application/software/trained machine learning model/etc. used in implementing/performing/applying the abstract idea/mental process and insignificant extra solution activities and as such, with broadest reasonable interpretation, further recites an insignificant extra solution activities to the judicial exception of merely updating/modifying/etc. data/information/software/trained model and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)), and instructions to apply/implement/perform the abstract idea/judicial exception using generic computer/computer components/etc.. Therefore, the additional elements/limitations of claim 2 does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process, and as such claim 2 is rejected for similar reasoning as claim 1, above.
As per claim 3, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the class that labels each historical support ticket identifies a support group that resolved the historical support ticket”, which, conceptually, with broadest reasonable interpretation, provides further as to the insignificant extra solution activities to the judicial exception of merely updating/modifying/etc. data/information/software/trained model and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)), and as such does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process, and therefore claim 3 is rejected for similar reasoning as claim 1, above.
As per claim 4, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the operations further comprise: validating the first trained machine learning model by determining a proportion of correct classifications of the historical support tickets classified by the trained machine learning model as the minority class out of a total number of classifications of the historical support tickets classified by the first trained machine learning model as the minority class” which, conceptually, with broadest reasonable interpretation, provides further as to the abstract idea/mental process/judging/evaluating/validating/etc. performed, which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process. Therefore, claim 4 is rejected for similar reasoning as claim 1, above.
As per claim 5, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the operations further comprise: validating the first trained machine learning model by determining a proportion of correct classifications of the historical support tickets classified by the first trained machine learning model as the minority class out of a total number of the historical support tickets labeled as the minority class” which, conceptually, with broadest reasonable interpretation, provides further as to the abstract idea/mental process/judging/evaluating/validating/ determining/etc. performed, which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process. Therefore, claim 5 is rejected for similar reasoning as claim 1, above.
As per claim 6, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the minority class comprises no more than 10% of the support tickets and the majority class comprises at least 70% of the support tickets” which, conceptually, with broadest reasonable interpretation, provides further as to the abstract idea/mental process/judging/evaluating/validating/determining/etc. performed, which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process. Therefore, claim 6 is rejected for similar reasoning as claim 1, above.
As per claim 7, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the generating of the first trained machine learning model comprises applying a balanced log loss function that penalizes misclassifications of the minority class more than misclassifications of the majority class” which, conceptually, with broadest reasonable interpretation, recites further clarification as to the insignificant extra solution activities of merely updating/modifying/training/generating/etc. data/information/software/trained model and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). Therefore, the additional elements/limitations of claim 7 does not integrate the abstract idea into a practical application and are not significantly more than the abstract idea/mental process, and as such claim 7 is rejected for similar reasoning as claim 1, above.
As per claim 9, it incorporates the deficiencies of claim 1, upon which it depends, and further recites “…wherein the first trained machine learning model consumes fewer resources than the second trained machine learning model” which, conceptually, with broadest reasonable interpretation, provides further clarification as to the high level/generic computer/computer components/software/machine learning models/etc. used to implement/perform the abstract idea/mental process/extra solution activities/etc., which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process. Therefore, claim 9 is rejected for similar reasoning as claim 1, above.
As per claim 10, it recites a non-transitory computer-readable medium having similar limitations as the system of claim 1, and as such recites a similar abstract idea and has similar deficiencies as claim 1. Claim 10 recites the further elements/limitations “A non-transitory computer-readable medium that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising…” and “…first trained machine learning model of a machine learning model stack comprising a plurality of machine learning models” which, conceptually, with broadest reasonable interpretation, recites high level/generic computer/computer components used to implement/perform/apply the abstract idea/mental process, which does not integrate the abstract idea into a practical application and is not significantly more than the abstract idea/mental process; and the additional element/limitation “the plurality of machine learning models ordered based on computing resources consumed” which, with broadest reasonable interpretation, provides a further clarification of the abstract idea/mental process as a human may mentally judge/evaluate/analyze/observe/etc. computing resources used/consumed by machine learning models/software/etc. and judge/decide/determine/etc. an order/sequence/etc. of the machine learning models based on the resources consumed. As such, the additional limitations/elements of claim 10 fail to correct the deficiencies of claim 1, and therefore claim 10 is rejected for similar reasoning as claim 1, above.
As per claims 11-16, they recite non-transitory computer-readable mediums having similar limitations as the systems of claims 2-7, respectively, and are therefore rejected for similar reasoning as claims 2-7, respectively, above.
As per claim 17, it recites a method having similar limitations as the operations performed by the system of claim 1, and as such recites a similar abstract idea and has similar deficiencies as claim 1. Claim 17 recites the further additional elements/limitations “processing historical support tickets using feature engineering that includes tokenization, feature extraction, and vectorization, wherein the feature extraction comprises generating n-grams from text of the historical support tickets and partitioning the n-grams into a first set comprising n-grams that appear only in support tickets resolved by a first support group, a second set comprising n-grams that appear only in support tickets resolved by a second support group, and a third set comprising n-grams that appear in support tickets resolved by both the first support group and the second support group” and “generating a trained machine learning model by providing a training set comprising a set of the processed historical support tickets”. The additional element “processing historical support tickets using feature engineering that includes tokenization, feature extraction, and vectorization, wherein the feature extraction comprises generating n-grams from text of the historical support tickets and partitioning the n-grams into a first set comprising n-grams that appear only in support tickets resolved by a first support group, a second set comprising n-grams that appear only in support tickets resolved by a second support group, and a third set comprising n-grams that appear in support tickets resolved by both the first support group and the second support group”, with broadest reasonable interpretation, recites a further clarification as to abstract idea/mental process performed as a human may mentally/with pen and paper judge/evaluate/analyze/etc. data/information/support tickets including tokenization/judging tokens/writing tokens corresponding to data/judging parts of text to be tokens/etc.; may mentally/with pen and paper judge/extract/decide/determine/etc. features/n-grams in text/historical support tickets and judge/determine/etc. features/n-grams in a first set/appearing tickets resolved by a first group/etc., features/n-grams in a second set/appearing tickets resolved by a second group/etc., and features/n-grams in a third set/appearing in tickets resolved by both the first and second groups/etc.; and may mentally/with pen and paper judge/decide/etc. vectors/write vectors/etc. for the data/information/support tickets. Further, the additional element/limitation “generating a trained machine learning model by providing a training set comprising a set of the processed historical support tickets” does nothing more than add an insignificant extra solution activity of transmitting data/providing a training set/transmitting processed historical support tickets/etc. to perform an insignificant extra solution activity of modifying data/updating software/training a model/generating a trained machine learning model/etc., which does not integrate the abstract idea into a practical application and the courts have identified functions such as gathering, displaying, updating, transmitting and storing data as well-understood, routine, conventional activity, thus do not amount to significantly more than the judicial exception (see MPEP 2106.05(d)). As such, the additional elements/limitations of claim 17 fail to correct the deficiencies of claim 1 and therefore claim 17 is rejected for similar reasoning as claim 1, above.
As per claims 18-20, they recite methods having similar limitations as the systems of claims 2-4, respectively, and are therefore rejected for similar reasoning as claims 2-4, respectively, above.
As per claim 21, it incorporates the deficiencies of claim 17, upon which it depends, and further recites “…performing unsupervised learning on the training set, the unsupervised learning comprising at least one of topic modeling using TF-IDF (term frequency-inverse document frequency) vectors or document clustering using contextual embeddings” which conceptually, with broadest reasonable interpretation, provides further clarification as to abstract idea/mental process performed as a human may mentally/with pen and paper/etc. learn/remember/etc. from data/information/training set including judging/deciding/determining/etc. clusters/groups/sets/etc. of data/information/documents based on context in/embedded in/etc. the documents/information, or may judge/analyze/determine/write/etc. models of topics using vectors, and as such the additional elements/limitations do not integrate the abstract idea into a practical application and are not significantly more than the abstract idea/mental process. Therefore, the additional elements/limitations of claim 21 fail to correct the deficiencies of claim 17 and claim 21 is rejected for similar reasoning as claim 17, above.
Reasons for Allowability Over Prior Art
The prior art of record (MacLaughlin et al. (herein called MacLaughlin) (US PG Pub. 2025/0165882 A1), Healy et al. (herein called Healy) (US PG Pub. 2021/0406369 A1), and Burton et al. (herein called Burton) (US PG Pub. 2019/0026697 A1), and Zhang (US Patent 11,182,691 B1)) teaches that support tickets for software/application/program/etc. are provided to trained machine learning models which determines a probability that the ticket may be addressed/issues in the ticket resolved/etc. and determines developers/programmers/groups/etc. to send the ticket to so the developers/programmers/group/etc. may resolve the support ticket/issues/etc., that the support ticket/issues/errors/etc. may be resolved/corrected/addressed/etc. by modifying code of the software/application/etc., that multiple/plurality/different/first and second/etc. trained models/machine learning models/etc. may be used to analyze/evaluate/etc. support tickets and select/determine/etc. developers/groups/programmers/etc. to send the ticket to for resolution/to address the ticket/etc., that the different/multiple/etc. models may consume/require different amounts of computing resources, and that the machine learning models/trained models/etc. may be trained/generated/etc. using historical/past/previous/etc. support tickets.
However, the prior art of record fails to render an obviousness of a trained machined learning model providing a probability that modifying source code of the software application will address a support ticket for a software application, selecting a support group to send the support ticket to based on the probability and a predetermined threshold, and sending the support ticket to the selected support group; when the trained machine learning model is a second trained machine learning model that the support ticket has been provided to as input after providing the support ticket to a first trained machine learning model as input, receiving a first probability that the support ticket is addressed by modification of source code of the software application from the first trained machine learning model, and based on the first probability and a predetermined threshold: determining that the first trained machine learning model was unable to determine which of a plurality of support groups to send the support ticket to, as required by independent claim 1; when the trained machine learning model is a first trained machine learning model of a machine learning model stack comprising a plurality of machine learning models ordered based on computing resources consumed, and after providing the support ticket for a software application as input to the first trained machine learning model, as required by independent claim 10; or after processing historical support tickets using feature engineering that includes tokenization, feature extraction, and vectorization, wherein the feature extraction comprises generating n-grams from text of the historical support tickets and partitioning the n- grams into a first set comprising n-grams that appear only in support tickets resolved by a first support group, a second set comprising n-grams that appear only in support tickets resolved by a second support group, and a third set comprising n-grams that appear in support tickets resolved by both the first support group and the second support group, generating the trained machine learning model by providing a training set comprising a set of the processed historical support tickets, and providing the support ticket to the trained machine learning model as input, as required by independent claim 17.
Response to Arguments
Applicant's arguments filed 6/1/2026 have been fully considered but they are not persuasive.
As per the 101 arguments on pg. 8 par. 5-pg. 10 par. 2 of the remarks that the amended independent claims and their respective dependent claims are allowable under 35 USC 101 because independent claim 1 recites the use of in inconclusive probability range that allows the system to try again with a different machine learning model which improves the likelihood that the routing of the support ticket is correct, independent claim 10 recites the use of a machine learning model stack including multiple machine learning models ordered based on the amount of computing resources they consume, allowing a less-resource intensive model to categorize support tickets it can handle and more resource intensive models to be used only when the first model is not able to generate a clean answer thereby saving computation resources, and independent claim 17 recites a particular way of generating a trained machine learning model that includes processing historical support tickets using feature engineering including tokenization, feature extraction, and vectorization, when the feature extraction partitioning n-grams into multiple sets which is a technical improvement to training the machine learning model, and further the machine learning model in the independent claims is used for the practical purpose of routing support tickets which allows for support staff resources to be conserved, which improves the support ticket routing system, and as such any abstract idea/mental process in the independent claims is integrated into a practical application, the examiner, respectfully, disagrees.
The examiner would first like to point out that, with broadest reasonable interpretation, independent claim 1 does not actually recite/require that the selected support group to send the support ticket to is correct/likely to be correct, only that the group is selected based on a probability that the ticket is addressed by modification of the source code and some sort of predetermined threshold. The examiner would further like to point out that the actual wording/phrasing of claim 1 does not recite/require any conservation of resources or that the support group actually correct/fix any issue in the support ticket, only that the support ticket is sent/transmitted to a selected support group, and sending/transmitting/etc. data/information is an insignificant extra solution activity that the courts have identified as well-understood, routine, conventional activity, thus does not integrate the abstract idea into a practical application and does not amount to significantly more than the judicial exception (see MPEP 2106.05(d)).
The examiner would also like to point out that, with broadest reasonable interpretation, the actual wording/phrasing of claim 10 only recites/requires that support ticket for a software application is input to a first trained machine learning model, and that the first trained machine learning model is of a model stack comprising a plurality of/multiple machine learning models that are somehow ordered based on the amount of computing resources they consume, the claim does not actually recite/require that any resources are conserved, any other models of the stack/multiple/plurality of models are selected to be used or that the order the models are selected to be used in is from least resources consumed to most resources consumed (ex: they could be ordered from most resources consumed to least resources consumed). As such, with broadest reasonable interpretation, independent claim 10 does not recite/require these argued features/limitations. If the applicant intended for the broadest reasonable interpretation of the claims to be limited to include these features/elements, the examiner would suggest further amendment/clarification to include these features/elements in independent claim 10.
The examiner would further like to point out that, with broadest reasonable interpretation, independent claim 17 does not actually recite/require any resources being conserved or that the generating the trained model includes the recited processing of the historical support tickets, rather, the actual wording/phrasing of claim 17 only recites/requires that the resulting processed historical support tickets/previously processed historical support tickets/the result of the processing/etc. art provided/transmitted/etc. as training data when generating/training/modifying/updating/ etc. a trained machine learning model, and as such, with broadest reasonable interpretation, the processing of historical support tickets may be performed separately from/before/outside/etc. the generation of the trained model, and by something other than the model being trained/generated/modified/etc.. As such, with broadest reasonable interpretation, the processing of the historical support tickets may be considered judging/analyzing/evaluating/etc. and therefore may be considered an abstract idea/mental process as it may be performed by a human with pen and paper/mentally/etc., as seen in the rejection of claim 17 under 35 USC 101 above, and providing/transmitting/etc. data/information/training set/etc. that is the result/output/etc. of the abstract idea when updating/modifying/generating trained models/software/etc. are insignificant extra solution activities which the courts have identified as well-understood, routine, conventional activity, thus does not integrate the abstract idea into a practical application and does not amount to significantly more than the judicial exception (see MPEP 2106.05(d)), as seen in the rejection of claim 17 under 35 USC 101, above.
Therefore, the examiner finds these arguments unpersuasive and maintains that the rejection under 35 USC 101 is proper.
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 DOUGLAS M SLACHTA whose telephone number is (571)270-0653. The examiner can normally be reached Monday-Friday 6:30am-4pm.
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/DOUGLAS M SLACHTA/Examiner, Art Unit 2193
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