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 .
REMARKS
On page 9, Applicant’s summary of the interview on August 25, 2025 is acknowledged.
The amendment to the specification, filed September 22, 2025, has been entered.
On page 9, Applicant’s comment directed to the allowable subject matter is noted. However upon further search and consideration necessitated the claim amendment, new prior art has been applied to the claims as amended.
On pages 9-17, Applicant’s argument by claim amendment to overcome the 35 USC 101 rejection as applied to claims 1-20 is not persuasive as discussed below.
On page 10, Applicant argues the new limitations “integrate the judicial exception into a practical application…provide improved model performance and/or scaling of a data labeling process for improved training of a model related to an intent engine.” Applicant points to paragraphs [0025] and [0029] to argue that the claims “provide improved model performance and/or scaling of a data labeling process for improved training of a model related to an intent engine.” Applicant’s argument is not persuasive because the pointed to disclosure is not directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field. The specification does not provide a technical explanation with as to how to implement the invention should be present in the specification. The disclosure should have sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.
On pages 10-18, Applicant when reasonably characterized, the recitations of claim 1 provides for "processing multi-channel service data objects to initiate automated resolution actions via an intent engine ... trained to recognize support intentions related to communications such as, for example, service tickets, service messages, email messages, application portal communications, widget communications, chat channels, web communications, API calls, alerts, notifications, telephone calls, video chats, and/or other communication data associated with an application framework." See Specification as filed, paragraph [0025]. The recitations of claim I also "provide improved model performance and/or scaling of a data labeling process for improved training of a model related to an intent engine." See Specification as filed, paragraph [0029]. Applicant’s argument is not persuasive because the claims are not limited to the limitations recite in paragraph [0029]. Therefore, the claims are not limited to the argued limitations.
As for the limitation of “wherein parameters of the machine learning model are tuned during training of the machine learning model to learn a mapping function for the intent recognition related to service message data", the limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea.
On pages 12-13, Applicant cites [0020], [0023], [0024], [0026], and [0029] to argue that the claimed invention is not directed to an abstract idea. Applicant’s argument is not persuasive because [0020] is a general characterization of an application framework which does not preclude claim 1 from the abstract idea interpretation. Applicant cites paragraph [0023] to argue that the complexity of the application framework at a high level, however, Applicant’s argument is not persuasive because [0023] is a general characterization of an application framework which does not preclude claim 1 from the abstract idea interpretation. Applicant cites paragraph [0024] to discuss the challenges of “traditional services”, however, the discussion does not preclude claim 1 from the abstract idea interpretation. Applicant cite paragraph [0026] and [0029] to argue that the use of “machine learning” to overcome the abstract idea interpretation, however, the discussion does not preclude claim 1 from the abstract idea interpretation.
On pages 12-14, Applicant’s citation of “relevant court precedent and noted in the MPEP §2106” to argue the claims do not recite a mental process.” Applicant’s citations are noted. However, when the claims are considered as a whole the claims are directed to a mental process as analyzed in light of the published guidance.
On pages 14-16, Applicant the pending claims recite a practical application such that they are directed to eligible subject matter. Applicant asserts "parameters of the machine learning model are tuned during training of the machine learning model to learn a mapping function for the intent recognition related to service message data," which enables correlation of "the intent support label to a resolution data object related to a service resolution for the service request" and initiation of "a first resolution action for the service request" or "a second resolution action for the service request." In doing so, claim I provides "substantial technical contributions to improving the efficiency and/or the effectiveness of an application framework system.” Applicant’s argument is not persuasive because the pointed limitation merely links the use of a judicial exception to a particular technological environment or field of use. The claim does not explicitly integrate the abstract idea into a practical application as argued by Applicant.
On pages 16-17, Applicant argues the claims recite an inventive concept because claim 1, 11, and 20 provide unconventional steps. Applicant’s argument is not persuasive because Applicant does not identify or provide support for the inventive concept and unconvention steps beyond the above cursory statements.
Claims 1-20, filed September 22, 2025, are examined on the merits.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
The claims 1-20 recite an apparatus, a method, and a computer program product, which are statutory categories of invention.
Step 2A Prong One:
Claim 1 recites “apply a machine learning model…” and “correlate the intent support label…” at a high level of generality such that it could be practically performed in the human mind with the physical aid of paper and pencil. The limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for generic computer components. These limitations, as drafted, are processes that, under its broadest reasonable interpretation, can be performed as a mental process (that is, “observation, evaluation, judgement, opinion”).
Claims 11 and 20 recite a method and a computer program product comprising the same steps as claim 1. These claims are directed abstract idea, e.g. mental process, under the same rationale as claim 1, supra.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite additional elements of a “processors”, “storage devices”, and “communication channel”, where the claim further recite generic elements of the apparatus, method, or computer program product. The “processors”, “storage devices”, and “communication channel” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic component (MPEP 2106.05(f)). The limitation of “receiving…” amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process. In addition, the limitations of “initiate a first resolution action…” and “initiate a second resolution action…” are post-solution activity is an element that is not integrated into the claim as a whole. The limitations represent extra-solution activity because it is a mere nominal or tangential addition to the claim (see MPEP 2106.05(g), which provide examples that the courts have found to be insignificant extra-solution activity, such as “[p]rinting or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55” and “[s]electing information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)”).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a “processors”, “storage devices”, and “communication channel” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The limitation of “receiving a service” amounts to no more than insignificant pre-activity of receiving data. Further, the “receiving” step simply appends well-understood and conventional activity of receiving data over a network (see MPEP 2106.05(d)(II)(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. In addition, the last limitations of “initiate a first resolution action…” and “initiate a second resolution action…” are post-solution activity is an element that is not integrated into the claim as a whole. The last limitations represent extra-solution activity because it is a mere nominal or tangential addition to the claim (see MPEP 2106.05(g), which provide examples that the courts have found to be insignificant extra-solution activity, such as “[p]rinting or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55” and “[s]electing information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)”).
Thus taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim 2 recites wherein machine learning model is a first machine learning model, and wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: apply a second machine learning model trained for automated service resolution to the intent support label to generate the resolution data object. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 3 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: query a set of predefined resolution data objects to select the resolution data object from the set of predefined resolution data objects based at least in part on the intent support label. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 4 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: select the resolution data object from a ranking of resolution data objects configured based at least in part on the intent support label. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 5 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: generate a response message object for a client device via a communication channel of the plurality of communication channels in response to a determination that the feature dataset does not satisfy defined intent criteria for the machine learning model; receive an additional service message object via the communication channel, wherein the additional service message object defines an additional feature dataset associated with the service request; and apply the machine learning model trained for intent recognition to the feature dataset and the additional feature dataset to generate the intent support label for the service message object. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 6 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, utilize a generative machine learning model to determine the first resolution information. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 7 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, query a knowledge base system to determine the first resolution information. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 8 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: transmit a resolution message object associated with the first resolution information or the second resolution information to a client device via the communication channel. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 9 recites wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
route a resolution ticket data object associated with the first resolution information or the second resolution information to a support device. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claim 10 recites the communication channel corresponds to an email communication channel, a network portal interface communication channel, a user interface widget communication channel, a chat communication channel, or an application programming interface (API) communication channel. These limitations further narrow the abstract idea or extra-solution activity, but are nonetheless part of the abstract idea identified in claim 1. They also do not amount to significantly more than the abstract idea. The claims are similarly rejected under the same rationale as claim 1, supra.
Claims 12-19 recite a method and a computer program product comprising the same steps as claims 2-10. These claims are directed abstract idea, e.g. mental process, under the same rationale as claims 2-10, supra.
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.
Claim(s) 1, 3, 4, 6-11, 13, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (Dunn hereafter, US 2020/0394360 A1) in view of Qu et al. (User Intent Prediction in Information-seeking Conversations, 2019).
The instant rejection relies on Applications disclosure of the term “resolution data object” refers to a data structure that represents one or more resolution actions for a service message object. A resolution data object can include and/or be configured as a message, an alert, a notification, a control signal, an API call, an email message, an application portal communication, a widget communication, a chat channel communication, a web communication, API calls, a set of executable instructions, a workflow, a resolution ticket, visual data, the like, or combinations thereof ([0062]).
Claim 1, Dunn discloses an apparatus comprising one or more processors and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to:
receive a service message object via a communication channel of a plurality of communication channels, wherein the service message object defines a feature dataset associated with a service request for an application framework ([0028], e.g. A communication system can receive communications with one or more words (e.g. text communications or data translated into text) and can part the words to identify associations between works in the communication and actions available through a communication system);
apply a machine learning model trained for intent recognition to the feature dataset to generate an intent support label ([0121], e.g. annotation of the intent may be facilitated. Annotation may define a quality of an association between the communication and the intent. Annotation may be done automatically by applying algorithms in one embodiment) for the service message object ([0005], e.g. “intent” refers to machine-based communication system categories associated with user issues addressable by a communication system. Devices in a communication system can use machine learning and AI with intent processing systems to manage communications with users),
correlate the intent support label to a resolution data object related to a service resolution for the service request ([0121], e.g. annotation may be completed manually based on the correlation of the original communication and the identified intent. The quality can be annotated in any suitable form, including words (e.g., “yes” and “no”), percentages (e.g., “80%”), numbers (e.g., on a scale from 1 to 10), etc.); and
based on whether the resolution data object satisfies defined resolution criteria for the intent support label,
initiate a first resolution action for the service request based at least in part on first resolution information related to a searchable database system or a generative system ([0127], e.g. As communications with users occur and the words from a user communication are associated with intent categories, and the communications result in a resolution of the user interaction (e.g. positive and negative resolutions), the words associated with an intent category and successfully resolved can be added to data 938 for a particular intent) or
initiate a second resolution action for the service request based at least in part on second resolution information related to an automated response message object (0127], e.g. Words that result in unsuccessful resolution, or that frequently result in a system shifting from one intent category and associated actions for the category to a different category with different associated actions can be removed from data 938 for one category and placed with data for another category).
However, Dunn does not disclose wherein parameters of the machine learning model are tuned during training of the machine learning model to learn a mapping function for the intent recognition related to service message data (Qu, page 30, column 2, e.g. Both CNN and BiLSTM start with an embedding layer initiated with pre-trained word embeddings. Preliminary experiments indicated that using MSDialog (complete set) to train word embeddings is more effective than using GloVe [18] in terms of final model performance. The embedding layer maps each token in the utterances to a word embedding vector with a dimension of d… We tuned the threshold with the validation data).
One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Qu to improve the system of Dunn. Therefore, it would have been obvious for one of ordinary skill in the art to use the system of Dunn with the machine learning model of Qu. The benefit would be to train word embeddings is more effectively and improved final model performance.
Claim 3, Dunn as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: query a set of predefined resolution data objects to select the resolution data object from the set of predefined resolution data objects based at least in part on the intent support label (Dunn, [0107], e.g. the intent identification engine 715 may query the intent datastore 745 with the operative words to locate a corresponding predefined intent. For example, the intent identification engine 715 may query the intent datastore 745 with the words “pay bill” to identify a closest matching intent of “pay_current_bill”. In some embodiments, the operative words may not correspond to an existing intent. In such embodiments, the intent identification engine 715 can create a new intent and save it to the intent datastore 745 in correlation with the operative words received. The intent identification engine 715 may pass the identified intent to the annotation engine 720).
Claim 4, Dunn as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to: select the resolution data object (Dunn, [0107], e.g. the intent identification engine 715 may query the intent datastore 745 with the operative words to locate a corresponding predefined intent. For example, the intent identification engine 715 may query the intent datastore 745 with the words “pay bill” to identify a closest matching intent of “pay_current_bill”) from a ranking of resolution data objects configured based at least in part on the intent support label ([0030], e.g. quality metrics can be based on user feedback and subjective user rankings of a quality of a communication session).
Claim 6, Dun as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, utilize a generative machine learning model to determine the first resolution information (Dunn, [0111], e.g. the performance monitoring system may implement an investigative algorithm that monitors client metrics to determine why certain routing paths are selected with negative or positive customer results. Based on this determination, the performance monitoring system can provide feedback to a machine learning algorithm to update operations, node selections, and routing paths to improve system performance).
Claim 7, Dunn as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
in response to a determination the resolution data object does not satisfy the defined resolution criteria for the intent support label, query a knowledge base system to determine the first resolution information (Dunn, [0107], e.g. the intent identification engine 715 may query the intent datastore 745 with the operative words to locate a corresponding predefined intent).
Claim 8, Dunn as modified discloses wherein the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
transmit a resolution message object associated with the first resolution information or the second resolution (Dunn, [0130], e.g. a first annotation group 1032, a second annotation group 1034, and a third annotation group 1036 are shown) information to a client device via the communication channel (Dunn, [0092], e.g. A continuous channel can be structured so as to facilitate routing of future communications from a network device to a specified endpoint. This bias can persist even across message series (e.g., days, weeks or months)).
Claim 9, Dunn as modified discloses the one or more storage devices store instructions that are operable, when executed by the one or more processors, to further cause the one or more processors to:
route a resolution ticket data object associated with the first resolution information or the second resolution information to a support device (Dunn, [0095], e.g. Each of the first two endpoints may have previously communicated with a network device having transmitted the communication…In this example, the rule does not include weighting or normalization parameters (though, in other instances, a rule may), resulting in scores of 14, 11 and 19. Thus, the rule may indicate that the message is to be routed to a device with the highest score, that being the third endpoint).
Claim 10, Dunn as modified discloses the communication channel corresponds to an email communication channel, a network portal interface communication (Dunn, [0131], e.g. a dashboard filter interface 1100 showing metrics for communications and intents).
Claims 11, 13, 14, and 16-20, Dunn as modified discloses a computer implemented method and a computer program product (Dunn, Figure 7) for implementing the above apparatus.
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (Dunn hereafter, US 2020/0394360 A1) in view of Qu et al. (User Intent Prediction in Information-seeking Conversations, 2019), as applied to claims 1, 3, 4, 6-11, 13, 14, and 16-20 above.
Claims 2 and 12, Dunn as modified discloses the claimed invention except for apply a second machine learning model trained for automated service resolution to the intent support label to generate the resolution data object. The limitation of “a second machine learning model” has been interpreted as a duplication of the cited machine learning model wherein the training of the second machine learning model achieves the same expected results. The court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
PERTINENT PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kaur et al. (11,487,948 B2) discloses a solution is needed for reducing both the manual effort required to build a high-quality labelled training dataset needed for training an intent recognition model, and the frequency of intent recognition errors (column 1, line 66, to column 2, line 14).
STATUS OF THE PRIOR ART
Claims 5 and 15 are free of any prior art.
CONCLUSION
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
1/16/2026