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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 21, 2026 has been entered.
REMARKS
On pages 9-18, Applicant’s argument by claim amendment to overcome the 35 USC 101 rejection as applied to claims 1-20 is persuasive. The 35 USC 101 rejection as applied to claims 1-20 is withdrawn.
On pages 18-20, Applicant argues Dunn et al. and Qu et al. fails to describe “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 provided by a searchable database system or a generative system, or initiate a second resolution action for the service request based at least in part on second resolution information associated with an automated response message object.” Applicant’s argument is not persuasive because the claim is not clear as to whether the “initiate…” steps are performed when “the resolution data object satisfies defined resolution criteria for the intent support label” or when the resolution data object does not satisfy defined resolution criteria for the intent support label. Further, are both “initiate…” steps performed when “the resolution data object satisfies defined resolution criteria for the intent support label.” The vague and indefinitely issued of the argued limitation supports that the prior art reasonably described the argued limitations under the broadest reasonable interpretation (BRI).
Claims 1-20, filed May 21, 2026, are examined on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, lines 14-21, recites “based on whether the resolution data object satisfies defined resolution criteria for the intent support label…initiate…”, wherein the claim is not clear as to whether the “initiate…” steps, respectively, is performed when “the resolution data object satisfies defined resolution criteria for the intent support label” or when the resolution data object does not satisfy defined resolution criteria for the intent support label. The claim is not clear as to which “initiate…” step corresponds to the satisfied or not satisfied condition. Or the decision to perform the respective “initiate…” steps is arbitrary. The same issue is in claim 11. Claims 2-10 and 12-20 are rejected for being dependent from claim 1 or 11, respectively.
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 Chung et al. (Chung hereafter, US 2018/0341851 A1).
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),
identify, based at least in part the intent support label, a resolution data object associated with 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 ([0127], e.g. When an intent list 910 is initially generated, this list can be seeded based on expected information for a client type, and the list of data 938 can then be updated with words and phrases actually used by users contacting a system. 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)),
initiate a first resolution action for the service request based at least in part on first resolution information provided by 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 until a define accuracy metric is achieved for a mapping function associated with the intent recognition.
Chung discloses wherein parameters of the machine learning model are tuned during training of the machine learning model until a define accuracy metric is achieved ([0040], e.g. For each iteration, links for the weights and values for the weights themselves are re-estimated and updated. Updates 185 are fed back into the program execution 120. Training parameters such as: maximum model size, maximum number of passes over the training data (iterations), shuffle type, regularization type, and regularization amount can be specified and stored in the parameter server 180, and [0041], e.g. Whereas the goal of a machine learning system 100 is training accuracy (convergence), the goal of the tuning server 150 can be modified e.g., defined by the operator, and can frequently change. The goal of the tuning server 150 can range from a general performance goal, such as “find an optimal (or near optimal) hardware/software configuration to more efficiently and expediently reach convergence,” to a more specific performance goal, such as “reduce cost by decreasing processors”) for a mapping function associated with the intent recognition. It is noted that the limitation of “for a mapping function…” has been reasonably interpreted as an intended use wherein the function is not actually being used in claim 1.
One of ordinary skill in the art at the time before the effective filing date of the instant invention would have been motivated by Chung 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 Chung. 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 Chung et al. (Chung hereafter, US 2018/0341851 A1), 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.
CONCLUSION
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
6/12/2026
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