DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the amendment filed 12 November 2025 and the RCE filed 1 December 2025.
Claims 1-6, 9-14, 16-18, 21-25 are pending. Claims 23-25 are newly added. Claims 1, 10, and 16 are independent claims.
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, 9-13, 15-18, 19, 21-22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hanekamp, Jr. et al. (US 10607298, patented 31 March 2020, hereafter Hanekamp) and further in view of Humphrey et al. (US 11689557, filed 19 February 2019, hereafter Humphrey) and further in view of Oral et al. (US 2016/0063595, published 3 March 2016, hereafter Oral) and further in view of Emigh et al. (US 8112483, patented 7 February 2012, hereafter Emigh) and further in view of Saha et al. (US 2022/0188181, filed 15 December 2020, hereafter Saha) and further in view of Jha et al. (US 2022/0284643, filed 26 February 2021, hereafter Jha).
As per independent claim 1, Hanekamp discloses a computer-implemented method, comprising:
receiving an explanation request from an application (column 21, lines 52-56: Here, a user interacts with the interface to request an explanation of why a particular calculation, operation, or decision was made by the system)
based upon content of the explanation request, traversing a homogenous cluster, in order to create a path (column 21, lines 13-27; column 21, line 67- column 22, line 25: Here, narrative explanations are associated nodes in a calculation graph. Based upon receiving a request, the calculation graph is traversed to identify explanation data associated with the node, or upstream from the node in the path. These explanations are used to create the displayed explanation (column 25, lines 4-26))
based upon the final path, inserting explanation data from the final path (column 25, lines 4-26: Here, explanation data obtained by walking the graph is inserted into the display to provide the user with explanation data)
based upon the inserted data and the final path, creating an explanation (column 25, lines 4-26: Here, explanation data obtained by walking the graph is inserted into the display to provide the user with explanation data)
providing the explanation to the application for display (column 22, lines 26-30: Here, the automatically generated explanations are displayed on a display of the computing device)
Hanekamp fails to specifically disclose:
storing data in a cache
selecting a template from a template store
inserting data into a template
storing the inserted template and the filled data as an outcome
processing the outcome according to a challenge function to create a challenged outcome
communicate the challenged outcome to the application
wherein the processing the outcome according to the challenge function includes:
storing a reward-based reinforcement model
generating a first intermediate outcome, wherein generating the first intermediate outcome includes applying the reward-based reinforcement model to the outcome
receiving a user input responsive to the explanation
generating a second intermediate outcome, wherein generating the second intermediate outcome includes applying the user input to the first intermediate outcome
providing feedback to the reward-based reinforcement model based on the user input
adjusting the reward-based reinforcement model based on the feedback
However, Humphrey, which is analogous to the claimed invention because it is directed toward selecting and filling a template, discloses:
selecting a template from a template store (column 4, lines 44-52: Here, the autonomous report composer cooperates with a library to select from the library, one of a “multitude of templates,” In this instance, the library is a template store)
inserting data into a template (column 34, lines 4-19: Here, a template is selected and fillable blanks within the template are populated with data to generate a report)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Humphry with Hanekamp, with a reasonable expectation of success, as it would have allowed for selection and filling of a template based upon the type information to be conveyed (Humphrey: column 4, lines 44-52). This would have provided the user the advantage of tailoring contents to meet user requests.
Additionally, Oral, which is analogous to the claimed invention because it is directed toward populating templates, discloses:
storing data in a cache (paragraph 0088: Here, data is stored in a local cache to reduce the number of times data is retrieved from bulk storage)
storing the inserted template and the filled data as an outcome (paragraph 0051: Here, populated templates are stored)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Oral with Hanekamp-Humphrey, with a reasonable expectation of success, as it would have allowed for conserving computing resources through both limiting requests to main memory via a cache and saving populated forms so that they are not generated each time a request is received. This would have provided both an increase in speed in providing data to an end user but also limited use of computing resources.
Further, Emigh, which is analogous to the claimed invention because it is directed toward performing a challenge-response function, discloses:
processing the outcome according to a challenge function to create a challenged outcome (column 4, line 45- column 5, line 12: Here, a challenge-response function is used to generate challenges to data)
communicate the challenged outcome to the application (column 6, lines 6-25: Here, based upon the response of the challenge-response being appropriate, the message data may be processed)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Emigh with Hanekamap-Humphrey-Oral, with a reasonable expectation of success, as it would have enabled a means for ensuring the generated response is appropriate. This would have facilitated providing correct explanation information to end users.
Additionally, Saha, which is analogous to the claimed invention because it is directed toward using a reinforcement learning model, discloses:
wherein the processing the outcome according to the challenge function includes:
storing a reward-based reinforcement model (Figure 10A; paragraph 0076: Here, a reinforcement learning model is a machine learning model (Figure 13, item 1330))
receiving a user input responsive to the output (paragraph 0076: Here, user input is received responsive to the output of the reinforcement learning model)
providing feedback to the reward-based reinforcement model based on the user input (paragraph 0076: Here, the user feedback may be positive or negative feedback. This feedback is used with a reward function to train the reinforcement model)
adjusting the reward-based reinforcement model based on the feedback (paragraph 0076: This feedback is used with a reward function to train the reinforcement model)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Saha with Hanekamap-Humphrey-Oral-Emigh, with a reasonable expectation of success, as it would have allowed for using reinforcement learning to train and update the machine learning model (Saha: paragraph 0076). This would have provided the advantage of continually adjusting the model based upon positive/negative rewards (Saha: paragraph 0076).
Finally, Jha, which is analogous to the claimed invention because it is directed toward reinforcement learning, discloses:
generating a first intermediate outcome, wherein generating the first intermediate outcome includes applying the reward-based reinforcement model to the outcome (paragraph 0084: Here, a reinforcement learning model receive a first reward signal based upon the reward signal definition and alter the decision-making model to receive a stronger reward signal)
generating a second intermediate outcome, wherein generating the second intermediate outcome includes applying the user input to the first intermediate output (paragraph 0084: Here, the decision-making model generates a ranked list of options to present to a user to receive user selection. This user selection results in a second intermediate outcome and is used to further train/update the decision-making model)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jha with Hanekamp-Humphrey-Oral-Emigh-Saha, with a reasonable expectation of success, as it would have allowed for training and improving a reinforcement model through updating (Jha: paragraph 0084).
As per dependent claim 2, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp further discloses prior to receiving the explanation request, fetching a configuration change from a service of the application and grouping the configuration change in the homogenous order (column 25, lines 28-47: Here, prior to receiving an explanation request, the underlying graphs and explanations can be updated as needed. The examiner is interpreting these changes as configuration changes. These changes are then associated with the associated calculation graphs to provided updated explanations).
As per dependent claim 4, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Hanekamp discloses wherein the fetching is based upon publication/subscription (column 23, lines 5-22: Here, based upon a user’s subscription level, different levels of explanation are provided. In the event that a user upgrades their subscription to “unlock” additional or more detailed explanations (column 23, lines 16-22). This is one of the instances (user tailors the tax preparation software, wherein tailoring includes purchasing an add-on to unlock explanations) in which the underlying explanations can be updated (column 25, lines 28-47)).
As per dependent claim 9, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp discloses wherein the homogenous cluster is stored in a database (column 21, lines 40-43: Here, the explanation data (homogenous cluster) is stored in a database).
Hanekamp fails to specifically disclose use of a cache. However, Oral discloses use of a cache (paragraph 0088). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Oral with Hanekamp-Humphrey, with a reasonable expectation of success, as it would have allowed for conserving computing resources through both limiting requests to main memory via a cache and saving populated forms so that they are not generated each time a request is received. This would have provided both an increase in speed in providing data to an end user but also limited use of computing resources.
With respect to independent claim 10, the applicant discloses the limitations substantially similar to those in claim 1. Claim 10 is similarly rejected.
Hanekamp further discloses a non-transitory computer-readable storage medium embodying a computer program for performing a method (column 33, lines 47-59).
With respect to claims 11-13, the applicant discloses the limitations substantially similar to those in claims 2, 4, and 9, respectively. Claims 11-13 are similarly rejected.
As per dependent claim 15, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Hanekamp fails to specifically disclose modelling the outcome as a Poisson distribution.
However, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date to have modeled data using a Poisson distribution because it defined the expectation of events in a challenge function. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Hanekamap-Humphrey-Oral, with a reasonable expectation of success, as it would have provided a probability distribution of receiving an appropriate response via the challenge function.
With respect to independent claim 16, the applicant discloses the limitations substantially similar to those in claim 1. Claim 10 is similarly rejected.
Hanekamp further discloses one or more hardware processors (Figure 23, item 304; column 33, lines 13-17), at least one memory coupled to the at least one of the one or more hardware processors (Figure 23, 300; column 33, lines 13-17), and one or more non-transitory computer-readable medium having stored thereon computer-executable instructions, that when executed by the computer system, cause the computer to perform a method (column 33, lines 47-59).
With respect to claims 17-18, the applicant discloses the limitations substantially similar to those in claims 2 and 4, respectively. Claims 17-18 are similarly rejected.
As per dependent claim 21, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp discloses an explanation (column 21, lines 52-56: Here, a user interacts with the interface to request an explanation of why a particular calculation, operation, or decision was made by the system).
Hanekamp fails to specifically disclose receiving a negative feedback in response to the explanation. However, Sasha discloses receiving a negative feedback in response to a failure of the recommendation determined by the user (paragraph 0076). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Saha with Hanekamap-Humphrey-Oral-Emigh, with a reasonable expectation of success, as it would have allowed for using reinforcement learning to train and update the machine learning model based upon the determination of a failure of the recommendation (Saha: paragraph 0076), wherein the recommendation is the explanation (Hanekamp: column 21, lines 52-56). This would have provided the advantage of continually adjusting the model based upon positive/negative rewards (Saha: paragraph 0076).
As per dependent claim 22, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp discloses an explanation (column 21, lines 52-56: Here, a user interacts with the interface to request an explanation of why a particular calculation, operation, or decision was made by the system).
Hanekamp fails to specifically disclose receiving a negative feedback in response to the explanation. However, Sasha discloses receiving a positive feedback based on the success of the recommendation determined by the user (paragraph 0076). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Saha with Hanekamap-Humphrey-Oral-Emigh, with a reasonable expectation of success, as it would have allowed for using reinforcement learning to train and update the machine learning model based upon the determination of a success of the recommendation (Saha: paragraph 0076), wherein the recommendation is the explanation (Hanekamp: column 21, lines 52-56).
As per dependent claim 25, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Emigh further discloses generating the challenged outcome, wherein generating the challenged outcome includes applying the challenge function to the outcome (Figure 10; column 10, line 43- column 11, line 31: Here, a plurality of processing steps are performed. Based upon the processing, a challenge is performed (Figure 10, item 160), and if the response is satisfactory, the content is retained). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Emigh with Hanekamp-Humphrey-Oral-Emigh-Saha-Jha, with a reasonable expectation of success, as it would have allowed for determining the validity of contents before incorporating the contents (Emigh: column 11, lines 15-26).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha and further in view of Mandyam et al. (US 8595186, patented 26 November 2013, hereafter Mandyam).
As per dependent claim 3, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Hanekamp fails to specifically disclose wherein the fetching is performed asynchronously in response to a scheduled job.
However, Mandyam, which is analogous to the claimed invention because it is directed toward scheduling updates, discloses wherein the fetching is performed asynchronously in response to a scheduled job (column 24, lines 40-48: Here, a fetching policy is defined to update data on a fixed schedule to maintain content freshness).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Mandyam with Hanekamp, Humphrey, and Oral, with a reasonable expectation of success, as it would have allowed for scheduling updates to maintain content freshness. This would have provided a user with an explanation interface capable of displaying up to date explanations.
Claims 5-6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha and further in view of Katz et al. (US 2016/0103858, published 14 April 2016, hereafter Katz) and further in view of Dhakshinamoorthy et al. (US 2019/0107827, published 11 April 2019, hereafter Dhakshinamoorthy).
As per dependent claim 5, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp fails to specifically disclose wherein the traversing involves a search key and jumps to a neighbor node based upon a ranking.
However, Katz, which is analogous to the claimed invention because it is directed toward traversing tree data based upon a search key, discloses wherein the traversing involves a search key and jumps to a neighbor node (paragraph 0037: Here, a search is performed to access a child node based upon the search key. The parent (neighbor) node of the child is accessed). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Katz with Hanekamp-Humphrey-Oral, with a reasonable expectation of success, as it would have allowed for searching a tree based upon a key. Such searching would have facilitated more efficient identification of nodes by eliminating the need to traverse the entire tree. Instead, the node can be accessed more efficiently based upon the stored key value.
Further, Dhakshinamoorthy, which is analogous to the claimed invention because it is directed toward ranking nodes based upon a query, discloses ranking nodes based upon a query (paragraph 0089: Here, a semantic analysis engine ranks the nodes based upon the most relevant nodes with respect to the query). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Dhakshinamoorthy with Hanekamp-Humphrey-Oral-Katz, with a reasonable expectation of success, as it would have allowed for accessing the most relevant nodes (Dhakshinamoorthy: paragraph 0089). This would have allowed a user to receive the most relevant content related to their query.
As per dependent claim 6, Hanekamp, Humphrey, Oral, Emigh, Saha, Jha, and Dhakshinamoorthy disclose the limitations similar to those in claim 5, and the same rejection is incorporated herein. Dhakshinamoorthy discloses wherein the ranking is created during store in the cache (paragraph 0089: Here, the query engine performs semantic analysis of cache stored in the repository. Additionally, the semantic analysis engine performs ranking of the nodes based upon query conditions). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Dhakshinamoorthy with Hanekamp-Humphrey-Oral-Katz, with a reasonable expectation of success, as it would have allowed for accessing the most relevant nodes (Dhakshinamoorthy: paragraph 0089). This would have allowed a user to receive the most relevant content related to their query.
With respect to claim 14, the applicant discloses the limitations substantially similar to those in claim 5. Claim 14 is similarly rejected.
Claims 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha and further in view of Adams et al. (US 9858592, patented 2 January 2018, hereafter Adams).
As per dependent claim 23, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp fails to specifically disclose applying the reward-based reinforcement model to the outcome according to a covariance function.
However, Adams, which is analogous to the claimed invention because it is directed toward optimizing a model, discloses applying the reward-based reinforcement model to the outcome according to a covariance function (column 13, line 57- column 14, line 34: Here, a covariance function relates hyper-parameters of a machine learning system to its performance). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Adams with Hanekamp-Humphrey-Oral-Emigh-Saha-Jha, with a reasonable expectation of success, as it would have allowed for tuning the machine learning model to the performance of the model (Adams: column 13, line 57- column 14, line 34).
As per dependent claim 24, Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Hanekamp fails to specifically disclose applying the reward-based reinforcement model to the outcome according to a correlation function.
However, Adams, which is analogous to the claimed invention because it is directed toward optimizing a model, discloses applying the reward-based reinforcement model to the outcome according to a correlation function (column 13, line 57- column 14, line 34: Here, a covariance function relates hyper-parameters of a machine learning system to its performance. The covariance function represents a correlation among sets of hyper-parameter values). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Adams with Hanekamp-Humphrey-Oral-Emigh-Saha-Jha, with a reasonable expectation of success, as it would have allowed for tuning the machine learning model to the performance of the model (Adams: column 13, line 57- column 14, line 34).
Response to Arguments
Applicant’s arguments have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Hanekamp, Humphrey, Oral, Emigh, Saha, and Jha.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gueret et al. (US 2022/0180225): Discloses a qualification module for determining whether a set of outcomes satisfies a qualification threshold and adjusting the feature values to generate a set of items satisfying the threshold (Abstract).
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/KYLE R STORK/Primary Examiner, Art Unit 2128