DETAILED ACTION
This action is in response to the claims filed 12/08/2025 for Application number 17/133,158. Claims 1, 4, 7-8, 12-13, and 15-16 have been amended. Thus, claims 1-2, 4, 7-9, and 12-25
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/08/2025 has been entered.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 7-9, 12-18, 20, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Ghatage et al. ("US 20170372231 A1", hereinafter "Ghatage") in view of Kamangar ("US 20220076851 A1", hereinafter "Kamangar") and further in view of Purushothaman et al. ("US 20220012605 A1", hereinafter "Purushothaman").
Regarding claim 1, Ghatage teaches A computer-implemented method for addressing and remedying problems associated with service requests, the computer-implemented method performed by one or more hardware processors (¶0058 “hardware-based processors”) comprising:
receiving at least one service request related to a problem (“In general, innovative aspects of the subject matter described in this specification can be embodied in methods that include operations of: receiving a request that indicates an issue to be resolved” [¶0004]) and to an [electronic medical record (EMR)] computer system];
inputting, via the one or more hardware processors, content associated with the at least one service request to a natural language processor (NLP) (“input from the user may be received in any appropriate form, including acoustic, speech, or tactile input.” [¶0071]), wherein inputting the content causes the NLP to output an indication of at least one key term associated with the at least one service request (“In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP).” [¶0015; See further ¶0028: “For example, in response to a request 106 that includes particular keywords and/or phrases…]);
accessing, via the one or more hardware processors (¶0058 “hardware-based processors”), a machine-learning electronic model (“The classification engine 118 (corresponds to “a machine-learning electronic model”) may receive the request 106 and the priority 116 from the preprocessing module(s) 110. The classification engine 118 may attempt to classify the request 106 by determining one or more categories that are relevant to the request 106.” [¶0032]), wherein
a) the machine-learning electronic model is trained based on (i) data associated with instances of terms associated with service requests (“In some implementations, the classification engine 118 may employ a classification model 128 that is trained using one or more ML techniques.” [¶0032; See FIG. 3]) and (ii) one or more decision elements associated with the instances of terms associated with service requests (“In some implementations, the classification engine 118 may access a rules engine 132(2) which applies one or more (e.g., business) rules to generate the ticket 120 in the ticketing service 122.” [¶0034; See further the training would be based on “rules” disclosed in ¶0026: “In some implementations, the priority determination module 114 may determine priority 116 by applying a set of (e.g., business) rules to the various word(s) and/or phrase(s) included in the text data of the request 106.”), and
(b) applying the machine-learning electronic model to data associated with items including the at least one key term generates information indicating the at least one classification group for the at least one service request (“Historical data may be accessed (302), the historical data describing past requests, tickets created to handle the past requests, and/or the resolution of the past requests. The historical data may include, for one or more requests, the text included in the request and the one or more categories into which the request was classified… The classification model 128 may be trained (306) or otherwise determined using the historical data and/or additional training data. (“applying the machine-learning electronic model to data associated with items…”) The classification model 128 may be stored and provided (308) for use in classifying subsequently received requests 106 into the appropriate categories” [¶0042-¶0043]);
Although Ghatage discloses service request routing in a computer implemented service environment, the reference does not explicitly disclose service request routing in an electronic medical record (EMR) computer system
Kamangar teaches receiving at least one service request related to an electronic medical record (EMR) computer system. (The patient's service request may be prioritized relative to other service requests based upon a number of prioritization factors, including e.g. evaluation of patient electronic medical records” [¶0008])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ghatage’s service request routing method by implementing it in an EMR computer system environment as taught by Kamangar. One would have been motivated to make this modification as machine learning based algorithms and classifiers would be utilized by service providers to assist with automatic evaluation of request suitability. [Kamangar, ¶0010]
However Ghatage/Kamangar fails to explicitly teach automatically determining, by the one or more hardware processors, that a rate of success associated with at least one remedy for the at least one classification group
and
in response to the rate of success associated with the at least one remedy exceeding a threshold, causing the at least one remedy to be implemented in the [EMR] computer system
Purushothaman teaches automatically determining, by the one or more hardware processors, that a rate of success associated with at least one remedy for the at least one classification group (“As shown in block 540, the system determines, via an artificial intelligence engine, an optimal variant from the one or more variants based on the one or more parameters and data present in a heuristic database. The data present in the heuristic database (e.g., knowledge base 385) comprises information associated with success rate of each of the one or more variants and/or each of the one or more actions/steps associated with resolving the application service request.” [¶0051; Note: See Abstract: “extracts one or more variants of standard operating procedure associated with the application service request, wherein the one or more variants are solutions associated with processing the application service request”])
and
in response to the rate of success associated with the at least one remedy exceeding a threshold, causing the at least one remedy to be implemented in the [EMR] computer system (“As shown in block 550, the system implements one or more actions associated with the optimal variant to process the application service request.” [¶0052; note “optimal variant” implies a rate of success exceeding a threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ghatage’s/Kamangar’s teachings in order to determine a rate of success of a solution to a service request and further implementing the solution if it exceeds a threshold as taught by Purushothaman. One would have been motivated to make this modification to process future service requests more efficiently. [¶0055, Purushothaman]
Regarding claim 2, Ghatage/Kamangar/Purushothaman teaches The computer-implemented method of claim 1, where Ghatage teaches wherein the at least one classification group is comprised of at least one of an access issue, an update issue, an email issue, or a payroll issue. (“As another example, a request may be submitted for financial, personal, and/or transactional data, to raise a dispute, or for other purposes. The text of the request may be analyzed to automatically determine a category of the request (e.g., a subject matter of the request, what the request is about). [¶0015; note: The claim recites “or”, thus under BRI, the examiner is only required to map to one of the recited elements, however, access issue is disclosed in ¶0019 and email issue is disclosed in ¶0047])
Regarding claim 7, Ghatage/Kamangar/Purushothaman teaches the computer-implemented method of claim 1, further comprising:
Ghatage teaches determining that the at least one remedy was not successful for the problem; and based on determining that the at least one remedy was not successful, flagging the at least one classification group for retraining via a machine learning algorithm. (“If a request is inappropriately classified into a (e.g., wrong) category, routed to the wrong agent(s) 126, and/or not resolved or unsatisfactorily resolved, the category classification of the request may be designated as wrongly classified data. Such data may be used to further train and/or refine the model to more appropriately classify subsequently received requests that include similar text to the wrongly classified data.” [¶0044])
Claim 8 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 8 additionally requires One or more non-transitory media (Ghatage, [¶0061])
Regarding claim 9, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 12, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, wherein the EMR computer system is remotely located at a client site. (“In some embodiments, service providers such as dental care providers, medical care providers, and others may provide some or all of their services remotely, in-person, or via combination of remote and in-person interactions with one or more clients.” [¶0032])
Same motivation to combine the teachings of Ghatage/Kamangar/Purushothaman as claim 8.
Regarding claim 13, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, wherein the operations further comprise:
Ghatage teaches determining that the at least one remedy was successful for the problem; and based on determining that the at least one remedy was successful, storing information associated with the at least one remedy, the at least one service request, and the at least one classification group, in a database associated with a machine learning component. (“For example, if a request 106 is appropriately classified into a category, appropriately routed to the suitable agent(s) 126, and/or resolved in a timely and/or successful manner, the category classification of the request may be designated as positive training data that is used to further train and/or refine the performance of the model.” [¶0044])
Regarding claim 14, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, where Ghatage teaches wherein the at least one classification group is associated with at least one application programming interface. (“For example, the automated response module(s) 130 may call one or more application programming interfaces (APIs) and/or access other types of interface(s) to perform action(s).” [¶0029])
Regarding claim 15, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 14, where Ghatage teaches wherein the at least one application programming interface is configured to implement the at least one remedy for the problem in the [EMR] computer system. (“The automated response module(s) 130 may access various systems to perform action(s). For example, the automated response module(s) 130 may call one or more application programming interfaces (APIs) and/or access other types of interface(s) to perform action(s).” [¶0029; note: Kamangar teaches the EMR system while Ghatage teaches implementing resolutions in a computer system by calling APIs, thus the combination of Ghatage/Kamangar teaches the limitation as recited.])
Same motivation to combine the teachings of Ghatage/Kamangar/Purushothaman as claim 8.
Regarding claim 16, it is substantially similar to claims 1 and 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 17, Ghatage/Kamangar/Purushothaman teaches The system of claim 16, where Ghatage teaches wherein the at least one classification group includes predetermined information associated with an input by a user device. (“Historical data may be accessed (302), the historical data describing past requests, tickets created to handle the past requests, and/or the resolution of the past requests. The historical data may include, for one or more requests, the text included in the request and the one or more categories into which the request was classified.” [¶0042; See FIG. 1, user device 104])
Regarding claim 18, Ghatage/Kamangar/Purushothaman teaches The system of claim 16, where Ghatage teaches wherein the at least one classification group is comprised of at least one classification subgroup. (“In some implementations, the category of a request 106 and/or ticket 120 may include one or more categories and/or subcategories within a hierarchy of categories and subcategories” [¶0039])
Regarding claim 20, Ghatage/Kamangar/Purushothaman teaches The system of claim 16, wherein the method further comprises:
Ghatage teaches causing displaying on a graphical user interface, an icon displaying the at least one remedy for the problem. (See FIG.2, “Implementations may provide a UI that includes any suitable number and type of UI elements such as controls, text, graphics, images, video, audio, frames, dynamic elements, static elements, and/or other UI element(s). The UI may include any suitable number of pages, windows, and/or other containers for presenting the UI elements.” [¶0040; See ¶0038; “In some example of FIG. 2, the priority 116 of the request 106 has been set to “High” to indicate that the resolution of the request 106 takes priority over other, lower priority requests.”])
Regarding claim 25, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, wherein the determining of the at least one key term, the determining of the at least one classification group (“The text of the request may be analyzed to automatically determine a category of the request.” [Abstract]),
the applying of the machine-learning electronic model to the data associated with the items (“In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP).” [Abstract]),
the automatic determining, and the causing of the at least one remedy to be implemented in the EMR computer system occur automatically without user input. (“In some instances, the automated response module(s) 130 may automatically perform one or more actions in response to a request 106. In some implementations, the automated response module(s) 130 may employ robotic processing analysis (RPA) to identify and perform action(s) automatically. Such action(s) may include retrieving and sending information to the requestor 102. For example, in response to a request 106 that includes particular keywords and/or phrases, the automated response module(s) 130 may retrieve and send knowledge base information, troubleshooting guidelines, answers to frequently asked questions (FAQs), and/or other information relevant to the request 106.” [¶0028])
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ghatage in view of Kamangar and Purushothaman and further in view of Gatti ("US 20130111488A1", hereinafter "Gatti").
Regarding claim 4, Ghatage/Kamangar/Purushothaman teaches The computer-implemented method of claim 1, wherein implementing the at least one remedy for the problem comprises:
where Ghatage teaches determining that a server associated with the EMR computer system is down (“For example, if a request 106 includes the text “network server down”, the priority 116 may be designated as high given the potentially severe (e.g., business) impact of the issue on the organization's ability to function internally, service customers, and so forth” [¶0026]); and
However Ghatage/Kamangar/Purushothaman fails to explicitly teach based on determining that the server associated with the EMR computer system is down, causing a reboot of the server.
Gatti teaches based on determining that the server associated with the EMR computer system is down, causing a reboot of the server. (“A service desk ticket in an IT setting may include a request for various computer related service tasks such as a request to update/install application software, backup a database/flat files, create a user account, add/replace memory, reset a password, reload an operating system, reboot a server, add disk space, troubleshoot a device (e.g., computer, printer, router, etc.), replace/add a device, etc.” [¶0022)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ghatage’s/Kamangar’s/Purushothaman’s teachings with the teachings of Gatti. One would have been motivated to make this modification because a service desk ticket in an IT setting may include a request for various computer related service tasks…[Gatti, [¶0022]
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Ghatage in view of Kamangar and Purushothaman and further in view of Hadad et al. ("US 20200202997 A1", hereinafter "Hadad").
Regarding claim 19, Ghatage/Kamangar/Purushothaman teaches The system of claim 16, however fails to explicitly teach wherein the machine-learning electronic model includes a random forest algorithm.
Hadad teaches wherein the machine-learning electronic model includes a random forest algorithm. (“Examples of machine learning algorithms or models that can be implemented at the machine learning model can include, but are not limited to: regression models such as linear regression, logistic regression, and K-means clustering; one or more decision tree models (e.g., a random forest model);” [¶0068])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ghatage’s/Kamangar’s/Purushothaman’s teachings by implementing a random forest algorithm as taught by Hadad. Ghatage discloses classification algorithms such as decision trees (¶0045) and a random forest algorithm is an ensemble of decision trees, thus one would have been motivated to make this modification to predict outcomes based on different variables and rules. [¶0067, Hadad]
Claims 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Ghatage in view of Kamangar and Purushothaman and further in view of Are et al. ("US 20170140114 A1", hereinafter "Are").
Regarding claim 21, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, however fails to explicitly teach wherein at least one of the operations is performed using a distributed electronic agent architecture that is associated with a geographically distributed electronic memory.
Are teaches wherein at least one of the operations is performed using a distributed electronic agent architecture that is associated with a geographically distributed electronic memory. (“Some embodiments of operating system 129 comprise a distributed adaptive agent operating system” [¶0040; See further “In a distributed computing environment, program modules may be located in local and/or remote computer storage media including, by way of example only, memory storage devices. (“geographically distributed electronic memory”) [¶0048])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Ghatage’s/Kamangar’s/Purushothaman’s teachings in order to implement the distributed adaptive agent system of Are. One would have been motivated to make this modification in order to facilitate accessing data from remote data sources/locations. [¶0052, Are]
Regarding claim 22, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, however fails to explicitly teach wherein one or more of the operations are performed using a geographically distributed medical-information computing system associated with at least one network coupled electronic memory.
Are teaches wherein one or more of the operations are performed using a geographically distributed medical-information computing system associated with at least one network coupled electronic memory. (“Embodiments of the present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and/or remote computer storage media including, by way of example only, memory storage devices.” [¶0048])
Same motivation to combine the teachings of Ghatage/Kamangar/Purushothaman/Are as claim 21.
Regarding claim 23, Ghatage/Kamangar/Purushothaman teaches The one or more non-transitory media of claim 8, however fails to explicitly teach wherein at least one of the operations is performed using a plurality of electronic agents that are associated with a distributed electronic memory of a health care computing system. (“In embodiments, a remote computer 108 is associated with a health records, data source such as data records systems 160, an electronic health record (EHR) system of a hospital or medical organization, a health information exchange EHR, insurance provider EHR, ambulatory clinic EHR, or patient-sensor, or other data source, and facilitates accessing data of the source and communicating the data to server 102 and/or other computing devices on a cloud computing platform, including other remote computers 108.” [¶0052. See also ¶0048])
Same motivation to combine the teachings of Ghatage/Kamangar/Purushothaman/Are as claim 21.
Regarding claim 24, Ghatage/Kamangar teaches The one or more non-transitory media of claim 8, however fails to explicitly teach wherein the one or more hardware processors comprise a distributed adaptive agent that includes a neural network and/or is associated with a distributed memory of an electronic records computing system.
Are teaches wherein the one or more hardware processors comprise a distributed adaptive agent that includes a neural network and/or is associated with a distributed memory of an electronic records computing system. (“In some embodiments, computer system 120 includes one or more software agents (implies “neural networks/machine learning algorithms” See also ¶0005), and in an embodiment includes an adaptive multi-agent operating system, but it will be appreciated that computer system 120 may also take the form of an adaptive single agent system or a non-agent system.” [¶0046])
Same motivation to combine the teachings of Ghatage/Kamangar/Are as claim 21.
Response to Arguments
Applicant's arguments filed 12/08/2025 have been fully considered and they are partially persuasive.
Regarding the 35 U.S.C §101 Rejection:
Applicant’s amendments to the claims appear to have overcome the 101 rejection and subsequent arguments were deemed to be persuasive. Thus, the previous 101 rejection has been withdrawn.
Regarding the 35 U.S.C. §103 Rejection:
Applicant’s arguments regarding the cited prior art references failing to teach “causing the remedy to be implemented in an EMR computer system based on a rate of success exceeding a threshold” (pg. 16 of the remarks) has been considered but are moot because the newly provided prior art of Purushothaman explicitly teaches using a rate of success within the context of processing service requests. Therefore, examiner asserts Ghatage/Kamangar/Purushothaman teaches all of the claimed limitations of claim 1 and similarly recited independent claims.
Applicant appears to assert that the cited prior art of Ghatage discloses ML algorithms configured on non-medical knowledge/training data rather than EMR related instances. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant merely asserts Ghatage doesn’t teach the use of medical related data however as noted above in the 103 rejection, the examiner relies upon Kamangar to teach that it would be obvious to apply the use of medical related data instances within a service request environment. Therefore, the examiner asserts the limitation is taught by the combination of Ghatage and Kamangar. Applicant also further asserts that Kamangar, Gatti, and Hadad do not teach the specific limitations recited in claim 1 however this argument is not persuasive because the rejection is based upon the combination of references while applicant’s arguments are directed towards the references individually.
Applicant further asserts that any general reference in Kamangar to an EMR computer system does not provide the required motivation to combine. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, as noted within the disclosure of Kamangar, para [0010] explicitly discloses the use of machine learning to assist with remote service request and patient consultation requests (falls within EMR computer system) therefore examiner asserts that there would be a motivation to combine the references. Therefore, examiner asserts that the combination of Ghatage/Kamangar is proper and there would be a motivation to combine the references.
Conclusion
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/MICHAEL H HOANG/Examiner, Art Unit 2122