Prosecution Insights
Last updated: April 19, 2026
Application No. 18/889,849

METHOD AND SYSTEM FOR CREATING BAYESIAN KNOWLEDGE NETWORKS (BKNS) FOR MEDICAL CONDITIONS

Non-Final OA §101§102
Filed
Sep 19, 2024
Examiner
COBANOGLU, DILEK B
Art Unit
3687
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Cloudphysician Healthcare Pvt Ltd.
OA Round
1 (Non-Final)
33%
Grant Probability
At Risk
1-2
OA Rounds
4y 9m
To Grant
61%
With Interview

Examiner Intelligence

Grants only 33% of cases
33%
Career Allow Rate
163 granted / 492 resolved
-18.9% vs TC avg
Strong +28% interview lift
Without
With
+27.9%
Interview Lift
resolved cases with interview
Typical timeline
4y 9m
Avg Prosecution
57 currently pending
Career history
549
Total Applications
across all art units

Statute-Specific Performance

§101
35.3%
-4.7% vs TC avg
§103
27.2%
-12.8% vs TC avg
§102
21.1%
-18.9% vs TC avg
§112
13.6%
-26.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 492 resolved cases

Office Action

§101 §102
DETAILED ACTION 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 . Claims 1-13 have been examined. Information Disclosure Statement The information disclosure statement filed 09/19/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. In particular, the Japanese foreign patent document, JP6969831 and non-patent literature publication, “Application of Bayesian network and regression method in treatment coast prediction” by Li-Li Tong et al. has not been provided. 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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-6 are drawn to a method which is within the four statutory categories (i.e. process). Claims 7-12 are drawn to a system which is within the four statutory categories (i.e. machine). Claim 13 is drawn to a non-transitory medium which is within the four statutory categories (i.e. manufacture). Step 2A, Prong 1: The independent claims 1, 7 and 13 recite: “retrieving, by a computing device, an audit trail dataset from an Electronic Medical Record (EMR) corresponding to each of a plurality of patients for one or more medical conditions, wherein the audit trail dataset comprises a plurality of events; determining, by the computing device, a generalized hospital event timeline for each of the one or more medical conditions based on the audit trail dataset, wherein the generalized hospital event timeline comprises a plurality of nodes corresponding to the plurality of events, and wherein the plurality of nodes is connected by unidirected edges in a chronological order of the plurality of events; generating, by the computing device, a fully connected network of the plurality of nodes of one or more generalized hospital event timelines of the one or more medical conditions, wherein each two of the plurality of nodes in the fully connected network are interconnected via an undirected edge; determining, by the computing device, a relation type corresponding to each two of the plurality of nodes of the fully connected network based on the one or more generalized hospital event timelines using a causal network learning algorithm; and creating, by the computing device, a BKN from the fully connected network based on the determined relation type for each two of the plurality of nodes.” The limitations of “retrieving, by a computing device, an audit trail dataset from an Electronic Medical Record (EMR) corresponding to each of a plurality of patients for one or more medical conditions, wherein the audit trail dataset comprises a plurality of events; determining, by the computing device, a generalized hospital event timeline for each of the one or more medical conditions based on the audit trail dataset, wherein the generalized hospital event timeline comprises a plurality of nodes corresponding to the plurality of events, and wherein the plurality of nodes is connected by unidirected edges in a chronological order of the plurality of events; generating, by the computing device, a fully connected network of the plurality of nodes of one or more generalized hospital event timelines of the one or more medical conditions, wherein each two of the plurality of nodes in the fully connected network are interconnected via an undirected edge;” correspond to an abstract idea of “certain methods of organizing human activity” with a recitation of generic computing component (the computing device). This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processor and generic network devices does not take the claim out of the methods of organizing human interactions grouping. Thus, the claims recite an abstract idea. The current specification recites “The system 100 may include a computing device 102 (for example, server, desktop, laptop, notebook, netbook, tablet, smartphone, mobile phone, or any other computing device), in accordance with some embodiments of the present disclosure. The computing device 102 may create the BKNs for medical conditions. In particular, the computing device 102 may create the BKNs based on medical conditions of different patients.” in [0019]. Therefore, the computing device is directed to a generic computing device. The limitations of “determining, by the computing device, a relation type corresponding to each two of the plurality of nodes of the fully connected network based on the one or more generalized hospital event timelines using a causal network learning algorithm” and “creating, by the computing device, a BKN from the fully connected network based on the determined relation type for each two of the plurality of nodes” correspond to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself. Claims 2-6, 8-12 are ultimately dependent from claims 1, 7 and include all the limitations of claims 1, 7. Therefore, claims 2-6, 8-12 recite the same abstract idea. Claims 2-6, 8-12 describe a further limitation regarding the basis for determining and generating plurality of nodes of one or more medical conditions. These are all just further describing the abstract idea recited in claims 1, 7, without adding significantly more. Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a computing device”, “a processor” “a memory communicatively coupled to the processor…”, using the computing device to perform the retrieving, determining, generating and creating functions (claims 1, 7 and 13), “iteratively updating in real-time, the BKN based on the retrieved audit trail” (claims 4, 10) and “modifying the BKN based on a user command” (claims 6, 12), which are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)). The computing device/processor in the claim steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining a generalized hospital event timeline and determining relation type corresponding to plurality of nodes and event timelines) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an 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 integration of the abstract idea into a practical application, the additional element of using a computing device/processor to perform the determining, generating, creating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 1-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Narain et al. (hereinafter Narain) (US 10,482,385 B2). Claim 1 recites a method for creating Bayesian Knowledge Networks (BKNs) for medical conditions, the method comprising: retrieving, by a computing device, an audit trail dataset from an Electronic Medical Record (EMR) corresponding to each of a plurality of patients for one or more medical conditions, wherein the audit trail dataset comprises a plurality of events (Narain discloses “…receiving data corresponding to a plurality of patients, where the data includes diagnostic information and/or treatment information for each patient…” in col. 1, lines 40-56, “…the data includes information from patient electronic health records” in col. 2, lines 11-12); determining, by the computing device, a generalized hospital event timeline for each of the one or more medical conditions based on the audit trail dataset, wherein the generalized hospital event timeline comprises a plurality of nodes corresponding to the plurality of events, and wherein the plurality of nodes is connected by unidirected edges in a chronological order of the plurality of events (Narain discloses “…the received data relates to a plurality of patient from a selected hospital…” in col. 3, lines 37-40, “…a graphical representation of part or all of the causal relationship network model may be displayed. In some embodiments, the information received from a user may be a selection of one or more nodes displayed in the graphical representation of part or all of the causal relationship network model.” in col. 14, lines 7-12, “…The thickness of the arrow indicates that this is a stronger relationships or connection…” in col. 24, lines 1-23 and fig. 17A); generating, by the computing device, a fully connected network of the plurality of nodes of one or more generalized hospital event timelines of the one or more medical conditions, wherein each two of the plurality of nodes in the fully connected network are interconnected via an undirected edge (Narain discloses “…a graphical representation of part or all of the causal relationship network model may be displayed. In some embodiments, the information received from a user may be a selection of one or more nodes displayed in the graphical representation of part or all of the causal relationship network model.” in col. 14, lines 7-12, “…The thickness of the arrow indicates that this is a stronger relationships or connection…” in col. 24, lines 1-23 and fig. 17A); determining, by the computing device, a relation type corresponding to each two of the plurality of nodes of the fully connected network based on the one or more generalized hospital event timelines using a causal network learning algorithm (Narain discloses “…a graphical representation of part or all of the causal relationship network model may be displayed. In some embodiments, the information received from a user may be a selection of one or more nodes displayed in the graphical representation of part or all of the causal relationship network model.” in col. 14, lines 7-12); and creating, by the computing device, a BKN from the fully connected network based on the determined relation type for each two of the plurality of nodes (Narain discloses “…a graphical representation of part or all of the causal relationship network model may be displayed. In some embodiments, the information received from a user may be a selection of one or more nodes displayed in the graphical representation of part or all of the causal relationship network model.” in col. 14, lines 7-12, “connection between nodes…” in col. 19, lines 28-45). Claim 2 recites the method of claim 1, wherein the relation type corresponds to one of a causal relation or a non-causal relation (Narain; col. 13, lines 12-34). Claim 3 recites the method of claim 2, wherein when the relation type of two nodes corresponds to causal relation, the two nodes in the BKN are connected via a directed edge, and wherein when the relation type of two nodes corresponds to non-causal relation, the two nodes in the BKN are not connected by an edge (Narain; col. 13, lines 12-34, col. 19, line 58 to col. 20, line 12). Claim 4 recites the method of claim 1, further comprising: retrieving the audit trail dataset in real-time from the EMR; and iteratively updating in real-time, the BKN based on the retrieved audit trail (Narain; col. 13, lines 12-34). Claim 5 recites the method of claim 1, further comprising rendering the BKN on a Graphical User Interface (GUI) (Narain; col. 14, line 66 to col. 15, line 4). Claim 6 recites the method of claim 1, further comprising modifying the BKN based on a user command (Narain; col. 1, lines 57-67, col. 13, lines 14-18). As per claims 7-12, they are system claims which repeat the same limitations of claims 1-6, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Narain disclose the underlying process steps that constitute the methods of claims 1-6, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 7-12 are rejected for the same reasons given above for claims 1-6. As per claim 13, it is an article of manufacture claim which repeats the same limitations of claim 1, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Narain disclose the underlying process steps that constitute the method of claim 1, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 13, are rejected for the same reasons given above for claim 1. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Mamon can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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Prosecution Timeline

Sep 19, 2024
Application Filed
Feb 04, 2026
Non-Final Rejection — §101, §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
33%
Grant Probability
61%
With Interview (+27.9%)
4y 9m
Median Time to Grant
Low
PTA Risk
Based on 492 resolved cases by this examiner. Grant probability derived from career allow rate.

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