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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/28/2026 has been entered.
Response to Amendment
The office action is responding to the amendments filed on 07/28/2026. Claims 1 and 10-11 have been amended. Claim 20 is cancelled.
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.
Claim(s) 1-3, 5, 9-13, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stoval et al. [US 2019/0189264 A1] in view of SEVENSTER et al. [US 2019/0311810 A1].
Claim 1 is rejected over Stoval and SEVENSTER.
Stoval teaches “A method for identifying differential diagnoses using a multi-classifier disease model, comprising: obtaining a multi-classifier disease model being a machine learning network based n at least one disease profile,” as “the server 102 may be configured to communicate with multiple image repositories, multiple cognitive systems, or a combination thereof.” [¶0040] and “the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.” [Abstract]
“receiving input patient data of a patient, indicating a condition of the patient wherein the input patient data is non-image data;” as “The viewer may also include a dictation and speech-to-text mechanism that that captures audio input data from a user and converts the audio input data to text data.” [¶0002] (The input data can be audio data, which is essentially non-image data)
“extracting by a processing circuitry, the plurality of structured health variables from the non-image data,” as “The viewer may also include a dictation and speech-to-text mechanism that that captures audio input data from a user and converts the audio input data to text data.” [¶0002] (Speech to text conversion is the extraction of the health data, which is done by the Electronic processor 104 as shown in Fig. 1)
“applying the multi-classifier disease model to the plurality of structured health variables to determine probabilities for the at least one disease;” as “the server 102 may present each differential diagnosis with a probability, such as a “high” probability of cancer or a “low” probability of cancer.” [¶0052]
“identifying, based on the probabilities of the at least one disease, one or more differential diagnoses for the input patient data; and” as “the methods and systems also perform a differential diagnosis, which provides a set of possible diagnoses and their associated probabilities.” [¶0008]
“providing the differential diagnoses.” as “generating a differential diagnosis for the patient based on the classification assigned by the model and data accessible via an electronic medical record of the patient” [¶0023]
Stoval does not explicitly teach each of the at least one disease profile associated with a disease, wherein each disease profile includes a plurality of structured health variables and relationships between at least one disease and the plurality of structured health variables;
However, SEVENSTER teaches “each of the at least one disease profile associated with a disease, wherein each disease profile includes a plurality of structured health variables and relationships between at least one disease and the plurality of structured health variables;” as “Health data may have a hierarchical relationship, when embedded ontologically. A disease state or disease profile may be a combination of one or more risk parameters. Example risk parameters may pertain to an individual's age, the individual's gender, whether the visit to the doctor is due to an emergency, or other health related parameters.” [¶0022] (Health profile and its relationship with various health variables/parameters is recited)
Stoval and SEVENSTER are analogous arts because they teach diagnosis of diseases using machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Stoval and SEVENSTER before him/her, to modify the teachings of Stoval to include the teachings of SEVENSTER with the motivation of among other benefits, system may resolve the need by identifying risk parameters that could have their relevancy confirmed to then narrow the number of risk models to be run. [SEVENSTER, ¶0015]
Claim 2 is rejected over Stoval and SEVENSTER.
Stoval teaches “further comprising: iteratively updating the multi-classifier disease model based on feedback data collected from applying the multi-classifier disease model.” as “This input from the radiologist may be used by the cognitive system 114 to update the model using machine learning.” [¶0051]
Claim 3 is rejected over Stoval and SEVENSTER.
Stoval teaches “further comprising: generating the at least one disease profile, wherein each of the at least one disease profile has the plurality of health variables associated with the disease of the at least one disease;” as “ a differential diagnosis may rule out particular diseases or conditions, may assign a probability to particular diseases or conditions, or a combination thereof.” [¶0068]
“connecting the plurality of structured health variables with the at least one disease; and” as “The server 102 may access patient data (separate from the image study) to perform the differential diagnosis. For example, the server 102 may be configured to access patient data through an electronic medical record of the patient. ” [¶0068]
Stoval does not explicitly teach constructing the multi-classifier disease model based on the at least one disease profile.
However, SEVENSTER teaches “constructing the multi-classifier disease model based on the at least one disease profile.” as “Another aspect of the present disclosure is therefore to assist users of system 10 to integrate the outcomes of multiple potentially inter-related risk parameters and risk models.” [¶0027]
Claim 5 is rejected over Stoval and SEVENSTER.
Stoval teaches “wherein a structured health variable of the plurality of structured health variables is any one of: risk factor, symptom, test, measurement of physiological condition, medication, treatment, and genetic factor.” as “ the server 102 may be configured to provide a differential diagnosis. In the medical industry, a differential diagnosis is the distinguishing of a particular disease or condition from others that present similar clinical features. Accordingly, the server 102 may be configured to interface with the differential diagnosis system 122 to automatically perform differential diagnostic procedures to diagnose a specific disease or condition in a patient or, at least, to eliminate one or more diseases, such as imminently life-threatening diseases or conditions.” [¶0052]
Claim 9 is rejected over Stoval and SEVENSTER.
Stoval teaches “further comprising: determining an operative action based on the differential diagnoses, wherein the operative action is a "to-do" action associated with at least one of the differential diagnoses.” as “ Differential diagnosis may also occur during diagnostic imaging and may make use of prioritization and classification.” [¶0046]
Claim 10 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 1.
Claim 11 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 1.
Claim 12 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 2.
Claim 13 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 3.
Claim 15 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 5.
Claim 19 is rejected by Stoval and SEVENSTER under the same rational of rejection of claim 9.
Claim(s) 4, 8, 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stoval et al. [US 2019/0189264 A1] in view of SEVENSTER et al. [US 2019/0311810 A1] and in further view of Brandt [US 2020/0105405 A1].
Claim 4 is rejected over Stoval, SEVENSTER and Brandt.
The combination of Stoval and SEVENSTER does not explicitly teach wherein the at least one disease profile is generated for the disease in a differential diagnoses set associated with a chief complaint, wherein the chief complaint is a common symptom related to a plurality of differential diagnoses.
However, Brandt teaches “wherein the at least one disease profile is generated for the disease in a differential diagnoses set associated with a chief complaint, wherein the chief complaint is a common symptom related to a plurality of differential diagnoses.” as “the typical differential diagnosis process begins with the identification of a chief medical complaint at step 100. The chief complaint is typically identified by the patient and is typically related to the symptom suffered by the patient without regard to the ultimate cause of the symptom.” [¶0035]
Stoval, SEVENSTER and Brandt are analogous arts because they teach diagnosis of diseases using machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Stoval, SEVENSTER and Brandt before him/her, to modify the teachings of combination of Stoval and SEVENSTER to include the teachings of Brandt with the motivation of tracking the performance of a medical practitioner over time by comparing their performance as measured by the inventive systems and methods to standard procedures that may be established by the medical community. [Brandt, ¶0034]
Claim 8 is rejected over Stoval, SEVENSTER and Brandt.
The combination of Stoval and SEVENSTER does not explicitly teach further comprising: connecting an operative action to a structured health variable of the plurality of health variables based on historical data.
However, Brandt teaches “further comprising: connecting an operative action to a structured health variable of the plurality of health variables based on historical data.” as “These selectable tabs may also provide access to display interfaces that display historical information about a patient, and information about treatment plans and options. ” [¶0063]
Stoval, SEVENSTER and Brandt are analogous arts because they teach diagnosis of diseases using machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Stoval, SEVENSTER and Brandt before him/her, to modify the teachings of combination of Stoval and SEVENSTER to include the teachings of Brandt with the motivation of tracking the performance of a medical practitioner over time by comparing their performance as measured by the inventive systems and methods to standard procedures that may be established by the medical community. [Brandt, ¶0034]
Claim 14 is rejected over Stoval, SEVENSTER and Brandt under the same rationale of rejection of claim 4.
Claim 18 is rejected over Stoval, SEVENSTER and Brandt under the same rationale of rejection of claim 8.
Claim(s) 6-7 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stoval et al. [US 2019/0189264 A1] in view of SEVENSTER et al. [US 2019/0311810 A1] and in further view of Rosenberg et al. [US 2012/0221251 A1].
Claim 6 is rejected over Stoval, SEVENSTER and Rosenberg.
The combination of Stoval and SEVENSTER does not explicitly teach wherein the multi-classifier disease model is a Bayesian Belief Network.
However, Rosenberg teaches “wherein the multi-classifier disease model is a Bayesian Belief Network.” as “statistical methods, such as Logistic Regression, CART, Naive Bayes, Bayesian Belief Networks, Support Vector Machines, and Random Forests, can be used to describe the associations, or a combination of these methods.” [¶0051]
Stoval, SEVENSTER and Rosenberg are analogous arts because they teach diagnosis of diseases using machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Stoval, SEVENSTER and Rosenberg before him/her, to modify the teachings of combination of Stoval and SEVENSTER to include the teachings of Rosenberg with the motivation of decision support is provided for measuring a patient's adherence to treatment, including diet and exercise, and for measuring the effectiveness, outcomes, benefits and costs of treatment, rehabilitation or prevention for a particular individual. [Rosenberg, ¶0022]
Claim 7 is rejected over Stoval, SEVENSTER and Rosenberg.
The combination of Stoval and SEVENSTER does not explicitly teach wherein the identified differential diagnoses have the probabilities greater than a threshold value.
However, Rosenberg teaches “wherein the identified differential diagnoses have the probabilities greater than a threshold value.” as “A test is recommended if, for the subset of the W.sub.j possible terms that were selected by the clinician, .SIGMA.W.sub.ij, is greater than some threshold (.SIGMA.W.sub.ij.gtoreq.threshold).” [¶0088]
Stoval, SEVENSTER and Rosenberg are analogous arts because they teach diagnosis of diseases using machine learning.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Stoval, SEVENSTER and Rosenberg before him/her, to modify the teachings of combination of Stoval and SEVENSTER to include the teachings of Rosenberg with the motivation of decision support is provided for measuring a patient's adherence to treatment, including diet and exercise, and for measuring the effectiveness, outcomes, benefits and costs of treatment, rehabilitation or prevention for a particular individual. [Rosenberg, ¶0022]
Claim 16 is rejected over Stoval, SEVENSTER and Rosenberg under the same rationale of rejection of claim 6.
Claim 17 is rejected over Stoval, SEVENSTER and Rosenberg under the same rationale of rejection of claim 7.
Response to Arguments
Applicant's arguments filed on 07/28/2026 have been fully considered but they are not persuasive. Applicant’s main argument is Stoval does not teach non-image patient data processing. Examiner respectfully disagrees. Previous office action didn’t require examiner to show non-image data processing. However, based on the amendment examiner changed the ground of rejection and mapped ¶0002 of Stoval, which teaches processing audio records of medical data and converting/processing them into text format. Therefore, Stoval teaches non-image data processing.
Also, the audio to text conversion is implied to be done by the processor as shown in Fig. 1 of Stoval. Therefore, rejection to the amended claims are maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MASUD K KHAN whose telephone number is (571)270-0606. The examiner can normally be reached Monday-Friday (8am-5pm).
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, Hosain Alam can be reached at (571) 272-3978. 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.
/MASUD K KHAN/ Primary Examiner, Art Unit 2132
