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 .
Response to Amendment
In the amendment filed 07/23/2026, the following has occurred: claims 1, 4, 7, 8, 14, 16, and 19 have been amended. Now, claims 1-20 remain pending.
Claim Objections
Claim 1 is objected to because of the following informalities: at the sevenths step of “determining, , by the one or more processors” it appears that a comma was inadvertently inserted twice. Appropriate correction is required.
Terminal Disclaimer
The terminal disclaimer filed on 07/23/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No. 12,254,966 has been reviewed and is accepted. The terminal disclaimer has been recorded.
The previous double patenting rejections are withdrawn based on the filing of the terminal disclaimer.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh, US Patent Application Publication No. 2025/0342975 in view of Holub, US Patent Application Publication No. 2021/0313061 and further in view of Hwang, US Patent Application Publication No. 2024/0428958.
As per claim 1, Singh teaches a computer-implemented method, comprising: receiving, by one or more processors, audio data comprising a portion of a conversation between a doctor and a patient (see paragraph 0007; computer-implemented system listening to patient-doctor conversations audio data); determining, by the one or more processors, a portion of a medical history of the patient (see paragraph 0011; system incorporates medical history of the patient from electronic health records (EHR)); providing, to at least one artificial intelligence executed by the one or more processors, the portion of the conversation and the portion of the medical history of the patient (see paragraph 0007; recommendation module integrates conversation data and historical patient records; paragraph 0043; recommendation module analyzes data with AI and ML), wherein the at least one artificial intelligence is trained using training data that includes multiple audio conversations between doctors and patients to create the at least one artificial intelligence (see paragraph 0027; AI and ML may be train on comprehensive dataset of patient-doctor interactions); receiving, by the one or more processors, decision support insights generated by the at least one artificial intelligence based at least in part on the portion of the conversation and the portion of the medical history of the patient (see paragraph 0007; recommendation module integrates conversation and historical record data to generate evidence-based suggestions); prioritizing the decision support insights, by the one or more processors, based on a medical urgency of individual insights of the decision support insights, to create prioritized decision support insights (see paragraph 0044; recommendation module provides evidence-based suggestions, such as presenting appropriate diagnostic tests or treatment options (i.e. prioritized suggestions) based on disease severity (urgency)); providing, by the one or more processors and to a computing device associated with the doctor, a text-based presentation of the prioritized decision support insights to the doctor in a graphical user interface displayed on the computing device (see paragraph 0044; suggestions presented to physicians on display device); and re-training, by the one or more processors, the at least one artificial intelligence using additional training data that includes the conversation between the doctorand the patient (see paragraph 0046; AI model is trained).
Singh does not explicitly teach the decision support insights are text-based, and modifying a graphical characteristic of the text-based presentation of the particular insight. Holub teaches provided decision support insights are text-based, and modifying a graphical characteristic of the text-based presentation of the particular insight (see paragraph 0078; provides medical recommendations, throttled (prioritized) based on importance to display to medical professional during exam with patient; paragraph 0035 describes that machine learning processing may modify the recommendations; Figure 7 shows an example of recommendations being displayed in different graphical representations). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize such a graphical display with the suggestion presentations of Singh with the motivation of improving the time and efficiency of patient data provided to medical professionals during patient visits (see paragraph 0001 of Holub).
Singh and Holub does not explicitly teach based on determining, by the one or more processors, that a subject matter of a particular insight of the prioritized decision support insights has been addressed in a subsequent portion of the conversation, updating the graphical user interface to indicate that the particular insight was addressed. Hwang teaches based on determining, by one or more processors, that a subject matter of a particular insight of prioritized decision support insights has been addressed in a subsequent portion of a conversation, updating the graphical user interface to indicate that the particular insight was addressed (see paragraph 0068; question recommendation prompts (decision support insights) may be prioritized and are dynamically changed during the conversation between patient and provider. NLP system recognizes if a prompt has been recognized (addressed) from conversation audio and dynamically updates the displayed prompts by removal or reprioritizing). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to dynamically update a display of insights in the system of Singh and Holub based on insights being addressed through conversation with the motivation of ensuring that the provider addresses displayed information (see paragraph 0068 of Hwang)
As per claim 2, Singh, Holub, and Hwang teaches the method of claim 1 as described above. Singh does not explicitly teach determining, based at least in part on the medical urgency, a criticality score associated with individual decision support insights to create criticality scores; and presenting the prioritized decision support insights with the criticality scores to the doctor in the graphical user interface. Holub further teaches determining, based at least in part on medical urgency, a criticality score associated with individual decision support insights to create criticality scores (see paragraphs 0033 and 0062 medical recommendations may be prioritized and ranked based on medical severity. Figures 6 and 7 show ranked recommendations with numerical representations, reflecting a “score” associated with the ranking); and presenting the prioritized decision support insights with the criticality scores to the doctor in the graphical user interface (see Figures 6 and 7 showing the graphical representation of the prioritized recommendations with adjacent corresponding “scores”). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize such a graphical display with the suggestion presentations of Singh for the reasons given above with respect to claim 1.
As per claim 3, Singh, Holub, and Hwang teaches the method of claim 1 as described above. Singh further teaches persisting a particular decision support insight in the graphical user interface based at least in part on determining that the doctor, during the portion of the conversation, failed to account for the particular decision support insight of the prioritized decision support insights (see paragraph 0058; actionable data being representative of failing to account for insight, resulting in graphical display of suggestion).
As per claim 4, Singh, Holub, and Hwang teaches the method of claim 1 as described above. Singh does not explicitly teach updating the graphical user interface to indicate that the particular insight was addressed comprises modifying a graphical characteristic of the text-based presentation of the particular insight being presented in the graphical user interface. Hwang further teaches updating the graphical user interface to indicate that the particular insight was addressed comprises modifying a graphical characteristic of the text-based presentation of the particular insight being presented in the graphical user interface (see paragraph 0068; question recommendation prompts (decision support insights) may be prioritized and are dynamically changed during the conversation between patient and provider. NLP system recognizes if a prompt has been recognized (addressed) from conversation audio and dynamically updates the displayed prompts by removal or reprioritizing (modifying a graphical characteristic of the text)).
As per claim 5, Singh, Holub, and Hwang teaches the method of claim 1 as described above. Singh does not explicitly teach determining, based at least in part on the decision support insights, one or more questions for the doctor to ask the patient; and providing, in the graphical user interface, the one or more questions. Holub further teaches determining, based at least in part on the decision support insights, one or more questions for the doctor to ask the patient; and providing, in the graphical user interface, the one or more questions (see paragraph 0035; presented recommendations include predictive questions to propose). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to utilize such a graphical display with the suggestion presentations of Singh for the reasons given above with respect to claim 1.
As per claim 6, Singh, Holub, and Hwang teaches the method of claim 1 as described above. Singh further teaches determining, based at least in part on the decision support insights, one or more suggestions to make to the patient; and providing, in the graphical user interface, the one or more suggestions (see paragraph 0031; treatment recommendations being an example of a suggestion to make to the patient).
Claim 7 recites substantially similar computing device limitations to method claim 1 and, as such, is rejected for similar reasons as given above.
As per claim 8, Singh, Holub, and Hwang teaches the computing device of claim 7 as described above. Singh further teaches accessing one or more medical knowledge databases to determine medical knowledge associated with at least the portion of the medical history of the patient (see paragraph 0058; historical patient records are integrated with the clinical knowledge database); and generating, by the at least one artificial intelligence, the decision support insights, based at least in part on the medical knowledge (see paragraph 0058; trained AI analyzes conversation data, historical patient records and clinical knowledge to generate suggestions).
As per claim 9, Singh, Holub, and Hwang teaches the computing device of claim 7 as described above. Singh further teaches determining the portion of the medical history of the patient comprises retrieving one or more electronic medical records associated with the patient from one or more databases (see paragraph 0054; patient data accessed from EHR).
As per claim 10, Singh, Holub, and Hwang teaches the computing device of claim 7 as described above. Singh further teaches determining the portion of the medical history of the patient comprises retrieving biometric data associated with the patient (see paragraph 0057; lab data such as blood tests and imaging results).
As per claim 11, Singh, Holub, and Hwang teaches the computing device of claim 10 as described above. Singh further teaches at least a portion of the biometric data associated with the patient is received while the patient is being examined by the doctor (see paragraph 0058; data is obtained during a conversation between a doctor and a patient).
As per claim 12, Singh, Holub, and Hwang teaches the computing device of claim 7 as described above. Singh further teaches determining, based at least in part on the decision support insights, one or more follow-up actions (see paragraph 0043; suggestion may be follow-up care); and providing, in the graphical user interface, the one or more follow-up actions (see paragraph 0043; suggestions provided through user-friendly interface).
As per claim 13, Singh, Holub, and Hwang teaches the computing device of claim 12 as described above. Singh further teaches receiving a confirmation from the doctor to perform at least one action of the one or more follow-up actions (see paragraph 0058; receives acceptance of suggestion from providing).
Claims 14-20 recite substantially similar computer medium limitations to method and device claims 1, 3-6, 8, and 12-13 and, as such, are rejected for similar reasons as given above.
Response to Arguments
Applicant’s arguments filed 07/23/2026 have been fully considered but are moot in view of the new grounds of rejection set forth above and the withdrawal of the double patenting rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zaidi, US Patent Application Publication No. 2025/0095807, discloses generating SOAP notes from AI processed doctor-patient conversations and removes doctor suggestions about how to address the problem.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683
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