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 .
Status of Claims
This action is in response to applicant arguments filled on 08/13/2026 for application 18/442167.
Claims 1, 15, and 19 have been amended.
Claims 1-19 are currently pending and have been examined.
Detailed Action
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-4, 6, 7, and 10-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paik et al (US 2021/0216822 A1) in view of Finley (US 2019/0065464 A1) and in further in view of Lin et al. (US 5666466)
In claim 1, a computer-implemented method for generating a medical examination report from sensor data of a medical examination, the method comprising:
Paik teaches:
receiving sensor data from a set of sensors in relation to the medical examination, wherein the set of sensors comprises at least one microphone and the sensor data comprise at least audio data (Para. 9 and 99 wherein “ the system comprises a microphone or audio detection component for detecting the dictation, and converts it to the corresponding text”),
Paik does not expressly disclose teach however Finley teaches:
wherein the audio data comprise utterances by a medical professional and a patient representative participating in the medical examination (Para. 31 wherein “ A speech diarizer 1 separates the voices of doctor and patient. Speech recognizers 2a and 2b transform the speech of the doctor and patient into unformatted text, respectively.”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the medical examination and dictation system of Paik to incorporate the speaker diarization and speaker specific medical concept processing as taught by Finley in order to distinguish speech attributable to different participants in a medical examination and associate clinically relevant information with appropriate speaker, thereby facilitating more accurate and organized generation of medical documentation form a multi-speaker medical encounter.
Paik further teaches:
processing the received sensor data according to a type of sensors, wherein the processing comprises at least transforming the audio data into text (Para. 99).
Paik does not expressly disclose however Finley teaches:
generating a verbatim report of the medical examination based on the processed sensor data, wherein the verbatim report comprises assigning each excerpt of the text to the medical professional or the patient representative (Para. 31 and 75).
Paik and Finley do not expressly disclose however Lin teaches:
by filtering the audio data according to at least one of a frequency pattern, a low pass filter, or a high pass filter, wherein the filtered audio data are classified according to characteristics determined by the filtering, and wherein the classifying characteristics are assigned to the medical professional and/or the patient representative (Col 5 lines 25-49 and Col 6 lines 15-30 wherein filtering process of speech spectrum is taught );
Finley further teaches:
converting the generated verbatim report into the medical examination report, wherein the medical examination report comprises a medical-professional summary generated by mapping clinically relevant concepts in the text excerpts assigned to the medical professional to a standard clinical vocabulary by accessing a predetermined ontology database in relation to a medical field of the medical examination, and a patient utterance summary, separate from the medical-professional summary, generated by aggregating the text excerpts assigned to the patient representative (Para. 34-37 teaches transforming speaker identified text into a conceptual representation by tagging semantic concepts in diarized speech, where the diarized text identifies whether words are attributable to the patient or doctor. Para. 39 teaches the semantic concepts are predefined concepts derived from medical ontologies such as SNOMED CT or ICD-10. Para. 40 teaches wherein both word and speaker vector are utilized in performing the concept tagging.).
And
Paik further teaches:
storing the medical examination report in an electronic medical report database (Para. 230).
It would have been obvious to one of ordinary skill in the art at the time of invention to further modify the combined system of Paik and Finley to identify and distinguish speakers based on voice characteristics, including frequency, as taught by LIn, in order to improve identification and separation and thereby facilitate accurate assignment of the corresponding speech to the respective participants.
As per claim 2, Paik teaches the computer-implemented method according to claim 1, wherein the set of sensors comprises at least one further sensor selected from the group of: a camera; and a motion sensor (Para. 236).
As per claim 3, Paik teaches the computer-implemented method according to claim 1, further comprising:
providing the text excerpts of the converted medical examination report, which are assigned to the patient representative, to the patient representative for approval (Fig. 5-7 wherein presenting generated medical text a user for review and receiving user input indicative of acceptance or rejection of the presented text is taught); and
receiving a user input by the patient representative, wherein the user input is indicative of approval and/or rejection of the provided text excerpts assigned to the patient representative (Fig. 5-7).
As per claim 4, Paik teaches the computer-implemented method according to claim 1, further comprising:
providing the text excerpts of the converted medical examination report, which are assigned to the medical professional, to the medical professional for verification (Para. 46 wherein “navigator-enabled image providing a user with the option to confirm accuracy of segmentation and labeling, reject, or edit segmentation and labeling.); and
receiving a user input, by the medical professional, indicative of a validation of the provided text excerpts assigned to the medical professional (Para. 46).
As per claim 6, Paik teaches the computer-implemented method according to claim 1, wherein storing the medical examination report in an electronic medical report database comprises storing the medical examination report in a centralized memory of a medical facility (Para. 231).
As per claim 7, Paik teaches the computer-implemented method according to claim 1, further comprising outputting the stored medical examination report (Para. 215).
As per claim 10, Paik teaches the computer-implemented method according to claim 1, further comprising:
identifying, based on the processed sensor data, medical imaging data that were examined during the medical examination (Para. 220); and
appending the medical imaging data to the medical examination report (Para. 219-220).
As per claim 11, Paik teaches the computer-implemented method according to claim 1, wherein converting the generated verbatim report into a medical examination report is based on a trained artificial intelligence, AI, a trained neural network, NN, deep learning, DL, and/or reinforcement learning, RL (Para. 222).
As per claim 12, Paik teaches the computer-implemented method according to claim 11, wherein the training of the AI and/or the NN, the DL, and/or the RL was based on training data sets, wherein each training data set comprised sensor data and an associated manually compiled medical examination report (Para. 212-216).
As per claim 13, Paik teaches the computer-implemented method according to claim 12, wherein the training, and/or learning, was based on optimizing a loss function (Para. 216 and 218).
As per claim 14, Paik teaches the computer-implemented method according to claim 1, wherein the medical examination comprises a radiology and/or an oncology examination (Para. 216).
Claims 15-19 recite substantially similar limitations as noted above and hence are rejected for similar rationale as above.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paik et al (US 2021/0216822 A1) in view of Finley (US 2019/0065464 A1) and Lin et al. (US Patent No. 5666466) as applied to claims above, and further in view of Soryal et al. (US 2018/0081927 A1).
As per claim 5, Paik in view of Finley and Lin teach the computer-implemented method according to claim 1. Paik in view of Finley and Lin does not explicitly teach however Soryal teaches:
further comprising:
temporarily storing intermediate data in a private cloud, wherein the intermediate data comprise the received sensor data, the generated verbatim report, and/or at least parts of the converted medical examination report (Para. 30, 32, and 33 wherein temporarily storing data in temporary cloud-based storage Is taught. and
deleting the temporarily stored intermediate data from the private cloud after a predetermined period of time, after a rejection by the patient representative, and/or after a verification by the medical professional (Wherein an expiation time is associated with the temporary storage (Para. 30), and upon expiration of the predetermined time period, the temporarily stored data is deleted as seen in Para. 33 in steps 510, 520, and 522).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the temporary cloud-based storage of Paik to incorporate the expiration-based deletion technique taught by Soryal in order to automatically remove temporarily stored data when the storage period expires, thereby reducing unnecessary retention of data and conserving storage resources.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paik et al (US 2021/0216822 A1) in view of Finley (US 2019/0065464 A1) and Lin et al. (US Patent No. 5666466) as applied to claims above, and further in view of Barkol et al. (US 2021/0257110 A1).
As per claim 8, Paik teaches the computer-implemented method according to claim 3. Paik in view of Finley and Lin do not explicitly teach however Barkol teaches wherein storing the medical examination report in an electronic medical report database comprises storing the medical examination report in a digital twin of the patient, and/or wherein the providing of the text excerpts assigned to the patient representative and the receiving of the user input by the patient representative are performed using the digital twin of the patient (Para. 85 and 107 wherein “ a digital twin of the patient with the medical information. The digital twin may be a digital replica/representation of the patient that is saved at the computing device (e.g., digital twin 108 saved on the server system 102). The digital twin may include patient demographic information, medical history, and other information to provide, to the extent possible, a simulation/representation of the current patient medical state”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combined system of Paik, Finley, and Lin to store the medical examination information in a digital twin of the patient, as taught by Barkol, in order to maintain a digital representation of the patient’s medical information, including medical history and current medical state, thereby providing a more complete and current representation of the patient for subsequent medical analyses and evaluation.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paik et al (US 2021/0216822 A1) in view of Finley (US 2019/0065464 A1) and Lin et al. (US Patent No. 5666466) as applied to claims above, and further in view of Beaumont et al. (US 2015/0088515 A1)
As per claim 9, Paik in view of Finley and Lin teach the computer-implemented method according to claim 1. Paik in view of Finley and Lin does not explicitly teach however Beaumont teaches:
wherein the assignment of each excerpt of the text to the medical professional or the patient representative in generating the verbatim report is based on at least one of:
filtering of the audio data according to a frequency pattern, and/or according to a low pass filter, and/or a high pass filter, wherein the filtered audio data are classified according to characteristics determined by the filtering, and wherein the classifying characteristics are assigned to the medical professional and/or the patient representative; extracting time stamps from the audio data and further sensor data, combining the audio data and the further sensor data according to the extracted time stamps, and assigning each excerpt of the text to the medical professional or the patient representative according to the combination of the audio data and the further sensor data per time stamp (receiving audio data and further sensor data comprising video/image data as seen in Para. 27-28, matching the audio data with the further sensor data based on time, wherein video data containing visual features associated with speech may include a timestamp of corresponding audio data as seen in Para. 32, and utilizing the matched audio and video data to identify a speaker as seen in Para. 35 and 39).
Response to Arguments
Applicant arguments with respect to the art rejection are moot in view of new grounds of rejection necessitate by claim amendments.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/ Primary Examiner, Art Unit 3687
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