Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
Claims 1-21 are present in this application. Claims 1-21 are pending in this office
action.
This office action is NON-FINAL.
Drawings
The Drawings filed on 04/25/24 are acceptable for examination purposes.
Specification
The Specification filed on 04/25/24 is acceptable for examination purposes.
Claim Rejections 35 U.S.C. §103
6. 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 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.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all
obviousness rejections set forth in this Office action:
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over
Paik et al. (US 2021/0216822 A1) in view Gnanasambandam et al. (US 2022/0384052 A1)
Regarding claim 1, Paik teaches a method for training a language model for a medical use or performing a medically-related procedure, (See Paik paragraph [0149], AI models (through the described UI components) and the words they dictate into the diagnostic report. This system ensures that the highest-fidelity ground truth data is fed back into the algorithms for training…to generate findings (e.g., AI-assisted findings) for insertion into a medical report), comprising: modeling a reward neural network on one or more physician preferences, (See Paik paragraph [0173], physician preferences and medico-legal duties…to produce the study assignments to physician worklists is an artificial neural network); and electronically training the language model by applying the reward neural network modeled, (See Paik paragraph [0215], natural language processing (NLP) algorithm(s) to provide labels for training the computer vision model(s)).
Paik does not explicitly disclose on the one or more physician preferences as a feedback to the language model to guide the language model to learn the one or more physician preferences.
However, Gnanasambandam teaches on the one or more physician preferences as a feedback to the language model to guide the language model to learn the one or more physician preferences, (See Gnanasambandam paragraph [0121], the physician may submit feedback pertaining to whether or not the cognified data is accurate for the patient. The feedback may be used to update the artificial intelligence engine that uses the knowledge graph and logical structure to generate the cognified data).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify to on the one or more physician preferences as a feedback to the language model to guide the language model to learn the one or more physician preferences of Gnanasambandam in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs.
Claims 8 and 15 recite the same limitations as claim 1 above. Therefore, claims
and 15 are rejected based on the same reasoning.
Regarding claim 2, Paik taught the method according to claim 1 as described above. Paik further teaches, wherein the reward neural network relies on an artificial intellegence (AI) model, (See Paik paragraph [0173], produce the study assignments to physician worklists is an artificial neural network…the artificial neural network), as a surrogate reward function for the feedback, (See Paik paragraph [0010], user interaction with said system and provide feedback for improving efficiency or quality).
Claims 9 and 16 recite the same limitations as claim 2 above. Therefore, claims
and 16 are rejected based on the same reasoning.
Regarding claim 3, Paik taught the method according to claim 2 as described above. Paik further teaches wherein the physician preferences are obtained from the AI model, (physician preferences and medico-legal duties…the mathematical function that is maximized to produce the study assignments to physician worklists is an artificial neural network), providing the feedback for the one or more physician preferences, (See Paik paragraph [0010], user interaction with said system and provide feedback for improving efficiency or quality).
Claims 10 and 17 recite the same limitations as claim 3 above. Therefore, claims
and 17 are rejected based on the same reasoning.
Regarding claim 4, Paik taught the method according to claim 2 as described above. Paik further teaches wherein the modeling of the reward neural network comprises obtaining implicit physician preferences, (See Paik paragraph [0173], physician preferences and medico-legal duties…to produce the study assignments to physician worklists is an artificial neural network).
Claims 11 and 18 recite the same limitations as claim 4 above. Therefore, claims
and 18 are rejected based on the same reasoning.
Regarding claim 5, Paik taught the method according to claim 4 as described above.
Paik does not explicitly disclose wherein the implicit physician preferences are derived from at least one of electronic health records, metadata, or artifacts.
However, Gnanasambandam teaches wherein the implicit physician preferences are derived from at least one of electronic health records, metadata, or artifacts, (paragraph [0115], physicians often generate patient notes before, during, and/or after consultation with a patient. The patient notes may be included in an electronic medical record).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify to wherein the implicit physician preferences are derived from at least one of electronic health records, metadata, or artifacts, preferences of Gnanasambandam in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs.
Claims 12 and 19 recite the same limitations as claim 5 above. Therefore, claims
and 19 are rejected based on the same reasoning.
Regarding claim 6, Paik taught the method according to claim 5 as described above.
Paik does not explicitly disclose wherein the implicit physician preferences are inferred from a physician written text provided in the at least one of the electronic health records, the metadata, or the artifacts.
However, Gnanasambandam teaches wherein the implicit physician preferences are inferred from a physician written text provided in the at least one of the electronic health records, the metadata, or the artifacts. (See Gnanasambandam paragraph [0435], the processing device of a server may receive an electronic medical record (EMR) including notes pertaining to a patient…the server may receive text input by the patient. F).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify to wherein the implicit physician preferences are inferred from a physician written text provided in the at least one of the electronic health records, the metadata, or the artifacts of Gnanasambandam in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs.
Claims 13 and 20 recite the same limitations as claim 6 above. Therefore, claims
and 20 are rejected based on the same reasoning.
Regarding claim 7, Paik taught the method according to claim 1 as described above. Paik further teaches wherein the reward neural network, (See Paik paragraph [0173], the artificial neural network is trained using reinforcement learning where the recommendation system is the agent with assignments as actions, trying to maximize rewards of combined efficiency and quality), is finetuned on one or more ground truth notes in electronic health records to approximate the language and thinking of physicians, (See Paik paragraph [0119], the AI-assisted query function is configured to find a broad spectrum of imaging abnormality categories. For example, in MRI of the spine, there may be approximately 50 different abnormality categories that can be observed, and detection of these abnormality categories can be made using a variety of different machine learning architectures).
Claims 14 and 21 recite the same limitations as claim 7 above. Therefore, claim
and 21 are rejected based on the same reasoning.
Conclusions/Points of Contacts
The prior art made of record and not relied upon is considered pertinent to
applicant’s disclosure. See form PTO-892.
Perez et al. (US 11, 875, 883 B1) provided for performing natural language understanding on voice conversations. Interactions between a clinician (or any healthcare provider) and a patient (or individual) are, in large part, spoken interactions.
Karampourniotis et al. (US 2021/0265063 A1) A processor may collect a first set of attributes associated with a first healthcare provider, the first healthcare provider having identified a first medical opinion for treating a medical condition of a patient. The processor may identify a plurality of healthcare providers that have treated a similar medical condition for one or more other patients.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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