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 1-20 are pending.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-9, 13-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US Patent 9,824,188 B2) in view of Kuusela et al. (US Pub. 2025/0073494 A1).
Regarding Claims 1, 8 and 15, Brown teaches a method (see Fig.1 and Col.8, Line 25-30, system and method for interacting with a patient), comprising:
initiating, by an artificial intelligence executed by one or more processors, a conversation with a human, the artificial intelligence configured to complete one or more tasks during the conversation (see Fig.2A (204,120), Col.11, Line 12-16 and Col.5, Line 6-15, completing healthcare tasks);
receiving, by the artificial intelligence, a portion of a first human response from the human (see Fig.3 (122) and Col.8, Line 66 – Col.9, Line 9, patient’s input or response to system);
generating, by the artificial intelligence, multiple possible responses to the portion of the received first human response (see Fig.3 (302) and Col.13, Line 46-55, presenting options for ambiguous query for clarification);
selecting, by the artificial intelligence, based at least in part on receiving additional portions of the first human response from the human, a first artificial intelligence response from the predicted multiple possible responses (see Fig.3 (304), Fig.4 (120) and Col.13, Line 55 – Col.14, Line 18, system response after patient’s clarification);
providing, by the artificial intelligence, the first artificial intelligence response to the human (see Fig.4 (120) and Col.14, Line 5-18, notifying patient of tracking walk);
and based at least in part on determining, by the artificial intelligence, that a task has been completed, ending the conversation with the human (see Fig,7B (120(3)) and Col.15, Line 58-65, completing task and confirming).
Brown fails to teach completing a checklist during the conversation.
Kuusela, however, teaches presenting a list of tasks for a patient to complete through communication or interaction with a machine learning language processing model (see Fig.3 (308) and paragraph [0054]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to Brown’s method the step for completing a checklist during the conversation. The motivation would be to present and track a series of healthcare tasks for a patient to complete.
Regarding Claim 3, Brown further teaches completing tasks included in the checklist after discussing a peripheral matter with the human during the conversation (see Fig.2B (120(1),122,120(2),222) and Col.13, Line 19-26, completing task after patient’s questions).
Regarding Claim 4, Brown further teaches answering a question posed by a human (see Fig.2B (120(1),122,120(2),222) and Col.13, Line 19-26, completing task after patient’s questions).
Regarding Claims 5 and 13, Brown further teaches providing the first human response to a second artificial intelligence to determine additional factors associated with the human in parallel with providing, by the artificial intelligence, the first artificial intelligence response to the human (see Fig.1 (106), Fig.3 (120,122) and Col.8, Line 38-50, healthcare entities server configured in parallel with the virtual assistant service to provide additional information for the patient).
Regarding Claim 6, Brown further teaches wherein the artificial intelligence is trained using multi-turn reinforcement learning through human feedback (see Fig.8 (818) and Col.19, Line 19-26, learn dictation or expression of a patient through time).
Regarding Claim 7, Brown further teaches wherein the artificial intelligence includes a retrieval module configured to access external sources of knowledge to supplement the artificial intelligence (see Fig.1 (106,116) and Col.8, Line 38-50, healthcare entities server configured in parallel with the virtual assistant service to provide additional information for the patient).
Regarding Claims 9 and 19, Brown further teaches predicting one or more possible human responses (see Fig.3 (302) and Col.13, Line 46-55, presenting options for ambiguous query for clarification); and predicting one or more answers, individual answers of the one or more answers corresponding to individual responses of the one or more possible human responses (see Fig.3 (304), Fig.4 (120) and Col.13, Line 55 – Col.14, Line 18, system response after patient’s clarification).
Regarding Claim 14, Brown further teaches confirming a date and time of a visit to a medical facility (see Fig.7B (120 (3) and Col.15, Line 58-65, confirming appointment and check-in).
Regarding Claim 16, Brown further teaches engaging in a in-take related task and a chronic care related task (see Fig.2B. Fig.3 and Col.13, Line 28-50).
Regarding Claim 17, Brown further teaches incorporating, by the artificial intelligence, one or more conversational strategies comprising open-ended questions, feedback loops, active listening techniques, or any combination thereof (see Fig.3 and Col.13, Line 44-64, resolving ambiguous query).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US Patent 9,824,188 B2) in view of Kuusela et al. (US Pub. 2025/0073494 A1), and in further view of Niehaus et al. (US Pub. 2023/0386626 A1).
Regarding Claim 2, Brown teaches wherein the artificial intelligence includes a safety engine configured to: based at least in part on determining that a kickout condition has occurred, transfer a conversation between the human and the artificial intelligence to a medical professional (see Fig.5 (502), Fig.9 (904), Col.14, Line 28-43 and Col.18, Line 7-16, diagnosis following blood pressure reading and contacting doctor), but Brown and Kuusela fail to teach providing the medical professional with a text-based summary of the conversation, including why the conversation was initiated and what information the human has provided.
Niehaus, however, teaches presenting a transcript of a medical consultation for analysis (see Fig.3 (304) and paragraph [0127]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the method of Claim 1 the step for providing the medical professional with a text-based summary of the conversation, including why the conversation was initiated and what information the human has provided. The motivation would be to present the patient’s referral information to a healthcare professional.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US Patent 9,824,188 B2) in view of Kuusela et al. (US Pub. 2025/0073494 A1), and in further view of Kidd et al. (US Patent 10,452,816 B2).
Regarding Claim 10, Brown and Kuusela teach the server of Claim 8 but fail to teach determining a tone of utterances of the human; correlate the tone with a particular mood; and adjust, based on the particular mood, an audio data output by the artificial intelligence.
Kidd, however, teaches determining the tone of utterance and mood of a patient via speech input (see Fig.3 (S110) and Col.5, Line 15-28) and providing a response in accordance with the determination (see Fig.3 (S130,S140) and Col.20, Line 22-40).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to configure the server of Claim 8 to determine a tone of utterances of the human; correlate the tone with a particular mood; and adjust, based on the particular mood, an audio data output by the artificial intelligence. The motivation would be to provide a response to the patient based on the determined sentiment of the patient.
Regarding Claim 11, Kidd further teaches determining a particular word in the utterance to determine the mood of the patient (see Fig.3 (120) and Col.8, Line 32-49).
Regarding Claim 12, Kidd further teaches inserting particular words in the audio response for the patient (see Fig.3 (S130,S140), Fig.6B and Col.15, Line 26-39).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US Patent 9,824,188 B2) in view of Kuusela et al. (US Pub. 2025/0073494 A1), and in further view of PIJL (US Pub. 2022/0076694 A1).
Regarding Claim 18, Brown teaches providing reminders to a patient (see Fig.9 (906) and Col.18, Line 25-29) but Brown and Kuusela fail to teach determining, by the artificial intelligence, that the human exhibits mild cognitive impairment.
PIJL, however, teaches processing speech data to determine a cognitive decline score and performing an action based on the score (see Fig.2 (210,212) and paragraphs [0047-0048]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the memory device of Claim 15 the instructions to determine that the human exhibits mild cognitive impairment and provide reminders to the human. The motivation would be to assist an elderly patient or a patient with a cognitive condition to complete a daily task.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US Patent 9,824,188 B2) in view of Kuusela et al. (US Pub. 2025/0073494 A1), and in further view of Hashish et al. (US Pub. 2023/0334076 A1).
Regarding Claim 20, Brown teaches outputting at least a portion of the conversation in a form suitable for input to an electronic health records (EHR) platform (see Fig.1 (106,142), Col.5, Line 48-52 and Col.9, Line 47-67, maintaining and storing health records via the virtual assistant and health entity server), but Brown and Kuusela fail to teach complying with relevant healthcare regulations, including Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).
Hashish, however, teaches storing medical records in compliance with the general data protection regulation (see paragraph [0021]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the memory device of Claim 15 the instructions to store medical records in compliance with General Data Protection Regulation (GDPR). The motivation would be to protect the patient’s privacy data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VU B HANG whose telephone number is (571)272-0582.
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/VU B HANG/Primary Examiner, Art Unit 2654
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