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 07/02/2026, the following has occurred: claims 1-12 have been canceled. Now, claims 13-20 are pending.
Election/Restrictions
Applicant’s election of Invention II, claims 13-20, in the reply filed on 07/02/2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claim Objections
Claim 14 is objected to because of the following informalities: within the claim the word “Taking” includes a capitalized “T.” Since this is not the first word of the claim, it appears that this was a typographical error.
Claim 20 is objected to because of the following informalities: within the claim the word “Implementing” includes a capitalized “I.” Since this is not the first word of the claim, it appears that this was a typographical error.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites “validating if the user’s inputs are medically relevant.” There are no previous recitations of “user inputs.” Therefore, this limitation lacks sufficient antecedent basis in the claim.
Claim 13 further recites “mapping the inputs to the medical database cluster.” As noted above, the claim recites “the user’s inputs,” lacking antecedent basis, but does not recite any other “inputs.” Additionally, there is no previous recitation of a “medication database cluster. Therefore, this recitation of “the inputs to the medication database cluster” lacks antecedent basis in the claim.
Claims 14-20 are rejected based on their dependencies on claim 13.
Claim 14 further recites “the outcomes from the Al agent that was attempting to resolve the workflow task but determined need for human intervention, followed by.” The claim is unclear and indefinite for several reasons: 1) there are no previous recitations of “outcomes,” 2) there are no previous recitations of “AI agent” nor reference to any entity “attempting to resolve the workflow task but determined need for human intervention,” 3) there is no previous recitation of “workflow task.” As a result, the claim limitations lack sufficient antecedent basis in the claims. Additionally, the claim ends in “followed by” with no period. It appears that the claim is incomplete, rendering the scope of the claim unclear and indefinite. For examination purposes, the claim will only be treated as “a step of building a medical database.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claim 13 recites a) submitting user instructions to an agentic Al system, b) validating if the user's inputs are medically relevant, c) annotating the user's input for one or more of context, semantics, ontologies, medical terms and medical phrases, d) mapping the inputs to the medical database cluster,e) establishing a scenario assessment score based on combining information from mapping relevance, medical database cluster and ontologies, and f) determining a score based on relevance, routing the conversation to a health expert or providing a response to the user and allowing the user to resume communication.
These limitations, as drafted, given the broadest reasonable interpretation, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. The broadest reasonable interpretation of these limitations encompass an interaction between a doctor and a patient, the doctor taking notes on information received from the patient and having the patient continue a conversation. For example, the claim encompasses a patient providing information that a doctor identifies as medically relevant, takes notes to annotate and map the patient information, and then generates scores related to the mapped information followed by continued communication. Note that the recitation of an “agentic AI system” in step a) is only recited as the destination of user information but the AI system is not involved in the step itself or any other steps. Therefore, this recitation is part of the abstract idea identified above. Additionally, broadest reasonable interpretation of “medical database cluster” encompasses an arrangement of medical data. Such an interpretation is consistent with the further recitation of “medical database cluster” in claim 15. Therefore, this recitation is also part of the abstract idea.
Claims 14-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 14-15 further expands on a medical database, explained above as encompassing an arrangement of medical data. Claim 16 further expands on the doctor’s understanding of the patient information, through analysis of the patient data. Claims 17-20 further steps of communication between a patient and a doctor. As explained above, these manual steps encompass Certain Methods of Organizing Human Activity.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea.
Claim 20 recites the following additional elements at a high level of generality and merely utilized as tools to implement the abstract idea:
Claim 20:
Implementing a text-voice conversational system that is managed by a command.
The broadest reasonable interpretation of “text-voice conversational system” managed by a “command” encompasses a generic computer component implementing software. Claim 13 recites “machine-assisted automated continuation” in the preamble. However, the body does not reference “machine-assisted automation.” Therefore, this is not an additional element to the abstract idea.
The written description discloses that the recited computer components encompass generic components including “The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer” (see paragraph 00102). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. These additional elements merely carry out the abstract idea through data collection, data processing, data communication, and data storage. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
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) 13-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salazar, US Patent Application Publication No. 2018/0218126 in view of Baldwin, US Patent Application Publication No. 2019/0206524.
NOTE to Applicant: the instant application is a Continuation in Part of application 17/993,772. The claimed subject matter of claims 13-20 is not fully supported by the subject matter of application 17/993,772. Therefore, the effective filing date for prior art purposes is 03/11/2025.
As per claim 13, Salazar teaches a method for machine-assisted automated continuation of conversations between a user, software system and healthcare professional, the method comprising steps of: a) submitting user instructions to an agentic Al system (see paragraph 0033; medical triage assistance system (agentic AI system) receives unstructured conversation between patient and a healthcare professional system or the medical triage assistance system), b) validating if the user's inputs are medically relevant (see paragraph 0034; medical triage assistance system extracts medically-relevant words and phrases), c) annotating the user's input for one or more of context, semantics, ontologies, medical terms and medical phrases (see paragraph 0034; tokenizes the medically-relevant phrases), d) mapping the inputs to the medical database cluster (see paragraph 0031; a particular phrase can be mapped to a particular symptom; paragraph 0045; mapping may be specific medical protocols to various medical concepts of the knowledge graph (medical database cluster)), e) establishing a scenario assessment based on combining information from mapping relevance, medical database cluster (see paragraphs 0045-0046; the scenario is established by summarizing protocol answers and patient systems, which is based on a combination of mapping and medical database), and f) determining based on relevance, routing the conversation to a health expert or providing a response to the user and allowing the user to resume communication (see paragraph 0046; relevance allows the nurse to quickly review the relevant information needed to properly route the patient).
In establishing the scenario and relevance, Salazar does not explicitly teach establishing a score and establishing it based on ontologies. Baldwin teaches establishing a score based on question and answer data received from a patient (see paragraph 0051; generates a score from comparisons, natural language analysis, lexical analysis of QA data), based in part on ontologies (see paragraph 0049; knowledge of QA system derived from ontologies). Since Baldwin is engaged in patient conversation data, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date to applying relevance scoring to the relevance assessment of Salazar and to include ontologies for assessing medically relevant patient text data with the motivation of further improving the accuracy of the information retrieval and extraction of Salazar, identified as a significant issue in the medical and health care domains by Baldwin (see paragraph 0002).
As per claim 14, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches a set of building a medical database (see paragraph 0018; patient information database, medical protocol database, training set database).
As per claim 16, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches a step of language analysis of the user's input using one or more of sentiment analysis and contextual identification of medical terminologies (see paragraphs 0023-0024; language processing of patient input identifies complaint, symptoms, etc., representing sentiment and context).
As per claim 17, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches a step of sending context of the conversation to the health expert (see paragraph 0040; symptoms, probabilities and confidence levels presented to nurse).
As per claim 18, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches a step of storing anonymized conversations that from the user and health expert for model training (see paragraph 0051; patient summary identified by an anonymized identifier; paragraph 0004; patient data used to train knowledge graph).
As per claim 19, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches the user submits the user instructions by spoken natural language (see paragraph 0003; system receives unstructured conversation between a patient and a healthcare professional).
As per claim 20, Salazar and Baldwin teaches the method of claim 19 as described above. Salazar further teaches the steps of: Implementing a text-voice conversational system that is managed by a command, allowing the user to enter queries in natural language, and validating the user's queries for medical accuracy (see paragraph 0003; unstructured conversation is organized into questions; paragraph 0030; conversation tokens are validated for accuracy).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salazar, US Patent Application Publication No. 2018/0218126 in view of Baldwin, US Patent Application Publication No. 2019/0206524 and further in view of Akinwande, US Patent Application Publication No. 2021/0209299.
As per claim 15, Salazar and Baldwin teaches the method of claim 13 as described above. Salazar further teaches a step of building a medical database cluster to store medical scenarios that require input from a health expert, the medical database clusters comprising data on signs/symptoms of medical conditions, data on severity of medical conditions (see paragraph 0023; data on patient complaint, duration, severity; paragraph 0031; feedback provided by healthcare professional).
Salazar and Baldwin do not explicitly teach the database includes drugs and side- effects of drugs. Akinwande teaches generating a corpus that includes medications and side effects (see paragraph 0020). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to incorporate this data into the data stored within the system of Salazar with the motivation of improving the treatment of patients by providing earlier recognition of adverse reactions to medications (see paragraph 0003 of Akinwande).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ito, US Patent Application Publication No. 2020/0013516, discloses processing doctor-patient conversations with a communicative model to identify medical information relevant to care for the patient.
Oliveira, US Patent Application Publication No. 2018/0330807, discloses applying natural language processing to annotate medical reports.
Uhl, US Patent Application Publication No. 2023/0153539, discloses inferring medical conditions by applying AI processing to conversation data.
Riskin, International Publication No. WO 2014/031541 A2, discloses applying natural language processing in annotating clinical assertions regarding patient condition.
Lan et al., Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats, discloses processing patient language from interaction with chatbots to diagnose depression.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683
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