DETAILED ACTION
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 .
Claim Objections
Claim 9 is objected to because of the following informalities: “the assistant of claim 1, wherein the analytics modules…” should be “the assistant of claim 1, “wherein the analytics module….” Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-20 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., system) and process (i.e., a method).
Although claims 1-20 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-20 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claim 17) recite
an input module configured to recognize, in real time, spoken language and convert the spoken language to a computer readable form to generate a patient embedding, wherein the computer readable form includes a mathematical vector associated with the patient embedding, and wherein the spoken language includes a conversation between a clinician and a patient during a patient visit;
a memory configured to store the patient embedding in a database of existing patient embeddings;
an analytics module configured to, in real time:
use one or more artificial intelligence based analytical techniques to parse the patient embedding and catalog portions of the patient embedding into a plurality of categories including patient history, patient symptoms, patient condition, patient medication, patient allergy, patient concerns, likely medical billing codes for services rendered during the patient visit;
compare the patient embedding to the database of existing patient embeddings and determine a confidence matching score of the patient embedding relative to one or more existing patient embeddings; and
automatically generate a set of post visit instructions and automatically generate a patient post visit record based at least in part on one or more existing patient embeddings having a confidence matching score greater than or equal to a first predetermined threshold; and
an output module configured to, in real time:
provide to the clinician via one or more different media the patient post visit record in a standardized format; and
automatically execute the set of post visit administrative instructions.
The identified limitations, under its broadest reasonable interpretation, cover managing personal behavior or interactions between people but for the recitation of generic computer components. For example, but for the memory and artificial intelligence (interpreted as computer environment), the context of the claim encompasses a transcribing clinician-patient conversation for the purpose of providing treatment plans or recommendations. The claim limitations fall within the Certain Methods of Organizing Human Activity groupings of abstract ideas. The performance of the claim limitations using generic computing components does not preclude the claim limitations from being in the Certain Methods of Organizing Human Activity grouping. Thus, the claim recites an abstract idea.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
As a whole, the additional elements recite using the memory to implement the abstract idea. The memory and artificial intelligence in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Dependent claims 2-16 and 18-20, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". For example, the steps require no more than a conventional computer to perform generic computer functions. As stated above, the memory and artificial intelligence in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-20, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-16 and 18-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-11 and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin (US 2023/0320642 A1).
Regarding claims 1 and 17, Lin discloses an artificial intelligence driven bi-directional medical assistant, comprising:
an input module configured to recognize, in real time, spoken language and convert the spoken language to a computer readable form to generate a patient embedding, wherein the computer readable form includes a mathematical vector associated with the patient embedding, and wherein the spoken language includes a conversation between a clinician and a patient during a patient visit (Paragraphs [0065]: The procedure includes obtaining 210 transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting 220 speech segments from the transcript data related to one or more of the patient or the therapist, [0066]: applying the topic model process to the extracted speech segments may include transforming one or more of the extracted speech segments into representations in a vector space to produce one or more vectored topic label representations);
a memory configured to store the patient embedding in a database of existing patient embeddings (Paragraph [0009]: for dialogue data analysis, is provided that includes one or more memory devices to store processor-executable instructions and dialogue data relating to one or more events involving a patient and at least another speaker);
an analytics module configured to, in real time (Paragraph [0013]: the one or more therapist similarity scores therapist advice output to dynamically manage the dialogue session in real-time by identifying, in response to the speech segment, therapy-relevant actionable items):
use one or more artificial intelligence based analytical techniques to parse the patient embedding and catalog portions of the patient embedding into a plurality of categories including patient history, patient symptoms, patient condition, patient medication, patient allergy, patient concerns, likely medical billing codes for services rendered during the patient visit (Paragraph [0065]: applying 230 a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, Paragraph [0072]: List of topics: topic 0 is chit-chat and interjections; topic 1 is low-energy exercises; topic 2 is fear; topic 3 is medication planning; topic 4 is the past, control and worry);
compare the patient embedding to the database of existing patient embeddings and determine a confidence matching score of the patient embedding relative to one or more existing patient embeddings (Paragraph [0085]: the speech segments can be compared to, for example, a working alliance inventory (or some other affinity inventory or ontology) transformed into embeddings. The comparison is performed by a machine learning comparator (schematically represented as ellipse 620) that is trained to produce embeddings (vectors) from conversational input and compare those embeddings to vector representations (derived from the same machine learning models) for a particular psychological inventory, Paragraph [0091]: Having transformed the speech segments and inventories into the embedding space, a similarity score (e.g., cosine similarity) between the embedding vectors of the turns (speech segment features) and its corresponding inventory vectors is computed); and
automatically generate a set of post visit instructions and automatically generate a patient post visit record based at least in part on one or more existing patient embeddings having a confidence matching score greater than or equal to a first predetermined threshold (Paragraph [0125]: The proposed system uses a turn-level rating mechanism that predicts the therapeutic outcome by computing a similarity score between the deep embedding of a scoring inventory, and the current speech segment (one or more sentences) that the patient is speaking.); and
an output module configured to, in real time (Paragraph [0126]):
provide to the clinician via one or more different media the patient post visit record in a standardized format (Paragraph [0126]: It transcribes the session in real-time, predicts the therapeutic outcome as a turn-level rating, and recommends treatment strategy that is best for the current context and state of the psychotherapy. ); and
automatically execute the set of post visit administrative instructions (Paragraph [0126]: It transcribes the session in real-time, predicts the therapeutic outcome as a turn-level rating, and recommends treatment strategy that is best for the current context and state of the psychotherapy. ).
Regarding claim 2, Lin discloses the assistant of claim 1, wherein the mathematical vector includes a mathematical representation of one or more datapoints in a multidimensional space, the one or more data points including the patient history, the patient symptoms, the patient condition, the patient medication, the patient allergy, the patient concerns, and/or the likely medical billing codes for services rendered during the patient visit (Paragraphs [0066] and [0072]).
Regarding claim 3, Lin discloses the assistant of claim 1, wherein the input module is configured to convert unstructured data to structured data, wherein the patient embedding is the structured data (Paragraph [0065]).
Regarding claims 4 and 18, Lin discloses wherein the input module is further configured to, in real time, recognize one or more of: written language, wherein the written language includes a patient existing record and/or clinician notes generated during the patient visit; imagery, wherein the imagery includes a patient imaging existing record and/or patient images generated during the patient visit; gestures, wherein the gestures include gestures performed by a clinician during the patient visit; and/or non-spoken language acoustics, wherein the non-spoken language acoustics include non-spoken language acoustics generated by the patient during the patient visit (Paragraph [0093]).
Regarding claim 5, Lin discloses the assistant of claim 4, wherein the input module is configured to perform one or more of: speech recognition, gesture recognition, image recognition, optical character recognition, and/or acoustic recognition on the spoken language, the written language, the imagery, the gestures, and the non-spoken language acoustics, and wherein the input module is configured to convert the written language, the imagery, the gestures, and the non-spoken language acoustics to a respective mathematical vector that is associated with the generated patient embedding, and wherein the analytics module is configured to automatically update the patient embedding with the respective mathematical vectors as the input is captured (Paragraphs [0093] and [0094]).
Regarding claim 6, Lin discloses the assistant of claim 5, wherein the spoken language, the written language, the gestures, and/or the non-spoken language acoustics are captured by the input module via an input device, wherein the input device includes one or more of a computerized medical equipment, a laptop, a desktop computer, a smart speaker, an internet browser, a mobile device, a tablet, a smart watch, smart glasses, an AR headset, a VR headset, and/or a XR headset (Paragraph [0146]).
Regarding claim 7, Lin discloses the assistant of claim 4, wherein the output module is configured to perform one or more of: speech synthesis, image generation, and/or document generation to generate and provide the patient post visit record via the one or more different media, wherein the one or more different media include: visual output, haptic output, and/or auditory output (Paragraph [0126]).
Regarding claim 8, Lin discloses the assistant of claim 7, wherein the output module is configured to provide the visual output and/or auditory output to the clinician via an output device, wherein the output device includes on one or more of a computerized medical equipment, a laptop, a desktop computer, a smart speaker, an internet browser, a mobile device, a tablet, a smart watch, smart glasses, an AR headset, a VR headset, and/or a XR headset (Paragraph [0253]).
Regarding claim 9, Lin discloses the assistant of claim 1, wherein the analytics module is further configured to pass the input data to a natural language processing module, a natural language understanding module, a large language model module, a neural network module, a mixed-initiative dialogue manager module (Paragraph [0062]).
Regarding claims 10 and 19, Lin discloses wherein the standardized format of the patient post visit record includes: a chief complaint, a subjective description, an objective description, an assessment, and a plan (Paragraph [0125]), wherein:
the chief complaint, the subjective description, and the objective description are automatically generated from directly the patient embedding prior to the comparison to existing patient embeddings (Paragraph [0125]), and
i) the assessment and the plan are automatically generated directly from the patient embedding prior to the comparison to existing patient embeddings, and the analytic module is further configured to automatically generate a secondary patient post visit record including, the chief complaint, the subjective description, the objective description, a secondary assessment, and a secondary plan, wherein the secondary assessment and the secondary plan are automatically generated by the analytic module based on the comparison of the patient embedding to the existing patient embeddings where a confidence matching score of the patient embedding to one or more existing patient embeddings is greater than or equal to a second predetermined threshold, or ii) the assessment and the plan are automatically generated by the analytic module based on the comparison of the patient embedding to the existing patient embeddings where a confidence matching score of the patient embedding to one or more existing patient embeddings is greater than or equal to a second predetermined threshold (Paragraph [0125]).
Regarding claim 11, Lin discloses the assistant of claim 1, wherein the set of post visit administrative instructions includes: input or upload information from the patient post visit record to an electronic heath records database; update an existing patient record for the patient with information from the patient post visit record; generate a referral letter to a specialty clinician; generate or begin a pre-authorization process for follow up appointments or procedures; schedule subsequent appointments for the patient with the clinician or with other clinicians based on information from the patient post visit record; send a prescription order to a pharmacy based on information from the patient post visit record; code and/or enter clinician services performed into an electronic billing system, and/or generate and provide patient friendly format of the patient post visit record to the patient before discharge (Paragraph [0063]).
Regarding claim 16, Lin discloses the assistant of claim 1, wherein the output module is configured to provide the clinician via one or more different media the patient post visit record in a manner that is not readily accessible to the patient during the visit (Paragraph [0092]).
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.
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.
Claims 12-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 2023/0320642 A1) in view of Bader et al. (US 2019/0392922 A1).
Regarding claims 12 and 20, Lin does not explicitly disclose wherein the analytic module is configured to, in real time,
automatically review a patient intake database and scheduling database to determine a list of patients to be seen by the clinician,
automatically review an existing patient electronic health record for each patient to be seen, and
automatically provide to the clinician, based on the review of the patient intake database and existing electronic health record for a respective patient to be seen, a pre patient visit report for each patient including:
bibliographic information of the respective patient to be seen; a medical history of the respective patient to be seen; a proposed assessment and plan to be included in the patient post visit record; a list of follow up questions to be asked of the respective patient during the visit; and/or a list of administrative tasks to be completed post patient visit,
wherein the output module is configured to provide the pre patient visit report to the clinician in the standardized format.
Bader teaches:
wherein the analytic module is configured to, in real time,
automatically review a patient intake database and scheduling database to determine a list of patients to be seen by the clinician (Paragraph [0054]: perioperative database 800 may allow delivery of a report that lists all patients to be seen for a day or session),
automatically review an existing patient electronic health record for each patient to be seen (Paragraph [0054]: with appointment time, age, type of surgery, surgeon, and flags 140, 214 for risk groups such as frailty, dementia, delirium, diabetes, anemia, and potentially other specific data that are highly relevant to this physician in this specific context ), and
automatically provide to the clinician, based on the review of the patient intake database and existing electronic health record for a respective patient to be seen, a pre patient visit report for each patient (Paragraph [0054]) including:
bibliographic information of the respective patient to be seen; a medical history of the respective patient to be seen; a proposed assessment and plan to be included in the patient post visit record; a list of follow up questions to be asked of the respective patient during the visit; and/or a list of administrative tasks to be completed post patient visit (Paragraph [0054]),
wherein the output module is configured to provide the pre patient visit report to the clinician in the standardized format (Paragraph [0053]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Lin to automatically review a patient intake database and scheduling database to determine a list of patients to be seen by the clinician, automatically review an existing patient electronic health record for each patient to be seen, and automatically provide to the clinician, based on the review of the patient intake database and existing electronic health record for a respective patient to be seen, a pre patient visit report for each patient including: bibliographic information of the respective patient to be seen; a medical history of the respective patient to be seen; a proposed assessment and plan to be included in the patient post visit record; a list of follow up questions to be asked of the respective patient during the visit; and/or a list of administrative tasks to be completed post patient visit, wherein the output module is configured to provide the pre patient visit report to the clinician in the standardized format as taught by Bader. Lin discloses a working alliance inventory (WAI) of 36 questions to provide a record of the mapping from the alliance measurement and the corresponding cognitive constructs underlying the measurement under a unified theory of therapeutic change (Lin Paragraph [0111]). Using the Perioperative Education And Engagement Of Surgical Patients of Bader would provide recommendations of at least one pathway to be implemented by the medical staff in the patient's care based on evaluation of EMR and questionnaire answers (Bader Abstract).
Regarding claim 13, Lin discloses wherein the analytic module is configured to, in real time, automatically modify the assessment and the plan of the pre patient visit report during the patient visit based on the patient embedding and the comparison to the database of existing patient embeddings if the confidence matching score of the patient embedding relative to one or more existing patient embeddings increases indicating a better fitting assessment and plan compared to the pre patient visit report (Paragraph [0133]).
Regarding claim 14, Lin discloses wherein the analytic module is configured to, in real time, automatically update the list of questions to be asked during the patient visit based on the patient embedding and the comparison to the database of existing patient embeddings (Paragraph [0112]).
Regarding claim 15, Lin discloses wherein the analytic module is configured to, in real time, automatically update the list of administrative tasks to be completed post patient visit based on the patient embedding and the comparison to the database of existing patient embeddings, wherein the set of post visit instructions includes the list of administrative tasks to be completed post visit (Paragraph [0126]).
Conclusion
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685