Patent Application 18491876 - SYSTEM AND METHOD TO ENHANCE THE CONTINUITY OF

Title: SYSTEM AND METHOD TO ENHANCE THE CONTINUITY OF CARE FOR A PATIENT

Application Information

Invention Title: SYSTEM AND METHOD TO ENHANCE THE CONTINUITY OF CARE FOR A PATIENT
Application Number: 18491876
Submission Date: 2025-05-14T00:00:00.000Z
Effective Filing Date: 2023-10-23T00:00:00.000Z
Filing Date: 2023-10-23T00:00:00.000Z
National Class: 705
National Sub-Class: 003000
Examiner Employee Number: 100289
Art Unit: 3684
Tech Center: 3600

Rejection Summary

102 Rejections: 1
103 Rejections: 3

Cited Patents

No patents were cited in this rejection.

Office Action Text

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 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.

Claim 2-3 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.

The term “clubbed” in claim 2 is undefine and unclear term which renders the claim indefinite. The term “clubbed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 15 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.

Regarding claim 15, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).

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 claimed invention is directed to an
abstract idea without significantly more.

Step 1:
The claims encompass two statutory categories: process and machine.
Process (Claims 1-12) These claims define a method to enhance the continuity of patient care.
Machine (Claims 13-20) These claims define a physical apparatus designed to carry out the claimed functions.
The claims encompass two statutory categories: process and machine. Having confirmed the claims fall within statutory categories, the analysis proceeds to Step 2A.
Step 2A Prong One:
The independent claims 1 (method) and 13 (system) recite abstract ideas related to processing information, making recommendations based on analysis and expert judgment, and managing interactions in a healthcare context.
Claim Recitations:
1. …
accessing the patient’s data available in one or more formats, wherein the patient’s data includes pre-stored data, real-time generated data, or a combination thereof;
processing the patient’s data using machine learning techniques and a language learning model by extracting relevant information from the patient’s data;
generating one or more patient-specific assignment recommendations automatically in correspondence with the processed patient’s data, wherein the one or more assignment recommendations are designed based on a correlation between the pre-stored data and the real-time generated data in order to address a condition management in the patient;
receiving one or more input from an expert on one or more assignment recommendations to generate one or more approved assignment recommendations;
communicating the one or more approved assignment recommendations to one or more remote users including, parents and caregivers of the patient, wherein the parents and caregivers may access and review the approved one or more assignment recommendations.
13. …
a server including a memory to store instructions;
a processing device operatively coupled to the server and is configured to execute instructions to:
access the patient’s data available in one or more formats, wherein the patient’s data includes a pre-stored data, a real-time generated data or a combination thereof;
process the patient’s data using machine learning techniques and language learning model based on extraction of relevant information from the patient’s data;
generate one or more patient specific assignment recommendations automatically in correspondence with the processed patient’s data, wherein the one or more assignment recommendations are designed based on a correlation between the pre-stored data and real-time generated data in order to address a condition management in the patient;
receive one or more inputs from an expert on one or more assignment recommendations to generate one or more approved assignment recommendations;
communicate the approved one or more assignment recommendations to one or more remote users which may include parents and caregivers of the patient, wherein the parents and caregivers can access and review the approved one or more assignment recommendations.

Under their broadest reasonable interpretation (BRI), Claim 1 and Claim 13 encompass steps and configurations for accessing, processing, and analyzing patient data using recited techniques, generating recommendations, incorporating expert input, and communicating with users. These core functions, particularly processing data to extract information and generating patient-specific recommendations based on analysis and expert input, align strongly with the "Mental Processes" category of abstract ideas, which includes concepts performed in the human mind such as observation, evaluation, and judgment. They also touch upon "Certain Methods of Organizing Human Activity," specifically managing personal behavior or relationships or interactions between people, as they involve coordinating actions and communication within a patient care workflow.
A human could replicate the core claimed functions manually. For instance, a healthcare professional could access patient records (accessing), read and analyze them (processing/mental process), form a judgment about appropriate recommendations based on their assessment (generating recommendations/mental process), discuss these with a colleague (receiving input from an expert/managing interaction), write down the final recommendations, and verbally or physically deliver them to a caregiver (communicating/managing interaction). This demonstrates that the fundamental steps of analysis, judgment, decision-making (recommendation generation), and coordinating actions described in the claims can be performed by a human using only their mind and manual actions, reinforcing their abstract nature under Step 2A, Prong One.
Dependent claims 2-6, 8, 11, 14-16, and 19 fall under mental processes, as they recite processing/analyzing specific patient data types/formats (2-6, 14 ), generating/editing recommendations using analytical judgment or assessment (8, 11 ), using AI/mathematical models for analysis (15-16), or adaptive analysis (19 ), to apply it the abstract idea, aligning with MPEP § 2106.04(a)(2) I & III. Dependent claims 7, 9, 10, 12, 17, 18, and 20 fall under certain methods of organizing human activity, as they recite managing interactions via communication formats (7 ), updates (9 ), query interfaces, consultation, or feedback (10, 12, 17, 18 ), or notifications (20 ), aligning with MPEP § 2106.04(a)(2) II C. Therefore, dependent claims 2-20 fit under the selected abstract ideas, reciting fundamental concepts matching MPEP § 2106 definitions.
The claims recite subject matter that corresponds to abstract ideas, such as mental processes and methods of organizing human activity. Therefore, the eligibility analysis must proceed to Step 2A Prong Two to determine if the claims integrate the exception into a practical application.
Step 2A, Prong Two
In Step 2A, Prong One, independent claims 1 (method) and 13 (system) were identified as reciting abstract ideas, primarily related to mental processes (analysis, evaluation, judgment) and methods of organizing human activity (managing interactions). Now, we examine the additional elements in these claims, to determine if they integrate these abstract ideas into a practical application.
Additional Elements: Machine learning techniques, language learning model, server, memory, processing device.
Machine learning techniques and language learning model (Claims 1, 13): The claimed machine learning techniques and language learning model represent software tools or computational approaches used for abstract data processing and analysis. They do not integrate the abstract idea into a practical application because they function merely as generic means to execute mathematical and linguistic operations. The claims do not describe how these specific techniques are applied in a novel or unconventional way within the healthcare context, nor do they detail any technical synergy between the AI models and the patient data beyond standard analytical applications. Their inclusion does not transform the abstract analysis into a concrete, practical application in a manner that meaningfully limits the abstract idea itself, serving as generic computational tools.
Server, memory, processing device (Claim 13): The server, memory, and processing device are fundamental hardware components of a computer system. Their recitation in the claim serves primarily as a technological environment for performing the abstract data access, processing, generation, receiving, and communication steps. The claim does not specify any unconventional arrangement or interaction between these components that provides a technical improvement or solves a technical problem inherent in the system's operation. They merely represent generic computing resources used to execute the abstract method, effectively functioning as generic tools without integrating the abstract idea into a practical application.
Based on the analysis under Step 2A, Prong Two, the independent claims 1 and 13 do not integrate the recited abstract ideas into a practical application. The additional elements identified are either generic computational tools or standard hardware components. These elements, whether considered individually or in combination, serve merely as a technological environment or implement the abstract steps using generic resources. The claims fail to describe any specific technical improvement or unconventional application that meaningfully limits the scope of the abstract ideas beyond executing them on a generic computer system using standard software tools.
We now evaluate the dependent claims to determine if the additional elements they introduce or elaborate upon integrate the abstract ideas into a practical application, focusing on whether they move beyond generic "apply it" instructions or generic tools, and grouping claims based on the additional elements present (or absent).
The dependent claims introduce additional elements or further specify aspects of the independent claims. Dependent Claims of Claim 1 (Claims 2-12): As evaluated, these claims do not introduce new additional elements. Claim 2 specifies data content; Claim 3 details SOAP notes structure; Claims 4 and 5 specify pre-stored and real-time data content; Claim 6 specifies data formats; Claim 7 specifies communication formats; Claim 8 specifies the basis for recommendations; Claim 9 describes user updates; Claim 10 specifies a User-query interface; Claim 11 refers to expert modification and ML; Claim 12 describes expert consultation; and Claim 17 describes a feedback loop. While adding specificity to the abstract steps and data/interaction flows, the absence of new additional elements means these claims do not integrate the abstract idea into a practical application under Step 2A Prong Two. Dependent Claims of Claim 13 (Claims 14-20): These claims introduce or elaborate on additional elements identified as data model, machine learning models (including convolutional neural networks, audio/video processing), Generative AI models (including GPT-3, GPT-3.5, BERT, ALBERT), and user interfaces (including graphical, voice, video, gesture interfaces). Claim 14 claims the data model; Claim 15 says the model may be ML models; Claim 16 says it comprises Generative AI models; Claim 18 specifies user interfaces for expert data access. Claim 17 describes a feedback loop; Claim 19 describes the data model's dynamic function; Claim 20 describes notifications. These elements are generic computational tools for "applying" the abstract processes [cite: MPEP § 2106.05(f)]. They function as mere instructions for execution using high-level hardware and software constructs, without integrating the abstract idea into a practical application.
Viewing the dependent claims as a whole, they introduce or elaborate upon additional elements that remain generic computational tools, standard interfaces, or specify characteristics of the input data or communication outputs. None of these additions, individually or in combination, integrate the underlying abstract ideas of data analysis, judgment, or organizing human activity into a practical application. They merely provide a technological environment or standard means for performing the abstract steps.
Based on the analysis under Step 2A Prong Two, the independent and dependent claims are directed to abstract ideas without integrating them into a practical application. Accordingly, the analysis proceeds to Step 2B to determine if the claims contain an inventive concept.
Based on the analysis under Step 2A Prong Two, the independent and dependent claims, even when considered as a whole and in combination, are directed to abstract ideas without integrating them into a practical application. Accordingly, the analysis proceeds to Step 2B to determine if the claims contain an inventive concept.
Step 2B:
In Step 2B, the question is whether the claim recites additional elements beyond the abstract idea identified in Step 2A that amount to "significantly more" than the abstract idea itself. Additional elements: machine learning techniques, language learning model, server, memory, processing device Claim 1, Claim 13 do not overcome step 2B.
The machine learning techniques and language learning model are described in paragraph [0012] –[0013] and [0044] as tools for performing preprocessing, extracting, and analyzing patient data. These are applications of computational and linguistic models to abstract tasks. The claims recite using these models to process data and generate recommendations. This represents automating abstract analytical and decision-making processes using general-purpose computational tools. The specification does not describe any specific technical improvement in the machine learning or language learning models themselves, or their application in a way that solves a technical problem beyond the abstract analysis.
Merely applying these models to patient data to automate tasks previously performed by humans or through less sophisticated means does not provide an inventive concept. The use of these models is described at a high level, focusing on the function they perform (extracting relevant information, generating recommendations) rather than a technical solution enabled by a specific or unconventional application of these models. Thus, the inclusion of machine learning techniques and a language learning model, individually considered, does not provide significantly more than the abstract idea being performed.
The server, memory, and processing device is described in paragraph [0013] and [0045] as generic computer hardware components used to store data and execute processes. These are the fundamental building blocks of any computational system. The specification does not describe any specific, unconventional architecture, configuration, or interaction of these components that provides a technical improvement in their functioning or solves a technical problem inherent in the system's operation.
Merely performing abstract steps on a generic computer or using standard hardware components does not amount to an inventive concept or provide significantly more. The specification does not disclose that these components are configured or combined in any special way that goes beyond their ordinary function to facilitate the abstract idea. Thus, the inclusion of a server, memory, and processing device, individually considered, does not provide significantly more than the abstract idea being performed.
When considering the independent claims 1 and 13 as a whole, the combination of additional elements – the generic server, memory, and processing device, and the use of machine learning techniques and language learning models – does not amount to significantly more than the recited abstract ideas.
The specification describes this combination in terms of its function in performing the abstract steps, it does not describe a technical problem solved by this specific combination, nor does it show a technical improvement in the computer or AI technology itself. The claims as a whole remain directed to the abstract concepts, implemented using standard technology, and lack the inventive concept required to transform their nature into something significantly more.
The additional elements, viewed individually or in combination across the independent claims, do not transform the nature of the claims into a patent-eligible application of the abstract idea. They represent generic computing components, standard software constructs, and basic interaction methods described at a high level. The specification does not disclose an inventive concept in the combination of these elements that provides a technical improvement beyond the abstract processes being automated.
Dependent Claims of Claim 1 (Group A: Claims 2-12) do not introduce new additional elements beyond those already considered for Claim 1 (data content/structure, interaction methods, recommendation basis). They add specificity to the abstract steps (e.g., specific data types like SOAP notes, specific communication formats, user update details) but do not introduce distinct components or functionalities beyond those encompassed by the base claim's abstract ideas and the already-analyzed additional elements. Therefore, these claims do not add significantly more than the judicial exception under Step 2B.
The following additional elements introduced or elaborated upon by the Dependent Claims of Claim 13 (Group B: Claims 14-20).
Claims 14 and 19 relate to the data model and its dynamic function, described in paragraph(s) [0045], [0051], [0069]. The specification describes the data model functionally for storing, managing, and dynamically processing data without detailing specific unconventional structures or technical improvements beyond implementing the abstract idea. The specification does not demonstrate that these limitations add significantly more than the judicial exception, either individually or in combination.
Claims 15 and 16 relate to specific machine learning and Generative AI models, described in paragraph(s) [0056], [0071]. These claims recite specific types of known computational models (ML, CNN, Generative AI like GPT, BERT) as tools for analysis without describing a specific technical improvement in the models themselves or their unconventional application. The specification does not demonstrate that these limitations add significantly more than the judicial exception, either individually or in combination.
Claim 18 relates to user interfaces for expert data access, described in paragraph(s) [0041], [0047], [0050]. The specification describes various user interfaces (graphical, voice, video, gesture) generically for the purpose of data access and interaction, lacking detail on any unconventional implementation or technical solution provided by these interfaces. The specification does not demonstrate that these limitations add significantly more than the judicial exception, either individually or in combination.
Note: Claims 17 (feedback loop) and 20 (notifications), also in Group B, relate to communication/interaction methods similar to those in Group A and were analyzed there.
The additional elements identified in Step 2A (machine learning techniques, language learning model, server, memory, processing device, data model, specific AI models, user interfaces) are described in the specification as generic computational tools or generic hardware/software components used to implement the abstract processes. Considered individually and in combination, these elements do not provide specific technical improvements or unconventional applications beyond their basic functions. Therefore, the claims as a whole do not amount to significantly more than the judicial exceptions identified in Step 2A.

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.
(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.

Claim(s) 1-2, 5-14, 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US20230052573A1-Gnanasambandam.

Claims:
Gnanasambandam teaches, A method to enhance the continuity of care for a patient comprising:
accessing the patient's data available in one or more formats, wherein the patient's data includes pre-stored data, real-time generated data, or a combination thereof; (Gnanasambandam, [0147-0148], [0149-0150], [0612]-[0615], [0631], [0642],)
Gnanasambandam discloses accessing patient data from various sources. Pre-stored data includes "health records that include doctor's notes... prescriptions, billing records, and insurance records" and facility data like "appointment times". Real-time data is also accessed, including data captured during interactions like "generating a recording of a conversation," which can be an "audio recording, a video recording", or generated by processing video, such as "tone data," "emotion data," and "movement data". Data formats include "text" input, audio via "microphone", and "video recording", satisfying access to pre-stored and real-time data in multiple formats.
It also describes receiving data directly from the user or capturing new data during interactions, such as generating "a recording of a conversation," including "audio recording, a video recording," or generating "tone data," "emotion data," and "movement data" by processing video, representing real-time generated data. Data can be received via "text," "microphone" (audio), and "video recording," and “during the appointment” demonstrating multiple formats.
Gnanasambandam teaches, processing the patient's data using machine learning techniques and a language learning model by extracting relevant information from the patient's data; (Gnanasambandam, paras [0094], [0343], [0134], [0138], [0136], [0619]-[0620])
Gnanasambandam describes a "critical thinking engine 108" that executes tasks using "artificial intelligence, such as recognizing and interpreting natural language". This engine includes an "artificial intelligence engine 109... that uses one or more machine learning models" trained to "transform input unstructured data (e.g., patient notes) into cognified data". The processing involves "natural language processing techniques" like "parts of speech tagging," "parsing," "named entity recognition (NER)," and "sentiment analysis" to identify "indicia (lexical items, words, phrases, and syntactic markers)" which constitutes extracting relevant information.

generating one or more patient-specific assignment recommendations automatically in correspondence with the processed patient's data, wherein the one or more assignment recommendations are designed based on a correlation between the pre-stored data and the real-time generated data in order to address a condition management in the patient; (Gnanasambandam, paras [0551-0554], [0121], [0123], [0586], [0313], [0524], [0666], [0631], figure 58, [0673], [0354])
A knowledge graph (pre-stored) + patient graph (real-time) produce a “care plan” that contains “action instructions,” functionally equivalent to the claimed “assignment recommendations.”
receiving one or more input from an expert on one or more assignment recommendations to generate one or more approved assignment recommendations; (Gnanasambandam, [0672], [0128], [0708], [0574-0575], [0678])
Gnanasambandam describes receiving input from "medical personnel" (expert) via a "clinic viewer", including a "desired medical outcome" or selection of "health artifacts to include in the updated care plan". It also discloses receiving an "indication that the goal is approved, denied, or modified by the medical personal". This input is used to "modify the care plan" or perform an action based on the indication, resulting in an "updated care plan" or transmitting an "approved" plan, thus generating approved recommendations.
communicating the one or more approved assignment recommendations to one or more remote users including, parents and caregivers of the patient, wherein the parents and caregivers may access and review the approved one or more assignment recommendations. (Gnanasambandam, paras [0123], [0589], [0708],[0681-0682], [0678], [0689], [0107], 0103).

Gnanasambandam discloses causing the generated or modified/approved "care plan" (which contains the recommendations/action instructions) to be "presented on a computing device" and explicitly teaches "transmitting the modified care plan to a computing device of the patient" or "transmitting the care plan including the goal to a computing device of a third party". These "third party" remote users are defined to include "a patient, a health coach, a clinician," "a nurse," "a family member of the patient, [or] a friend of the patient," which encompasses parents and caregivers. Medical person “may use the user interface 7400 to update the goal in real-time or near real-time, thereby updating the modified care plan for the patient.” A medical person for example could be a nurse that is analogous to caregiver and parent, since a parent could be a nurse or pediatrician for example.
2. Gnanasambandam teaches, The method as claimed in claim 1, wherein the patient's data comprises medical session report, progress report, patient's health records, or a combination thereof, where the medical session report further comprises one or more previous or ongoing session reports, the progress report comprises one or more medical session reports clubbed to define progress made by the patient over a period, and the patient's health record comprises one or more additional health related details of the patient. (Gnanasambandam, [0092],[0148], [0154])
Gnanasambandam disclose, “medical session report” and “progress report” correspond to “patient notes before, during, and/or after consultation” and “numerous EMRs for the patient,” which reflect individual sessions and cumulative health insights. Similarly, the limitation’s “patient’s health record” aligns with “health records that include doctor’s notes... prescriptions, billing records, and insurance records,” capturing comprehensive health-related details.
5. Gnanasambandam’s teaches, The method as claimed in claim 1, wherein the real-time generated data comprises session notes of the ongoing session, patient's data, medical data of one or more patients with similar condition or a combination thereof.
Gnanasambandam teaches real-time data comprises "patient notes...during...consultation" (ongoing session notes) ([0092]). It also includes other real-time "patient data" like user queries via the "cognitive agent" ([0402]) and captured "recording[s]" ([0612-0614]). Furthermore, its AI engine utilizes the consolidated "knowledge cloud" / "master dataset" (containing data from many patients) when processing current data ([0151-0152], [0598-0600]), thus comprising "medical data of one or more patients with similar condition".

6. Gnanasambandam’s teaches, The method as claimed in claim 1, wherein the patient's data may be in one or more of the following formats-text, audio, video, image, recording of the session or a combination thereof.
Gnanasambandam teaches accessing patient data in the required formats by disclosing receiving "text" input ([0166]), capturing "audio recording [0612]" and "video recording" of patient-professional conversations ("recording of the session") ([0612]-[0615]), and performing "imaging extraction" ("image") ([0454]).
7.Gnanasambandam teaches, The method as claimed in claim 1, wherein the one or more approved assignment recommendations may be communicated to the one or more remote users in one or more of the following formats-text, audio, video, image, gaming task, recording, or a combination thereof.
Gnanasambandam teaches the approved recommendations (care plans/action instructions) are generated ( para [0530-0532]), finalized via expert input ( para [0590], [0708]), and communicated electronically ( para [0532], [0708], [0683]) text format.
8. Gnanasambandam teaches, The method as claimed in claim 1, wherein the one or more assignment recommendations are generated based on the session notes of the ongoing and previous sessions, medical data of one or more patients with similar condition, where the assignment recommendations are tailored according to the patient's requirement and generated to improve patient's condition. Gnanasambandam teaches generating care plans/recommendations based on comparing a "patient graph [0121]" (accumulated from previous sessions/interactions) with a "knowledge graph [0115],[0378], [0313], [0524]” and also processes current "patient notes" into "cognified data" ([0097]), thereby utilizing both previous and ongoing session data.
9.Gnanasambandam teaches, The method as claimed in claim 1, wherein the one or more remote user may share an update with the expert related to the communicated one or more approved assignment recommendations, thereby facilitating a seamless communication between the one or more remote user and the expert in order to enhance the continuity of care for the patient. Gnanasambandam disclose that the remote " user provides data responsive to the microsurvey 116 using the user device 104" ([0151, 0291-0292]). Furthermore, the system tracks user interactions with health artifacts (recommendations/action instructions) like performing tests, exercises, or consuming content ([0557]) to generate an "engagement profile" ([0559]), thereby enabling the remote user to share updates related to the communicated recommendations back to the system/expert. This communication tracking user interactions with recommendations to update an "engagement profile" ([0557]-[0560]) establishes a feedback loop. This integrated system, where user input and actions directly update the system data used for care management, inherently facilitates seamless communication as described.
10.Gnanasambandam teaches, The method as claimed in claim 1 establishes a user-query interface to enable one or more remote users to pose questions or seek clarification from the expert regarding the one or more approved assignment recommendations and/or one or more health condition of the patient, wherein the expert may be a physician, therapist, clinician, doctor and/or any other person having experience in providing recommendations and/or treatment related to the condition of the patient. (Gnanasambandam, [0089], [0091], [0133],[01555], [0402], [0498])
Gnanasambandam teaches establishing a user-query interface by describing a "cognitive agent" allowing users to enter natural language queries via channels like chat or voice regarding their health condition or through questionnaires sent to physicians ("expert"). This enables remote users to pose questions about their condition or implicitly about their care plan ("assignment recommendations") to qualified medical personnel involved in the system.
11.Gnanasambandam teaches, The method as claimed in claim 1, wherein the one or more assignment recommendations is automatically generated by using machine learning model, which is then edited and/or modified by the expert based upon his assessment of the patient's condition or one or more preferences of receiving the assignment by the patient or caregiver to get the one or more approved assignment recommendations.
Gnanasambandam teaches generating assignment recommendations ("action instructions" within a "care plan") automatically using an "artificial intelligence engine" employing "machine learning models" ([0136], [0313]), which are then modified based on input ("desired medical outcome") reflecting the expert's assessment ([0590, 0586]), resulting in an "approved" care plan ([0491], [0713], [0750]).
12.Gnanasambandam teaches, The method as claimed in claim 1, wherein the expert addresses user queries, provide guidance, and maintain an interactive consultation with the one or more remote user in order to maintain the continuity of care. (Gnanasambandam, , 0132-0133, 0004, 0141, 0147, 0090-0092, 0155, 0016, 0491)
The "cognitive agent" described in Gnanasambandam acts as an interface to the “expert” system/personnel by addressing user queries through a "question and answering system" (and "conversation streams” using the cognitive agent, thereby providing guidance and maintaining interaction.

13.Gnanasambandam teaches, A system to enhance the continuity of care for a patient comprising: a server including a memory to store instructions; (Gnanasambandam, [0004-0005], abstract)
Gnanasambandam disclosed a system that includes a memory storing instructions, along with a processor for executing those instructions.

a processing device operatively coupled to the server and is configured to execute instructions to: access the patient's data available in one or more formats, wherein the patient's data includes a pre-stored data, a real-time generated data or a combination thereof; (Gnanasambandam, [0004-0005], abstract, [0598-0600], [0612-0615])
Gnanasambandam teaches accessing both pre-existing and real-time patient data for care planning, satisfying the requirement for accessing patient data “in one or more formats” and including “pre-stored data, real-time generated data or a combination.”
process the patient's data using machine learning techniques and language learning model based on extraction of relevant information from the patient's data; (Gnanasambandam, [0565-0566], [0134]), disclose machine learning model and natural language processing to extract tone/emotion and clinical context.

generate one or more patient specific assignment recommendations automatically in correspondence with the processed patient's data, wherein the one or more assignment recommendations are designed based on a correlation between the pre-stored data and real-time generated data in order to address a condition management in the patient; (Gnanasambandam, paras [0551-0554], [0121], [0123], [0586], [0313], [0524], [0666], [0631], figure 58, [0673], [0354])
A knowledge graph (pre-stored) + patient graph (real-time) produce a “care plan” that contains “action instructions,” functionally equivalent to the claimed “assignment recommendations.”

receive one or more inputs from an expert on one or more assignment recommendations to generate one or more approved assignment recommendations; (Gnanasambandam, [0672], [0128], [0708], [0574-0575], [0678])
Gnanasambandam describes receiving input from "medical personnel" (expert) via a "clinic viewer", including a "desired medical outcome" or selection of "health artifacts to include in the updated care plan". It also discloses receiving an "indication that the goal is approved, denied, or modified by the medical personal". This input is used to "modify the care plan" or perform an action based on the indication, resulting in an "updated care plan" or transmitting an "approved" plan, thus generating approved recommendations.

communicate the approved one or more assignment recommendations to one or more remote users which may include parents and caregivers of the patient, wherein the parents and caregivers can access and review the approved one or more assignment recommendations. (Gnanasambandam, paras [0123], [0589], [0708],[0681-0682], [0678]).

Gnanasambandam discloses causing the generated or modified/approved "care plan" (which contains the recommendations/action instructions) to be "presented on a computing device" and explicitly teaches "transmitting the modified care plan to a computing device of the patient" or "transmitting the care plan including the goal to a computing device of a third party". These "third party" remote users are defined to include "a patient, a health coach, a clinician," "a nurse," "a family member of the patient, [or] a friend of the patient," which encompasses parents and caregivers. Presentation on their device inherently allows them to access and review the recommendations.

14. Gnanasambandam teaches, The system as claimed in claim 13, further comprises a data model configured to perform a contextual analysis of the pre-stored data and the real-time stored data in order to identify the intend behind the patient's data.
Gnanasambandam, para [0089] mentions "performing conversational analysis which includes analyzing conversational context." Para [0134] mentions the engine "recognizing and interpreting natural language." Para [0093] describes cognifying "unstructured data" like "patient notes entered into one or more EMRs". Paragraph [0612] discusses analyzing real-time "recording of a conversation." These combined show analysis of pre-stored and real-time data for context/intent.

17. Gnanasambandam teaches, The system as claimed in claim 13, wherein the system further allows an interactive feedback loop between the expert and the one or more remote users which may include parents and/or caregivers of the patient in order to improve patient's engagement and treatment outcome. (Gnanasambandam, [0151-0156])
Gnanasambandam disclosed, Microsurvey tool collects ongoing responses from patient and returns data to platform and cognitive agent engages in dynamic, human-like conversations, persists over multiple interactions. Two-way, iterative communications between expert platform and patient/caregiver.
18. Gnanasambandam teaches, The system as claimed in claim 13, wherein the expert accesses the pre-stored data and the real-time data in one or more formats via a user interface including a graphical user interface, a voice user interface, a video user interface, a gesture user interface, or combination therefor.
Gnanasambandam's system teaches the expert ("medical professional") accessing pre-stored and real-time patient data ([0606-0609], [0596-0598]) via an expert-specific user interface, namely a "clinic viewer" or "patient chart interface" ([0128],[0532], [0597]). This interface is shown to be a Graphical User Interface (GUI) ([0682], FIG. 68)19. Gnanasambandam teaches, The system as claimed in claim 13, wherein the data model updates the one or more approved assignment recommendations and provides a dynamic response based on inputs provided by the one or more remote users.
Gnanasambandam's system teaches its data model ("machine learning model") modifying and generating "updated care plans" (updating recommendations) ([0568-0569]) dynamically in "real-time or near real-time" ([0668]). This updating is done "based on the patient data" ([0668-0670]), which includes inputs provided by remote users via "microsurveys" ([0151]) or tracked interactions ([0554]).20. Gnanasambandam teaches, The system as claim in claim 13 is configured to share notifications with the one or more remote users for completing and updating the one or more approved assignment recommendations.
Gnanasambandam's system is configured to share notifications with remote users by presenting information like updated care plans ([0562], [0588]) or via an "action calendar" ([0302-0304]). The action calendar serves as notification for completing assignment recommendations ([0303]), and the presentation of modified care plans serves as notification for updating the recommendations ([0588]).

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 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) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20230052573A1-Gnanasambandam in combination with US20060095298A1-Bina.

3. Gnanasambandam teaches, The method as claimed in claim 2, wherein the medical session report and the progress report comprises one or
Gnanasambandam disclose medical session report and the progress report in paragraphs 0092-0093 and 0598 and does not disclose that such records are SOAP notes.
However, Bina describes a system for managing patient data and simplifying workflow which explicitly utilizes SOAP notes, because Bina teaches generating specific workflow forms including "Daily SOAP notes (attending provider notes)" which are populated by the system from entered data. in paragraph 0044/0015/0081.
A person of ordinary skill in the art, seeking to improve the efficiency and standardization of documentation within Gnanasambandam's advanced health management system (aimed at efficient "health management" and "patient care" [0090]), would be motivated to incorporate the standardized "Daily SOAP note" format explicitly taught by Bina ([0015], [0018]). Bina teaches that using such standardized forms simplifies provider workflow ([0015]: "simplifying the workflow of attending physicians..."). Therefore, incorporating Bina's standardized, workflow-simplifying SOAP note format into Gnanasambandam's system for handling patient notes ([0090-0093], [0598]) would have been an obvious optimization to enhance documentation consistency and workflow efficiency.
Gnanasambandam in combination with Bina teaches, The method as claimed in claim 1, wherein the pre-stored data comprises one or more medical session reports or progress reports of the patient including .
Bina explicitly teaches generating "Daily SOAP notes (attending provider notes)" ([0015], [0044], [0081]). Refer to claim 3 for combination and motivation rational under 35 USC 103.

Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20230052573A1-Gnanasambandam in combination with US 20230380762 A1-Kuss.

15. Gnanasambandam teaches, The system as claimed in claim 13, wherein the data model may be a machine learning models configured to process and analyse the patient's data using techniques such as (Gnanasambandam, [0159-0161], [0628-0630], [0564-0566])
Gnanasambandam described video-signal processing (facial-recognition) and audio-signal processing (tone detection) and does not disclosed convolutional neural networks. However, Kuss describe convolutional neural networks in paragraph 0053, it is obvious to combine CNN of Kuss with Gnanasambandam, because CNN could be trained in images to determine a medical status as fluid status for example, since may benefit from an easy and automated method that is reliable and accurate. (paragraph 0028-0029, 0032, 0050)

Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20230052573A1-Gnanasambandam in combination with US-20220375605-A1 -Lipton.

16. Gnanasambandam teaches, The system as claimed in claim 13, wherein the data model further comprises a Generative AI model including one or more of
Gnanasambandam teaches a system using an "artificial intelligence engine" with "one or more machine learning models" employing "natural language processing techniques" to process patient data and generate personalized care plans ([0136-0137], [0313], [0437]) and does not explicitly disclose using specific named natural language processing models such as BERT, GPT-3, T5, etc., within its AI engine. Lipton disclosed CBERT for example in paragraph 0006/0036, that is obvious to combine for a PHOSITA since, pre-filtering reduces a processing time.

Conclusion
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/J.D.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684