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 .
Status of the Claims
The status of the claims as of the filing of the response is as follows: Claims 1-20 are pending. Claims none are canceled. The applicant has amended Claims 1, 2, 13, and 15, which are amended and have been considered below. Claims 3-12, 14, 16-20 are original.
Response to Amendments
35 U.S.C. 112(b)
Applicant’s arguments, see page 7, filed 11/14/2025, with respect to Claims 2 and 15 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejections are withdrawn.
The Applicant asserts that the term "clubbed" should be interpreted as "combined" based on the specification's context and has further clarified Claim 15 by removing exemplary language. The Examiner is respectfully agreed and withdraws the rejection because the amendments provide clear notice of the boundaries of the inventive subject matter.
Response to Arguments
35 U.S.C. § 101
Applicant’s arguments, see page 8-17, filed 11/14/2025, with respect to amended Claims 1 and 13 have been fully considered and are not persuasive. The 35 U.S.C. § 101 rejection is sustained.
The applicant asserts claim 1 includes "technical details that cannot be performed in the human mind" such as "a contextual analysis model configured to extract information under different categories as set forth in pre-defined subjective, objective, assessment, planning data." They argue the claims provide a "technical solution" to the "problem... identified in the present application [as] the lack of continuity of care."
The Examiner is respectfully disagreed and sustains the rejection because the extraction of information into "subjective, objective, assessment, and planning data" represents the "evaluation and judgment" of clinical observations, which are acts that can be performed in the human mind. Furthermore, the rules emphasize that an improvement to a "method of organizing human activity" such as enhancing the continuity of patient care does not constitute a "technical improvement to computer functionality" under MPEP § 2106.05(a). The reliance on McRo is misplaced because the present claims do not recite a "specific technical mechanism" that improves a technological field, but rather automate the "management of personal behavior or relationships" through the communication of care plans (MPEP § 2106.04(a)(2)(II)). Refer to 35 U.S.C 101 rejection for further details.
Applicant asserts that the Data Model and its Contextual/ML sub-modules are "significantly more" than the abstract idea of "processing information" because they are not necessary components. Furthermore, the Applicant argues that Claim 1 presents a technical solution that improves communication and the quality of recommendations, thereby resolving the problem of "lack of continuity of care" (Spec. para [0050]). Finally, the Applicant contends that the Examiner has not met the Berkheimer and Aatrix standard for establishing a prima facie case that the elements are "well-understood, routine, and conventional" (WRC).
Examiner respectfully disagrees because, under Step 2B, the additional elements fail to transform the abstract idea into a patent-eligible application.
Specifically, the use of a "contextual analysis model" to sort data into "SOAP" categories represents the mere automation of "traditional" documentation framework (Spec. para [0005]). According to MPEP § 2106.05(g), this constitutes "insignificant pre-solution activity", gathering and organizing data for the subsequent analytical step. The Applicant’s contention that these elements are not "necessary" to the abstract idea is legally insufficient to establish an inventive concept; per MPEP § 2106.05(h), these limitations merely narrow the abstract idea to the field of healthcare, which is not a technical transformation.
Furthermore, the alleged technical improvement to the "behavior of the user interface" and "quality of recommendations" (Spec. para [0050]) is focused on the clinical result, not the computer's technical operation. Under MPEP § 2106.05(a), a claim that uses a computer as a tool to achieve a better business or healthcare outcome does not recite a technical improvement. Since the claims rely on "any device" ([0045]) and utilize conventional platforms like WhatsApp and Alexa ([0080]), the combination of elements represents a generic computer implementation that lacks "significantly more" than the judicial exceptions identified.
35 U.S.C. § 102
Applicant's arguments, see page 18-19, filed 11/14/25, with respect to amended Claims 1 and 13 have been fully considered and are not persuasive.
The applicant "respectfully submits that Gnanasambandam neither teaches nor discloses" a data model that determines "intent" or includes a "contextual analysis model" for SOAP categories (subjective, objective, assessment, planning), noting the reference only discusses "conversational context."
Examiner respectfully disagrees because the applicant’s argument relies on a narrow interpretation of the claim terms that is inconsistent with the Broadest Reasonable Interpretation (BRI) standard required by MPEP 2111. Specifically, Gnanasambandam explicitly teaches identifying the "intent of the user" in paragraph [0159], directly contradicting the applicant’s assertion. Furthermore, while the reference may not use the specific acronym "SOAP," it discloses the extraction of "conclusions" and "recommendations" from patient notes ([0097]), which are the functional equivalents of the "assessment" and "planning" categories of the claimed SOAP data. Under MPEP 2131, a prior art reference anticipates a claim if it discloses every element, and here, the "contextual analysis" and "intent" functions are clearly present within the "cognitive intelligence platform" of Gnanasambandam.
35 U.S.C. § 103
Applicant's arguments, see page 19-20, filed 11/14/2025, with respect to amended Claims 3, 4, 15, and 16 have been fully considered and are not persuasive.
Applicant Argument: The applicant asserts that dependent claims 3, 4, 15, and 16 are allowable because the references (Bina, Kuss, Lipton) combined with Gnanasambandam fail to disclose the "intent" and "SOAP" data model limitations of independent claim 1.
The rejections of Claims 3, 4, 15, and 16 under 35 U.S.C. § 103 are maintained. The applicant's traversal fails because it relies on the alleged deficiency of the independent claim, which has been refuted by pointing to the express disclosures in Gnanasambandam paragraphs [0097] and [0159]. Applying the "Guide Respond to Applicant" rule, the examiner finds that the applicant has failed to address the "rational underpinning" of the combination of references. Specifically, the applicant does not dispute that Bina, Kuss, and Lipton teach the specific technical limitations added by the dependent claims (e.g., GPT/BERT models or SOAP formatting). The amended claims do not overcome the rejection because the primary technical hurdle (the automated medical intent/SOAP categorization) is already present in the primary prior art reference.
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.
Subject Matter eligibility Rejection 35 U.S.C 101
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
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: Identification of Abstract Ideas
Step 2A, Prong One is to evaluate whether the claim recites a judicial exception. According to MPEP 2106.04, this requires identifying whether the claim language, when given its Broadest Reasonable Interpretation (BRI), falls into one of the three categories of abstract ideas: mathematical concepts, certain methods of organizing human activity, or mental processes. The goal is to determine if the claim is "directed to" a fundamental concept that exists independently of the recited technology.
Under the Broadest Reasonable Interpretation (BRI) standard of MPEP 2111, the whole invention is related to a computer-implemented method and system that enhances the continuity of care for a patient by executing code to process patient data using a data model (comprising a contextual analysis model and a machine learning model) to automatically generate approved, patient-specific assignment recommendations for communication to remote users. Refer to par. 0012
More specifically, the claims 1-20 recite a series of functional steps and system components for information management.
Independent Claims 1 and 13 function as a data processing pipeline: (1) gathering data (accessing patient data), (2) evaluating that data (processing via a data model/machine learning to determine "intent"), (3) making a clinical judgment (generating assignment recommendations), (4) seeking secondary review (receiving expert input), and (5) transmitting instructions (communicating to remote users).
Dependent Claims (2-12 and 14-20) further specify the types of data being organized (Claim 2: progress reports; Claim 3: SOAP notes) or the format of the communication (Claim 6: text/audio; Claim 10: user-query interface). These limitations refine the "what" and "how" of the information transfer but do not change the underlying character of the process, which remains the management of information and human interactions.
Therefore, the claims 1-20 have recitations that fall into the following categories of abstract ideas:
The claims 1-20 have abstract ideas, specifically the Mental Process of clinical evaluation and the Method of Organizing Human Activity related to coordinating a care plan. The core steps of evaluating patient data and managing interactions are considered fundamental human activities, even with the use of machine learning.
Independent Claim 13 Recited the Following Non-bold abstract idea:
Claim 13.
A system to enhance the continuity of care for a patient comprising: a computer system that includes including a memory to store instructions and a processing device operatively coupled to the memory and configured to execute instructions to cause the computer system 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 a data model by extracting relevant information from the patient's data, wherein the data model is operational to determine an intent of the patient's data and includes
a contextual analysis model configured to extract information under different categories as set forth in pre-defined subjective, objective, assessment, planning data and
a machine learning model that receives the extracted information and categories under which the information pertains to assist the machine learning model to generate one or more patient-specific assignment recommendations;
generating the 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 inputs 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 selected from any of multiple formats 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 via a user interface.
Claim Abstract Classification Rational
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claims 1 and 13 abstract idea recite the collection of patient health data to determine clinical intent through the extraction of SOAP (Subjective, Objective, Assessment, Planning) categories and the generation of therapeutic recommendations based on data correlations, finalized through human-expert validation and transmission to a caregiver network. This process aligns with the following abstract idea categories:
Mental Process (MPEP § 2106.04(a)(2)(III)): This category encompasses concepts performed in the human mind, including acts of observation, evaluation, and clinical judgment. The independent claims 1 and 13 recite "determine an intent of the patient's data," "extract information under different categories as set forth in pre-defined subjective, objective, assessment, planning data," and "generate one or more patient-specific assignment recommendations."
When comparing the MPEP 2111 interpretation to MPEP 2106, these limitations describe cognitive steps typically performed by a therapist who observes a patient, evaluates their data to extract clinical meaning (intent), and exercises judgment to decide on a course of action (recommendations). (Spec., para. [0048], [0049])
The paragraph confirms the data model automates a clinician's evaluative judgments ("identify," "intent," "appropriate response") when reviewing patient progress to determine next steps.
Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II)): This category includes fundamental methods of managing personal behavior or relationships and interactions between people, such as clinical care workflows. The independent claims 1 and 13 recite "receiving one or more inputs from an expert on one or more assignment recommendations to generate one or more approved assignment recommendations" and "communicating the one or more approved assignment recommendations... to one or more remote users including, parents and caregivers.access the patient's data available in one or more formats, …"
This describes a managed workflow of interaction, which falls under the sub-category of Managing Personal Behavior or Relationships / Interactions Between People. It coordinates the collaborative relationship between a professional (expert) and a support network (caregivers) to ensure a shared understanding of the patient’s treatment plan. (Spec., para. [0035]) This paragraph is relevant as it identifies the primary objectives: "communication method" and enhanced "continuity of care." It grounds the claim in organizing expert-to-caregiver interactions, focusing the invention on healthcare team activity, not hardware or software performance.
Manual Replication Scenario (Human Equivalence)
While the applicant’s system provides for the automation of these tasks using a computer to achieve greater speed, consistency, and a wider "correlation" of data, these benefits are does not overcome prong one.
To demonstrate that a human (or a group of humans) could mirror the limitations of independent Claim 13 without the use of specialized hardware or software, consider the following scenario involving a traditional therapy clinic setting:
The abstract nature of the claims is reinforced because a human—or a group of humans—can replicate the entire sequence without a computer. The following 5-step scenario mirrors the limitations of the independent claims:
A therapist reviews a patient’s history file while observing their current behavior to access all necessary care data.
The therapist mentally processes these observations by sorting them into standard clinical categories like subjective and objective findings.
Based on clinical experience, the therapist determines the patient's intent and drafts specific home-based exercises to manage their condition.
A senior doctor reviews the draft to provide expert input and signs off on the final approved recommendations.
The therapist hands the printed instructions to the mother and explains the tasks to ensure continuity of care, mother is agreed after review instructions with the therapist.
Because each step can be performed through human observation, evaluation, and human activities the claim is directed to an abstract idea that a human could replicate, confirming its classification as a judicial exception.
Dependent Claims Analysis
The dependent claims 2-12 and 14-20 are also directed to an abstract idea, as they further specify the data types, formats, and organizational structures of the clinical workflow without adding a technical improvement to the computer itself.
Claims 2-6 and 14: These claims recite under BRI specific categories of health data such as "medical session reports," "SOAP notes," and "real-time generated data", which is a Mental Process (MPEP § 2106.04(a)(2)(III)). These limitations specify the information being gathered and analyzed an act of data collection and classification that mirrors a clinician's mental intake process.
Claims 7, 9, and 20: These claims recite the communication of recommendations via "text, audio, video" or "sharing an update" and "notifications" which is a Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II) - Managing Interactions). These limitations describe the protocol for informing a human caregiver of a clinical decision, which is an administrative task in the management of a care relationship.
Claims 8, 11, and 19: These claims recite "generating assignment recommendations" based on previous sessions and allowing an expert to "edit and/or modify" the output, which is a Mental Process (MPEP § 2106.04(a)(2)(III)). This describes the specific evaluative logic used to reach a clinical conclusion, representing the subjective judgment of a healthcare professional.
Claims 10 and 12: These claims recite establishing a "user-query interface" to "pose questions or seek clarification", which is a Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II) - Managing Interactions). This is the digital equivalent of a "Q&A" session between a doctor and a parent, organizing the interaction between people within a healthcare workflow.
Claims 15 and 16: These claims recite the use of specific computational models like "convolutional neural networks" or "Generative AI models including GPT-3" , which is a Mental Process (MPEP § 2106.04(a)(2)(III)). These are mathematical concepts and tools used to perform the mental act of analysis; reciting a specific high-level AI model does not shift the focus away from the underlying abstract idea of data evaluation.
Claims 17 and 18: These claims specify an "interactive feedback loop" and "user interfaces" like "voice" or "gesture", which is a Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II) - Managing Interactions). These specify the medium of human communication, but the substance remains the organization of feedback and data access between an expert and a remote user.
Having established that both the independent and dependent claims recite judicial exceptions in the form of mental processes and methods of organizing human activity, we must now evaluate whether these claims include additional elements that integrate the abstract ideas into a practical application.
Step 2A, Prong Two: Integration into a Practical Application
Step 2A, Prong Two evaluates whether the claim as a whole integrates the judicial exception into a practical application by applying the abstract idea to solve a technical problem or improve a technology. The additional elements in Claims 1 and 13 fail to overcome this prong because they do not provide a specific technical improvement to the computer itself, but rather use the computer as a tool to automate the abstract process. The claims recite a functional result—enhancing care continuity—without disclosing a specific technical mechanism that transforms the underlying abstract ideas.
Evaluation of Independent Claims 1 and 13 Additional Elements
General-Purpose Computing Hardware: The recitation of a "computer system," "memory," and "processing device" fails to integrate the abstract idea into a practical application because it merely provides a generic environment for data processing.
Under MPEP 2106.05(f), these elements are "no more than a mere instruction to 'apply it' on a computer" and do not constitute a specific technological implementation. The specification describes these components performing their basic functions of storage and execution (Spec., para. [0013]), which is insufficient to integrate the exception because "merely using a computer to accelerate an abstract process is not a technological improvement" (MPEP 2106.05(a)).
Computational Processing Models: The recitation of a "data model," "contextual analysis model," and "machine learning model" fails because these represent mathematical concepts and linguistic processing tools used to implement the mental process of clinical evaluation.
These models are recited as functional tools to "extract information" and "generate recommendations," which are the very steps identified as the judicial exception in Prong One. According to MPEP 2106.05(a), an improvement in the "accuracy of a mathematically calculated statistical prediction" is an improvement to the abstract idea itself, not to the underlying computer technology. The claim lacks any "specific technical mechanism" (e.g., a specific modified neural network architecture) that solves a technical bottleneck, instead focusing on the "goal or result" of better condition management.
Interactive User Interface: The recitation of a "user interface" used by parents and caregivers to "access and review" recommendations fails to integrate the abstract idea into a practical application because it acts as a generic tool for the nominal display of information.
Under MPEP 2106.05(h), an additional element does not integrate an abstract idea if it is "merely a link to the technological environment" or serves to "convey the results of the abstract idea." The interface recited in Claim 13 is a generic conduit that allows a remote user to view the outcome of the clinical evaluation (the recommendations). It does not reflect an "improvement in the functioning of the computer itself" or a technical solution to a user-interface problem (e.g., a specific new way to render complex medical data on limited-bandwidth devices). Instead, it "simply provides a way to interact with the judicial exception" (MPEP 2106.04(d)(2)), which remains a non-integrated application of a method for organizing human activity.
Evaluation of the Combination "Viewed as a Whole"
When viewed as a whole, the combination of these elements—the generic hardware, the computational models, and the communication interface does not amount to an integration into a practical application. The combination simply describes a "care management workflow" that has been digitized.
The synergy between the models and the hardware is purely functional; the hardware stores the data, the model analyzes it, and the interface displays the result. There is no technical interaction between these components that results in a "specific technical solution to a technical problem" (MPEP 2106.04(d)(1)). Instead, the combination reflects a "method of organizing human activity" (managing expert-caregiver communication) executed via "mental processes" (AI-based clinical judgment) on a computer, which remains a judicial exception under Step 2A.
Dependent Claims Analysis
The additional elements in the dependent claims 2-12 and 14-20 fail to integrate the judicial exception into a practical application. While these claims add layers of specificity to the data types and tools used, they do not recite a specific technical mechanism that improves the functioning of the computer or provides a technological solution to a medical problem.
Claims 2-5, 8, 12, 14, and 19: These claims do not introduce new additional elements. Instead, they specify the content and structure of the data being analyzed (e.g., SOAP notes, medical session reports, and progress made over time). These limitations "merely narrow the abstract idea" to a specific field of use—clinical documentation management—which does not reflect a technological improvement. Narrowing a mental process to a specific set of clinical observations remains a mental process under the MPEP 2106.04(d)(2) framework. Neither Claims 9-11 and 17 these claims add Interactive Feedback and Human-in-the-loop Modifications, which fails to improve computer functionality (MPEP 2106.05(a)). This is not a technological improvement because it merely describes human interaction and organization, making the computer a passive conduit rather than demonstrating how feedback improves the computer's function.
Claims 6, 7, 18, and 20: These claims add Input/Output Formats and Notifications, which is a mere link to a technological environment (MPEP 2106.05(h)).
Reciting that the data may be in "text, audio, video, image" (Claim 6) or that the system shares "notifications" (Claim 20) merely defines the medium through which the abstract idea is performed. These elements do not solve a technical problem in data transmission; they simply utilize existing communication channels (like WhatsApp or SMS, as cited in Spec., para. [0080]) to convey the results of the mental process. They are "nominal" in that they do not change how the computer operates but only what information is displayed.
Claims 15 and 16: These claims add Specific AI and Model Architectures (CNN, Audio/Video processing, GPT-3, BERT), which fails to improve computer functionality (MPEP 2106.05(a)).
Identifying a high-level mathematical model (like a Convolutional Neural Network or a Transformer) as the tool for "extracting relevant information" (Claim 15) is a functional application of a mathematical concept. The claims do not describe a "specific technical mechanism" such as a proprietary modification to the transformer architecture that reduces latency or memory usage—that would reflect an improvement in AI technology itself. Per MPEP 2106.04(a)(2)(I), these remain mathematical tools used to execute the abstract analytical steps.
Evaluation of the Combination "Viewed as a Whole"
When viewed as a whole, the combination of these elements in the dependent and independent claims does not pass Step 2A, Prong Two. The combination reflects a business-as-usual healthcare workflow: gathering files, using a calculator/model to analyze them, and calling the patient to report the result.
Because the claims are directed to abstract ideas (mental processes and methods of organizing human activity) and fail to integrate those ideas into a practical application, the analysis must proceed to Step 2B to determine if the elements represent an "inventive concept."
Step 2B: Inventive Concept Analysis
Step 2B is the search for an "inventive concept"—an element or combination of elements that is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself (MPEP 2106.05). The additional elements identified in Step 2A fail to overcome Step 2B because they represent the mere automation of a clinical care workflow using existing, general-purpose technological tools and mathematical models. Per MPEP 2106.05(d), adding a "computer-implemented" limitation to an abstract idea does not supply an inventive concept when the computer is used as a tool to perform its ordinary functions.
Evaluation of Independent Claims 1 and 13 Additional Elements
The additional elements evaluated below computer system, memory, processing device, data model, machine learning model, contextual analysis model, and user interface do not individually or in combination provide an inventive concept.
Processor and Storage (General-Purpose Hardware)]: The recitation of a "computer system," "memory," and "processing device" does not amount to significantly more because these components are used as a generic technological environment.
The specification admits that these are generic components, stating that the "processing device 112 can be any device that is equipped with the necessary components" such as a "smartphone, tablet, [or] personal computer" (Spec., para. [0045]). These limitations are generic as the applicant describes them as generic hardware (e.g., a "server" or "memory") performing their basic functions of storing and executing instructions to automate the care workflow.
Machine Learning and Contextual Analysis Models: The recitation of a "data model" including "machine learning" and "contextual analysis" does not provide an inventive concept because it represents the high-level automation of clinical evaluation.
The applicant characterizes these models as using "fine-tuned large language models" and "machine learning techniques" to extract information into pre-defined categories (Spec., para. [0044]). This is an application of mathematical and linguistic principles to a mental process; per MPEP 2106.05(a), "merely using a computer as a tool to perform the abstract idea" through known AI methodologies does not constitute an inventive concept. The claims do not recite a specific, non-conventional algorithm or a technical improvement to the ML models themselves, but rather their ordinary function of categorizing data (Spec., para. [0048]).
Interactive User Interface and Communication]: The recitation of a "user interface" and "communication module" fails to provide an inventive concept because it utilizes existing communication infrastructures to deliver instructions.
The specification identifies the user interface as consisting of "commonly used user interfaces" such as a "graphical user interface [or] voice user interface" (Spec., para. [0040]). Furthermore, the communication of assignments is explicitly admitted to occur over existing third-party platforms such as "WhatsApp, Telegram, SMS... Alexa, [and] Siri" (Spec., para. [0080]). Under MPEP 2106.05(h), these elements are merely "conveying the results" of the abstract clinical judgment through conventional technological channels, which does not amount to significantly more than the abstract method of organizing human activity.
When viewed as a whole, the combination of additional elements using a generic processor to run a standard machine learning model to generate a report that is then sent via WhatsApp is not enough to constitute an inventive concept.
Because the combination does not result in a technological improvement to computer functionality the components interact in a predictable, manner where the software performs the analysis and the hardware provides the storage and transmission, mirroring a "care management business process" rather than a technological breakthrough.
Dependent Claims Analysis
In Step 2B, we evaluate whether the additional elements in the dependent claims, either alone or in combination, transform the judicial exception into an inventive concept. Following our Prong Two analysis, these claims fail to provide "significantly more" as they utilize conventional technologies to perform the abstract steps.
Claims 2-5, 8, 9-11, 12, 14, 17, and 19 do not provide an inventive concept because they merely narrow the abstract ideas to a specific field-of-use (autism therapy) and describe insignificant pre- and post-solution activities (MPEP § 2106.05(g) and (h)). Specifically, while these claims add details regarding clinical data structures (SOAP notes) and administrative feedback loops between experts and caregivers, the specification confirms that these are "traditional methods of communication" and "comprehensive documentation" digitized for "continuity between sessions" (Spec., para. [0005 “Traditionally, therapists create SOAP notes…” ], [0008]). Because these elements only describe an administrative workflow where the computer serves as a passive conduit for human interaction and data organization, they fail to provide a technical solution or "significantly more" than the underlying mental processes and methods of organizing human activity.
(Claims 6, 7, 18, 20): These claims add Input/Output Formats and Notifications, which is insignificant pre/post-solution activity (MPEP § 2106.05(g)). The specification confirms this is an insignificant post-solution step, utilizing existing messaging and audio infrastructures: "[assignments] may be communicated... using messaging Apps like WhatsApp, Telegram, SMS... audio devices like Alexa, Siri" (Spec., para. [0080]). These elements are "well-understood, routine, and conventional" means of delivering the results of the abstract clinical judgment to a human user, providing no technological improvement to the communication devices themselves.
(Claims 15-16): These claims add Specific Machine Learning and Generative AI Model Architectures, which constitutes MPEP § 2106.05(f) - Mere Instructions to apply the abstract idea. The specification admits these are standard tools: "data model 204 further comprises a Generative AI model including one or more of GPT-3... BERT... or a combination thereof... or any other suitable models known to those skilled in the art" (Spec., para. [0063]). Reciting a specific, commercially available AI model to execute the abstract task of "extracting information" is a generic computer implementation that lacks an inventive concept, as it does not involve a non-conventional modification to the AI architecture itself.
Evaluation of the Combination "Viewed as a Whole"
When viewed as a whole, the combination of dependent claims and additional elements—utilizing generic mobile apps (WhatsApp), standard AI models (GPT-3), and conventional clinical categories (SOAP)—is not enough to constitute an inventive concept. The combination simply describes the digitization of a standard care-coordination workflow, where each component performs its well-understood and routine function to automate the abstract clinical evaluation process.
Step 2B Synthesis
The additional elements, considered individually and in combination, fail to transform the abstract ideas into a patent-eligible application because they represent the mere implementation of clinical judgments and interpersonal management on a generic computer system. The claims are directed to an abstract idea and lack an inventive concept that provides "significantly more."
Therefore, Claims 1-20 are rejected under 35 U.S.C. § 101.
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.
2. 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.
Claim 1.
Gnanasambandam teaches, A method to enhance the continuity of care for a patient comprising:
executing code by one or more processors of a computer system to cause the computer system to perform operations comprising: (Gnanasambandam, See at least, [0005] In some embodiments, a system includes a memory storing instructions and a processor communicatively coupled with the memory. The processor may execute the instructions to perform one or more of the operations described above. [0008] ...configured to provide a population health management service.)
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.
processing the patient's data using a data model by extracting relevant information from the patient's data, wherein the data model is operational to determine an intent of the patient's data and includes: (Gnanasambandam, See at least, [0089] extract concepts, relationships, and draw conclusions from a given text posed in natural language... by performing conversational analysis which includes analyzing conversational context. [0159] the cognitive intelligence platform 102 uses conversational analysis to identify the intent of the user (e.g., find data, ask a question, search for facts, find references, and find products).)
a contextual analysis model configured to extract information under different categories as set forth in pre-defined subjective, objective, assessment, planning data and (Gnanasambandam, par. 0093 “ sentences … organized“, 0094 “ knowledge graph may represent a model that includes individual elements (nodes) and predicates that describe properties and/or relationships between …“ 0096 “ Tags related to possible health related information may be generated and associated with “, 0525 “ curated medical knowledge“, , 0097 “The cognified data may include a summary of a health related condition of a patient, where the summary includes insights, conclusions, recommendations, identified gaps (e.g., in treatment, risk, quality of care, guidelines, etc. “Par. 0101 “text entered by the user”, par. 0299 “blood sugar levels”, )
Gnanasambandam discloses a contextual analysis model within a cognitive intelligence platform configured to extract concepts and indicia from unstructured data, such as patient notes. This extraction is categorized into information types, including symptoms (subjective), vital signs (objective), conclusions (assessment), and care plans (planning) which directly correspond to the claimed categories, where the indicia are pre-defined by an ontological knowledge graph.
.
a machine learning model that receives the extracted information and categories under which the information pertains to assist the machine learning model to generate one or more patient-specific assignment recommendations; (Gnanasambandam, abstract “based on a patient graph of the patient and a knowledge graph” , fig. 65 “generate the care plan based on the type selected”, 0018 - 0020)
The Gnanasambandam reference discloses a machine learning model utilizing a knowledge graph and patient graph that receives extracted patient data and clinical categories. This system assists in generating patient-specific assignment recommendations, described as care plans and action instructions.
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.
Gnanasambandam teaches, Claim 2.
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 combined 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.
Claim 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".
Claim 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]).
Claim 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.
Claim 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.
Claim 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.
Claim 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.
Claim 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]).
Claim 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.
Claim 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.
Claim 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]).
Claim 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]).
Note: Claims 13, and 17-18 are rejected with the same analysis above from being very similar to claims 1,2, 5-12, 14-15 and 19-20.
Claim Rejections - 35 USC § 103
1. 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.
2. 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.
3. 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.
4. Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20230052573A1-Gnanasambandam in combination with US20060095298A1-Bina.
Claim 3.
Gnanasambandam teaches, The method as claimed in claim 2, wherein the medical session report and the progress report comprises
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.
Claim 4.
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 SOAP notes of the previous sessions indicating assessment of the condition and progress made by the patient in previous sessions.
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.
5. 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.
Claim 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 at least one of (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)
6. 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.
Claim 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