DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 09/25/2025.
Claims 1, 3-5, 8-10, 12, 16 and 18-20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 § 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:
Claims 1-11 are directed to a method (i.e., a process) and claim 12-20 are directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 12 includes limitations that recite an abstract idea. Note that independent claim 12 is the system claim, while claim 1 covers a method claim.
Specifically, independent claim 12 recites:
A system for automatically generating a medical session report for one or more sessions of a patient with a Clinician, the system comprising:
one or more processors; and
one or more memories, coupled to the one or more processors, storing code that when executed by a computer system causes the computer system to perform operations comprising:
receive a patient data in one or more formats from one or more sources;
enable the Clinician to selectively activate a machine learning data model to enhance the medical session report;
guide a machine learning data model using bifurcated components of a machine learning model and a contextual model, wherein the contextual analysis module is configured to communicatively operate with the machine learning module to generate a medical session report;
guide the machine learning data model to access multiple sources of data;
guide the machine learning data model to identify information in the multiple sources of data that is relevant to the medical session report;
guide the contextual analysis module of the machine learning data model to categorize the patient's data in the respective categories of Subjective, Objective, Assessment and Planning (SOAP) notes;
process the patient data using machine learning techniques and language learning models by extracting relevant information from the patient data;
generate one or more session notes automatically based on the patient's data, the processed patient, and one or more pre-stored information, wherein generating the one or more session notes comprises:
guiding the machine learning model to select one of a plurality of templates in corresponding to the generated one or more session notes, wherein multiple templates are pre-stored in the machine learning model to automatically generate the SOAP note;
guide the machine learning model to select one or more goals for the patient, wherein guiding the machine learning model to select one or more goals for the patient comprises:
guiding the selection of goals based on patient data provided by the Clinician;
guide the machine learning model to map and track progress made by the patient on the selected one or more goals; and
compile the one or more session notes to automatically generate the medical session report of the patient.
A system for automatically generating a medical session report for one or more sessions of a patient with a Clinician, the system comprising:
a server including a memory to store various treatment plans and a patient’s previous
and ongoing medical data; and
a processing device operatively coupled to the server, wherein the processing device is configured to execute the instructions to:
receive a patient’s data in one or more formats from one or more sources;
process the patient’s data using machine learning techniques and language learning models by extracting relevant information from the patient’s data;
generate one or more session notes automatically based on the patient’s data, the processed patient’s data, and one or more pre-stored information; and
compile the one or more session notes to automatically generate the medical session report of the patient.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because generating a medical session report for one or more sessions of a patient with a Clinician, generating session notes based on patient’s data, processed patient’s data, pre-stored information then compiling session notes generate the medical session report of the patient are medical workflow tasks, which are managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “mathematical concepts” because guiding a machine learning data model using bifurcated components of a machine learning model and a contextual model, guiding the machine learning data model to identify information in the multiple sources of data that is relevant to the medical session report, guiding the contextual analysis module of the machine learning data model to categorize the patient's data in the respective categories, guiding the machine learning model to select one of a plurality of templates and guiding the machine learning model to map and track progress made by the patient on the selected one or more goals are mathematical relationships. The foregoing underlined limitations also relate to claim 12 (similarly to claim 1).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 3-11, 13-14 and 18-20, these claims merely recite determining steps such as: claim 3 - generating a medical session report as claimed in claim 1, wherein the one or more sources may include Clinician’s short-hand notes from the session, audio recording of the session, video recording of the session, transcript of the session, patient’s past electronic health records, past session notes, clinical assessments, patient’s interviews and other relevant health condition information, claim 4 - generating a medical session report wherein the patient’s data may be in one or more of the following formats — text, audio, video, or a combination thereof, claim 5 – generating a medical session report wherein generating the one or more session notes further includes populating the 30 patient’s data with the one or more pre-stored information, wherein the one or more pre-stored information includes one or more previously stored patient’s data, another patient’s data having similar manifestation of the condition and/or information obtained from a database, claim 6 - generating a medical session report wherein the at least one or more session notes are edited by the Clinician, claim 7 - generating a medical session report wherein the machine learning techniques includes a machine learning algorithm or a natural language processing (NLP) algorithm and the large language models include one or more generative AI models either in combination or alone, claim 8 - selecting a template in correspondence to the generated one or more session notes of the patient, wherein a template is selected from one or more pre-stored templates, compiling the one or more session notes in the selected template to generate the medical session report of the patient, claim 9 – generating a medical session report wherein the patient’s past electronic health records is analysed and adapted in the at least one or more session notes in order to enhance the specificity and relevance of the generated session notes, claim 10 - generating a medical session report wherein an Insurance Claim is automatically generated based on the patient’s medical session report, claim 11 - generating a medical session report wherein the medical session report is communicated to one or more remote users including one or more insurance companies, caregivers of the patient and other Clinicians, claim 13 - a data model configured to perform a contextual analysis on the received patient’s data and based on the contextual analysis, one or more goals related to the condition of the patient are automatically selected, claim 14 - process and analyse the patient’s data inputted by the Clinician using techniques such as convolutional neural networks, audio signal processing, or video signal processing, claim 18 - updates one or more goals for the patient based on the patient’s data, wherein the updates include marking one or more previously assigned goals as complete, assigning new goals, and mapping of goals against the patient’s data or inputs provided by the Clinician, claim 19 - generate one or more progress reports corresponding to the progress made by the patient over a period, wherein the progress reports are generated based on one or more medical records of the patient, previous session notes, previous medical session reports, or a combination thereof and claim 20 - providing one or more insights related to the progress made by the patient on one or more goals in the form of an infographic such as charts, graphs, tables in combination or alone, wherein the infographics are dynamic and a user interacts with the infographic to obtain one or more insights of interest.
In relation to claims 2 and 15-17, these claims merely recite specific kinds of data, such as: claim 2 - wherein the medical session report of the patient includes a SOAP note, wherein the 15 SOAP note includes details related to subjective information, objective observations, assessment findings, and plans for future treatment of the patient, claim 15 - the data model uses Generative AI model which may include any one of the GPT-3 (Generative Pre-trained Transformer 3), GPT- 3.5, BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Approach), ALBERT (A Lite BERT), TS (Text-to-Text Transfer Transformer) or a combination thereof, claim 16 - the patient’s data may include past session notes made by the Clinician, ongoing session notes, patient’s past electronic medical records, treatment plans and a combination thereof., and claim 17 - one or more user interfaces including a graphical user interface, a voice user interface, a video user interface, a gesture user interface, or a combination thereof.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1 and 12, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations among humans but for the recitation of generic computer components. That is, other than reciting a system, one or more processors, one or more memories, a processing device, one or more interfaces and processing device configured to execute the instructions to perform the limitations, nothing in the claim elements precludes the steps from practically being performed interactively with the humans. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment between patients and therapist but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system, one or more processors, one or more memories, processing device, one or more interfaces and processing device configured to execute the instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “guiding a machine learning data model to automatically generate a medical session report”, “activate a machine learning data model to enhance the medical session report”, “a machine learning algorithm or a natural language processing (NLP) algorithm”, and “convolutional neural networks” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receive patient data in one or more formats from one or more sources” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 12, regarding the additional limitations of the system, one or more processors, one or more memories, processing device, one or more interfaces and processing device configured to execute the instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 12 and analogous independent claim 1 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over by Lin (US 2023/0320642 A1) in view of Shope (US 2024/0404669 A1).
Claim 1:
Lin discloses a method for automatically generating a medical session report for one or more sessions of a patient with a Clinician (See P0203-P0204, P0208-P0209 generating analytics reports from data collected during psychotherapy sessions.), the method comprising: performing operations in a computer system comprising:
receiving patient data in one or more formats from one or more sources (See Fig. 2, [P0065] obtaining 210 transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting 220 speech segments from the transcript data related to one or more of the patient or the therapist.);
processing the patient data using machine learning techniques and language learning models by extracting relevant information from patient data (See Fig. 2, Fig. 10 and [P0065] applying 230 a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing. Also, see P0083-P0085);
generating one or more session notes automatically based on the patient's data, the processed patient, and one or more pre-stored information, (See Fig. 2, P0065, where derived psychiatric assessment serves as generating session notes. Also, see P0018-P0019, specific user notes determined by machine learning.); and
compiling the one or more session notes to automatically generate the medical session report of the patient in accordance with the guidance of the machine learning data model (Besides Fig. 10, P0136 where the Recommendation System Unit is for reporting, see P0214 using automatic text summarization and generated summaries and P0140 performed using a machine learning topic modeling engine 1026.).
Although Lin discloses a method and system for generating a medical session report using a machine learning data model mentioned above, Lin does not explicitly teach performing additional machine learning steps when reporting generated SOAP notes and tracking features. Shope teaches:
enabling the Clinician to selectively activate a machine learning data model to enhance the medical session report (Taught in P0017, P0061 as highlighted portions of generated report. See Fig. 7-8, [P0083] The machines 700, 800 can automatically find relationships between data from a telehealth visit to suggest content for documenting a provider record or a patient record. The machines 700, 800 can run intelligent tools including generative artificial intelligence, large language models, machine learning that uses algorithms to generate new content, code, or data relating to telehealth visits, within the context of the present disclosure.);
guiding a machine learning data model using bifurcated components of a machine learning model and a contextual model, wherein the contextual analysis module is configured to communicatively operate with the machine learning module to generate a medical session report (Taught in P0028-P0029 as splitting SOAP notes into training and validation sets. Also, see virtual audio/video communication session mentioned in P0051-P0053, Fig. 4.);
guiding the machine learning data model to access multiple sources of data (See [P0042] access one or more patient records from one or more sources, including pharmacy claims, benefit information, prescribing physician information, dispensing information (e.g., where and how the patient obtains their current medications), demographic information, prescription information including dose quantity and interval.);
guiding the machine learning data model to identify information in the multiple sources of data that is relevant to the medical session report (Taught in P0017 as confidence scored data calculated using large language models. Also, see highlighted portions of generated report in P0017, P0062);
guiding the contextual analysis module of the machine learning data model to categorize the patient's data in the respective categories of Subjective, Objective, Assessment and Planning (SOAP) notes (See P0015, P0023-P0024, [P0027] The second machine learning model (e.g., the artificial network including an LLM, or other diffusion network) can be used for SOAP note generation from transcriptions of clinical encounters by training the second machine learning model on a large dataset of clinical encounter transcriptions and their corresponding ground truth SOAP notes (the actual SOAP notes written by the clinicians for each clinical encounter associated with the transcription).);
wherein generating the one or more session notes comprises:
guiding the machine learning model to select one of a plurality of templates in corresponding to the generated one or more session notes, wherein multiple templates are pre-stored in the machine learning model to automatically generate the SOAP note (See P0023-P0024 using the machine learning model to simplify and expedite the management of patient information and the generation of both post visit documents and preliminary SOAP notes).;
guiding the machine learning model to select one or more goals for the patient, wherein guiding the machine learning model to select one or more goals for the patient comprises:
guiding the selection of goals based on patient data provided by the Clinician (See Fig. 10-11, P0061 where progress towards goals notes and entry fields are optionally accepted or rejected.); and
guiding the machine learning model to map and track progress made by the patient on the selected one or more goals (Taught in Fig. 10-11, P0061 as progress towards goals notes. Also, see tracking information in P0024, machine vector diffusion step in P0027, P0029, P0042.).
Therefore, it would have been obvious to one of ordinary skill in the art of physician assistant generative modeling before the effective filing date of the claimed invention to modify the method and system of Lin to include performing additional machine learning steps when reporting generated SOAP notes and tracking features as taught by Shope to save a clinician time and effort when navigating through multiple pages of information that includes notes as mentioned in Shope’s P0015.
Regarding 2, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the medical session report of the patient includes a SOAP note, wherein the SOAP note includes details related to subjective information, objective observations, assessment findings, and plans for future treatment of the patient (See P0156-P0158 disorder-specific multi-objective policies (DISMOP) allow for subjective information and objective observations details. See Fig. 4, P0071-P0072 where analysis and trajectories lead to specific adopted therapy questions during patient-therapist sessions.).
Regarding 3, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the one or more sources may include Clinician’s short-hand notes from the session, audio recording of the session, video recording of the session, transcript of the session, patient past electronic health records, past session notes, clinical assessments, patient’s interviews and other relevant health condition information (See notes taken from database records (P0017-P0019), transcript data, derived psychiatric assessment (P0064), extracting speech segments during therapy sessions (Abstract, P0011-P0012, P0020).).
Regarding 4, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the patient data may be in one or more of the following formats – text, audio, video, or a combination thereof (See P0057 different types of narrative formatting for patient and therapist, regarding spoken dialogue in P0084.).
Regarding 5, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein generating the one or more session notes further includes populating the patient data with the one or more pre-stored information, wherein the one or more pre-stored information includes one or more previously stored patient’s data, another patient data having similar manifestation of the condition and/or information obtained from a database (See P0073-P0074 annotated topics during psychotherapy sessions serve as populating the patient’s data with pre-stored information.).
Regarding 6, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the at least one or more session notes are edited by the Clinician (See P0232 where therapist can make real-time modifications. Also, see P0214 where users writing outlines and choosing a subset of sentences allows one to edit.).
Regarding 7, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the machine learning techniques includes a machine learning algorithm or a natural language processing (NLP) algorithm and the large language models include one or more generative AI models either in combination or alone (See the artificial intelligence include realistic speaker recognition and diarization system in P0187.).
Regarding claim 9, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the patient past electronic health records is analysed and adapted in the at least one or more session notes in order to enhance the specificity and relevance of the generated session notes (See P0179 notes taken by therapist during psychotherapy sessions. See notes taken from database records (P0017-P0019), transcript data, derived psychiatric assessment (P0064), extracting speech segments during therapy sessions (Abstract, P0011-P0012, P0020).).
Regarding claim 10, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein an Insurance Claim is automatically generated based on the patient medical session report (See P0203-P0204, P0208-P0209 generating analytics reports from data collected during psychotherapy sessions.).
Regarding claim 11, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the medical session report is communicated to one or more remote users including one or more insurance companies, caregivers of the patient and other Clinicians (See P0026-P0227 products for stakeholders and clinical studies and input and present output relating to sensors.).
Claim 12:
Lin discloses a system for automatically generating a medical session report for one or more sessions of a patient with a Clinician (See P0203-P0204, P0208-P0209 generating analytics reports from data collected during psychotherapy sessions.), the system comprising:
one or more processors; and
one or more memories, coupled to the one or more processors, storing code that when
executed by a computer system causes the computer system to perform operations (See processor in P0007-P0009 memory devices to store processor-executable instructions. See P0017, P0227 databases storing information and server.) comprising:
receive a patient data in one or more formats from one or more sources (See Fig. 2, [P0065] obtaining 210 transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting 220 speech segments from the transcript data related to one or more of the patient or the therapist.);
process the patient data using machine learning techniques and language learning models by extracting relevant information from the patient’s data (See Fig. 2, Fig. 10 and [P0065] applying 230 a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing. Also, see P0083-P0085);
generate one or more session notes automatically based on the patient's data, the processed patient, and one or more pre-stored information, wherein generating the one or more session notes comprises (See Fig. 2, P0065, where derived psychiatric assessment serves as generating session notes. Also, see P0018-P0019, specific user notes determined by machine learning.); and
compile the one or more session notes to automatically generate the medical session report of the patient (Besides Fig. 10, P0136 where the Recommendation System Unit is for reporting, see P0214 automatic text summarization and generated summaries.).
Although Lin discloses a method and system for generating a medical session report using a machine learning data model mentioned above, Lin does not explicitly teach performing additional machine learning steps when reporting generated SOAP notes and tracking features. Shope teaches:
enable the Clinician to selectively activate a machine learning data model to enhance the medical session report (Taught in P0017, P0061 as highlighted portions of generated report. See Fig. 7-8, [P0083] The machines 700, 800 can automatically find relationships between data from a telehealth visit to suggest content for documenting a provider record or a patient record. The machines 700, 800 can run intelligent tools including generative artificial intelligence, large language models, machine learning that uses algorithms to generate new content, code, or data relating to telehealth visits, within the context of the present disclosure.);
guide a machine learning data model using bifurcated components of a machine learning model and a contextual model, wherein the contextual analysis module is configured to communicatively operate with the machine learning module to generate a medical session report (Taught in P0028-P0029 as splitting SOAP notes into training and validation sets. Also, see virtual audio/video communication session mentioned in P0051-P0053, Fig. 4.);
guide the machine learning data model to access multiple sources of data (See [P0042] access one or more patient records from one or more sources, including pharmacy claims, benefit information, prescribing physician information, dispensing information (e.g., where and how the patient obtains their current medications), demographic information, prescription information including dose quantity and interval.);
guide the machine learning data model to identify information in the multiple sources of data that is relevant to the medical session report (Taught in P0017 as confidence scored data calculated using large language models. Also, see highlighted portions of generated report in P0017, P0062);
guide the contextual analysis module of the machine learning data model to categorize the patient's data in the respective categories of Subjective, Objective, Assessment and Planning (SOAP) notes (See P0015, P0023-P0024, [P0027] The second machine learning model (e.g., the artificial network including an LLM, or other diffusion network) can be used for SOAP note generation from transcriptions of clinical encounters by training the second machine learning model on a large dataset of clinical encounter transcriptions and their corresponding ground truth SOAP notes (the actual SOAP notes written by the clinicians for each clinical encounter associated with the transcription).);
wherein generating the one or more session notes comprises:
guiding the machine learning model to select one of a plurality of templates in corresponding to the generated one or more session notes, wherein multiple templates are pre-stored in the machine learning model to automatically generate the SOAP note (See P0023-P0024 using the machine learning model to simplify and expedite the management of patient information and the generation of both post visit documents and preliminary SOAP notes).;
guide the machine learning model to select one or more goals for the patient, wherein guiding the machine learning model to select one or more goals for the patient comprises:
guide the selection of goals based on patient data provided by the Clinician (See Fig. 10-11, P0061 where progress towards goals notes and entry fields are optionally accepted or rejected.); and
guide the machine learning model to map and track progress made by the patient on the selected one or more goals (Taught in Fig. 10-11, P0061 as progress towards goals notes. Also, see tracking information in P0024, machine vector diffusion step in P0027, P0029, P0042.);
Therefore, it would have been obvious to one of ordinary skill in the art of physician assistant generative modeling before the effective filing date of the claimed invention to modify the method and system of Lin to include performing additional machine learning steps when reporting generated SOAP notes and tracking features as taught by Shope to save a clinician time and effort when navigating through multiple pages of information that includes notes as mentioned in Shope’s P0015.
Regarding 2, Lin discloses the method for automatically generating a medical session report as claimed in claim 1, wherein the medical session report of the patient includes a SOAP note, wherein the SOAP note includes details related to subjective information, objective observations, assessment findings, and plans for future treatment of the patient (See P0156-P0158 disorder-specific multi-objective policies (DISMOP) allow for subjective information and objective observations details. See Fig. 4, P0071-P0072 where analysis and trajectories lead to specific adopted therapy questions during patient-therapist sessions.).
Regarding claim 13, Lin discloses the system as claimed in claim 12, further includes a data model configured to perform a contextual analysis on the received patient’s data and based on the contextual analysis, one or more goals related to the condition of the patient are automatically selected (See P0005-P0006, P0020 psychotherapy data from obtained transcription data and patient's psychiatric diagnosis by machine learning in P0081-P0082.).
Regarding claim 14, Lin discloses the system as claimed in claim 13, wherein the data model may be a machine learning model configured to process and analyse the patient’s data inputted by the Clinician using techniques such as convolutional neural networks, audio signal processing, or video signal processing (convolutional neural networks in P0081.).
Regarding claim 15, Lin discloses the system as claimed in claim 13, wherein the data model uses Generative AI model which may include any one of the GPT-3 (Generative Pre-trained Transformer 3), GPT-3.5, BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Approach), ALBERT (A Lite BERT), T5 (Text-to-Text Transfer Transformer) or a combination thereof (See forms of Generative Pre-Trained Transformers (GPT) in P0178.).
Regarding 16, Lin discloses the system as claimed in claim 12, wherein the patient’s data may include past session notes made by the Clinician, ongoing session notes, patient past electronic medical records, treatment plans and a combination thereof (See notes taken from database records (P0017-P0019), transcript data, derived psychiatric assessment (P0064), extracting speech segments during therapy sessions (Abstract, P0011-P0012, P0020).).
Regarding 17, Lin discloses the system as claimed in claim 12 includes one or more user interfaces including a graphical user interface, a voice user interface, a video user interface, a gesture user interface, or a combination thereof (See user interface in P0021-P0022.).
Regarding 18, Lin discloses the system as claimed in claim 12 automatically updates one or more goals for the patient based on the patient data, wherein the updates include marking one or more previously assigned goals as complete, assigning new goals, and mapping of goals against the patient’s data or inputs provided by the Clinician (See treatment relating to short term task and long-term goals in P0127.).
Regarding 19, Lin discloses the system as claimed in claim 18, wherein the code that when executed by a computer system causes the computer system to perform further operations comprising automatically generate one or more progress reports corresponding to the progress made by the patient over a period, wherein the progress reports are generated based on one or more medical records of the patient, previous session notes, previous medical session reports, or a combination thereof (See [P0231] data derived from the transcript data and/or the topic labels output is used to generate image data that provides a visual representation of a patient's psychotherapy data, and thus can provide a rolling temporal visual representation of emotional/psychological progress of the psychotherapy session.).
Regarding 20, Lin discloses the system as claimed in claim 12 wherein the code that when executed by a computer system causes the computer system to perform further operations comprising: providing one or more insights related to the progress made by the patient on one or more goals in the form of an infographic such as charts, graphs, tables in combination or alone, wherein the infographics are dynamic and a user interacts with the infographic to obtain one or more insights of interest (See P0240-P0244, progress shown in dashboard with line graph. Also, see Fig. 13 and Fig. 24.).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 8 is rejected under 35 U.S.C. 103 as being unpatentable over by Lin (US 2023/0320642 A1) in view of Shope (US 2024/0404669 A1) further in view of Walker (US 2002/0170565 A1).
Regarding claim 8, although Lin and Shope teach the method as claimed in claim 1 mentioned above, Lin and Shope do not explicitly teach selecting from pre-stored templates and compiling the session notes in the selected template to generate the medical session report of the patient. Walker teaches further comprises: performing operations in the computer system comprising: selecting a template in correspondence to the generated one or more session notes of the patient, wherein a template is selected from one or more pre-stored templates; compiling the one or more session notes in the selected template to generate the medical session report of the patient (See P0095 appropriate template selected from a list specific to diseases and diagnosis mentioned in P0101-P0103.).).
Therefore, it would have been obvious to one of ordinary skill in the art of medical records management before the effective filing date of the claimed invention to modify the method and system of Lin and Shope to include selecting from pre-stored templates and compiling the session notes in the selected template to generate the medical session report of the patient as taught by Walker to document patient encounters in a chronological way for good record keeping mentioned in Walker’s P0011-P0012.
Response to Arguments
Applicant argues that claim 1 solves technical problems that improve computer technology, by new and improved specific guidance of a machine learning data model, which includes bifurcated models, to improve the computer system to provide a technical solution to the technical limitations, e.g. see pgs. 14-16 of Remarks – Examiner disagrees.
Besides no technological implementations or improvements being explained or claimed, the instant case is a problem that has already been solved and the claims are well-understood, routine, and conventional. That is, compiling the one or more session notes to automatically generate the medical session report of the patient is well-known in the art, as evidenced by at least Lin et al. (US 2023/0320642 A1) Fig. 10, P0136, P0214; Shope et al. (US 2024/0404669 A1) P0015 & P0055; and Lipton et al. (US 2022/0375605 A1) Fig. 1, P0037-P0038, P0043, P0046 & P0103. Accordingly, compiling notes documented in the form of a SOAP (i.e., “Subjective, Objective, Assessment, and Plan”) used to generate medical session reports are merely invoked as well-known means of data gathering.
Also, guiding a machine learning data model to automatically generate a medical session report for one or more sessions of a patient with a Clinician is well-known in the art, as evidenced by at least Shope et al. (US 2024/0404669 A1) Fig. 10-11, P0061; and Lipton et al. (US 2022/0375605 A1) P0005-P0006, P0037-P0038 & P0056. Accordingly, using a machine learning model as basis to generate medical session reports are merely invoked as well-known means of data gathering.
Regarding the prior art rejections, Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied new art and art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See Lipton (US 2022/0375605 A1).)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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/T.S.W./Examiner, Art Unit 3687
12/31/2025
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687