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 .
Notice to Applicant
Receipt of Applicant’s Claims filed March 14, 2025 is acknowledged.
Response to Amendment
Claims 1-98 have been cancelled. Claims 99-118 have been added. Claims 99-118 are pending and are provided to be examined upon their merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 14, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
The claims are given an effective filing date of September 18, 2023, corresponding to PCT/US2023/033066, as the provisional application (63/407,584) from which the instant application claims priority to does not support several features of the claims, including the conversational AI agent, natural language processing model, and analysis of the prognostic values by the server.
Claim Objections
Claims 99-118 are objected to because of the following informalities:
Claim 99 introduces “a conversational AI agent”. The first instance of the acronym must be written out for clarity (“artificial intelligence (AI)”).
Claims 100-108 are objected to by virtue of their dependency on claim 99.
Claim 100 introduces “a conversational AI model”. The first instance of the acronym must be written out for clarity (“artificial intelligence (AI)”).
Claim 109 introduces “a generative AI agent”. The first instance of the acronym must be written out for clarity (“artificial intelligence (AI)”).
Claims 110-118 are objected to by virtue of their dependency on claim 109.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 99-118 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a system and a method (99-118). Accordingly, claims 99-118 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claim 99 as the claims that represents a claimed invention for analysis; method claim 99 being similar to system claim 109.
Claim 99:
A computer-implemented method for prognostic evaluation of autoimmune disorders comprising:
presenting, with a conversational Al agent at one or more timepoints, a plurality of clinical conversational prompts to a patient user;
receiving, with the conversational Al agent at the one or more timepoints, a plurality of conversational responses from the patient user in response to the plurality of clinical conversational prompts;
receiving, with at least one server, a first set of text data comprising the plurality of conversational responses from the patient user;
receiving, with the at least one server via at least one application programming interface, a first set of medical record data for the patient user,
wherein the first set of medical record data for the patient user comprises at least one of blood biomarker test data and biometric measurement data;
processing, with the at least one server, the first set of text data and the first set of medical record data for the patient user according to a natural language processing model,
wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data,
wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features;
analyzing, according to the natural language processing model, the one or more text segments to determine one or more temporal or circumstantial association between the one or more text segments and the blood biomarker test data and/or the biometric measurement data;
assigning, according to at least one output of the natural language processing model, one or more prognostic values to the one or more text segments;
analyzing, with the at least one server, the one or more prognostic values for the one or more text segments to generate a prognostic assessment for at least one autoimmune disorder for the patient user; and
providing, with the at least one server, the prognostic assessment to a practitioner user via a graphical user interface of a client device.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activities. The claim elements are directed towards “presenting,…, a plurality of clinical conversational prompts to a patient user”, “receiving,…, a plurality of conversational responses from the patient user in response to the plurality of clinical conversational prompts”, “processing,…, the first set of text data and the first set of medical record data for the patient user”, “analyzing,…, the one or more text segments to determine one or more temporal or circumstantial association between the one or more text segments and the blood biomarker test data and/or the biometric measurement data”, “assigning,…, one or more prognostic values to the one or more text segments”, “analyzing,…, the one or more prognostic values for the one or more text segments to generate a prognostic assessment”, and “providing,…, the prognostic assessment to a practitioner user”. The claim limitations encompass human activities that are typically performed by medical care providers for their patients during health appointments by asking patients questions about their health, obtaining patient records, and analyzing the obtained data for the purpose of obtaining a prognostic assessment.
Accordingly, the claim recites an abstract idea.
Claim 109 is abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), 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 of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the Claims:
conversational Al agent (99); at least one server (99-100); at least one application programming interface (99,109); natural language processing model (99,109); graphical user interface (99); client device (99); conversational AI model (100-102,108); large language model (101,113); at least one body-worn sensor (107,111); first client device (109,112); first graphical display (109); first input/output device (109); second client device (109); second input/output device (109); application server (109); generative Al engine (109); natural language processing engine (109); processor (109); non-transitory computer readable medium (109); generative AI model (109,113); network communications interface (109); biometric sensor (110-112)
Any computing systems that would be able to perform the method (server, first client device, first input/output device, application server, processor) and associated elements (application programming interface, first graphical display, non-transitory computer readable storage medium) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Pg. 17 of Applicant specification recites: “Embodiments may be implemented with numerous other general-purpose or special- purpose computing devices and computing system environments or configurations. Examples of well-known computing systems, environments, and configurations that may be suitable for use with an embodiment include, but are not limited to, personal computers, handheld or laptop devices, personal digital assistants, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network, minicomputers, server computers, game server computers, web server computers, mainframe computers, and distributed computing environments that include any of the above systems or devices.” No specific, technical improvements are being made to the technology of computing devices as any generic computing components may be applied to perform the abstract idea of performing disease prognosis.
Output devices (graphical user interface, client device, second client device, second input/output device) are also taught at a high level of generality. Pgs. 15-16 recite: “ Output device 108 produces or generates output data 120 and can comprise, for example, a display device or monitor in which case output data 120 is visual, a printer in which case output data 120 is printed, a port for example a USB port, a peripheral component adaptor, a data transmitter or antenna such as a modem or wireless network adaptor, etc. Output data 120 could be distinct and derived from different output devices, for example a visual display on a monitor in conjunction with data transmitted to a network. A user could view data output, or an interpretation of the data output, on, for example, a monitor or using a printer. The storage device 114 can be any form of data or information storage means, for example, volatile or non-volatile memory, solid state storage devices, magnetic devices, etc… In general, the processor 102 can receive instructions as input data 118 via input device 106 and can display processed results or other output to a user by utilizing output device 108. More than one input device 106 and/or output device 108 can be provided. It should be appreciated that the processing system 100 may be any form of terminal, server, specialized hardware, or the like.” No specific, technical improvements are made being to output devices as a wide variety of generic output devices are applied to perform an insignificant extra-solution activity of merely outputting data; MPEP 2106.05(g).
Artificial intelligence models (conversational Al agent, conversational AI model, large language model, generative Al engine, generative AI model) are also taught at a high level of generality. Pg. 21 recites: “In accordance with certain embodiments, AI engine 218 may comprise one or more sub-engines, including a generative AI (gAI) engine 220, a natural language processing (NLP) engine 222, and a machine learning (ML) engine 224. In accordance with certain aspects of the present disclosure, gAI engine 220 comprises a neural network architecture configured to identify the patterns and structures within existing data to generate new and original content (e.g., clinical conversational interactions). In certain embodiments, gAI engine 220 comprises a large language model.” Pg. 23 further recites: “Exemplary ML models that may be incorporated into ML engine 224 may include, but are not limited, deep learning models, such as the deep Boltzmann machine, deep belief networks, Recurrent Neural Network (RNN), Fully Convolutional Neural Network (FCN), Dilated Residual Network (DRN), Generative Adversarial Network (GAN), and Deep Neural Network (DNN); ensemble, such as random forest, gradient boost machines, boosting, adaboosting, stacked generalization, and gradient boosted regression trees; neural networks, such as perception, back- propagation, Hopfield, ridge regression, LASSO, and elastic; rule systems, such as cubist, one rule, and zero rule; linear regression, such as ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, and logistic regression; Bayesian, such as naive, averaged one-dependent estimators, Gaussian naive, and multinomial naive; decision tree, such as classification and regression, iterative dichotomiser, and condition-decision; instance-based, such k-nearest neighbor, learning vector quantization, and locally weighted learning; and clustering, such as k-means, k-medians, expectation max, and hierarchical.” No specific, technical improvements are being made to the technology of machine learning as a wide variety of generic machine learning models methods may be applied to perform the abstract idea of generating prompts and prognostic assessments based on obtained data.
Natural language processing (natural language processing model, natural language processing engine) are also taught at a high level of generality. Pg. 13-14 recites: “In accordance with certain aspects of the present disclosure, NLP may comprise one or more computer-implemented machine learning framework for the analytical deconvolution of free text into informative granules and broad trends.” Pg. 22 further recites: “In accordance with certain aspects of the present disclosure, NLP engine 222 comprises one or more NLP model comprising one or more NLP algorithms configured to process text data derived from medical record data (e.g., received from OCR engine 216) and patient conversational data (e.g., in response to a plurality of generative outputs from gAi engine 220) to derive one or more subsets of data that is of direct (or statistically probable) relevance to criteria that inform a disease-relevant health status of patient user 21.” No specific, technical improvements are being made to the technology of natural language processing as any generic NLP models are applied to perform the abstract idea of analyzing medical record data and patient conversations to extract relevant medical data.
Sensors (body-worn sensor, biometric sensor) are also taught at a high level of generality. Pg. 19 recites: “Physiological measurement device 240 and body-worn sensor device 242 may be configured to collect a plurality of biometric data from patient user 21. In accordance with certain aspects of the present disclosure, the biometric data may comprise heart rate data, heart rate variability data, ECG data, sleep data, activity (i.e., movement/telemetry) data, blood pressure, temperature, pulse oximetry, and the like.” No specific, technical improvements are being made to the technology of sensor devices as a wide variety of generic sensors are only applied to perform an insignificant extra-solution activity of data gathering; MPEP 2106.05(g).
Networks (network communications interface) are also taught at a high level of generality. Pg. 19 recites: “It is to be appreciated that the processing system 100 may be a part of a networked communications system. Processing system 100 could connect to a network, for example the Internet or a WAN. Input data 118 and output data 120 could be communicated to other devices via the network. The transfer of information and/or data over the network can be achieved using wired communications means or wireless communications means. A server can facilitate the transfer of data between the network and one or more databases. A server and one or more databases provide an example of an information source. Thus, the processing computing system environment 100 illustrated in FIG. 1 may operate in a networked environment using logical connections to one or more remote computers. The remote computer may be a personal computer, a server, a router, a network PC, a peer device, or other common network node, and typically includes many or all of the elements described above.” No specific, technical improvements are being made to the technology of sensor devices as a wide variety of network modalities are only applied to perform an insignificant extra-solution activity of data transmission; MPEP 2106.05(g).
Looking at the additional elements as an ordered combination adds 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 with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the 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 other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 100: This claim recites the method further comprising configuring, with the at least one server, a conversational Al model according to the one or more prognostic values for the one or more text segments; which only serves to limit the conversational AI model such that it only serves to generally link the abstract idea of identifying prognostic values from text to the field of use of machine learning.
Claim 101: This claim recites wherein the conversational Al model comprises a large language model; which only serves to further limit the type of conversational AI model without providing any specific technical improvements to the model’s functioning.
Claim 102: This claim recites wherein the plurality of clinical conversational prompts comprise a plurality of generative prompts according to the conversational Al model; which only serves to limit the abstract idea of the prompts.
Claims 103 and 115: These claims recite wherein the prognostic assessment comprises a diagnostic assessment for the at least one autoimmune disorder; which only serves to limit the type of disease that is subject to the abstract idea of the diagnostic assessment.
Claims 104 and 116: These claims recite wherein the prognostic assessment comprises a predictive assessment of at least one pathophysiological event associated with the at least one autoimmune disorder; which only serves to limit the abstract idea of the predictive assessment.
Claims 105 and 117: These claims recite wherein the prognostic assessment comprises a clinical recommendation for at least one pharmaceutical intervention for the patient user; which only serves to limit the abstract idea of the prognostic assessment.
Claims 106 and 118: These claims recite wherein the prognostic assessment comprises a clinical recommendation for at least one blood biomarker test for the patient user; which only serves to limit the abstract idea of the prognostic assessment.
Claim 107: This claim recites wherein the biometric measurement data comprises data from at least one body-worn sensor of the patient user; which teaches body-worn sensors at a high level of generality such that they are only applied to perform an insignificant extra-solution activity of data gathering.
Claim 108: This claim recites the method further comprising modifying, via the conversational Al model, the plurality of clinical conversational prompts according to the one or more prognostic values for the one or more text segments; which teaches an abstract idea of certain methods of organizing human activity as modifying conversational prompts according to prognostic values is an activity typically performed by healthcare providers asking patients follow-up questions about their condition. This claim further teaches the conversational Al model at a high level of generality such that it is only applied to perform the abstract idea.
Claim 110: This claim recites the system further comprising at least one biometric sensor configured to collect the biometric measurement data for the patient user; which teaches body-worn sensors at a high level of generality such that they are only applied to perform an insignificant extra-solution activity of data gathering.
Claim 111: This claim recites wherein the at least one biometric sensor comprises a body- worn sensor configured to continuously collect the biometric measurement data when worn by the patient user; which only serves to further limit the type of biometric sensor without providing any specific technical improvements to the functioning of biometric sensors.
Claim 112: This claim recites wherein the at least one biometric sensor is communicably engaged with the first client device to communicate a plurality of sensor inputs comprising the biometric measurement data to the first client device in real-time; which only serves to limit the timing of the insignificant extra-solution activity of data gathering.
Claim 113: This claim recites wherein the generative AI model comprises a large language model; which only serves to further limit the type of conversational AI model without providing any specific technical improvements to the model’s functioning.
Claim 114: This claim recites wherein the biometric measurement data comprises at least one data type selected from the group consisting of electrocardiogram data, heartrate data, heartrate variability data, sleep data, and activity data; which only serves to limit the type of data gathered.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Large language models that utilize natural language processing models are known, as evidenced by:
Thirunavukarasu; Arun James, Large language models in medicine, 17 Jul 2023, Nature Medicine, 29, 1930-1940: pg. 1930, “Large language models (LLMs) are artificial intelligence (AI) systems that are trained on billions of words derived from articles, books and other internet-based content. Typically, LLMs use neural network architectures (see Box 1 for a glossary of terms) that leverage deep learning — already used with impressive results across medicine — to represent the complicated associative relationships between words in the text-based training dataset1,2. Through this training process, which may be multi-staged and involve variable degrees of human input, LLMs learn how words are used with each other in language and can apply these learned patterns to complete natural language processing tasks. Natural language processing describes the broad field of computational research aiming to facilitate automatic analysis of language in a way that imitates human ability3. Generative AI developers aim to produce models that can create content on demand and intersect with natural language processing within applications, such as chatbots and text prediction — in other words, ‘natural language generation’ tasks4.”
Sallam; Malik, ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns, 19 Mar 2023, Healthcare, 11(6),887: pg. 1, “ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations.” Pg. 2, “Launched in November 2022, “ChatGPT” is an AI-based large language model (LLM) trained on massive text datasets in multiple languages with the ability to generate humanlike responses to text input [6]. Developed by OpenAI (OpenAI, L.L.C., San Francisco, CA, USA), ChatGPT etymology is related to being a chatbot (a program able to understand and generate responses using a text-based interface) and is based on the generative pre-trained transformer (GPT) architecture [6,7]. The GPT architecture utilizes a neural network to process natural language, thus generating responses based on the context of input text [7].”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 100-107 and 110-118, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 100-107 and 110-118, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 100-107 and 110-118, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 99-118 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 99-100 and 103-105 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Molero (WO 2021252090).
Regarding claim 99, Molero teaches a computer-implemented method for prognostic evaluation of autoimmune disorders ([0051], “the cloud-based application may be configured to identify potential diagnoses or potential treatments for any disease, condition, area of study, or disorder, including, but not limited to,… autoimmune disorders,…”) comprising:
presenting, with a conversational Al agent at one or more timepoints, a plurality of clinical conversational prompts to a patient user ([0101], “cloud server 135 embeds a chatbot into the cloud-based application. The chatbot is configured to automatically communicate with user devices. The chatbot can communicate with a user device in a communication session, in which messages are exchanged between the user device and the chatbot.” [0032], “a third interface may be availed to a subject and may include one or more pages configured to receive self-evaluation responses or self-assessments. For example, a subject interface may be configured to present wellness-related questions and receive corresponding answers.” [0096], “Cloud server 135 may be configured to execute intelligent functionality to process the data records stored in data registry 140. For example, executing intelligent functionality may include inputting at least a portion of the data records stored in data registry 140 into a trained machine-learning or artificial-intelligence models to generate outputs for further analysis.”);
receiving, with the conversational Al agent at the one or more timepoints, a plurality of conversational responses from the patient user in response to the plurality of clinical conversational prompts ([0032], “a subject interface may be configured to present wellness-related questions and receive corresponding answers… the cloud-based application may automatically detect, or detect via input, a time period (e.g., date, time, date period and/or time period) corresponding to an assessment, medical test or input.”);
receiving, with at least one server, a first set of text data comprising the plurality of conversational responses from the patient user ([0032], “a subject interface may be configured to present wellness-related questions and receive corresponding answers” [0101], “cloud server 135 embeds a chatbot into the cloud-based application. The chatbot is configured to automatically communicate with user devices. The chatbot can communicate with a user device in a communication session, in which messages are exchanged between the user device and the chatbot.”);
receiving, with the at least one server via at least one application programming interface, a first set of medical record data for the patient user ([0034], “The cloud-based application may further receive information about a particular subject. The information may include demographic information, medical-history information, employment information, etc.”),
wherein the first set of medical record data for the patient user comprises at least one of blood biomarker test data and biometric measurement data ([0035], “the cloud-based application processes the data to build, for each subject, time series data that associates individual time periods with (for example) any medication being received by the subject, disability level/score, MRI statistics or characteristics, well-being indices, symptom presentation and/or adverse events. In some instances, an event chain may be generated based on the data that includes a series of time stamps and associated clinical, biomarker, medical-test and/or self-assessment metrics.” [0032], “receive medical test results (e.g., blood-test results, MRI images, MRI statistics, etc.)” [0103], “a recent symptom include a subject currently or recently (e.g., at a last visit, at intake, within 24 hours, within a week) experienced a particular symptom (e.g., difficulty breathing, fever above a threshold temperature, blood pressures above a threshold blood pressure, etc.).”);
processing, with the at least one server, the first set of text data and the first set of medical record data for the patient user according to a natural language processing model ([0031], “The unstructured data may include (for example) physician notes, subject’s textual self-assessment reports, etc. The unstructured data may be processed to identify corresponding structured data (e.g., using natural language processing and/or keyword detection).”),
wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data ([0126], “cloud server 135 extracts a set of subject attributes from the subject record received at block 410. A subject attribute characterizes an aspect of the subject. Non limiting examples of subject attributes include any information found in an electronic health record, any demographic information, an age, a sex, an ethnicity, a recent or historical symptom, a condition, a severity of the condition, and any other suitable information that characterizes the subject.” [0141], “cloud server 135 queries the knowledge base using at least some words extracted from the particular question. The words may be extracted from the string of text representing the particular question using natural language processing techniques.”),
wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features ([0157], “At block 730, a classifier is used to assign the subject to one of the sub-groups based on characteristics of the subject. The classifier may include (for example) a clustering classifier” [0160], “As another example, an automated technique or a predefined criteria may be used to divide the records at block 725. The automated technique may include (for example) a clustering algorithm applied to MRI-change data, disability-change data, or length-of- treatment use data.”);
analyzing, according to the natural language processing model, the one or more text segments to determine one or more temporal or circumstantial association between the one or more text segments and the blood biomarker test data and/or the biometric measurement data ([0031], “The unstructured data may include (for example) physician notes, subject’s textual self-assessment reports, etc. The unstructured data may be processed to identify corresponding structured data (e.g., using natural language processing and/or keyword detection).” [0035], “the cloud-based application processes the data to build, for each subject, time series data that associates individual time periods with (for example) any medication being received by the subject, disability level/score, MRI statistics or characteristics, well-being indices, symptom presentation and/or adverse events. In some instances, an event chain may be generated based on the data that includes a series of time stamps and associated clinical, biomarker, medical-test and/or self-assessment metrics. Thus, the event chain may identify which treatments were received by a subject, time periods during which various treatments were received, clinical assessments performed during treatment time periods, results of one or more MRIs received during treatment time periods, subject reports or self-evaluations during treatment time periods, etc.”);
assigning, according to at least one output of the natural language processing model, one or more prognostic values to the one or more text segments ([0036], “The cloud-based application may use the subject’s event chain to (for example) generate a prediction corresponding to an effectiveness of a given treatment, predict an effectiveness of a given treatment, predict a prognosis, and/or identify a recommended treatment… A prediction of the given subject’s responsiveness to the given treatment may be defined based on the responsiveness of the subset of subjects to the given treatment” [0131], “the record data identifies a treatment received by the subject and the subject’s responsiveness to the treatment. The responsiveness to the treatment may be represented by text (e.g., “subject responded positively to treatment”) or a score indicating an extent to which the subject responded positively or negatively to the treatment (e.g., a score from “0” to “1” with “0” indicating a negative responsiveness and “1” indicating a positive responsiveness).”). Examiner notes pg. 6 of Applicant specification specifies that “the prognostic assessment comprises one or more recommended actions for management of the autoimmune disorder for the patient user”, which is analogous to the treatment response prediction score of Molero.
analyzing, with the at least one server, the one or more prognostic values for the one or more text segments to generate a prognostic assessment for at least one autoimmune disorder for the patient user ([0045], “Thus, the aggregate statistics can be used to predict what types of subject attributes and/or contexts are predictive of treatment efficacy and/or of a positive prognosis while on the treatment. A care provider or subject may use the prediction and particular attributes of the subject to determine whether to select the treatment or continue use of the treatment. In some instances, a prediction of efficacy and/or prognosis may be generated for the subject for each of multiple treatment strategies (e.g., associated with different corresponding subject populations), and a treatment can be selected for the subject based on the predictions.”); and
providing, with the at least one server, the prognostic assessment to a practitioner user via a graphical user interface of a client device ([0158], “an output is generated that corresponds to a treatment prediction or recommendation based on the classification generated at block 730. For example, the output may identify a binary prediction as to whether the subject will respond to the treatment, a probability that the subject will respond to the treatment, a predicted progression of the subject on the treatment over a time period, a predicted change in MRI statistics for the subject on the treatment over a time period, a probability of progression to another disease sub-type, etc. The output may be presented at and/or transmitted to (for example) a device associated with a care provider, neurologist, physician or subject.” [0095]).
Regarding claim 100, Molero claim teaches the method of claim 99. Molero further teach the method further comprising configuring, with the at least one server, a conversational Al model according to the one or more prognostic values for the one or more text segments ([0159], “the query at block 720 may be performed at routine time intervals, in response to receiving a particular type of input about a subject (e.g., identification of MRI or disability progression, identification of a new treatment being used or considered for the subject, or identification of a new diagnosis of an MS sub-type for the subject). The query may be structured to include a constraint identifying a particular treatment being used or considered for the subject as reflected in the subject’s record. The query may request particular predefined field values (e.g., corresponding to MRI results, relapse detections, disability assessments, adverse events, subsequent treatment changes, subject attributes, diagnoses and/or prior medications used) for those records satisfying the constraint.”).
Regarding claim 103, Molero claim teaches the method of claim 99. Molero further teaches wherein the prognostic assessment comprises a diagnostic assessment for the at least one autoimmune disorder ([0147], “A test result can include a result of one or more tests used to diagnosis and/or assess multiple sclerosis, such as a test identified in Section II (e.g., an MRI, CSF analysis, visually evoked potential test and/or a blood test).” [0158], “an output is generated that corresponds to a treatment prediction or recommendation based on the classification generated at block 730. For example, the output may identify… a probability of progression to another disease sub-type, etc.”).
Regarding claim 104, Molero claim teaches the method of claim 99. Molero further teaches wherein the prognostic assessment comprises a predictive assessment of at least one pathophysiological event associated with the at least one autoimmune disorder ([0035], “the cloud-based application processes the data to build, for each subject, time series data that associates individual time periods with (for example) any medication being received by the subject, disability level/score, MRI statistics or characteristics, well-being indices, symptom presentation and/or adverse events. In some instances, an event chain may be generated based on the data that includes a series of time stamps and associated clinical, biomarker, medical-test and/or self-assessment metrics” [0036], “The cloud-based application may use the subject’s event chain to (for example) generate a prediction corresponding to an effectiveness of a given treatment, predict an effectiveness of a given treatment, predict a prognosis, and/or identify a recommended treatment.” [0158], “an output is generated that corresponds to a treatment prediction or recommendation based on the classification generated at block 730. For example, the output may identify… a probability of progression to another disease sub-type, etc.” [0051], “the cloud-based application may be configured to identify potential diagnoses or potential treatments for any disease, condition, area of study, or disorder, including, but not limited to, oncology,… autoimmune disorders”). Under the broadest reasonable interpretation, both a negative prediction of a patient’s prognosis and a progression to another disease sub-type encompass pathophysiological events.
Regarding claim 105, Molero claim teaches the method of claim 99. Molero further teaches wherein the prognostic assessment comprises a clinical recommendation for at least one pharmaceutical intervention for the patient user ([0036], “The cloud-based application may use the subject’s event chain to (for example) generate a prediction corresponding to an effectiveness of a given treatment, predict an effectiveness of a given treatment, predict a prognosis, and/or identify a recommended treatment.” [0036], “Interacting with treatment section 925 can allow a user to select one or multiple MS treatments (e.g.. alemtizumab, azatioprina, copaxone, deaclizumab, dimethyl fumarate, methotrexate, mitoxantrone, natalizumab, interfron, ocrelizumab, fmgolimod, rituximab, teriflunomide, other, or no treatment);…. and/or identify one or more symptom treatments (e.g., prednisone, cortisone, dexametasone, hydroortisone, methylprednisolone, prednisone). The interface may further be configured to receive, for each MS and/or symptom treatment, a dose, unit, period, route (sub-cutaneous, intramuscular, intravenous, oral, other), start/end date and/or reason for ending the treatment.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 101-102, 108-110, 113, and 115-117 are rejected under 35 U.S.C. 103 as being unpatentable over Molero (WO 2021252090) in view of Yuan (Yuan; Jiajia, Advanced prompting as a catalyst: Empowering large language models in the management of gastrointestinal cancers, 14 Aug 2023, The innovation Medicine 1(2): 100019).
Regarding claim 101, Molero claim teaches the method of claims 99-100. Molero does not teach wherein the conversational Al model comprises a large language model.
However, Yuan does teach wherein the conversational Al model comprises a large language model (pg. 2, “Large Language Models (LLMs), exemplified by cutting-edge architectures like GPT-4,1 have demonstrated considerable potential in transforming healthcare delivery2-4 and competency in medical examinations.5” ).
Molero in view of Yuan are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Yuan for the advantage of “leading to the generation of personalized and targeted treatment plans, as opposed to general guideline recommendations offered through rudimentary prompts” (Yuan; pg. 7).
Regarding claim 102, Molero claim teaches the method of claims 99-100. Molero does not teach wherein the plurality of clinical conversational prompts comprise a plurality of generative prompts according to the conversational Al model.
However, Yuan does teach wherein the plurality of clinical conversational prompts comprise a plurality of generative prompts according to the conversational Al model (pg. 2, “we iteratively refined this template to align GPT-4’s responses with physicians’ requirements and added elements to generate comforting responses for patients.” Pg. 3, “This involves defining the central task that GPT-4 needs to perform. Given the context of our research, our model, acting as a digestive oncology specialist, is tasked with generating personalized treatment plans for digestive system cancer patients.”).
Molero in view of Yuan are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Yuan for the advantage of “enabl[ing] GPT-4 to produce complex and contextually accurate responses” (Yuan; pg. 3).
Regarding claim 108, Molero teaches the method of claims 99-100. Molero does not teach the method further comprising modifying, via the conversational Al model, the plurality of clinical conversational prompts according to the one or more prognostic values for the one or more text segments.
However, Yuan does teach the method further comprising modifying, via the conversational Al model, the plurality of clinical conversational prompts according to the one or more prognostic values for the one or more text segments (pg. 4, “As highlighted in Figure 3, the clinician directed GPT-4 to offer a detailed staging diagnosis, subsequently pointing out its error. Following multiple question-and- answer interactions with the clinician, GPT-4 acknowledged its mistake and adjusted its response. It began by determining the cancer's stage, before suggesting a specific treatment plan. This revised response is not only more suitable for the patient but also provides her with hope.”). See Fig. 3, below, which demonstrates how ChatGPT’s prompts are adjusted based on the provided prognostic values.
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Molero in view of Yuan are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Yuan for the advantage of “enabl[ing] GPT-4 to produce complex and contextually accurate responses” (Yuan; pg. 3).
Regarding claim 109, this claim is rejected for the same reasons as claim 99. Molero further teaches a system for prognostic evaluation of autoimmune disorders comprising:
a first client device associated with a patient user, wherein the first client device comprises a first graphical display and a first input/output device ([0168], “The output may be transmitted to a user device and/or presented locally (e.g., to a care provider, physician, neurologist or subject)” [0092], “The user devices of computer 105, laptop 110, and mobile device 115 are examples for the purpose of illustration, and thus, the present disclosure is not limited thereto.”);
a second client device associated with a practitioner user, wherein the second client device comprises a second graphical display and a second input/output device ([0097], “cloud server 135 is configured to enable a user device (e.g., operated by a doctor) to access the cloud-based application to transmit consult broadcasts to a set of destination devices.” [0092], “The user devices of computer 105, laptop 110, and mobile device 115 are examples for the purpose of illustration, and thus, the present disclosure is not limited thereto.”);
an application server communicably engaged with the first client device and the second client device via a network communications interface ([0011], “a cloud-based application server” [0016], “FIG. 1 illustrates a network environment in which the cloud-based application is hosted” [0030], “a cloud-based application may provide an interface through which users (e.g., care providers, radiologists, laboratories and/or subjects) can provide input that characterizes a state of a subject.”),
wherein the application server comprises a natural language processing engine ([0141], “cloud server 135 queries the knowledge base using at least some words extracted from the particular question. The words may be extracted from the string of text representing the particular question using natural language processing techniques.”),
wherein the application server comprises at least one processor and a non-transitory computer readable medium having instructions stored thereon that, when executed by the at least one processor, cause the processor to perform one or more operations ([0012], “a system is provided that includes one or more data processors and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods disclosed herein.”).
Molero does not teach wherein the application server comprises a generative Al engine and a natural language processing engine.
However, Yuan does teach wherein the application server comprises a generative Al engine (pg. 2, “we aimed to unleash GPT-4’s potential to provide personalized digestive system cancer treatment plans through prompt engineering.”). It would be obvious to one of ordinary skill in the art that GPT-4 is a generative AI engine available through an application server.
Molero in view of Yuan are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Yuan for the advantage of “enabl[ing] GPT-4 to produce complex and contextually accurate responses” (Yuan; pg. 3).
Regarding claim 110, Molero in view of Yuan teaches the system of claim 109. Molero teaches the system further comprising at least one biometric sensor configured to collect the biometric measurement data for the patient user ([0121], “the reported state is any information characterizing a state of the subject in an aspect, such as whether the subject has been discharged, whether the subject is alive, measurements of the subject’s blood pressure, the number of times the subject wakes up during a sleep stage, and other suitable states.”). It would be obvious to one of ordinary skill in the art that blood pressure measurements would require a biometric sensor, such as a blood pressure monitor.
Regarding claim 113, Molero in view of Yuan teaches the system of claim 109. Molero does not teach wherein the generative AI model comprises a large language model.
However, Yuan does teach wherein the generative AI model comprises a large language model (pg. 2, “Large Language Models (LLMs), exemplified by cutting-edge architectures like GPT-4,1 have demonstrated considerable potential in transforming healthcare delivery2-4 and competency in medical examinations.5” ).
Molero in view of Yuan are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Yuan for the advantage of “leading to the generation of personalized and targeted treatment plans, as opposed to general guideline recommendations offered through rudimentary prompts” (Yuan; pg. 7).
Regarding claims 115, 116, and 117, these claims are rejected for the same reasons as claims 103, 104, and 105, respectively.
Claims 106-107 are rejected under 35 U.S.C. 103 as being unpatentable over Molero (WO 2021252090) in view of Nakatsugawa (US 20230284938).
Regarding claim 106, Molero teaches the method of claim 99. Molero further teaches wherein the prognostic assessment comprises a clinical recommendation for at least one blood biomarker test for the patient user
However, Nakatsugawa does teach wherein the prognostic assessment comprises a clinical recommendation for at least one blood biomarker test for the patient user ([0084], “the control unit 16 may output a comment of the content further recommending a test in a case where the level of blood glucose is evaluated to be inappropriate in the first evaluation. For example, a comment may be output, which has a content recommending a postprandial blood glucose test, a glucose tolerance test, an intestinal bacterium test, and the like.” [0098], “the evaluation unit 14 may use the correlation data which is obtained by performing big data analysis (for example, deep learning by artificial intelligence (AI)) based on optional biological information obtained by the monitoring device 4 and the level of blood glucose in addition to the above-mentioned values.”).
Molero in view of Nakatsugawa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Nakatsugawa for the advantage of “acquir[ing] appropriate measurement results” (Nakatsugawa; [0027]).
Regarding claim 107, Molero claim teaches the method of claim 99. Molero does not teach wherein the biometric measurement data comprises data from at least one body-worn sensor of the patient user.
However, Nakatsugawa does teach wherein the biometric measurement data comprises data from at least one body-worn sensor of the patient user ([0140], “a wearable terminal such as a wristwatch type, a glasses type, an earphone type, or a ring type may be applied as the monitoring device 4. The wearable terminal may derive the level of glucose by analyzing a signal emitted by glucose in blood, for example, by irradiating the skin of the user with infrared rays.”)
Molero in view of Nakatsugawa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero with Nakatsugawa for the advantage of utilizing a system wherein “the monitoring value is measurable in a non-invasive manner” (Nakatsugawa; [0140]).
Claims 111-112, 114, and 118 are rejected under 35 U.S.C. 103 as being unpatentable over Molero (WO 2021252090) in view of Yuan (Yuan; Jiajia, Advanced prompting as a catalyst: Empowering large language models in the management of gastrointestinal cancers, 14 Aug 2023, The innovation Medicine 1(2): 100019) further in view of Nakatsugawa (US 20230284938).
Regarding claim 111, Molero in view of Yuan teaches the system of claims 109-110. Molero in view of Yuan does not teach wherein the at least one biometric sensor comprises a body- worn sensor configured to continuously collect the biometric measurement data when worn by the patient user.
However, Nakatasugawa does teach wherein the at least one biometric sensor comprises a body- worn sensor configured to continuously collect the biometric measurement data when worn by the patient user ([0140], “a wearable terminal such as a wristwatch type, a glasses type, an earphone type, or a ring type may be applied as the monitoring device 4. The wearable terminal may derive the level of glucose by analyzing a signal emitted by glucose in blood, for example, by irradiating the skin of the user with infrared rays.” [0004], “a measurement device (hereinafter, referred to as “continuous blood glucose measurement device”) that measures not the level of blood glucose itself but a level of glucose of an interstitial fluid having a correlation with the level of blood glucose by a sensor mounted on the skin of a user.”).
Molero in view of Yuan further in view of Nakatsugawa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero in view of Yuan with Nakatsugawa for the advantage of utilizing a system wherein “the monitoring value is measurable in a non-invasive manner” (Nakatsugawa; [0140]).
Regarding claim 112, Molero in view of Yuan teaches the system of claims 109-110. Molero in view of Yuan does not teach wherein the at least one biometric sensor is communicably engaged with the first client device to communicate a plurality of sensor inputs comprising the biometric measurement data to the first client device in real-time.
However, Nakatasugawa does teach wherein the at least one biometric sensor is communicably engaged with the first client device to communicate a plurality of sensor inputs comprising the biometric measurement data to the first client device in real-time ([0140], “at least one of the electrocardiographic signal, the blood pressure, and the body temperature of the user, which is measured by a sensor included in the wearable terminal, may be used as the monitoring value.” [0141], “Further, the wearable terminal includes an input/output unit such as a button, a display, and a touch panel, and a network I/F that performs wired or wireless communication between the information processing apparatus 20 and an external network (not shown in the drawing).” [0004], “a measurement device (hereinafter, referred to as “continuous blood glucose measurement device”) that measures not the level of blood glucose itself but a level of glucose of an interstitial fluid having a correlation with the level of blood glucose by a sensor mounted on the skin of a user.”).
Molero in view of Yuan further in view of Nakatsugawa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero in view of Yuan with Nakatsugawa for the advantage of “measur[ing] fluctuation in the level of blood glucose with time” (Nakatsugawa; [0004]).
Regarding claim 114, Molero in view of Yuan teaches the system of claim 109. Molero in view of Yuan does not teach wherein the biometric measurement data comprises at least one data type selected from the group consisting of electrocardiogram data, heartrate data, heartrate variability data, sleep data, and activity data.
However, Nakatsugawa does teach wherein the biometric measurement data comprises at least one data type selected from the group consisting of electrocardiogram data, heartrate data, heartrate variability data, sleep data, and activity data ([0012], “the measured value may be a level of blood glucose, the monitoring value may be at least one of a level of glucose included in interstitial fluid, sweat, or saliva, an electrocardiographic signal, a blood pressure, or a body temperature which has a correlation with the level of blood glucose,”).
Molero in view of Yuan further in view of Nakatsugawa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Molero in view of Yuan with Nakatsugawa for the advantage of “acquiring an appropriate measurement result” (Nakatsugawa; [0006]).
Regarding claim 118, this claim is rejected for the same reasons as claim 106, as described above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Machine-Learning Model For Generating Hemophilia Pertinent Predictions Using Sensor Data (US 20230377747), which teaches systems and methods for building and using a machine-learning model to facilitate intelligent selection of treatment strategies for subjects suffering from hemophilia. Sensor data (e.g., that characterizes movement of or a physiological characteristic of particular a subject) can be used as an input to a machine-learning model workflow. The sensor data may affect a model selection, model configuration, model result, pre-processing and/or post-processing. A result of the workflow may inform or influence a treatment selection, treatment schedule, treatment dosage and/or activity recommendation for the particular subject.
Systems And Methods For Responding To Healthcare Inquiries (US 20190311814), which teaches techniques for responding to a healthcare inquiry from a user. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; identifying the intent as a need to obtain a dermatological diagnosis or treatment; soliciting, from the user and via the computing device, an image of the user's skin area to be diagnosed or treated; instantiating an image classification system to identify a dermatological disease from the image; and presenting one or more medical recommendations to the user based on the identified dermatological disease.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
