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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority to application NZ809888 filed in New Zealand on 4/5/2024. It is noted, however, that applicant has not filed a certified copy of the foreign application as required by 37 CFR 1.55.
Status of the Claims
Claims 1-20 are currently pending and have been considered below.
Claim Objections
Claims 8, 10, and 18 are objected to because of the following informalities:
Claims 8 and 18 each introduce the abbreviation “GenAI” without explaining its meaning (e.g. in parentheses, as is done for the abbreviations “AI” and “ML” in claim 1).
Claim 10 recites “the users’ health data,” which indicates that health data for multiple users is being tracked despite parent claim 1 only introducing a single user; this phrase should be amended to “the user’s health data” for grammatical clarification.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 12, and 20 each recite “analyze the processed data types for detecting abnormalities and deviations in the collected data types by providing a sequence of prompts to AI models.” Claim 4 similarly recites “generate the sequence of intelligent prompts which are provided to the AI models to detect abnormalities and deviations in the collected data types and generate warnings for one or more abnormal health parameters associated with the data types.” Applicant’s original specification does not provide sufficient written support for analyzing data to detect abnormalities and deviations or generate warnings by specifically generating and providing a sequence of prompts to AI models. At most, paras. [0006]-[0008] restate the same outcome-based functional wording of the claims, while para. [0025] states that “the data analysis unit 114 generates a sequence of one or more intelligent prompts which are employed to detect abnormalities and deviations by analyzing the processed data types…. the sequence of intelligently generated prompts is provided by the data analysis unit 114 to Artificial Intelligence (AI) models to determine abnormalities and deviations in the data types.” There is no explanation of how the sequence of prompts are actually generated, what the sequence of prompts actually entails, or how the sequence of prompts would actually operate to facilitate detection of unspecified “abnormalities and deviations” of any type beyond being input into a black-box-type AI model. Accordingly, Applicant has not shown that they were in possession of any specific method of generating or providing a sequence of prompts to AI models in order to detect abnormalities and deviations in collected data types, and each claim is rejected under 35 U.S.C. 112(a). Claims 2-11 and 13-19 are also rejected on this basis because they inherit the unsupported limitation due to their dependence on claims 1 and 12, respectively.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 9, 11, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the text present in the collected data types" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim because there is no previously introduced text in the collected data types.
The term “irrelevant” in claims 2 and 14 is a relative term which renders the claim indefinite. The term “irrelevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For purposes of examination, Examiner will consider data to be “irrelevant” if it is deleted or otherwise removed during a data cleansing or preprocessing step.
Claims 9 and 11 each recite the limitation "the insights and recommendations generation unit." There is insufficient antecedent basis for this limitation in each claim because they both depend on claim 1, which does not introduce an insights and recommendations generation unit. For purposes of examination, claims 9 and 11 will each be interpreted as depending on claim 8, which does introduce an insights and recommendations generation unit.
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
In the instant case, claims 1-11 are directed to a system (i.e. a machine), claims 12-19 are directed to a method (i.e. a process), and claim 20 is directed to a non-transitory computer-readable medium (i.e. a manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1, 12, and 20 recite steps that, under their broadest reasonable interpretations, cover mental processes. Specifically, claim 1 (as representative) recites:
An Artificial Intelligence (AI) based system for personalized health data management, the system comprises: a memory storing program instructions; a processor executing instructions stored in the memory; and a health data management engine executed by the processor and configured to:
perform one or more data extraction operations on one or more pre-processed data types to obtain processed data types, wherein the data types are collected from multiple data sources;
analyze the processed data types for detecting abnormalities and deviations in the collected data types by providing a sequence of prompts to Al models;
extract one or more health features data from the analyzed data types by using one or more feature extraction techniques;
employ one or more Machine Learning (ML) models to augment the extracted health features data in order to identify anomalies and patterns in the health features data; and
generate insights and recommendations associated with health of a user based on processing of the analyzed features data, wherein one or more action items are triggered based on the generated insights and recommendations.
But for the recitation of generic computer components like a memory, a processor, AI models, and ML models, the italicized functions, when considered as a whole, describe a health data management, analysis, and recommendation operation that could be achieved by a human actor such as a clinician or other medical professional either mentally or with the aid of pen and paper. For example, a clinician could look at and process various types of data obtained from different data sources to extract features or other health information from the data (e.g. calculating physiological data averages, trends, or other health implications), analyze the data to detect anomalies, deviations, or other patterns (e.g. by prompting themselves to look for certain patterns and using their judgement to compare new data against reference norms or baselines), and come up with health-related insights and recommendations for action to share with a patient based on the analyzed feature data. Such operations reflect how a clinician or other medical professional may use their medical expertise and judgment to provide personalized care and health recommendations to a patient based on analysis of various types of patient-related data (e.g. health records, sensor readings or other physiological measurements, patient testimonials, etc.) collected over time, such that it also fits into the abstract idea grouping of “certain methods of organizing human activity” such as managing personal behavior and interactions with others. Accordingly, claim 1 recites an abstract idea. Claims 12 and 20 recite substantially similar subject matter as claim 1 and are found to recite an abstract idea under the same analysis.
Dependent claims 2-11 and 13-19 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 12, respectively, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2, 4-11, and 14-19 recite additional limitations that further describe the abstract idea identified in the independent claims.
Specifically, claims 2 and 14 describe various data cleaning operations that a human actor would be capable of performing either mentally or with aid of pen and paper when preparing and analyzing health-related data, such as removing irrelevant data, correcting misspellings, standardizing the text format, parsing text into individual tokens, and converting the data to a structured format by aggregating data over specific time intervals or implementing other data conversion techniques.
Claim 4 recites generating a sequence of intelligence prompts for detecting abnormalities and deviations and generate warnings for anormal health parameters, which a clinician could achieve mentally by mentally prompting or reminding themselves to look for specific types of deviations or abnormalities in the patient’s data (e.g. telling themselves to remember that a given patient has previously suffered from a heart attack, so make sure to check the patient data for any abnormalities related to cardiovascular functioning).
Claims 5 and 15 recite identifying and classifying entities in the analyzed data types, which a clinician could achieve by looking at the data and mentally identifying different concepts and categorizing them according to some health-based schema.
Claims 6 and 16 recite extracting features from time-series data including average heart rate, step count trends, and sleep duration patterns, which a clinician could achieve by looking at these types of time-series data and thinking about their implications or performing statistical calculations. These claims also recite converting text into numerical representations called embeddings that capture semantic meaning of the text, and computing statistical features of the data, which describe mathematical concepts and thus fall into the “mathematical concepts” grouping of abstract idea.
Claims 7 and 17 recite employing a classification model to classify the extracted health features data into pre-defined categories comprising diagnosis, treatment, and patient history, employing a clustering model to group similar data points together for identifying patterns and trends, and employing a predictive model to predict health outcomes and compute one or more future health metrics based on historical data. These functions describe mental and/or mathematical data calculations and evaluations that a clinician could achieve either mentally or with the aid of pen and paper, e.g. by utilizing simple models like regression equations, decision trees, checklists, etc. to classify and cluster data as well as to make predictions about future health metrics and outcomes.
Claims 8 and 18 recites generating summaries of extracted health feature data and identifying anomalies and outliers in the feature data that indicate user’s potential health issues, which a clinician could achieve mentally by thinking about the data and using their medical expertise and judgment to summarize the data and recognize any anomalies that may be indicative of certain health issues.
Claim 9 recites providing predictive actions for identifying expected health challenges to guide the user on health improvement and provide user’s health information to healthcare services providers and support functions for enabling them to forecast user’s health parameters, which a clinician could accomplish mentally and/or by managing their personal behavior and interactions with others (e.g. other healthcare service providers) by using their medical expertise and judgment to come up with treatment or care recommendations for a patient to mitigate any future health concerns and communicating their findings and insights to colleagues who will also care for the patient.
Claim 10 recites proactively monitoring and tracking the users’ health data and alerting the user with immediate remediation actions, which a clinician could accomplish by using their medical expertise and judgment to continually take in new information from a patient (e.g. at different appointments over time) and communicating any concerns or warnings to the patient immediately upon consideration of any new or dangerous information.
Claims 11 and 19 describe providing various types of actions that are related to communicating with various entities, which a clinician could achieve either mentally or by managing their personal behavior and/or interactions with others by thinking about and/or executing different types of actions that are related to communication with various entities.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1, 12, and 20 do not include additional elements that integrate the abstract idea into a practical application. The additional elements of claims 1, 12, and 20 include a memory and processor cooperating to implement a health data management engine that performs the claimed functional steps, analyzing the processed data types by providing a sequence of prompts to AI models, and augmenting the extracted health features data by employing one or more ML models. These additional elements, when considered in the context of each claim as a whole, merely serve to automate operations that could otherwise be performed mentally by a human actor (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components (see MPEP 2106.05(f)). The claims do not include any specific technical details about the structure, arrangement, or operation of the computer elements or AI/ML models that provide any improvements to the functioning of a computer or another technical field; instead, these elements appear to be claimed as high-level tools with which the otherwise-abstract health data management and analysis functions are digitized and/or automated. Accordingly, claims 1, 12, and 20 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-11 and 13-19 is also not integrated into a practical application under a similar analysis as above. The functions of claims 14, 16-17, and 19 are performed with the same additional elements introduced in the independent claims, without introducing any new additional elements of their own, and accordingly also amount to mere instructions to apply the abstract idea with these same additional elements.
Claims 2-11 introduce various “units” executed by the processor that perform the claimed functional steps of each claim (e.g. a data collection and pre-processing unit in claims 2-3, a data analysis unit in claim 4, a feature extraction unit in claims 5-6, a feature augmentation unit in claim 7, and an insights and recommendations generation unit in claims 8-11). These units also amount to mere instructions to “apply” the judicial exception with generic computing components, because they merely function as tools with which the otherwise-abstract data cleaning, analysis, augmentation, recommendation, etc. steps are digitized and/or automated.
Claims 3 and 13 recite performing data extraction techniques like OCR and NLP as well as employing a real-time data streaming technique to continuously collect data from one or more health monitoring devices. The OCR and NLP functions of these claims amount to mere instructions to “apply” the exception because they represent additional high-level computing techniques that are merely utilized to digitize and/or automate the main abstract data management and analysis steps such that they occur in a computerized environment. The continuous collection of real-time streaming data from health monitoring devices amounts to insignificant extra-solution activity, because this step nominally collects data but provides no further technical details regarding how the data is processed or utilized in any way such that it is tangential to the main data management and analysis steps of the invention.
Claims 5 and 15 specify that an NER technique is used as a basis for identifying and classifying entities in the analyzed data types, which amounts to mere instructions to “apply” the exception because it represents another high-level computing technique that is merely utilized to digitize and/or automate the main abstract data management and analysis functions of the invention such that they occur in a computerized environment by computerized components.
Claims 8 and 18 recite employing one or more GenAI models for generating the summaries, which also amounts to mere instructions to “apply” the exception because the unspecified GenAI models represent high-level computing techniques that are merely utilized to digitize and/or automate the otherwise-abstract step of generating summaries from health data.
Accordingly, the additional elements of claims 1-20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-20 are directed to an abstract idea.
Step 2B
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 integration of the abstract idea into a practical application, the additional elements of a processor and memory implementing a health data management engine and various functional “units” as well as high-level AI and ML models to perform the performing, analyzing, extracting, augmenting, generating, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. The use of high-level computing techniques like OCR, NLP, NER, and GenAI as in dependent claims 3, 5, 8, 13, 15, and 18 also amounts to mere instructions to “apply” the judicial exception, as explained above. As evidence of the generic nature of the above recited additional elements, Examiner notes the following portions of Applicant’s specification:
[0024], noting OCR and NLP techniques include commercially available techniques like tesseract library, Google cloud vision, spaCy, and Natural Language Toolkit;
[0033]-[0034], noting use of commercially available AI and ML frameworks like TensorFlow, PyTorch, Hugging Face, Google Cloud AI, Azure Cognitive Services, etc.
[0052], noting examples of existing processor-based computing systems like a programmed microprocessor, a micro-controller, a peripheral integrated circuit, etc.
Though Applicant asserts in [0018] that “the processor 106 is a specific-purpose processor which is specifically programmed to execute instructions stored in the memory 108 for executing respective functionalities of the units of the engine 104,” Examiner notes that merely asserting that a general purpose computer is “specially programmed” to be a particular machine does not make it so (see MPEP 2106.05(b)(I)); in the present case, a processor executing instructions to perform health data management and analysis functions is not a particular machine when considered under the guidance of MPEP 2106.05(b), at least because it does not refer to a specific machine but rather a generic type of device that is capable of executing software programs. The cited MPEP section notes that the particularity or generality of the elements of a machine are considered by “the degree to which the machine in the claim can be specifically identified (not any and all machines).” Mackay Radio & Tel. Co. v. Radio Corp of America is cited as an example of a particular machine integrating a mathematical formula into a practical application, because “the claim recited the particular type of antenna and included details as to the shape of the antenna and the conductors, particularly the length and angle at which they were arranged.” The instant claims’ recitation of “a processor executing instructions stored in the memory” contrasts this type of particularity because it merely describes a broad class of devices capable of performing the particular functions, without any details about a single specific and narrowly-defined device.
The continuous collection of real-time streaming data from one or more health monitoring devices as in claims 3 and 13 amounts to insignificant extra-solution activity, as explained above. Receiving or transmitting data over a network is also recognized as a well-understood, routine, and conventional computer functions performed using generic computer components, as outlined in MPEP 2106.05(d)(II).
Further, obtaining real-time streaming data from health monitoring devices is well-understood, routine, and conventional in the field of health data management and analysis, as evidenced by at least Gallix et al. (US 20250292910 A1) [0089] & [0259]; Chanan et al. (US 20240296958 A1) [0048] & [0411]; and Hariprasad (WO 2025147613 A1) [0067] & [00173].
Further, the combination of these additional elements is not expanded upon in the specification as a unique arrangement and as such relies on the knowledge of one of ordinary skill in the art to understand the combination of components within a computer system as a well-known and generic combination for automating an abstract idea and thus do not provide an inventive concept. The combination of a processor and memory executing various functional engines or units, AI/ML/GenAI models, data extraction techniques like OCR, NLP, and NER, and continuous data collection from health monitoring devices is well-understood, routine, and conventional in the field of health data management and analysis, as evidenced by at least Gallix [0076]-[0081], [0089], [0164], [0369]; Zou (WO 2022174161 A1) [0004], [0037], [0057], [0063], & [0076]; and Wagner et al. (WO 2024086537 A1) Pgs 8, 20-21, & 36.
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the computer implementation, AI/ML models, and specific computerized data extraction techniques in combination is to digitize and/or automate a health data management and analysis operation that could otherwise be achieved by a human actor mentally and/or with aid of pen and paper. Thus, when considered as a whole and in combination, claims 1-20 are not patent eligible.
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 1-2, 4-12, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gallix et al. (US 20250292910 A1).
Claims 1, 12, and 20
Gallix teaches an Artificial Intelligence (AI) based system for personalized health data management (Gallix abstract, [0081]-[0082]), the system comprises: a memory storing program instructions; a processor executing instructions stored in the memory; and a health data management engine executed by the processor and configured to (Gallix [0076]-[0082], [0093], noting a system implemented via a processor executing instructions stored in memory to perform health data management operations):
perform one or more data extraction operations on one or more pre-processed data types to obtain processed data types, wherein the data types are collected from multiple data sources (Gallix [0036], [0040]-[0042], [0048], noting data extraction techniques are utilized on different data types collected from multiple data sources);
analyze the processed data types for detecting abnormalities and deviations in the collected data types by providing a sequence of prompts to Al models (Gallix [0048], [0134], [0139], [0158], [0166], [0172]-[0173], [0195], noting a multi-layer model evaluates the processed data to identify abnormalities and deviations for a user; the model analyzing the data sequentially using stacked layers is considered equivalent to providing a sequence of prompts to AI models because the passing of data from one model layer to another to facilitate sequential processing is functionally equivalent to providing sequential prompts/inputs to each different layer model);
extract one or more health features data from the analyzed data types by using one or more feature extraction techniques (Gallix [0149], [0248], noting feature extraction of the data);
employ one or more Machine Learning (ML) models to augment the extracted health features data in order to identify anomalies and patterns in the health features data (Gallix [0149]-[0150], noting the extracted features include labels (i.e. the features are augmented/labeled with additional data); see also [0223], noting use of machine learning to enrich (i.e. augment) the data so that anomalies and other health statuses may be predicted, as well as [0195], [0220], [0266], noting additional analysis of the data to identify patterns, trends, and anomalies); and
generate insights and recommendations associated with health of a user based on processing of the analyzed features data, wherein one or more action items are triggered based on the generated insights and recommendations (Gallix [0033], [0057]-[0065], [0186]-[0189], noting the system analyzes the data to provide health insights and actionable instructions/recommendations for the user; certain actions such as sending an alert or communicating with a peripheral device may be automatically triggered/implemented by the system).
Claims 12 and 20 recite substantially similar subject matter as claim 1, and are also rejected as above.
Claim 2
Gallix teaches the system as claimed in claim 1, and further teaches wherein a data collection and pre- processing unit pre-processes the collected data type by cleaning the collected data types using data cleaning techniques, removing irrelevant data from the collected data types, correcting misspellings, and standardizing text format of the collected data, and wherein the text present in the collected data types is broken down into individual tokens for easier analysis, and wherein the collected data types are converted to a structured format by the data collection and pre-processing unit by aggregating data over specific time intervals or implementing data conversion techniques (Gallix [0135], [0140], [0144], [0164], noting the data is preprocessed with cleaning techniques to remove irrelevant data, correct inaccuracies (i.e. misspellings), standardize the format, provide word embeddings and/or extract concepts from text data (i.e. break the text into tokens), and convert the data to a desired structure).
Claim 4
Gallix teaches the system as claimed in claim 1, and further teaches wherein the health data management engine comprises a data analysis unit executed by the processor and is configured to generate the sequence of intelligent prompts which are provided to the Al models to detect abnormalities and deviations in the collected data types (Gallix [0048], [0134], [0139], [0158], [0166], [0172]-[0173], [0195], noting a multi-layer model evaluates the processed data to identify abnormalities and deviations for a user; the model analyzing the data sequentially using stacked layers is considered equivalent to the system generating and providing a sequence of prompts to AI models because the passing of data from one model layer to another to facilitate sequential processing is functionally equivalent to generating and providing sequential prompts/inputs to each different layer model) and generate warnings for one or more abnormal health parameters associated with the data types (Gallix [0189], [0206], [0343], [0594], noting the system may generate alerts (i.e. warnings) when a monitored parameter is outside of normal range or passes a threshold).
Claim 5
Gallix teaches the system as claimed in claim 1, and further teaches wherein the health data management engine comprises a feature extraction unit executed by the processor and is configured to extract one or more health features data from the analyzed data types by applying the one or more feature extraction techniques to identify and classify entities in the analyzed data types based on a Named Entity Recognition (NER) technique (Gallix [0164], noting feature extraction may involve identifying clinical entities of different classifications in the text (e.g. diseases, medications, or symptoms), which is functionally equivalent to use of an NER technique).
Claim 6
Gallix teaches the system as claimed in claim 5, and further teaches wherein the feature extraction unit converts text present in the analyzed data types into numerical representations referred to as embeddings, the embeddings capture the semantic meaning of the text present in the collected data types and are used for analysis of the collected data types (Gallix [0140], [0162], noting textual data is converted into embeddings for further processing), and wherein the feature extraction unit extracts features data from time-series data associated with the analyzed data types, the time series data comprises average heart rate, step count trends, and sleep duration patterns (Gallix [0144], [0165], noting the system preprocesses time series data like physiological signals from health monitoring devices; such physiological signals can include data like heart rate, activity or exercise data (i.e. step count trends), and sleep patterns per [0192], [0194], [0259]), and wherein the feature extraction unit computes statistical features data of the analyzed data types for summarizing the data types (Gallix [0141], noting preprocessing includes statistical handling techniques that summarize data such as interquartile range or Z-score detection).
Claim 7
Gallix teaches the system as claimed in claim 1, and further teaches wherein the health data management engine comprises a feature augmentation unit executed by the processor and is configured to employ a classification model to classify the extracted health features data into one or more pre-defined categories, the pre-defined categories comprise diagnosis, treatment, and patient history (Gallix [0164], noting NLP and LLM models can be used to identify classifications of extracted clinical entities, including diseases (i.e. diagnosis), medications (i.e. treatment), and symptoms (i.e. patient history)), and wherein a clustering model is employed to group similar data points associated with the extracted features data together by using clustering techniques for identifying patterns and trends in the health data associated with the extracted features data (Gallix [0145], [0161], noting use of clustering algorithms to process data so that it may be used to identify patterns and trends as in [0220]), and wherein a predictive model is employed to predict health outcomes based on the extracted features data, and wherein outcome of the predictive models is used to compute one or more future health metrics based on historical data (Gallix [0173], [0220]-[0221], [0239], [0251], noting use of predictive models to predict future outcomes, risk scores, and other metrics based on a patient’s historical data).
Claim 8
Gallix teaches the system as claimed in claim 1, and further teaches wherein the health data management engine comprises an insights and recommendations generation unit executed by the processor and is configured to employ one or more GenAI models for generating summaries of the extracted health feature data associated with the health data (Gallix [0164], noting generative AI models like LLMs can be used for sentiment analysis and topic modeling of textual data to summarize the sentiment or prominent themes or trends present in the text), and wherein the insights and recommendations generation unit identifies anomalies and outliers in the features data that indicate user's potential health issues (Gallix [0139], [0187]-[0189], [0206], [0343], [0594], noting generated insights and recommendations may be related to triggering alerts or notifications about detected anomalies of various types that may indicate potential health issues that should be investigated or treated).
Claim 9
Gallix teaches the system as claimed in claim 8, and further teaches wherein the insights and recommendations generation unit provide one or more predictive actions for identifying expected health challenges to guide the user on health improvement and provides user's health information to healthcare service providers and support functions for enabling them to forecast user's health parameters (Gallix [0187]-[0189], [0207], [0604]-[0613], noting generated insights and recommendations may include an instruction/action to guide the user and/or a healthcare provider in medical decision-making).
Claim 10
Gallix teaches the system as claimed in claim 8, and further teaches wherein the insights and recommendations generation unit proactively monitor and tracks the users' health data and alerts the user with immediate remediation actions (Gallix [0082], [0187]-[0189], [0234], [0372], [0623]-[0628], noting the system continuously monitors the user’s health data over time and proactively provides alerts other actions when critical health events are predicted to occur).
Claim 11
Note: the content/descriptors of what the action items relate to does not change or affect the structure or functioning of the claimed invention in any meaningful way, such that they are interpreted as nonfunctional descriptive language. The content/descriptors of the action items as “relating to” categories like “communicating with one or more emergency services based on the generated insights and recommendations,” “communicating with one or more collaborative services based on the insights and recommendations rendered for consumption based on the user’s choice of subscription,” and “communicating with other applications or internet applications for fetching regular feeds to render a consolidated view of the health parameters” do not impact the only positively claimed function of the claim, which is “providing one or more predictive actions” which could entail simply outputting (i.e. providing) the actions for display to a user. The specific content or type of the provided action items merely describes the content that may be displayed to a user, which is non-functional descriptive language because the function would remain unchanged regardless of the type of action that each item is “related” to such that these limitations are not patentably limiting in this case (see MPEP 2111.05). Further, the limitation “for fetching regular feeds to render a consolidated view of the health parameters” is an intended functional result of the described type of third action item and is not positively recited. Claim 11 will be considered to be met by the prior art disclosing a system that can provide action items of any type.
Gallix teaches the system as claimed in claim 8, and further teaches wherein the insights and recommendations generation unit provides one or more predictive actions including a first action item relating to communicating with one or more emergency services based on the generated insights and recommendations, a second action item relating to communicating with one or more collaborative services based on the insights and recommendations rendered for consumption based on the user's choice of subscription, and a third action item relating to communicating with other applications or internet applications for fetching regular feeds to render a consolidated view of the health parameters (Gallix [0057], [0187]-[0189], noting the provided instructions can relate to various types of recommended actions or interventions, including triggering an alert for a critical condition (which is considered to be “related to” communicating with one or more emergency services, because notifying a user of a critical condition would help facilitate them to call emergency services or another urgent care provider for intervention), scheduling an appointment or communicating with a caregiver or other user (which is considered to be “related to” communicating with one or more collaborative services based on the user’s choice of subscription, because communicating with a medical office to schedule an appointment or directly speaking with a medical professional would be based on the user’s choice of health insurance or other subscription and involve the user and medical personnel collaborating to make decisions), and controlling a peripheral device or other medical device (which is considered to be “related to” communicating with other applications because a computing device directly controlling another computing device would necessitate communication between their control programs (i.e. applications))).
Claim 19
Note: the limitation “for fetching regular feeds to render a consolidated view of the health parameters” is an intended functional result of the described type of third action item and is not positively recited. This limitation will be considered to be met by the prior art disclosing triggering of an action item that is in any way related to communicating with other applications or internet applications for any purpose.
Gallix teaches the method as claimed in claim 12, and further teaches wherein the one or more action items comprises a first action item relating to communicating with one or more emergency services based on the generated insights and recommendations, a second action item relating to communicating with one or more collaborative services based on the insights and recommendations rendered for consumption based on the user's choice of subscription, and a third action item relating to communicating with other applications or internet applications for fetching regular feeds to render a consolidated view of the health parameters (Gallix [0057], [0187]-[0189], noting the provided instructions can relate to various types of recommended actions or interventions, including triggering an alert for a critical condition (which is considered to be “related to” communicating with one or more emergency services, because notifying a user of a critical condition would help facilitate them to call emergency services or another urgent care provider for intervention), scheduling an appointment or communicating with a caregiver or other user (which is considered to be “related to” communicating with one or more collaborative services based on the user’s choice of subscription, because communicating with a medical office to schedule an appointment or directly speaking with a medical professional would be based on the user’s choice of health insurance or other subscription and involve the user and medical personnel collaborating to make decisions), and controlling a peripheral device or other medical device (which is considered to be “related to” communicating with other applications because a computing device directly controlling another computing device would necessitate communication between their control programs (i.e. applications))).
Claim Rejections - 35 USC § 103
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.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Gallix as applied to claims 1 or 12 above, and further in view of Ravi (US 20240013898 A1).
Claims 3 and 13
Gallix teaches the system as claimed in claim 1, and further teaches wherein the health data management engine comprises a data collection and pre-processing unit executed by the processor and is configured to perform the data extraction operations on the collected data types comprising (Gallix [0164], noting NLP techniques utilized to understand and structure textual data), and wherein a real-time data streaming technique is employed to continuously collect data from one or more health monitoring devices (Gallix [0259], [0262], noting collection of real-time data from wearable health monitoring devices/sensors).
Though Gallix describes various data cleaning and pre-processing steps for textual data such as NLP, it fails to explicitly disclose performance of OCR techniques to convert collected data types into a machine-readable format. However, Ravi teaches an analogous clinical data aggregation and analysis system that utilizes OCR to convert collected data types into a machine-readable format as part of the NLP preprocessing techniques (Ravi [0011], [0039]-[0042]). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the text preprocessing, cleaning, and NLP pipeline of Gallix to include an OCR step as in Ravi in order to convert images of typed, handwritten, or printed text into text that machines can process so that the collected data is in a format usable for further NLP and other computing processing tasks (as suggested by Ravi [0011] & [0042]).
Claim 13 recites substantially similar subject matter as claim 3, and is also rejected as above.
Claim 14
Gallix in view of Ravi teaches the method as claimed in claim 13, and the combination further teaches wherein collected data type are pre- processed by cleaning the collected data types by using data cleaning techniques, removing irrelevant data from the collected data types, correcting misspellings, and standardizing text format of the collected data, and wherein text present in the collected data types is broken down into individual tokens for easier analysis, and wherein the collected data types are converted to a structured format by aggregating data over specific time intervals or implementing data conversion techniques (Gallix [0135], [0140], [0144], [0164], noting the data is preprocessed with cleaning techniques to remove irrelevant data, correct inaccuracies (i.e. misspellings), standardize the format, provide word embeddings and/or extract concepts from text data (i.e. break the text into tokens), and convert the data to a desired structure).
Claim 15
Gallix in view of Ravi teaches the method as claimed in claim 13, and the combination further teaches wherein the step of performing one or more data extraction operations comprises extracting one or more health features data from the analyzed data types by identifying and classifying entities in the analyzed data types based on a Named Entity Recognition (NER) technique (Gallix [0164], noting feature extraction may involve identifying clinical entities of different classifications in the text (e.g. diseases, medications, or symptoms), which is functionally equivalent to use of an NER technique).
Claim 16
Gallix in view of Ravi teaches the method as claimed in claim 15, and the combination further teaches wherein the step of extracting one or more health features data from the analyzed data types comprises converting text present in the analyzed data types into numerical representations referred to as embeddings, the embeddings capture the semantic meaning of the text present in the collected data types and the embeddings are used for analysis of the collected data types (Gallix [0140], [0162], noting textual data is converted into embeddings for further processing), and wherein features data is extracted from time-series data associated with the analyzed data types, the time series data comprises average heart rate, step count trends, and sleep duration patterns (Gallix [0144], [0165], noting the system preprocesses time series data like physiological signals from health monitoring devices; such physiological signals can include data like heart rate, activity or exercise data (i.e. step count trends), and sleep patterns per [0192], [0194], [0259]), and wherein statistical features data of the analyzed data types is computed for summarizing the data types (Gallix [0141], noting preprocessing includes statistical handling techniques that summarize data such as interquartile range or Z-score detection).
Claim 17
Gallix in view of Ravi teaches the method as claimed in claim 13, and the combination further teaches wherein the step of employing one or more ML models comprises employing a classification model to classify the extracted health features data into one or more pre-defined categories, the pre-defined categories comprises diagnosis, treatment, and patient history (Gallix [0164], noting NLP and LLM models can be used to identify classifications of extracted clinical entities, including diseases (i.e. diagnosis), medications (i.e. treatment), and symptoms (i.e. patient history)), employing a clustering model to group similar data points associated with the extracted features data together by using clustering techniques for identifying patterns and trends in the health data associated with the extracted features data (Gallix [0145], [0161], noting use of clustering algorithms to process data so that it may be used to identify patterns and trends as in [0220]), and employing a predictive model to predict health outcomes based on the extracted features data, and wherein outcome of the predictive models is used to compute one or more future health metrics based on historical data (Gallix [0173], [0220]-[0221], [0239], [0251], noting use of predictive models to predict future outcomes, risk scores, and other metrics based on a patient’s historical data).
Claim 18
Gallix in view of Ravi teaches the method as claimed in claim 13, and the combination further teaches wherein the step of generating insights and recommendations comprises employing one or more GenAI models for generating summaries of the extracted health feature data associated with the health data (Gallix [0164], noting generative AI models like LLMs can be used for sentiment analysis and topic modeling of textual data to summarize the sentiment or prominent themes or trends present in the text), and wherein anomalies and outliers are identified in the features data that indicate user's potential health issues (Gallix [0139], [0187]-[0189], [0206], [0343], [0594], noting generated insights and recommendations may be related to triggering alerts or notifications about detected anomalies of various types that may indicate potential health issues that should be investigated or treated).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Su et al. (Reference U on the accompanying PTO-892) describes the state of the art of using LLMs for health forecasting and anomaly detection.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684
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