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 .
Respond to Applicant Arguments
Applicant arguments remarks filled on 05/26/2026 page 12 regarding rejection of the previous non-final 35 U.S.C 112(b), were fully considers and are persuasive, applicant remove the indefinite languages of Claims 3, 4, 8-16 and 18, therefore previous rejection 112(b) is withdraw.
Applicant arguments remarks filled on 05/26/2026 page 13-17 regarding rejection 35 U.S.C 101 subject matter eligibility, were fully considers and are not persuasive, for the reasons below.
Applicant argues that scale and complexity of the data and volume, multidimensionality, and temporal dependence of such data far exceed human mental capabilities because claim 13 requires resampling and fusing data from thousands of time points-a task impossible to perform mentally.
Examiner respectfully disagreed, because claim 13 does not require thousands of time and was classify as mathematical concept because it centers on data manipulation and mathematical calculations for prediction, rather than a specific technological improvement in the functioning of a computer or medical device.
Applicant argues that mathematical precision of the operations such as cubic spline
interpolation, mm1mum entropy binning, and high/low-frequency resampling exact
mathematical computations, including second-order derivative continuity constraints and
conditional entropy minimization.
Examiner respectfully disagreed, because claim 13 was classify as mathematical concept because it centers on data manipulation and mathematical calculations for prediction, rather than a specific technological improvement in the functioning of a computer or medical device.
Applicant argues that the claims do not simply" apply" mathematical formulas or calculations the mathematical operations in the claims are applied within a specific, practical technical process and produce concrete technical effects. Accordingly, the claims do not recite mathematical concepts.
Examiner respectfully disagreed, because claim 6, 9, 13 recite mathematical concept in the frame of step 2A Prong One, because claim 6 just summon “cubic spline”, Claim 9 just named different models ARIMA, informer… and claim 13 generate manipulation of data using math low or high frequency, greater or lower is just software logic. The intent of the use of mathematical abstract idea to ensure second-order… balance…efficiency…accuracy…improve prediction does not overcome prong one, since prong one ask whether the claim recite judicial exception, as in claim 6, 9, and 13.
Applicant argues that claims do not recite methods of organizing human activity, questionnaire
pushing and survey data collection in the present application are not merely social interactions or
information gathering.
Examiner respectfully disagreed, because claims 1, 7 and 17 recite methods of organizing human activity, because prong one just ask whether the claim recite a judicial exception, as proper explain prong one determination below, for example acquire multimodal data that include image and survey data and pushing a questionnaire, receiving survey data, according to preset rules recite managing personal behavior or relationships or interactions between people, because including social activities, teaching and following rules or instructions, because recites questions/instructions, selects answers, and submits symptom information maps with following rules or instructions and an interaction between a person and the system.
Applicant argues that "binning [Wingdings font/0xE0]grouping [Wingdings font/0xE0] cubic spline [Wingdings font/0xE0] interpolation" workflow solves the technical problem of using irregular, intermittent monitoring data for accurate assessment.
Examiner respectfully disagreed because "binning [Wingdings font/0xE0]grouping [Wingdings font/0xE0] cubic spline [Wingdings font/0xE0] interpolation" workflow is an improvement to the abstract idea identified in prong one below. There are not new technology of binning, grouping and just apply the cubic spline and interpolation to the abstract idea.
Applicant argues that resampling and addition of high/low-frequency data improves prediction accuracy. These features constitute a specific technical improvement, not an abstract concept.
Examiner respectfully disagreed because improve the accuracy of the abstract idea ((acquisition ----- binning -----grouping -----interpolation -----resampling -----multi-model integration)) not overcome prong two. The claims gathering health data, organizing (binning…grouping) and supplementing it by mathematical operations (interpolation, resampling, multi model integration without how are integrated), and judging the probability of a disease, which is an abstract idea, and the additional elements apply that idea with generic acquisition and models used as tool. Refer also spec 0121-0123
Applicant argues that claims are specifically limited to the technological environment of healthcare/disease prediction.
Examiner respectfully disagreed because claims does not recite an specify technology improve, is just recited an abstract idea ((acquisition ----- binning -----grouping -----interpolation -----resampling -----multi-model integration)) in generic models, used to apply the abstract idea.
Applicant argues that technical problem solved is how to accurately assess health status using irregular,
intermittently collected physiological data.
Examiner respectfully disagreed because an improvement in the abstract idea (accurately assess health status using irregular, intermittently collected physiological data) does not add eligibility under 35 U.S.C 101.
Applicant argues the additional elements, alone and combined, provide an inventive concept significantly exceeding the abstract idea.
The elements relied on are the judicial exception itself, not additional elements, so they cannot supply the inventive concept Step 2B requires under MPEP 2106.05. The genuine additional elements remain the generic processor, memory, and storage, which the specification admits run on a general-purpose processor, such as a central processing unit (CPU) (Spec., para. 0184). Generic hardware performing ordinary functions is not significantly more.
Applicant argues cubic spline interpolation and minimum entropy binning are themselves technical in nature.
Examiner respectfully disagreed because both are mathematical concepts under MPEP 2106.04(a)(2)(I), shown by the specification's own entropy formula and iterative calculation (Spec., paras. 0088 to 0091) and its cubic functions with continuous second-order derivatives (Spec., para. 0102). A mathematical technique is part of the exception and cannot integrate or transform itself. The benefit the specification ties to them is improving the accuracy of the completed data (Spec., para. 0081), an improvement to the abstract result, not to technology under MPEP 2106.05(a).
Applicant argues binning plus grouping plus cubic spline interpolation synergistically keeps imputed data within the same impact bin and smooth across cycles.
Examiner respectfully disagreed because the ordered combination is evaluated under MPEP 2106.05, and the asserted synergy is more accurate and smoother data, which is a better output of the abstract idea, not a technical effect on any device. Each step performs its ordinary mathematical function in the inherent gather, sort, then interpolate sequence. The specification confirms the purpose is to improve the accuracy of the obtained data and improve the diagnostic effect (Spec., para. 0086), which does not integrate the exception.
Applicant argues resampling and fusion of high and low frequency data build multi-scale features that improve prediction accuracy.
Examiner respectfully disagreed because resampling and difference processing are mathematical operations, and improved prediction accuracy is by definition an improvement to the abstract prediction, not to technology, so it fails MPEP 2106.05(a). The specification frames the step as obtaining the prediction result of the periodic trend (Spec., para. 0128), an abstract output. A more accurate analysis is not an inventive concept under Step 2B.
Applicant argues integrating ARIMA, Informer, N-BeatXs, and LightGBM leverages each model's advantages for more accurate integrated predictions.
Examiner respectfully disagreed because the specification admits these are known tools, stating the ARIMA model is a time series model in traditional machine learning and that the Informer is built on the basis of the Transformer model (Spec., para. 0122). Selecting and combining known models to obtain more accurate health assessment results (Spec., para. 0123) applies the exception with computational tools under MPEP 2106.05(f). Using known models for their known strengths is not significantly more.
Applicant argues the claims provide a specific solution to a specific technical problem, namely assessing health status from irregular, intermittently collected physiological data.
Examiner respectfully disagreed because the problem that the specification recites is that continuous monitoring ... is difficult to achieve and the resulting data cannot be used directly (Spec., paras. 0068 to 0069), which is a data-completeness and analysis problem, not a malfunction in any machine. The claimed answer improves the accuracy of the data and the diagnosis, which is an improvement to the abstract idea, not a technical solution under MPEP 2106.05(a). Solving an information problem with mathematics on generic hardware is mere instructions to apply under MPEP 2106.05(f).
Applicant argues that, compared to the prior art, the application achieves significant technical advancements.
Examiner respectfully disagreed because eligibility under Section 101 is separate from novelty and obviousness, so an advance over the prior art does not confer eligibility per MPEP 2106.05, and a claim to a judicial exception is not made eligible merely because it is new or improved. Any advantage over prior art is weighed under 35 U.S.C. 102 and 103, not Section 101. The Step 2B inquiry asks for an inventive concept beyond the exception, which the admitted generic hardware and known models do not supply.
Applicant arguments remarks filled on 05/26/2026 page 17-21 regarding rejection 35 U.S.C 102 subject matter eligibility, were fully considers and are persuasive for the follows reasons:
The prior art of record fails to expressly teach or suggest, either alone or in combination, each and every feature of the independent claims. In particular, the prior art fails to teach Claim 1 binning the monitoring data according to a degree of impact of the monitoring data on a target disease, the monitoring data within each bin is provided with a same degree of impact on the target disease. Upon completion of an updated prior art search, Examiner submits that the closest related art includes:
- Reference Narziev, disclosing Distribution of feature importance for depression classification model, Fig. 7, and features had a sleep level with 34% contribution to the model performance, Sec. 5, which reads on sorting by degree of impact on a target disease because Narziev measures how much each item of sensed data influences one disease and ranks the data by that influence, extracting five factors influencing depression, Abstract, but failing to explicitly disclose a post-classification analysis of the impact of each input variable type on the predicted output;
Though many aspects of the independent claims are disclosed in the prior art, it would not have been obvious to one of ordinary skill in the art to combine the disparate features into the invention of the instant claims. In particular, it would not have been obvious to binning the monitoring data according to a degree of impact of the monitoring data on a target disease, the monitoring data within each bin is provided with a same degree of impact on the target disease. Accordingly, the prior art, either alone or in combination, does not disclose or render obvious all the features of the independent claims and they are found to recite subject matter free from prior art, as are the claims depending therefrom.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claim 17, terms, “vital sign data acquisition module,” “multimodal data acquisition module,” and “physical state data generating module” invoke 35 U.S.C. 112(f) interpretation.
Claim 17.
…
a vital sign data acquisition module, configured to …
a multimodal data acquisition module, configured to …
a physical state data generating module, configured to ….
A binning, completion, grouping, confirmation and acquisition submodules used….
Claim 18, terms, “physical state data acquisition module” and “health assessment module” invoke 35 U.S.C. 112(f) interpretation.
Claim 18.
A health assessment device, comprising:
a physical state data acquisition module, configured to … the data processing method according to claims 1;
a health assessment module, configured to …
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
(Claim 17) “vital sign data acquisition module”
Under §112(f), this module is interpreted be a component of a processing device as shown in Fig. 7 & [0141]-[0149] & [0183]-[0184] that receives sensor data from separate sensor components and analyzes the data.
(Claim 17) “multimodal data acquisition module”
Under §112(f), this module is interpreted as be a component of a processing device as shown in Fig. 7 & [0143] & [0183]-[0184] that receives imaging or survey data from separate imaging and/or survey input components and analyzes the data.
(Claim 17) “physical state data generating module”
Under §112(f), this module is interpreted be a component of a processing device as shown in Fig. 7 & [0144] & [0183]-[0184] that performs some kind of analysis. please make it clearer how you are interpreting this module
(Claim 17) A binning, completion, grouping, confirmation and acquisition submodules
The elements above are interprets as software algorithms that comprise the vital sign data acquisition module according to fig.7, par. 0051, 0145-0149, 0085-0092-0093, 0099, 0102, 0150
(Claim 18 )“health assessment module”
The specification provides explicit software submodules/algorithms and a hardware execution context: it states the “health assessment module comprises: a resampling submodule… a difference processing submodule… the input submodule” ([0171]–[0173]). It also discloses concrete model structures used by that module, e.g., “ARIMA… Informer… N-BeatXs” and “the fifth model is a LightGBM model” ([0031]–[0043]). Finally, it anchors execution to hardware by describing an electronic device with “a memory, a processor, and a program… executable on the processor” ([0051]), so the corresponding structure is software algorithms running on processor-based hardware.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Subject Matter eligibility Rejection 35 U.S.C 101
Claims 1, 4-20 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis (35 U.S.C. §101)
The claims do fall within at least one of the four statutory categories (process, machine, manufacture).
Process (Claims 1, 4-16)
Claims 1, 4–16 recite methods (a series of acts/steps), e.g., Claim 1 recites “acquiring vital sign data… collecting multimodal data… generating physical state data,” and the dependent method claims further recite steps such as “binning,” “grouping,” “generating supplementary data,” “standardizing,” and “inputting… into a health assessment model.” These are classic “process” limitations under Step 1.
Machine (Claims 17–19)
Claims 17–19 recite devices with components, e.g., Claim 17 “A data processing device, comprising: a vital sign data acquisition module… a multimodal data acquisition module… a physical state data generating module,” Claim 18 “A health assessment device, comprising…,” and Claim 19 “An electronic device, comprising: a memory, a processor, and a program….” This language describes “a concrete thing consisting of parts,” i.e., a machine for Step 1.
Manufacture (Claim 20)
Claim 20 recites a “readable storage medium, storing a program,” which is interpreted as directed to a tangible article (a manufacture) in the purpose of compact prosecution and future applicant amendment.
The analysis proceeds to Step 2A Prong One for compact prosecution.
2A, Prong One:
Step 2A Prong One asks whether the claim recites a judicial exception (law of nature, natural phenomenon, or abstract idea).
Recitation of Independent representation Claims; bold are additional elements and non-bold abstract idea
Claim 17.
A data processing device, comprising:
a vital sign data acquisition module, configured to acquire vital sign data of a target object, wherein the vital sign data includes monitoring data obtained by monitoring the vital signs of the target object and supplemented data supplemented by the vital sign data according to the monitoring data;
a multimodal data acquisition module, configured to acquire multimodal data of the target object, wherein the multimodal data includes at least one of image data of the target object and survey data for preset symptoms;
and a physical state data generating module, configured to generate the physical state data of the target object according to the vital sign data and the multimodal data.
the vital sign data acquisition module comprising:
an acquisition submodule, used to acquire monitoring data obtained by monitoring the vital signs of the target object;
a binning submodule, used for binning the monitoring data;
a grouping submodule, used for grouping the monitoring data after binning according to a preset monitoring period;
a completion submodule, used to generate supplementary data to supplement a missing monitoring data in each monitoring cycle; and
a vital sign data confirmation submodule, used to use the monitoring data and the supplemented data as the vital sign data of the target object;
wherein the binning submodule is used to bin the monitoring data according to a degree of impact of the monitoring data on a target disease, the monitoring data within each bin is provided with a same degree of impact on the target disease.
Claim Abstract Classification Rational (Independent Claims 1 and 17)
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claims 1 and 17 recite the abstract idea of collecting disparate physiological and subjective data points to synthesize a comprehensive diagnostic profile of an individual’s health status. This process aligns with the following abstract idea categories:
Mental Process (MPEP § 2106.04(a)(2)(III)): Mental processes are concepts that can be performed in the human mind, including observations, evaluations, and judgments used to reach a conclusion. The independent claims 1 and 17 recite "generating a physical state data of the target object according to the vital sign data and the multimodal data." This step describes an analytical evaluation where a processor (or a clinician) reviews various data inputs to form a judgment regarding a patient's condition. This fits the definition in MPEP 2106 because the "generating" of a state from data is a cognitive exercise in data synthesis that does not require a specialized computer to perform. The specification supports this, stating: "It is possible to obtain more accurate and comprehensive physical data of the target object through limited monitoring means... thereby improving the data collection effect" (Spec., para. [0074]). This paragraph confirms that the "generation" of data is an analytical improvement on data collection efficiency rather than a technical improvement to computer hardware. Additional binning steps just classify monitoring data which could be performed mentally or with pen and paper.
Applicant adding limitations 4-10 in claim 17 not overcome prong one, because when is interpreted under BRI merely describe included more abstract data to the vital sign data acquisition module.
For example, limitations 6-10 is mental process because a recite the observation, evaluation, judgment, opinion and cognitive skill with a paper and a pen of a doctor about binning and grouping data to evaluate according experiences or previous events supplementary data about a patient data, wherein the binning is according to the impact of the disease of the patient.
Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II)): This category includes managed workflows and interactions between people, such as social or professional relationships. The independent claims 1 and 17 recite "collecting multimodal data... [including] survey data for preset symptoms." This describes a managed workflow of interaction, which falls under the sub-category of Managing Personal Behavior or Relationships / Interactions. This fits the category because the collection of survey data through questionnaires is a traditional method of patient-provider interaction used to gather subjective evidence. The specification supports this, stating: "The survey data can be set up with corresponding questionnaires for different target diseases... [the] target subject can log in... and fill in the corresponding questionnaire" (Spec., para. [0109-0110]). These paragraphs are relevant as they describe a social/professional interaction (question and answer) that organizes how a human provides information to a system, which is a fundamental method of organizing human activity. Claim 17 limitation 5 recite the interaction of a patient an a doctor, where the doctor monitoring vital sign of a patient.
Manual Replication Scenario (Human Equivalence)
The abstract nature of the claims is reinforced because the entire process is analogous to fundamental human activities:
Under MPEP 2106.04(a), a claim is directed to a mental process if it describes a concept that can be performed in the human mind, or by a human using a pen and paper. Although the Applicant’s claims utilize a computer to process data with greater speed and efficiency than a human, the USPTO and the courts have consistently held that the mere automation of an abstract idea does not transform it into a patent-eligible invention. As stated in MPEP 2106.05(a), "the use of a physical computer to perform an abstract idea with more speed and accuracy" does not constitute a technical improvement to the computer itself, but rather an improvement to the abstract process. Therefore, the "generating" of physical state data remains an abstract mental evaluation regardless of the computational power used to execute it.
Step-by-Step Human Analogy for the Independent Claimed Process:
To demonstrate that the independent claims 1 and 17 recite a process that can be performed through manual human activity, consider the following clinical analogy mirroring the limitations of the claims:
Acquiring vital sign data (Claim 1/17 limitation): A nurse monitors a patient's vital signs (e.g., blood oxygen or heart rate) at specific intervals throughout the day and records them in a medical chart. To account for "supplemented data," the nurse notices a missing entry from a 2:00 PM check and uses her medical knowledge to interpolate a likely value based on the 1:00 PM and 3:00 PM readings to ensure the chart is "complete" for the doctor's review.
Collecting multimodal data (Claim 1/17 limitation): A receptionist provides the patient with a COPD screening questionnaire (survey data) and requests the patient's previous CT scan results (image data). The patient fills out the questionnaire—selecting options for symptoms like shortness of breath or smoking history and hands both the survey and the images to the medical staff.
Generating physical state data (Claim 1/17 limitation): A doctor reviews the completed vital sign chart (the monitoring and supplemented data) alongside the patient’s survey answers and CT scan (multimodal data). By mentally synthesizing these disparate data points, the doctor produces a "physical state data" summary—essentially a clinical evaluation of the patient's current health status.
This manual process entirely mirrors the "parent functions" of claims 1 and 17. The specification reinforces this human equivalence by noting that the data collection is intended to help professionals "analyze and predict the user's disease status" (Spec., para. [0003]). The added binning steps is like a clinician sorting blood-pressure reading into “low concern”, “moderate concern,” and “high concern” piles based on how much each reading affects the suspected disease does not remove judicial exception. This confirms that the claimed invention is merely a computerized version of a standard medical evaluation protocol.
Dependent claims
Claims 4-5: These claims recite under BRI mathematical and mental process using entropy calculation and filling a missing value from another cycle both judicial exceptions.
Claim 6: This claim recites under BRI "generating the complementary data... by cubic spline interpolation," which is a Mathematical Concept consisting of mathematical formulas and calculations used to derive new data points.
Claim 7: This claim recites under BRI "pushing a questionnaire" and "receiving survey data" to be used as "external variables" [Claim 7], which is a Certain Method of Organizing Human Activity (Managing Personal Behavior or Relationships / Interactions).
Claim 8Claim 8 is directed to the abstract idea of performing a mathematical evaluation to determine a “probability of suffering from a target disease.” The claim recites inputting data into a health assessment model and outputting a numerical probability, which constitutes a mathematical calculation applied to data. Under MPEP § 2106.04(a)(1), this is a mathematical concept because it merely applies formulas or statistical techniques to generate a quantitative result.
Claim 9Claim 9 recites a health assessment model that applies a sequence of statistical algorithms, including an ARIMA model and an Informer model, to health-related data. These limitations define specific mathematical relationships and time-series forecasting calculations used to transform input data. As such, the claim is directed to a mathematical concept performed by automated algorithms rather than to a technological improvement.
Claim 10Claim 10 further specifies that the first (ARIMA) and second (Informer) models process vital sign data, while the third (N-BEATSx) model processes both vital sign data and multimodal data. This refinement merely assigns different data sets to different mathematical models within the same analytical workflow. The focus remains on organizing and applying mathematical calculations to data, which is an abstract idea under MPEP § 2106.04(a)(1).
Claim 11Claim 11 recites inclusion of a third N-BEATSx model within the health assessment model to further analyze the data. N-BEATSx is itself a mathematical forecasting framework that relies on numerical optimization and statistical modeling. The claim therefore remains directed to a mathematical concept implemented through algorithmic processing.
Claim 12Claim 12 recites a fourth Informer model applied within the same sequence of statistical models. This limitation adds another layer of mathematical analysis but does not alter the nature of the invention beyond additional mathematical calculations. Accordingly, the claim is directed to an abstract idea because it continues to rely solely on mathematical concepts to generate predictive outputs.
Claim 13 recites the abstract idea of mathematical processing of physiological time-series data to derive a predicted trend, by “resampling… into high-frequency data and low-frequency data,” “performing difference processing,” and “inputting… into the fourth model to obtain the prediction result of the periodic trend.” These limitations are mathematical concepts because resampling and differencing are statistical/mathematical transformations applied to data, and the model-based “prediction result” reflects computation of a numerical/trend output from those transformations. Accordingly, under Prong One, Claim 13 is directed to a judicial exception since it centers on data manipulation and mathematical calculations for prediction, rather than a specific technological improvement in the functioning of a computer or medical device.
Claims 14–15 recite the abstract idea of mathematically combining model outputs and training a statistical classifier to generate a disease-probability prediction, by “splicing output results… in time and input into the fifth model for integrated training” and specifying the fifth model is a “LightGBM model.” These limitations are mathematical concepts because they describe data aggregation (splicing) and algorithmic model training/inference that applies statistical/machine-learning calculations to transform inputs into a predictive output. Accordingly, under Prong One, Claims 14–15 are directed to a judicial exception since the focus is on mathematical/statistical computation over data, not on an improvement to computer functionality or another specific technological process.
Claims 18-20: These claims recite under BRI the performance of the "data processing method" steps within a device or storage medium environment. These claims recite Mental Processes and Mathematical Concepts because they incorporate by reference the automated execution of the data collection, interpolation, and algorithmic assessment steps identified in the method claims.
Having identified that the claims as a whole recite judicial exceptions under Step 2A, Prong One, the analysis must now determine whether the additional elements integrate these abstract ideas into a practical application.
Step 2A, Prong Two
Prong Two asks whether the claims, as a whole, add elements that apply the recited abstract idea in a manner that meaningfully limits it to a practical application, rather than merely using the abstract idea in a particular setting.
Evaluation of Independent Claims 1 and 17 Additional Elements
Acquisition and Generating Modules: The recitation of a "vital sign data acquisition module," "multimodal data acquisition module," and "physical state data generating module" fails to integrate the abstract idea into a practical application.
These elements describe generic functional modules used solely to facilitate the mental and mathematical exceptions identified in Prong One. According to MPEP 2106.05(f), integration is not present when the additional elements "do no more than describe the judicial exception... and describe that the judicial exception is applied in a generic computer environment." The specification confirms these modules are merely "division[s] of logical functions" that can be "implemented in the form of software" or "hardware associated with program instructions" (Spec., para. [0180], [0183]). This indicates that the modules serve as a mere conduit for the "analytical and prediction" capabilities of the process, rather than a technical improvement to signal processing or medical sensing hardware.
When viewed as a whole, the combination of these modules reflects an automated version of a clinician’s data organization workflow. The specification acknowledges the goal is to "reduce manual participation and save human resources" by providing results "conveniently and quickly" (Spec., para. [0120]). Under MPEP 2106.05(a), an improvement in the "speed or accuracy" of an abstract process through a generic computer does not integrate the exception into a practical application, as the improvement is to the efficiency of the abstract idea itself rather than to the underlying technology.
Dependent claims analysis
Claims 8–15
The recitation of a "pre-trained model" including "ARIMA," "Informer," "N-BeatXs," and "LightGBM" fails to integrate the abstract idea into a practical application.
These elements fail to improve computer functionality (MPEP 2106.05(a)). While the models provide a refined "health assessment result" [Claim 8], an improvement in the "accuracy of a mathematically calculated statistical prediction" is an improvement to the abstract idea itself, not a technology. The claims do not recite a technical mechanism that reduces computational resource usage or increases processor speed; they merely "use the computer as a tool" to perform complex calculations (MPEP 2106.05(f)). Consequently, these models do not move the claim beyond Step 2A.
Claims 18–20
The recitation of a "memory," "processor," "program," and "readable storage medium" fails to integrate the abstract idea into a practical application.
These are additional elements but reflect "mere instructions to 'apply it' on a computer" (MPEP 2106.05(f)). Under BRI, these components are "invoked to perform the abstract idea" without a specific functional relationship that improves the computer itself. These elements "simply link the expression of the judicial exception to a particular technological environment" [Claims 19, 20], which constitutes "insignificant extra-solution activity" (MPEP 2106.05(h)). There is no specific technical mechanism described that alters how the processor or memory operates or provides a solution to a technical problem in computer technology.
Claims 4–7, 16
The remaining claims do not pass Prong Two as they recite no additional elements beyond the judicial exceptions identified in Prong One.
When viewed as a whole, the combination of these elements does not pass Prong Two. The claims link generic hardware with mathematical refinements to notify a user of a disease probability, which is an improvement to a medical conclusion rather than a technological process. Because Step 2A is not satisfied, the analysis must proceed to Step 2B.
Step 2B: Inventive Concept Analysis
Step 2B asks whether the additional elements (identified in Prong Two) “amount to significantly more than the judicial exception” when evaluated individually and in combination, after construing the claim under BRI.
Step 2B determines whether the additional elements, alone or in combination, amount to an "inventive concept" that is "significantly more" than the judicial exception. The claims fail this requirement because the recited components are invoked solely to automate a medical evaluation process without providing a specific technical solution to a computer-centric problem.
Evaluation of Independent Claims 1 and 17 Additional Elements
Functional “Modules” / Generic Implementation: The recitation of a “vital sign data acquisition module,” “multimodal data acquisition module,” and “physical state data generating module” fails to provide an inventive concept.
These elements represent "mere instructions to 'apply' the exception" on a computer (MPEP 2106.05(f)). The specification explicitly admits these are not specialized hardware but rather “a division of logical functions” that can be “fully or partially integrated into one physical entity” or “implemented in the form of software” (Spec., para. [0183]). Consequently, these modules do not reflect a technological mechanism; they are functional placeholders for the "analytical and prediction" capabilities of the clinician’s mental process, which is insufficient to supply "significantly more" (MPEP 2106.05(g)).
When viewed as a whole (Claims 1 and 17): The combination of these modules reflects a generic automation of collecting health information to reach a diagnostic conclusion. The specification confirms the goal is to “obtain more accurate and comprehensive physical data... thereby improving the data collection effect” (Spec., para. [0074]). Under MPEP 2106.05(a), an improvement to the efficiency of the abstract idea itself (the medical evaluation) does not constitute an inventive concept for the underlying computer technology.
Dependent Claims Analysis
(Claims 8–15): “Pre-trained model” / ARIMA / Informer / N-BeatXs / LightGBM
These claims add specific algorithmic labels and training steps which fail to provide an inventive concept.
These limitations represent "insignificant pre-solution activity" (MPEP 2106.05(g)). The specification describes using these models—such as the “ARIMA model,” “Informer model,” and “LightGBM model”—to “accurately and quickly generate preliminary prediction results” (Spec., para. [0121], [0135]). Per Stanford II, an improvement in the "accuracy of a mathematically calculated statistical prediction" is an improvement to the abstract idea itself, not a technological improvement. Because the claims do not recite a technical mechanism that alters the computer's internal functioning (e.g., reducing memory footprint or increasing processing speed), they remain mere instructions to apply math on a computer.
“Memory,” “Processor,” “Program,” “Readable Storage Medium” (Claims 18-20)
These claims add generic computer hardware that fails to provide an inventive concept.
These elements reflect a "mere field-of-use limitation" (MPEP 2106.05(h)). The specification confirms the hardware comprises a “processor... memory, and a program” where the processor is “configured to read the program in the memory” (Spec., para. [0051]). These components perform their "well-understood" functions to execute the abstract method identified in Prong One. There is no functional relationship described that improves the hardware itself; rather, the hardware is “associated with program instructions” to perform the process (Spec., para. [0180]).
Considering the additional elements together (functional modules + trained models + generic hardware), the claims amount to a computerized version of a standard clinical data-organization workflow. The combination lacks an inventive concept because the elements perform their routine functions to facilitate the automated execution of mental processes and mathematical calculations.
The claims are directed to judicial exceptions and, considering the additional elements individually and in combination, lack an inventive concept under Step 2B. Therefore, Claims 1, 4-20 are rejected under 35 U.S.C. § 101.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOSHUA DAMIAN RUIZ/ Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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