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 .
DETAILED ACTION
In the preliminary amendment filed on 21 October 2025, the following has occurred: claim 5 has been amended.
Now claims 1-6 are pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-073320, filed on 27 April 2023.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 21 October 2025, has been considered by the Examiner.
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. Such claim limitation(s) is/are:
an acquirer in claim 1
an estimator in claim 1
The acquirer is being read from Figure 4, paragraph [0039] as a generic off-the-shelf processor implementing software to receive data (i.e., a user interface), and the estimator is being read from Figure 4, paragraphs [0040]-[0042] as a generic off-the-shelf machine learning model.
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.
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.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an apparatus (i.e., a system), for performing the limitations of:
[… obtain …] at least one of: a lifelog that records a history of one or more types of specific activities that affect variations in immune function of a user; and a meteorological log that records a history of one or more types of meteorological elements at locations at which the user was present, wherein the one or more types of meteorological elements affect variations in the immune function of the user; and […] estimate an immune function index representing an index of the immune function during a predetermined period from an estimation target time point, based on at least one of: the history of the one or more types of specific activities recorded in the lifelog; and the history of the one or more types of meteorological elements recorded in the meteorological log, wherein: the estimator estimates the immune function index, based on at least one of: a history of a type of specific activity, from among the one or more types of specific activities, at a first time point that precedes the estimation target time point by a first appearance period corresponding to the type of specific activity; and a history of a type of meteorological element, from among the one or more types of meteorological elements, at a second time point that precedes the estimation target time point by a second appearance period corresponding to the type of meteorological element, the first appearance period is defined as a period from a time point at which the user has performed the type of specific activity to a time point at which variations in the immune function index, the variations being caused by the type of specific activity, become apparent, and the second appearance period is defined as a period from a time point at which the user has been present at a location with the type of meteorological element to a time point at which variations in the immune function index, the variations being caused by the type of meteorological element, become apparent.
, as drafted is a system that under its broadest reasonable interpretation, covers performance of
the limitation in the mind but for the recitation of generic computer components. That is, other
than reciting an acquirer and an estimator, the claimed invention amounts to a person performing a mental process. For example, but for an acquirer and an estimator the claims encompass a human user acquiring log data for specific type of activities and meteorological data over a predetermined period of time and using the collected data to make a determination for a patient about the patient’s immune function. If a claim limitation, under its broadest
reasonable interpretation, covers performance of the limitation in the mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the
claim recites the additional elements of an acquirer, which implements the abstract idea. The acquirer is recited at a high-level of generality (i.e., a general-purpose computers/ computer component implementing generic computer functions; see Applicant’s specification Figure 5, Fig. 4, paragraphs [0039]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed toward an abstract idea.
The claim recites the additional elements of “acquiring…” and an estimator. The “acquiring…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The estimator is recited at a high-level of generality. (i.e., generic off-the shelf machine learning model) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does 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 an acquirer to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “acquiring…” and an estimator were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “acquiring…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The estimator steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Shelton (20220237999): see below but at least paragraph [0359]; Mikhno (20230298764): see below but at least paragraph [0067]; Walker (20210327588): paragraph [0094]; training and use of a generic off-the-shelf machine learning model is well-understood, routine and conventional. Well- understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-6 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 2 describes maximation of a period (i.e., mathematical operations), however high-level mathematical concepts are not an additional elements, as such the claim does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 3-4 and 6 describe use of additional labeled data, however the labels that describe that data that is used is not an additional element, as such the claim does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 5 describes the use of labeled data in use of a machine learning model, however a machine learning model was already considered above and incorporated herein, as the estimator is described as a machine learning model in paragraphs [0050]-[0052], as such the claim does not recite any additional elements not already considered above are therefore cannot provide a practical application and/or significantly more
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20220237999 (hereafter “Shelton”), in view of U.S. Patent Pub. No. 20230298764 (hereafter “Mikhno”).
Regarding (Original) claim 1, Shelton teaches an estimation apparatus (Shelton: Figs. 1, 4, paragraph [0004], “methods, systems, and apparatus for predictive based system adjustments based on biomarker trending”) comprising:
an acquirer configured to acquire (Shelton: Figs. 1, 4, paragraph [0006], “The computing system may comprise a processor that may be configured to perform the method. A biomarker for a patient may be received from a sensing system”, paragraphs [0265]-[0267], “at least one wireless interface to provided communication connection… The computer system 20063 may comprise a processor 20102 and a network interface 20100. The processor 20102 may be coupled to a communication module 20103, storage 20104, memory 20105, non-volatile memory 20106, and input/output (I/O) interface 20107 via a system bus”. The Examiner notes a processor configured to receive data reads on a “data acquisition controller”, under the broadest reasonable interpretation and is in agreement with Applicant’s specification paragraph [0039]) at least one of:
a lifelog that records a history of one or more types of specific activities that affect variations in immune function of a user; and a meteorological log that records a history of one or more types of meteorological elements at locations at which the user was present, wherein the one or more types of meteorological elements affect variations in the immune function of the user (Shelton: paragraph [0040], “The biomarkers 20005 measured by the one or more sensing systems 20001 may include, but are not limited to, sleep, core body temperature, maximal oxygen consumption, physical activity, alcohol consumption… coughing and sneezing… and/or menstrual cycle”, paragraphs [0049]-[0052], “detect physical activities… during physical activities, including running and/or walking”, paragraphs [0055]-[0057], “A physical activity sensing system may measure physical activity data, including heart rate, motion, location, posture, range-of-motion, movement speed, and/or cadence. The physical activity sensing system may measure physical activity data including accelerometer, magnetometer, gyroscope, global positioning system (GPS), PPG, and/or ECG… Based on the measured physical activity data, the physical activity sensing system may detect physical activity-related biomarkers, including but not limited to exercise activity, physical activity intensity, physical activity frequency, and/or physical activity duration. The physical activity sensing system may generate physical activity summaries based on physical activity information… The computing system may store the physical activity information in user profiles”, paragraph [0083], “consider various factors, including exercise, fluid intake, and/or temperature”, paragraph [0129], “An environmental sensing system may measure environmental data including environmental temperature, humidity, mycotoxin spore count, and airborne chemical data. The environmental sensing system may include a digital thermometer, air sampling, and/or chemical sensors”, paragraph [0135], “The environmental sensing system may locally process environmental data or transmit the data to a processing unit”, paragraph [0239], “the patient and the environment surrounding the patient may be monitored by one or more environmental sensing systems 20015 including, for example, a microphone (e.g., for detecting ambient noise associated with or around a patient), a temperature/humidity sensor, a camera for detecting breathing patterns of the patient, etc.”, paragraph [0460], “an event may be logged. The event may be logged based on a biomarker measurement. The event may be logged based on a biomarker measurement spike… The event may be logged based on an environment… The logged event may indicate at least one of a biomarker measurement and an environment at a point in time. The logged event may include a timestamp based on the time of event logging”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) a lifelog and (b) a meteorological log, the tracking of activity and environmental data into a log as taught by the system of Shelton teaches the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the log that tracks physical activity (i.e., lifestyle data) and environmental (i.e., meteorological data) of Shelton separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04); and
an estimator configured to estimate an immune function index representing an index of the immune function during a predetermined period from an estimation target time point, based on at least one of: the history of the one or more types of specific activities recorded in the lifelog; and the history of the one or more types of meteorological elements recorded in the meteorological log (Shelton: Figs. 1, 4, paragraph [0006], “The computing system may comprise a processor that may be configured to perform the method”, paragraphs [0041]-[0045], “The biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to… immune system… Information from the biomarkers may be determined and/or used… The surgical hub may detect potential risk factors or conditions, including systemic inflammation and/or reduced immune function”, paragraph [0059], “Based on the measured alcohol consumption data, the sensing system may calculate a blood alcohol concentration. The sensing system may detect alcohol consumption conditions and/or risk factors. The sensing system may detect alcohol consumption-related biomarkers including reduced immune capacity”, paragraph [0074], “data over a set time interval”, paragraph [0133], “Based on the environmental information, the sensing system may generate environmental aspects conditions including inflammation, reduced lung function, airway hyper-reactivity, fibrosis, and/or reduce immune functions”, paragraph [0143], “immune function may be determined… Reduced immune function may be predicted”, paragraph [0190], “based on the selected biomarker sensing systems data… contextual information may be determined, including… immunity level, and/or infection risk… score may be generated based on the determined blood vessel health. Immunity capability may be determined… Infection risk may be determined”, paragraphs [0359]-[0365], “Machine learning is a branch of artificial intelligence”, paragraphs [0460]-[0465], “The patient may be monitored for a predefined set of time. The patient may be monitored for a predefined set of time after a biomarker measurement spike… The change in biomarker measurement may be analyzed after a monitoring period”, paragraph [0505], “The biomarker may be monitored for a set time, such as 24 hours. The biomarker may be analyzed after the monitoring period”), wherein:
the estimator estimates the immune function index, based on at least one of: a history of a type of specific activity, from among the one or more types of specific activities, at a first time point that precedes the estimation target time point […] corresponding to the type of specific activity; and a history of a type of meteorological element, from among the one or more types of meteorological elements, at a second time point that precedes the estimation target time point […] corresponding to the type of meteorological element, […] (Shelton: paragraphs [0350]-[0355], “training a machine learning model for predicting surgical complications and/or post-surgical recovery rates, data sources containing pre-surgical data, such as a patient's medical conditions and biomarker measurement data, may be identified”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data. In such case, the deployed model may be further trained, validated, and tested with such additional live production data. In an example, the updated biases and weights of a further-trained MLP model may update the deployed MLP model's biases and weights”, paragraph [0450], “The machine learning may include updating the previous patient outcomes data and risk models with recent analyses. The machine learning's training process may update the risk model for predicting an outcome on new data set. For example, previous patient outcomes data and risk models may be updated with new analyses and determinations”. Also see at least paragraphs [0059], [0133] for specific activities and type of meteorological elements identified above respectively incorporated herein. The Examiner notes a history of previously logged specific activity/meteorological data is used to update a machine learning model for estimation of an immune function index).
Shelton may not explicitly teach (underlined below for clarity):
the estimator estimates the immune function index, based on at least one of: a history of a type of specific activity, from among the one or more types of specific activities, at a first time point that precedes the estimation target time point by a first appearance period corresponding to the type of specific activity; and a history of a type of meteorological element, from among the one or more types of meteorological elements, at a second time point that precedes the estimation target time point by a second appearance period corresponding to the type of meteorological element, the first appearance period is defined as a period from a time point at which the user has performed the type of specific activity to a time point at which variations in the immune function index, the variations being caused by the type of specific activity, become apparent, and the second appearance period is defined as a period from a time point at which the user has been present at a location with the type of meteorological element to a time point at which variations in the immune function index, the variations being caused by the type of meteorological element, become apparent.
Mikhno teaches the estimator estimates the immune function index, based on at least one of: a history of a type of specific activity, from among the one or more types of specific activities, at a first time point that precedes the estimation target time point by a first appearance period corresponding to the type of specific activity; and a history of a type of meteorological element, from among the one or more types of meteorological elements, at a second time point that precedes the estimation target time point by a second appearance period corresponding to the type of meteorological element, the first appearance period is defined as a period from a time point at which the user has performed the type of specific activity to a time point at which variations in the immune function index, the variations being caused by the type of specific activity, become apparent, and the second appearance period is defined as a period from a time point at which the user has been present at a location with the type of meteorological element to a time point at which variations in the immune function index, the variations being caused by the type of meteorological element, become apparent (Mikhno: paragraphs [0245]-[0249], “the duration of the time window corresponds to a sensor wear period that a user wears a sensor to acquire data that is used to calibrate the personalized model of that user. Data of each user may be filtered such that only data that is within a specified time window for each user is selected for training… the time window of data that was selected for each user can be used as training data during a personalized learning process at 2450 to adapt the population model 2420 resulting in a personalized model 2455 for each user. In other words, the personalized learning process adapts (e.g., calibrates) the population model 2420 using the time window of data that was selected for that user (at 2440) to generate or derive a personalized model 2455 that is personalized for each user”. The Examiner notes a calibration period to personalize a model to learn personalized variations, teaches what is claimed of the appearance period in which user specific variations become apparent under the broadest reasonable interpretation).
Regarding (Original) claim 2, Shelton and Mikhno teach the limitations of claim 1, and further teach wherein the history of the type of specific activity includes a history of the type of specific activity during a first selected duration that precedes the first time point, the first selected duration corresponds to a statistical period during which a degree of detectability of variations in the immune function index, with respect to a statistic of the type of specific activity, is maximized, the history of the type of meteorological element includes a history of the type of meteorological element during a second selected duration that precedes the second time point, and the second selected duration corresponds to a statistical period during which the degree of detectability of variations in the immune function index, with respect to a statistic of the type of meteorological element, is maximized (Shelton: paragraphs [0350]-[0357], “training a machine learning model for predicting surgical complications and/or post-surgical recovery rates, data sources containing pre-surgical data, such as a patient's medical conditions and biomarker measurement data, may be identified… the analytics system 20338 may determine optimal and/or preferred operating parameters”, paragraph [0360], “iterative optimization of an objective function (e.g., cost function)”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data. In such case, the deployed model may be further trained, validated, and tested with such additional live production data. In an example, the updated biases and weights of a further-trained MLP model may update the deployed MLP model's biases and weights”, paragraph [0450], “The machine learning may include updating the previous patient outcomes data and risk models with recent analyses. The machine learning's training process may update the risk model for predicting an outcome on new data set. For example, previous patient outcomes data and risk models may be updated with new analyses and determinations”. Also see at least paragraphs [0059], [0133] for specific activities and type of meteorological elements identified above respectively incorporated herein; Mikhno: paragraph [0079], “maximize some notion of cumulative reward”, paragraph [0125], “Parameter optimization generally includes an objective function that is minimized. The objective function measures the mathematical agreement between the estimated output of the model with the actual measured data. Parameters are typically interactively adjusted until the objective function is optimized. The optimization process terminates once the level of agreement reaches a desired threshold or no longer improves”, paragraphs [0245]-[0249], “the duration of the time window corresponds to a sensor wear period that a user wears a sensor to acquire data that is used to calibrate the personalized model of that user”. The Examiner notes the optimization period to personalize the determinations of immune function for a specific user reads on what is required of maximized under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 3, Shelton and Mikhno teach the limitations of claim 2, and further teach wherein the estimator further uses attribute information of the user to estimate the immune function index (Shelton: paragraphs [0041]-[0045], “The biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to… immune system… Information from the biomarkers may be determined and/or used… The surgical hub may detect potential risk factors or conditions, including systemic inflammation and/or reduced immune function”, paragraph [0366], “the patient's integrated data record may include personal identifier information or other information that may identifier a patient such as an age, an employer, a body mass index (BMI), demographic information, and the like”; Mikhno: paragraph [0097], “using different combinations of demographic parameters such as age, gender, location and diabetes history such as diabetes onset, years on insulin, therapy type using existing BG, insulin, meal information, activity data from an activity tracker, and CGM data”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 4, Shelton and Mikhno teach the limitations of claim 1, and further teach wherein the estimator estimates the immune function index according to a type of lifestyle habits of the user (Shelton: paragraphs [0041]-[0045], “The biomarkers 20005 may relate to physiologic systems 20007, which may include, but are not limited to… immune system… Information from the biomarkers may be determined and/or used… The surgical hub may detect potential risk factors or conditions, including systemic inflammation and/or reduced immune function”, paragraph [0058], “An alcohol consumption sensing system may measure alcohol consumption data including alcohol and/or sweat”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 5, Shelton and Mikhno teach the limitations of claim 1, and further teach wherein: the estimator includes an estimation model that learns, using machine learning applied to learning data, a relationship between an immune function index and at least one of: a history of one or more types of specific activities; and a history of one or more types of meteorological elements (Shelton: paragraph [0198], “A computing system, as described herein, may select one or more biomarkers (e.g., data from biomarker sensing systems) from immune system-related biomarkers, including alcohol consumption, pH, respiratory rate, edema, sweat, and/or environment for analysis.”, paragraphs [0359]-[0365], “Machine learning is a branch of artificial intelligence that seeks to build computer systems that may learn from data without human intervention. These techniques may rely on the creation of analytical models that may be trained to recognize patterns within a dataset, such as a data collection. These models may be deployed to apply these patterns to data, such as biomarkers, to improve performance without further guidance… A supervised learning algorithm may create a mathematical model from training a dataset (e.g., training data). The training data may consist of a set of training examples. A training example may include one or more inputs and one or more labeled outputs.”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data”),
the learning data includes: a learning immune function evaluation value representing an evaluation value of the immune function index; and at least one of a learning history of the type of specific activity, and a learning history of the type of meteorological element (Shelton: paragraphs [0350]-[0355], “training a machine learning model for predicting surgical complications and/or post-surgical recovery rates, data sources containing pre-surgical data, such as a patient's medical conditions and biomarker measurement data, may be identified”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data. In such case, the deployed model may be further trained, validated, and tested with such additional live production data. In an example, the updated biases and weights of a further-trained MLP model may update the deployed MLP model's biases and weights”, paragraph [0450], “The machine learning may include updating the previous patient outcomes data and risk models with recent analyses. The machine learning's training process may update the risk model for predicting an outcome on new data set. For example, previous patient outcomes data and risk models may be updated with new analyses and determinations”. Also see at least paragraphs [0059], [0133] for specific activities and type of meteorological elements identified above respectively incorporated herein),
the learning history of the type of specific activity includes a history of the type of specific activity at a third time point that precedes a measurement day of the immune function index by the first appearance period, the history of the type of specific activity includes a history of the type of specific activity during a third selected duration that precedes the third time point, the third selected duration corresponds to a statistical period during which a degree of detectability of variations in the immune function index, with respect to a statistic of the type of specific activity, is maximized, the learning history of the type of meteorological element includes a history of the type of meteorological element at a fourth time point that precedes the measurement day of the immune function index by the second appearance period, the history of the type of meteorological element includes a history of the type of meteorological element in a fourth selected duration that precedes the fourth time point, and the fourth selected duration corresponds to a statistical period during which the degree of detectability of variations in the immune function index, with respect to the statistic of the type of meteorological element, is maximized (Shelton: paragraphs [0350]-[0357], “training a machine learning model for predicting surgical complications and/or post-surgical recovery rates, data sources containing pre-surgical data, such as a patient's medical conditions and biomarker measurement data, may be identified… the analytics system 20338 may determine optimal and/or preferred operating parameters”, paragraph [0360], “iterative optimization of an objective function (e.g., cost function)”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data. In such case, the deployed model may be further trained, validated, and tested with such additional live production data. In an example, the updated biases and weights of a further-trained MLP model may update the deployed MLP model's biases and weights”, paragraph [0450], “The machine learning may include updating the previous patient outcomes data and risk models with recent analyses. The machine learning's training process may update the risk model for predicting an outcome on new data set. For example, previous patient outcomes data and risk models may be updated with new analyses and determinations”. Also see at least paragraphs [0059], [0133] for specific activities and type of meteorological elements identified above respectively incorporated herein; Mikhno: paragraph [0079], “maximize some notion of cumulative reward”, paragraph [0125], “Parameter optimization generally includes an objective function that is minimized. The objective function measures the mathematical agreement between the estimated output of the model with the actual measured data. Parameters are typically interactively adjusted until the objective function is optimized. The optimization process terminates once the level of agreement reaches a desired threshold or no longer improves”, paragraphs [0245]-[0249], “the duration of the time window corresponds to a sensor wear period that a user wears a sensor to acquire data that is used to calibrate the personalized model of that user”. The Examiner notes the optimization period to personalize the determinations of immune function for a specific user reads on what is required of maximized under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 6, Shelton and Mikhno teach the limitations of claim 5, and further teach wherein the estimation model is a machine learning model that learns a relationship, based on lifestyle habits of human subjects, between: the immune function index; and at least one of the history of one or more types of specific activities and the history of one or more types of meteorological elements (Shelton: paragraph [0198], “A computing system, as described herein, may select one or more biomarkers (e.g., data from biomarker sensing systems) from immune system-related biomarkers, including alcohol consumption, pH, respiratory rate, edema, sweat, and/or environment for analysis.”, paragraphs [0359]-[0365], “Machine learning is a branch of artificial intelligence that seeks to build computer systems that may learn from data without human intervention. These techniques may rely on the creation of analytical models that may be trained to recognize patterns within a dataset, such as a data collection. These models may be deployed to apply these patterns to data, such as biomarkers, to improve performance without further guidance”, paragraph [0371], “a deployed model may be updated as more live production data become available as training data. In such case, the deployed model may be further trained, validated, and tested with such additional live production data. In an example, the updated biases and weights of a further-trained MLP model may update the deployed MLP model's biases and weights”).
The motivation to combine is the same as in claim 1, incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20190271713 (hereafter “Heinemann”) teaches a immune state failure prediction system calibrated to a patient.
U.S. Patent Pub. No. 20210327588 (hereafter “Walker”) teaches a immune status determination using historical patient data.
U.S. Patent Pub. No. 20250191730 (hereafter “Nakagawa”) teaches a health state prediction using learned patient behaviors over a period of time.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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