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 .
Information Disclosure Statement
The information disclosure statement (IDS) received on September 24, 2025, has been considered by examiner.
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 claims are not directed to patent eligible subject matter.
Claims 1-20 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., computer readable medium1 and system) and process (i.e., a method).
Although claims 1-20 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-20 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claims 15 and 20) recite (in bold and italics):
receiving values for characteristics of an individual, wherein the characteristics include physical properties or medical history properties of the individual;
selecting a profile for the individual, wherein selecting includes using the values of the characteristics, wherein the profile specifies expected ranges for event data;
configuring a health monitoring application with the profile to generate notifications following an event exceeding an expected range;
collecting an event data set and a notification data set from the individual;
customizing the profile for the individual to form a custom profile, wherein customizing includes using a trained machine learning model to process a data set to output accurate notifications, wherein the data set includes the event data set, the notification data set, and a set of labels indicating the accuracy of notifications in the notification data set; and
configuring the health monitoring application with the custom profile
The identified limitations, under their broadest reasonable interpretation, cover managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, the identified limitations encompass a user receiving values (e.g., medical history) of an individual and selecting and creating a profile for the individual to set health monitoring notifications to alert the individual when said values exceed an expected range. The claim limitations fall within the Certain Methods of Organizing Human Activity groupings of abstract ideas. Thus, the claimed invention recites a judicial exception.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
This judicial exception is not integrated into a practical application. As a whole, the additional elements of “wherein customizing includes using a trained machine learning model to process a data set to output accurate notifications” in the steps are recited as nominal expected use (i.e., trained machine learning model functioning as required without improvements to the technology itself) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does 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 to an abstract idea.
Dependent claims 2-14 and 16-20, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claim, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". As a whole, the additional elements of “wherein customizing includes using a trained machine learning model to process a data set to output accurate notifications” in the steps are recited as nominal expected use (i.e., trained machine learning model functioning as required without improvements to the technology itself) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-20, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2-14 and 16-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-6, 9-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kochura et al. (US 2019/0074089 A1).
Regarding claims 1, 15, and 20, Kochura discloses a computer implemented method comprising:
receiving values for characteristics of an individual, wherein the characteristics include physical properties or medical history properties of the individual (Paragraph [0059]: user baseline program 200 obtains historical data. Historical data refers to monitoring data and medical-related information associated with a user);
selecting a profile for the individual, wherein selecting includes using the values of the characteristics, wherein the profile specifies expected ranges for event data (Paragraphs [0056]: user baseline program 200 receives information from a user based on processing one or more queries associated with various aspects of medical monitoring program 300….medical monitoring program 300 expects a range of sensor information (i.e., values) for a model based on one set of information, such as a food choice, input by the user and Paragraph [0021]: User data 104 includes a plurality of profiles of users that utilize system 102 to generate analytical and predictive models utilized for determining a state of health, or predicting a change associated with the state of health of a user based on monitoring data corresponding to the user);
configuring a health monitoring application with the profile to generate notifications following an event exceeding an expected range (Paragraph [0037]: In various embodiments, based on the results obtained from one or more models, medical monitoring program 300 determines a level of urgency associated with the state of health of the user and communicates responses (e.g., notifications) to the user);
collecting an event data set and a notification data set from the individual (Paragraph [0057]: user baseline program 200 receives information from another individual based on medical monitoring program 300 determining or predicting one state and/or level of urgency associated with a user (referring to FIG. 3, No branch of decision step 309). However, the actual state of the user differs from the state of the user determined and/or predicted by one or more models. In one example, a doctor reviews a notification generated by medical monitoring program 300 and the received sensor data);
customizing the profile for the individual to form a custom profile, wherein customizing includes using a trained machine learning model to process a data set to output accurate notifications, wherein the data set includes the event data set, the notification data set, and a set of labels indicating the accuracy of notifications in the notification data set (Paragraph [0064]: user baseline program 200 modifies one or more models. User baseline program 200 may utilize a combination of functions and/or programs associated with machine learning program 107 and analytics suite 106 to modify, update, or replace one or more models); and
configuring the health monitoring application with the custom profile (Paragraph [0065]: user baseline program 200 stores information associated with the user.).
Regarding claims 2 and 16, Kochura discloses further comprising:
detecting an anomalous event using the health monitoring application configured with the custom profile (Paragraph [0078]); and
sending a notification of the anomalous event (Paragraph [0078]).
Regarding claims 3 and 17, Kochura discloses wherein:
the event data set is a first event data set (Paragraph [0078]); and
detecting the anomalous event includes:
collecting a second event data set (Paragraph [0078]),
analyzing a first portion of the second event data set to determine a first baseline (Paragraph [0078]),
analyzing a second portion of the second event data set to determine a second baseline (Paragraph [0078]),
comparing the first baseline to the second baseline (Paragraph [0034]), and
determining that no baseline shift has occurred based on the comparison (Paragraph [0056]).
Regarding claims 4 and 18, Kochura discloses further comprising: classifying the anomalous event by generating a severity score for the anomalous event (Paragraph [0036]).
Regarding claim 5, Kochura discloses wherein sending the notification of the anomalous event includes at least one of the following: (a) sending a communication to the individual, (b) sending a communication to a service monitoring the individual, or (c) sending a communication to a contact designated by the individual (Paragraph [0037]).
Regarding claim 6, Kochura discloses further comprising:
detecting whether an acknowledgment of the communication is received (Paragraph [0050]),
calling a medical professional upon not detecting the acknowledgment within a time window (Paragraph [0050]).
Regarding claim 9, Kochura discloses further comprising:
detecting an anomalous event using the health monitoring application configured with the custom profile (Paragraph [0078]);
collecting a second event data set (Paragraph [0078]);
analyzing a first portion the second event data set to determine a first baseline (Paragraph [0078]);
analyzing a second portion of the second event data set to determine a second baseline (Paragraph [0078]);
comparing the first baseline to the second baseline (Paragraph [0034]); and
determining that a baseline shift has occurred based on the comparison (Paragraph [0056]).
Regarding claim 10, Kochura discloses further comprising: prompting the individual to confirm the baseline shift (Paragraph [0057]).
Regarding claim 11, Kochura discloses further comprising: sending a notification to the individual that the anomalous event is a result of the baseline shift (Paragraph [0078]).
Regarding claim 12, Kochura discloses wherein the event data includes heartrate data, step data, sleep data, activity data, appliance usage data, dietary data, medication data, location data, accelerometer data, blood pressure data, body temperature data, ambient temperature data, blood sugar data, or other sensor data (Paragraph [0014]).
Regarding claim 13, Kochura discloses wherein:
the profile is a first profile (Paragraph [0031]), and selecting the first profile for the individual includes:
determining similarities between the individual and a plurality of profiles using the values of the characteristics (Paragraph [0031]), and
identifying the first profile as having the highest similarity with the individual among the plurality of profiles (Paragraph [0031]).
Regarding claim 14, Kochura discloses wherein:
the profile is a first profile (Paragraph [0031]), and
selecting the first profile for the individual includes generating the first profile from a plurality of profiles using the values of the characteristics (Paragraph [0031]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7, 8, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kochura et al. (US 2019/0074089 A1) in view of Sundaram et al. (US 2024/0050032 A1).
Regarding claims 7 and 19, Kochura does not explicitly disclose wherein customizing the profile is preceded by:
calculating an accuracy score for notifications generated by the profile from the event data set, and
determining the accuracy score is less than a threshold value.
Sundaram teaches:
calculating an accuracy score for notifications generated by the profile from the event data set (Paragraph [0154]), and
determining the accuracy score is less than a threshold value (Paragraph [0154]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kochura to disclose calculating an accuracy score for notifications generated by the profile from the event data set, and determining the accuracy score is less than a threshold value as taught by Sundaram. Using the devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion of Sundaram would provide a method to assess and improve the muscular skeletal (MSK) health of a patient or user managing symptoms of a chronic condition, recovering from or preparing for a surgical procedure or as a means of early detection of health problems (Sundaram Paragraph [0004]).
Regarding claim 8, Kochura does not explicitly disclose wherein configuring the health monitoring application with the custom profile includes:
calculating an accuracy score for notifications generated by the custom profile using the event data set, and
determining the accuracy score is greater than a threshold value.
Sundaram teaches:
calculating an accuracy score for notifications generated by the custom profile using the event data set (Paragraph [0154]), and
determining the accuracy score is greater than a threshold value (Paragraph [0154]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kochura to disclose calculating an accuracy score for notifications generated by the profile from the event data set, and determining the accuracy score is greater than a threshold value as taught by Sundaram. Using the devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion of Sundaram would provide a method to assess and improve the muscular skeletal (MSK) health of a patient or user managing symptoms of a chronic condition, recovering from or preparing for a surgical procedure or as a means of early detection of health problems (Sundaram Paragraph [0004]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINYERE MPAMUGO whose telephone number is (571)272-8853. The examiner can normally be reached Monday-Friday, 9am-5pm.
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685
1 Claim interpretation affects the evaluation of both criteria for eligibility. For example, in Mentor Graphics
v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017), claim interpretation was crucial to
the court’s determination that claims to a “machine-readable medium” were not to a statutory category. In
Mentor Graphics, the court interpreted the claims in light of the specification, which expressly defined the
medium as encompassing “any data storage device” including random-access memory and carrier
waves. In this case, Paragraph [0097] of Applicant’s specification discloses, “a hardware service or hardware module such as service 708, that performs a function can include a software component stored in a non-transitory computer-readable medium that, in connection with the necessary hardware components…” The specification discloses non-transitory storage devices only. Thus, claim 15 is not directed to a transitory signal per se.
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