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 Amendment dated 17 February 2026, the following occurred:
Claims 1-3, 5-7, 11, 13, and 17 were amended.
Claims 4, 8-10, 14, and 20 were canceled.
Claims 1-3, 5-7, 11-13, and 15-19 are pending.
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 1-3, 5-7, 11-13, and 15-19 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.
Claims 1, 11, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a system, method, and a non-transitory computer readable medium for automated monitoring of a patient, and therefore meet step 1.
Step 2A1
The limitations of (Claim 17 being representative) …receiving sensory data […] to obtain sensory data from a vicinity of a patient, and wherein the sensory data comprises information about an amount of movement made by the patient; parsing the sensory data to derive a plurality of features, wherein the plurality of features comprise features that are indicative of a risk of a pressure injury; parsing the sensory data to derive a plurality of features, wherein the plurality of features comprises features that are indicative of a risk of a pressure injury; querying…[a data store]… to retrieve local historical patient-related data collected at a location of the patient; querying…[a data store]… to retrieve remote patients’ data, wherein the remote patients’ data comprises data from other patients with at least one of: a same diagnosis, age, gender, or race as the patient; generating… at least one feature vector based on the plurality of features and the historical patient-related data; […] generating, based on the at least one feature vector, a predictive model indicating at least one alert parameter for a generation of a patient-related alert to a medical authority; and generating a patient-related alert based on the at least one alert parameter…, wherein the patient-related alert is generated if the amount of movement is less than a threshold amount within a defined time threshold, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting a system, method, or apparatus implemented by circuitry (generic computer components) or a non-transitory computer readable medium and processor (a generic computer), the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a communication circuit (claims 1 and 11), a decision circuit connected to an alert circuit (claims 1 and 11), a non-transitory computer readable medium (claim 17), and a processor having a local database (claim 17) that implement the identified abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., a general-purpose computing, see, e.g., Para. 0080) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). 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. Further, obtaining data is considered insignificant extra solution activity such as pre-solution activity e.g., data gathering (performed by receiving/transmitting/etc.) See MPEP 2106.05(g).
The claims recite the additional elements of a machine learning (ML) module the generates a predictive model. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to generate a predictive model merely confines the use of the abstract idea (i.e., a trained model) to a particular technological environment or field of use (e.g., a neural network; see Spec. Para. 0059) and thus fails to add an inventive concept to the claims.
The claims further recite the additional elements of a sensor array, a remote database, and a communication modality. The sensor array, remote database, and communication modality merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a general-purpose processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the machine learning model to generate a predictive model was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (e.g., a neural network). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a sensor array, a remote database, and a communication modality were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, these additional elements do not provide significantly more. As such, the claim is not patent eligible.
Claims 2, 3, 5-7, 12, 13, and 15, 16, 18, and 19 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claims 2 and 12 merely describe generating at least one rescheduling parameter, which further defines the abstract idea.
Claims 3 and 13 merely describe the remote patients’ data, which further defines the abstract idea.
Claim 5 merely describes acquiring the sensory data, which further defines the abstract idea.
Claims 6, 15, and 18 merely describe continuously monitoring the sensory data, which further defines the abstract idea.
Claims 7, 16, and 19 merely describe generating the updated feature vector, which further defines the abstract idea.
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.
Claims 1-3, 5-7, 11-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Christiansson (U.S. 2018/0122509) in view of Murray et al. (U.S. 12,131,819), Halperin et al. (U.S. 2015/0164438), referred to hereinafter as Murray and Halperin, respectively, and Pipke (U.S. 2007/0149862).
REGARDING CLAIM 1
Christiansson teaches the claimed system for an automated monitoring of a patient, comprising: a sensor array, wherein the sensor array comprises at least one of: an infrared sensor, a motion sensor, a proximity sensor, a remote pressure sensor, or an imaging sensor; a communication circuit connected to the sensor array and configured to: [Para. 0039 teaches sensors measuring motion (interpreted as an array).]
query a local patient's database to retrieve local historical patient-related data collected at a location of the patient; and [Para. 0032, 0064 teaches that the server is connected to a database and extracts raw/historical patient data (historical patient-related data).]
query a remote database to retrieve […] data… [Para. 0082 teaches scanning a cloud (remote) database for multiple parameters.]
[…]
parse the sensory data to derive a plurality of features, […] [Para. 0072 teaches a heart rate monitor device (as sensor) with the ability to extract (parse) heart rate variability (HRV) and respiratory sinus arrhythmia (RSA). Para. 0032 teaches that this may alternately be performed by the server.]
Christiansson may not explicitly teach
…remote patients’ data… wherein the remote patients’ data comprises data from other patients with at least one of: a same diagnosis, age, gender, or race as the patient;
generate at least one feature vector based on the plurality of features, the historical patient-related data, and the remote patients’ data; and
However, Murray teaches the following:
…remote patients’ data… wherein the remote patients’ data comprises data from other patients with at least one of: a same diagnosis, age, gender, or race as the patient; [Col. 50, Line 60-61 teaches the data received from the remote memory (interpreted as the cloud database of Christiansson) may be associated with any suitable number of users. Col. 24, Line 38-43 teaches the data includes user-pertinent information (e.g., diagnosis and/or demographic information). The Examiner notes that a person having ordinary skill in the art would recognize that users may be related or grouped using such information.]
generate at least one feature vector based on the plurality of features, the historical patient-related data, and the remote patients’ data; and [Col. 49, Line 28-32 teaches feature vectors including a plurality of features (the historical features of Christiansson). Col. 51, Line 7-10 teaches the features include statistical data. The statistical data is generated based in part on historical data. Col. 51, Line 13-14 teaches defining at least a portion of the features relative to other users (remote patients).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the system of Christiansson to generate a feature vector based on data from multiple users as taught by Murray, with the motivation of improving the detection of data patterns associated with anomaly events (see Murray at Col. 34, Line 52-55).
Christiansson in view of Murray may not explicitly teach
a communication circuit connected to the sensor array and configured to: receive sensory data from the sensor array to obtain data from a vicinity of a patient, wherein the sensory data comprises information about an amount of movement made by the patient;
…wherein the plurality of features comprise features that are indicative of a risk of a pressure injury;
an alert circuit connected to the decision circuit and configured to generate a patient-related alert based on the at least one alert parameter via a communication modality selected from among SMS, in-app notifications, and a real-time alerting system, wherein the patient-related alert is generated if the amount of movement is less than a threshold amount within a defined time threshold.
However, Halperin teaches the following:
a communication circuit connected to the sensor array and configured to: receive sensory data from the sensor array to obtain data from a vicinity of a patient, wherein the sensory data comprises information about an amount of movement made by the patient; [Para. 0121 teaches two or more motion sensors (interpreted as an array) are disposed under a patient’s mattress to be used as a motion sensor.]
…wherein the plurality of features comprise features that are indicative of a risk of a pressure injury; [Para. 0148 teaches patient motion and moisture, which are indicative of a risk of a pressure ulcer.]
and generate a patient-related alert based on the at least one alert parameter via a communication modality selected from among SMS, in-app notifications, and a real-time alerting system, wherein the patient-related alert is generated if the amount of movement is less than a threshold amount within a defined time threshold. [Para. 0235 teaches generating an alert via a pager (real-time alerting system) when a patient’s movement level is detected below a threshold within a time period.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the system of Christiansson in view of Murray to receive sensory data, include features that are indicative of a risk of a pressure injury, and generate an alert based on patient inactivity as taught by Halperin, with the motivation of preventing pressure ulcers (see Halperin at Para. 0145).
Christiansson in view of Murray and Halperin may not explicitly teach
a decision circuit with a machine learning (ML) module configured to:
cause the ML module to generate, based on the at least one feature vector, a predictive model indicating at least one alert parameter for a generation of a patient-related alert to a medical authority;
However, Pipke teaches the following:
a decision circuit with a machine learning (ML) module configured to: [Para. 0029 teaches a model module.]
cause the ML module to generate, based on the at least one feature vector, a predictive model indicating at least one alert parameter for a generation of a patient-related alert to a medical authority; [Para. 0016 teaches inputting a set of measurements (feature vectors) from each patient to a model. The model generates estimates that are compared to the actual measurements to generate residuals (alert parameters) for each patient. The residuals are available for inspection to medical personnel, and mapped to alerts. Medical staff utilizes the alerts.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the system of Christiansson in view of Murray and Halperin to generate a model to indicate alert parameters as taught by Pipke, with the motivation of providing accurate, actionable, and early detection of health problems (see Pipke at Para. 0011).
REGARDING CLAIM 2
Christiansson in view of Murray, Halperin, and Pipke teaches the claimed system of claim 1.
Halperin further teaches
wherein the decision circuit is further configured to generate at least one rescheduling parameter for resetting a patient's rounding schedule based on the at least one alert parameter. [Para. 0235 teaches resetting a timing counter if the patient leaves the bed prior to the preset time limit. The Examiner notes that “for resetting a patient's rounding schedule” is an intended use of the rescheduling parameter and is not required to occur.]
REGARDING CLAIM 3
Christiansson in view of Murray, Halperin, and Pipke teaches the claimed system of claim 1.
Murray further teaches
wherein the remote patients' data is collected from one or more medical facilities. [Col. 50, Line 60-66 teaches collecting data from one or more facilities.]
REGARDING CLAIM 5
Christiansson in view of Murray, Halperin, and Pipke teaches the claimed system of claim 1.
Murray further teaches
wherein the communication circuit is further configured to acquire the sensory data periodically based on pre-set time intervals. [Col. 4, Line 43-45 teaches sensor measurements. Col. 26, Line 22-25 teaches generating a training data set, including multiple samples, based on a predetermined time interval.]
REGARDING CLAIM 6
Christiansson in view of Murray, Halperin, and Pipke teaches the claimed system of claim 1.
Murray further teaches
wherein the decision circuit is further configured to continuously monitor current sensory data received from individual sensors of the sensor array to determine if at least one reading of at least one individual sensor deviates from a previous reading of the at least one individual sensor by a margin exceeding a pre-set threshold value. [Col. 3, Line 57-60 teaches a sensor that detects a sensor measurement. This includes detecting measurements that exceed a threshold value. Col. 20, Line 34-38 teaches continuously receiving data.]
REGARDING CLAIM 7
Christiansson in view of Murray, Halperin, and Pipke teaches the claimed system of claim 6.
Murray further teaches
wherein the decision circuit is further configured to, responsive to the at least one reading deviating from the previous reading by the margin exceeding the pre-set threshold value, generate an updated feature vector based on the current sensory data and wherein the alert circuit is further configured to generate the alert based on the at least one alert parameter produced by the predictive model in response to the updated feature vector. [Col. 3, Line 60-64 teaches transmitting a communication upon detecting a measurement exceeding a threshold value. Col. 16, Line 1-3 teaches generating one or more alerts based on data. Col. 24, Line 53-56 teaches updating prediction models with newly received data.]
REGARDING CLAIMS 11 AND 17
Claims 11 and 17 are analogous to Claim 1, thus Claims 11 and 17 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
REGARDING CLAIMS 12 AND 13
Claims 12 and 13 are analogous to Claims 2 and 3, respectively, thus Claims 12 and 13 are similarly analyzed and rejected in a manner consistent with the rejections of Claims 2 and 3.
REGARDING CLAIMS 15 AND 18
Claims 15 and 18 are analogous to Claim 6, thus Claims 15 and 18 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 6.
REGARDING CLAIMS 16 AND 19
Claims 16 and 19 are analogous to Claim 7, thus Claims 16 and 19 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-3, 5-7, 11-13, and 15-19, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
…claim 1 is a machine comprising a specific combination of electronic devices: a sensor array, a communication circuit, a decision circuit and real-time alert circuitry.
Regarding (a), the Examiner respectfully disagrees. The communication circuit, decision circuit, and real-time alert circuitry are merely components of a computer (otherwise a 112(a) – written description rejection would be warranted). The particular combination of the additional elements of a computer (comprising the communication circuit, decision circuit, and real-time alert circuitry) and the sensor array was analyzed at Step 2B and found to not provide significantly more. There is nothing about a computer and, at minimum, two sensors that would render the claim eligible, nor has the Applicant provided any evidence that there is.
…improves the functioning of machines that perform automated patient monitoring… Claim 1 improves computer functionality by reciting specific mechanisms that allow computers to process healthcare monitoring data more effectively… improves the computer’s functionality by enabling it to autonomously analyze sensor data in conjunction with data from multiple databases and generate context-appropriate outputs.
Regarding (b), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer (see Spec. Para. 0080). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the function of the computer, a practical application is not present.
…claim 1 provides a particular technical solution to technical problems.
Regarding (c), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Examiner cannot find, nor has the Applicant identified, any technological problem that was caused by the technological environment to which the claims are confined.
Human beings cannot perform such continuous, sensor-linked detection with machine precision.
Regarding (d), the Examiner respectfully disagrees. Human beings can perform continuous, sensor-linked detection with machine precision when aided by the tools of a computer and a sensor array.
The human mind cannot retrieve data from sensors, local databases, and remote databases without use of a machine… The decision circuit is expressly designed to automate healthcare decisions using a large amount of data from different sources, which cannot be performed with the same efficiency in the human mind… The human mind cannot generate electronic signals for an alert in real time… [T]he claimed features cannot be practically performed in the human mind…
Regarding (e), the Examiner submits that the abstract idea was not characterized as being directed to a mental process. The claimed invention was characterized as falling under Certain Methods of Organizing Human activity (see Final Office Action dated 12/17/2025 at Pg. 3). As such, this argument cannot be persuasive.
Much like Example 47’s claim 3, the instant claim 1 includes specially defined hardware coupled with an automated, machine-driven response.
Regarding (f), the Examiner respectfully disagrees. MPEP 2106.04(II)(1) indicates that subject matter eligibility is satisfied where a claimed invention is not directed to an abstract idea. Example 47, Claim 1 is an illustration of this where the claim is directed to the physical aspects of a particular integrated circuit. While there may be an abstraction present in the storage of data, the claim as a whole is not directed to any of the enumerated groupings abstract ideas present in MPEP 2106. This is contrasted with Applicant’s claims which are directed to an abstract idea (see basis of rejection) and are not directed to the physical aspects of an integrated circuit.
The system… activates a physical response.
Regarding (g), the Examiner respectfully disagrees. The system generates a patient-related alert. The alert is part of the abstraction.
Rejections under 35 U.S.C. § 103
Regarding the rejection of Claims 1-3, 5-7, 11-13, and 15-19, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
… while Murray may suggest accessing a remote memory to retrieve data, Applicant has not found Murray to suggest retrieving remote patients' data that includes data from other patients with at least one of: a same diagnosis, age, gender, or race as the patient, much less generating "at least one feature vector based on the plurality of features, the historical patient-related data, and the remote patients' data," as required by amended claim 1.
Regarding (h), the Examiner respectfully disagrees. Murray teaches generating feature vectors comprising a plurality of features. Murray further teaches that at least a portion of the features are defined relative to other users and that user-pertinent information may include diagnosis and/or demographic information. Thus, Murray teaches utilizing data associated with other users (remote patients), including diagnosis and demographic information, in generating feature vectors. Accordingly, Murray teaches generating a feature vector “based on” patient-related features and data associated with other patients having diagnosis and/or demographic characteristics corresponding to the patient.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Panneer Selvam et al. (U.S. 2021/0052221) which discloses a system, method, and smartwatch for protecting a user.
Collins, JR. et al. (U.S. 2020/0121186) which discloses a system that monitors various conditions of a plurality of hospital beds located in different rooms of a healthcare facility.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert W Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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