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 .
Claims 1, 2, 5, 10-13 and 16 have been amended. Claims 4, 7, 15 and 18 have been cancelled. Claims 1-3, 5-6, 8-14, 16-17 and 19-20 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, 5-6, 8-14, 16-17 and 19-20 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.
Step 1
Claims 1-3, 5-6 and 8-10 are drawn to a method for using machine learning to monitor cognitive or physical impairment, which is within the four statutory categories (i.e. process). Claim 11 is drawn to a non-transitory medium for using machine learning to monitor cognitive or physical impairment, which is within the four statutory categories (i.e. manufacture). Claims 12-14, 16-17 and 19-20 are drawn to a system for monitoring, which is within the four statutory categories (i.e. machine).
Step 2A | Prong One
Claims 1-3, 5-6 and 8-10 (Group I) recite a method using machine learning to monitor cognitive or physical impairment, the method comprising:
performing an initial configuration of an assistance software during a deployment phase by:
receiving, by a processing unit of a monitoring system (MPEP § 2106.05(f), apply it), personal profile data (MPEP § 2106.05(g), insignificant extra solution activity) comprising at least one of a monitoring survey and a personal information form related to a monitored person; and
executing, by the processing unit (MPEP § 2106.05(f), apply it), a first machine learning algorithm using a first predictive model to select one or more functionalities of the assistance software to be activated based on the personal profile data, the assistance software (MPEP § 2106.05(f), apply it) providing assistance in the daily life of the monitored person;
receiving subsequent to the deployment phase by the processing unit of a monitoring system (MPEP § 2106.05(f), apply it) data (MPEP § 2106.05(g), insignificant extra solution activity for receiving data) from a plurality of devices located in a living environment of the monitored person;
generating by the processing unit (MPEP § 2106.05(f), apply it) monitoring data based on the received data; and
executing by the processing unit (MPEP § 2106.05(f), apply it) a second machine learning algorithm, the second machine learning algorithm using a second predictive model to determine one or more outputs based at least on the monitoring data, the one or more outputs comprising at least one of a cognitive impairment indicator and a physical impairment indicator, the cognitive impairment indicator indicating whether the monitored person is affected by cognitive impairment, the physical impairment indicator indicating whether the monitored person is affected by physical impairment; and
adjusting, by the processing unit (MPEP § 2106.05(f), apply it), the one or more activated functionalities of the assistance software in response to the at least one of the cognitive impairment indicator and the physical impairment indicator.
The bolded limitations, given the broadest reasonable interpretation, cover a mathematical concept and/or a certain method of organizing human activity because it recites mathematical relationships, formulas, equations, and/or mathematical calculations (machine learning algorithm using a predictive model) and/or fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of the abstract idea are underlined and are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 11 and 12-14, 16-17 and 19-20 is identical as the abstract idea for Claims 1-3, 5-6 and 8-10 (Group I), because the only difference between is they are directed towards different statutory categories.
Claim 12 further recites the limitations of at least one communication interface (MPEP § 2106.05(f), apply it); and a memory storing a predictive model (MPEP § 2106.05(f), apply it and (MPEP § 2106.05(g), insignificant extra-solution activity).
Dependent Claims 2-10 and 13-20 include other limitations, for example Claims 2 and 13 recite wherein the one or more outputs of the second machine learning algorithm comprises the cognitive impairment indicator, Claims 3 and 14 recite transmitting the cognitive impairment indicator to a third party device (MPEP § 2106.05(f), apply it and (MPEP § 2106.05(g), insignificant extra-solution activity), Claims 5 and 16 recite wherein the one or more outputs of the second machine learning algorithm comprises the physical impairment indicator, Claims 6 and 17 recite transmitting the physical impairment indicator to a third party device (MPEP § 2106.05(f), apply it and (MPEP § 2106.05(g), insignificant extra-solution activity), Claims 8 and 19 recite wherein the plurality of devices located in the living environment of the monitored person comprise at least one of the following: a sensing device, a personal electronic device and a smart appliance (MPEP § 2106.05(f), apply it), Claims 9 and 20 recite wherein the monitoring data comprise at least one of the following: an occurrence of an activity performed by the monitored person, a duration of an activity performed by the monitored person, a number of occurrences of an activity performed by the monitored person, an occurrence of an interaction of the monitored person with a device or an object located in the living environment of the monitored person, a duration of an interaction of the monitored person with a device or an object located in the living environment of the monitored person, a number of occurrences of an interaction of the monitored person with a device or an object located in the living environment of the monitored person, an occurrence of a fall of the monitored person, a number of occurrences of a fall of the monitored person, an average speed of the monitored person when walking in the living environment, an average time spent in an area, a maximum time spent in an area, a minimum time spent in an area, a number of visits to an area, a sleep quality metric, a health metric, a variation in the value of a metric generated based on the received data, information related to at least one of physical and cognitive capabilities of the monitored person, and personal information related to the monitored person, and Claim 10 recites wherein the second machine learning algorithm implements a neural network, the second predictive model comprising weights of the neural network, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 11 and 12.
Step 2A | Prong Two
Furthermore, Claims1-3, 5-6, 8-14, 16-17 and 19-20 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a processing unit of a monitoring system, communication interface, memory, plurality of devices, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0013] and [0040-0042] of the present Specification, see MPEP 2106.05(f); and
add insignificant extra-solution activity to the abstract idea – for example, the receiving data, which amounts to pre-solution activity and/or recitation of storing data and transmitting data, which amounts to an insignificant application, see MPEP 2106.05(g).
Step 2B
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs [0013] and [0040-0042] of the Specification discloses that the additional elements (i.e. a monitoring system, communication interface, memory, plurality of devices, processing unit, software) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receiving, storing and transmitting data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare).
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention transmits the data to third party device over a network, for example the Internet; and
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of predictive model data on a database and/or electronic memory.
Dependent Claims 2-3, 5-6, 8-10, 13-14, 16-17 and 19-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than receiving or transmitting data over a network (e.g. the transmitting feature of dependent Claims 3, 6, 14 and 17) and electronic recordkeeping (e.g. the storing feature of dependent Claim 12).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-3, 5-6, 8-14, 16-17 and 19-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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, 5-6, 8-14, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor (U.S. Pub. No. 2025/0288773 A1) in view of Arslan (U.S. Pub. No. 2025/0152043 A1).
Regarding claim 1, Taylor discloses a method using machine learning to monitor cognitive or physical impairment, the method comprising:
performing an initial configuration of an assistance software during a deployment phase by (Paragraph [0151] and [0419-0420] discuss an app that is used for the functions, construed as assistance software, including user onboarding and profile setup wizard. See also figures 1 and 2 showing the burble app.):
receiving, by a processing unit of a monitoring system, personal profile data comprising at least one of a monitoring survey and a personal information form related to a monitored person (Paragraphs [0041], [0231-0240] and [0419-0426] discuss using a user onboarding and profile setup wizard to collect information about the user for a computer-implemented method.); and
executing, by the processing unit, a first machine learning algorithm using a first predictive model to select one or more functionalities of the assistance software to be activated based on the personal profile data, the assistance software providing assistance in the daily life of the monitored person (Paragraphs [0282] and [0313-0314] discuss using artificial intelligence, including LLMs and neural networks, to make predictions or classifications regarding content a user is likely to find beneficial or enjoyable through personalized recommendations.);
receiving subsequent to the deployment phased by the processing unit data from a plurality of devices located in a living environment of the monitored person (Paragraphs [0044 and [0160] discuss receiving data from various data inputs, including sensors in the user’s smart home devices and wearables or other devices. Paragraphs [0041] and [0044-0070] and discuss various types of monitors or sensors used for the computer-implemented method.);
generating by the processing unit monitoring data based on the received data (Paragraphs [0041] and [0135] discuss using data collected form various inputs, including sensors, to assess the user’s condition, construed as generating monitoring data based on the received data. For example, paragraphs [0067-0068] discusses using the received data to analyze characteristics of the user, such as their facial expressions and their voice.); and
executing by the processing unit a second machine learning algorithm, the second machine learning algorithm using a predictive model to determine one or more outputs based at least on the monitoring data (Paragraphs [0282], [0316] and [0396-0397] discuss using the monitoring data to make predictions using a neural network.), the one or more outputs comprising a physical impairment indicator, the physical impairment indicator indicating whether the monitored person is affected by physical impairment (Paragraphs [0088] and [0098-0101] discusses determining if the monitoring data inputs indicate cognitive issues, including cognitive decline. Paragraphs [0162] and [0237] discuss alerting emergency services or a third party if needed.);
adjusting, by the processing unit, the one or more activated functionalities of the assistance software in response to the at least one of the cognitive impairment indicator and the physical impairment indicator (Paragraphs [0085-0086], [0118-0134] and [0248] discuss adjusting recommendations based on real-time data and in response to any impairment indicators.).
but Taylor does not appear to explicitly disclose:
wherein the one or more outputs comprises a cognitive impairment indicator, the cognitive impairment indicator indicating whether the monitored person is affected by cognitive impairment.
Arslan teaches wherein the one or more outputs comprises a cognitive impairment indicator, the cognitive impairment indicator indicating whether the monitored person is affected by cognitive impairment (Paragraphs [0035], [0057] and [0059] discuss the output of the analysis including indicating the user’s cognitive function is impaired based on monitoring of the user.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify Taylor to include a cognitive impair indicator, as taught by Arslan, in order to “serve as a surrogate to identify early dementia states and neurodegenerative diseases, and to track disease progression (Arslan, Paragraph [0029]).”
Regarding claim 2, Taylor does not appear to explicitly disclose wherein the one or more outputs of the second machine learning algorithm comprises the cognitive impairment indicator.
Arslan teaches wherein the one or more outputs of the second machine learning algorithm comprises the cognitive impairment indicator (Paragraphs [0035] and [0057] discuss the output of the machine learning including indicating the user’s cognitive function is impaired.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify Taylor to include wherein the one or more outputs of the machine learning algorithm comprises the cognitive impairment indicator, as taught by Arslan, in order to “serve as a surrogate to identify early dementia states and neurodegenerative diseases, and to track disease progression (Arslan, Paragraph [0029]).”
Regarding claim 3, Taylor does not appear to explicitly disclose transmitting the cognitive impairment indicator to a third party device.
Arslan teaches transmitting the cognitive impairment indicator to a third party device (Paragraphs [0049] and [0058] discuss reporting the results to the caregivers for immediate medical attention or to view relevant user metrics or feedback on a mobile app.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify Taylor to include wherein the one or more outputs of the machine learning algorithm comprises the cognitive impairment indicator, as taught by Arslan, in order to “serve as a surrogate to identify early dementia states and neurodegenerative diseases, and to track disease progression (Arslan, Paragraph [0029]).”
Regarding claim 5, Taylor does not appear to explicitly disclose wherein the one or more outputs of the second machine learning algorithm comprises the physical impairment indicator.
Arslan teaches wherein the one or more outputs of the second machine learning algorithm comprises the physical impairment indicator (Paragraphs [0035], [0040] and [0064] discuss the output from the machine learning including a score related to the user’s cognitive function.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare before the effective filing date of the claimed invention to modify Taylor to include wherein the one or more outputs of the machine learning algorithm comprises the physical impairment indicator, as taught by Arslan, in order to “alert the user or caregivers on which function specifically is on decline using mobile app (Arslan, Paragraph [0064]).”
Regarding claim 6, Taylor discloses further comprising transmitting the physical impairment indicator to a third party device (Paragraph [0237] discusses automatically alerting emergency services if needed.).
Regarding claim 8, Taylor discloses wherein the plurality of devices located in the living environment of the monitored person comprise at least one of the following (Examiner notes that the claim only requires one of the following to meet the claim limitation, but provides explanation regarding several that exist in the Taylor disclosure.): a sensing device, a personal electronic device and a smart appliance (Paragraphs [0044], [0160] and [0193] discuss using sensors, mobile devices and smart home devices to gather data regarding the user.).
Regarding claim 9, Taylor discloses wherein the monitoring data comprise at least one of the following (Examiner notes that the claim only requires one of the following to meet the claim limitation, but provides explanation regarding several that exist in the Taylor disclosure.): an occurrence of an activity performed by the monitored person (Paragraph [0119] discusses the monitoring data including exercise data, construed as an occurrence of an activity.), a duration of an activity performed by the monitored person (Paragraph [0236] discusses the monitoring data including a workout duration.).
Regarding claim 10, Taylor discloses wherein the machine learning algorithm implements a neural network, the predictive model comprising weights of the neural network (Paragraphs [0315-0142] and [0279-0282] discuss using a neural network with weights that can be adjusted.).
2025Attorney Docket No. 317EP.001US01
Claim 11 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above.
Claim 12 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above. Claim 12 further recites the limitations of at least one communication interface (Taylor paragraph [0084] discusses the system using an interface.); and a memory storing a predictive model (Taylor Paragraph [0051] discusses the method being implemented on a server, which includes a memory for storing data, construed as including storing the predictive model.).
Claim 13 recites substantially similar limitations as those already addressed in claim 2, and, as such, is rejected for similar reasons as given above.
Claim 14 recites substantially similar limitations as those already addressed in claim 3, and, as such, is rejected for similar reasons as given above.
Claim 16 recites substantially similar limitations as those already addressed in claim 5, and, as such, is rejected for similar reasons as given above.
Claim 17 recites substantially similar limitations as those already addressed in claim 6, and, as such, is rejected for similar reasons as given above.
Claim 19 recites substantially similar limitations as those already addressed in claim 8, and, as such, is rejected for similar reasons as given above.
Claim 20 recites substantially similar limitations as those already addressed in claim 9, and, as such, is rejected for similar reasons as given above.
Response to Arguments
Applicant's arguments filed 07/21/2026 have been fully considered.
Claim Rejections under 35 U.S.C. § 101
Applicant asserts that the amended claims “integrate the machine learning algorithms into a specific, closed-loop technical control system that autonomously configures and reconfigures an assistive software environment (Remarks, page 9).” Examiner maintains that the machine learning model using predictive models is part of the abstract idea and thus, cannot be used to integrate the abstract idea into a practical application. The performing and configuration steps are considered part of the abstract idea and also cannot be used to integrate the abstract idea into a practical application.
Applicant further asserts that the amended claims “provide a specific technical improvement over conventional monitoring systems, satisfying the "significantly more" requirement (Remarks, page 10)” because it claims an “ordered combination of steps that establishes a functional baseline during a deployment phase using static profile data, which is subsequently and dynamically adjusted once real-time sensor data becomes available” and that this “specific, multi-tiered algorithmic architecture is not a well-understood, routine, or conventional activity in the healthcare or computer science fields (Remarks, page 10).” Examiner maintains that these limitations are part of the abstract idea, and therefore, cannot result in significantly more than the abstract idea itself. For the Step 2B significantly more inquiry, the focus is on the additional elements. The additional elements have been identified as being recited at an “apply it” level or reciting insignificant extra-solution activity. Receiving, transmitting and storing are all well-understood, routine and conventional as indicated by the rejection above citing to Berkheimer evidence as required by MPEP §2106.
Therefore, the claims remain rejected as being directed towards ineligible subject matter.
Claim Rejections under 35 U.S.C. § 103
Applicant’s arguments with respect to the prior art rejection have been fully considered but are moot in view of the new ground of rejections necessitated by Applicant’s amendment.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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