DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to a request for continued examination (“RCE”) filed 17 April 2026, on an application filed 26 May 2023, which is a continuation of an application that claims foreign priority to a provisional application filed 13 June 2022.
Claims 1, 13 and 15 are amended.
Claims 16 and 17 have been canceled.
Claim 21 has been added by amendment.
Claims 1, 3-5, 7, 8, 10, 12, 13, 15 and 18-21 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 17 April 2026 has been entered.
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, 8, 10, 12, 13, 15 and 18-21 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, 7, 8, 10, 12, 13, 15 and 18-21 are within the four statutory categories. Claims 1, 3-5, 7, 8, 10, 12, 18, 20 and 21 are drawn to a method for mitigating physical accidents associated with a user in an Internet of Things (IoT) environment, which is within the four statutory categories (i.e. process). Claims 13 and 19 are drawn to a system for mitigating physical accidents associated with a user in an Internet of Things (IoT) environment, which is within the four statutory categories (i.e. machine). Claim 15 is drawn to a non-transitory computer readable medium for storing computer readable program code or instructions which are executable by a processor to perform a method for mitigating physical accidents associated with a user in an Internet of Things (IoT) environment, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 13 recites: A system for mitigating physical accidents associated with a user in an Internet of Things (IoT) environment, the system comprising:
a memory storing instructions; and at least one processor configured to execute the instructions to:
monitor multi-modal input data over a first time period, the multi-modal input data being associated with at least one multi-modal interaction with at least one IoT device in the IoT environment,
generating multi-modal cognitive data corresponding to the user based on the monitored multi-modal input data, the multi-modal cognitive data comprising information related to a plurality of cognitive health indexes,
identify a change in at least one cognitive ability of the user due to chronic illness, based on the multi-modal cognitive data, by,
obtaining predefined cognitive decline criteria from a knowledge database,
converting the generated the multi-modal cognitive data into a plurality of weighted standard cognitive indexes based on the obtained predefined cognitive decline criteria,
comparing a corresponding cognitive health index of the plurality of cognitive health indexes with a corresponding weighted standard cognitive index among the plurality of weighted standard cognitive indexes, and
identifying the change in the at least one cognitive ability of the user based on a result of the comparing;
estimate a cognitive ability index of the user based on the identified change in the at least one cognitive ability of the user;
predict at least one possible physical accident associated with a current user activity in the IoT environment based on a correlation of the estimated cognitive ability index with at least one cognitive health index from a plurality of cognitive health indexes,
predicting a physical location within the IoT environment where the predicted at least one possible physical accident may occur during the current user activity based on a detection of a user's location within the IoT environment and an identification of a type of the current user activity
determine at least one corrective action affecting the IoT environment to avoid the predicted at least one possible physical accident associated with the current user activity, and
performing the determined at least one corrective action such that the predicted at least one possible physical accident is mitigated, using the at least one IoT device in the IoT environment and based on the predicted physical location,
wherein the at least one corrective action comprises at least one of activating a smart assistant, changing lighting, or changing ventilation in the IoT environment.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of “mathematical concepts” (herein the mathematical steps) and/or a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the steps of monitoring the data of a user to determine whether they have a cognitive deficiency, predicting a risk associated with that deficiency and determine a corresponding intervention is properly interpreted as following rules or instructions based on the determination), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 1 and 15 are identical as the abstract idea for claim 13, because the only difference between claims 1, 13 and 15 is that claim 1 recites a method, whereas claim 8 recites a system and claim 15 recites a non-transitory computer-readable media.
Dependent claims 3-5, 7, 8, 10, 12 and 18-21 include other limitations, for example claims 5 and 10 further describes processing of the predicted risk, claims 3, 4 and 14 further describes the index or the ability, claims 12, 18, 19 and 21 classifies or describes data, claims 7 and 8 describes monitoring data, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 3-5, 7, 8, 10, 12 and 18-21 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 3-5, 7, 8, 10, 12 and 18-21 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 13 and 15.
Prong 2 of Step 2A
Claims 1, 13 and 15 are not integrated into a practical application because the additional elements (i.e. any limitations that are 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 the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea or the application of the treatment, e.g. see paragraphs 100-106 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language directed to the IOT device or the multi-model input data, which amounts to limiting the abstract idea to an IOT environment, see paragraph 48 of the present invention; see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data transmission, gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 3-5, 7, 8, 10, 12 and 18-21 include other limitations, but these limitations also amount to no more than amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. claims 12, 18 and 19 recite various data types and claim 20 recites a machine learning model), and/or do not include any additional elements beyond those already recited in independent claims 1, 13 and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1, 13 and 15 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural components of the computing devices), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises 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 100-106 of the present Specification discloses that the additional elements (i.e. the structural components of the computing devices) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare).
Dependent claims 3-5, 7, 8, 10, 12 and 18-21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 13 and 15, and/or the additional elements recited in the aforementioned dependent claims similarly amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. claims 12, 18 and 19 recite various data types and claim 20 recites a machine learning model), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. 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, 7, 8, 10, 12, 13, 15 and 18-21 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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1, 3-5, 7, 8, 10, 12, 13, 15 and 18-21 are rejected under 35 U.S.C. 103 as being obvious over Lu et al. (US PG-Pub 2019/0156296 a1), in view of Cook et al. (U.S. PG-Pub 2016/0314255 A1), hereinafter Cook, in view of Cadavid et al. (U.S. PG-Pub 2015/0110741 A1), hereinafter Cadavid, further in view of Molapo et al. (U.S. PG-Pub 2021/0374469 A1), hereinafter Molapo.
As per claims 1, 13 and 15, Lu discloses a system and a non-transitory computer readable medium for storing computer readable program code or instructions which are executable by a processor to perform a method for mitigating physical accidents associated with a user in an Internet of Things (IoT) environment (Lu, Figs. 1-3.), the system comprising:
a memory storing instructions; and at least one processor configured to execute the instructions (Lu, Figs. 1-3.) to:
monitor multi-modal input data over a first time period, the multi-modal input data being associated with at least one multi-modal interaction with at least one IoT device in the IoT environment (IoT device monitors a plurality of user interactions, such as sleep patterns, social and health habits and/or medical treatments, see Lu paragraphs 20 and 22-24.),
identify a deficiency in at least one cognitive ability of the user due to chronic illness, based on the monitored multi-modal input cognitive data, by: (Sleep patterns of the user correspond to a sleep deficiency, see paragraphs 22-24. The claimed invention performs the same regardless of the name of the source of the change, correspondingly, the phrase due to chronic illness comprises non-functional descriptive material and is afforded limited patentable weight.),
generating multi-modal cognitive data corresponding to the user based on the monitored multi-modal input data … (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28.);
comparing a corresponding cognitive health index of the plurality of cognitive health indexes with a corresponding weighted standard cognitive index among the plurality of weighted standard cognitive indexes (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Note the definition of the cognitive ability in originally filed claim 3.); and
identifying the change in the at least one cognitive ability of the user based on a result of the comparison (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Note the definition of the cognitive ability in originally filed claim 3.);
comparing a corresponding cognitive health index of the plurality of cognitive health indexes with a corresponding weighted standard cognitive index among the plurality of weighted standard cognitive indexes (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Note the definition of the cognitive ability in originally filed claim 3.), and
identifying the change in the at least one cognitive ability of the user based on a result of the comparing (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Note the definition of the cognitive ability in originally filed claim 3.);
estimate a cognitive ability index of the user based on the identified deficiency in the at least one cognitive ability of the user (System uses monitored data to determine that the user has an issue or has a deficiency with respect to a population of patients, see Lu paragraphs 26 and 27. A user’s sleep quality rating would comprise a cognitive ability index. Note the definition of the cognitive ability in originally filed claim 4.);
predict at least one possible physical risk associated with a current user activity in the IoT environment based on a correlation of the estimated cognitive ability index based on a correlation of the estimated cognitive ability index with at least one cognitive health index from the plurality of cognitive health indexes (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Note the definition of the cognitive ability in originally filed claim 3.),
identifying … an identification of a type of the current user activity (System identifies a current planned user activity, which would comprise an identification of a type of the current user activity, see Lu, paragraphs 26-28.);
determine at least one corrective action affecting the IoT environment to avoid the predicted at least one possible physical risk associated with the current user activity (System determines that the user needs to be alerted to an issue or that the user’s schedule needs to be adjusted based on the user’s lack of sleep, see Lu paragraphs 28-33.), and
performing the determined at least one corrective action to mitigate the predicted at least one possible physical risk, using the at least one IoT device in the IoT environment … (System determines that the user needs to be alerted to an issue or that the user’s schedule needs to be adjusted based on the user’s lack of sleep, and sends the alert or changes the schedule using the IoT device either by accessing data or presenting data on IoT device, see Lu paragraphs 28-33.),
wherein the at least one corrective action comprises at least one of activating a smart assistant, changing lighting, or changing ventilation in the IoT environment (Lu discloses that the user needs to be alerted to an issue or that the user’s schedule needs to be adjusted based on the user’s lack of sleep, and sends the alert or changes the schedule using the IoT device either by accessing data or presenting data on IoT device, see Lu paragraphs 28-33; both of which would comprise activating a smart assistant.).
Lu fails to explicitly disclose:
changes due to chronic illness;
a change in at least one cognitive ability of the user,
estimating ability based on the identified change
predicting a physical accident,
predicting a physical location within the IoT environment where the predicted at least one possible physical accident may occur during the current user activity based on a detection of a user's location within the IoT environment …;
mitigating the hazard based on the predicted location; and
Cook teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to disclose changes due to chronic illness and a change in at least one cognitive ability of the user, and estimating ability based on the identified change (Cook, see paragraphs 67 and 243 wherein sleep symptoms arise due to chronic illness such as dementia and Alzheimer’s, and Fig. 4, wherein Cook establishes a cognitive baseline and corresponding prediction of cognitive and/or mobility scores, and then reexams the same user in order to detect changes in the user’s cognitive ability and a new corresponding prediction of cognitive and/or mobility scores.) in order to provide a process to “transform smart home based sensor data into activity performance features and statistical activity features which are then processing through a machine learning engine to predict clinical cognitive assessment values” (Cook, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the health condition monitoring system of Lu to include to disclose a change in at least one cognitive ability of the user, and estimating ability based on the identified change, as taught by Cook, in order to provide a health condition monitoring system that can “transform smart home based sensor data into activity performance features and statistical activity features which are then processing through a machine learning engine to predict clinical cognitive assessment values” (Cook, Abstract.).
Lu/Cook fails to explicitly disclose the multi-modal cognitive data comprising information related to a plurality of cognitive health indexes, obtaining predefined cognitive decline criteria from a knowledge database and converting the generated the multi-modal cognitive data into a plurality of weighted standard cognitive indexes based on the obtained predefined cognitive decline criteria.
Cadavid teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to disclose the multi-modal cognitive data comprising information related to a plurality of cognitive health indexes, obtaining predefined cognitive decline criteria from a knowledge database and converting the generated the multi-modal cognitive data into a plurality of weighted standard cognitive indexes based on the obtained predefined cognitive decline criteria (Cadavid discloses converting cognitive data into a plurality of weighted cognitive indexes based on cognitive decline criteria obtained from a database, see paragraphs 346, 347 and 410-421.) in order to provide a process for “a practical and valid cognitive measurement tool for use in MS patient evaluation, which are not only tailored to assess the cognitive domains affected by MS, but are also efficient, and easily administered with cross-cultural utility” (Cadavid, paragraph 5.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the health condition monitoring system of Lu/Cook to include the multi-modal cognitive data comprising information related to a plurality of cognitive health indexes, obtaining predefined cognitive decline criteria from a knowledge database and converting the generated the multi-modal cognitive data into a plurality of weighted standard cognitive indexes based on the obtained predefined cognitive decline criteria, as taught by Cadavid, in order to provide a health condition monitoring system provide a process for “a practical and valid cognitive measurement tool for use in MS patient evaluation, which are not only tailored to assess the cognitive domains affected by MS, but are also efficient, and easily administered with cross-cultural utility” (Cadavid, paragraph 5.).
Lu/Cook/Cadavid fails to explicitly disclose:
predicting a physical location within the IoT environment where the predicted at least one possible physical accident may occur during the current user activity based on a detection of a user's location within the IoT environment …; and
mitigating the hazard based on the predicted location.
Molapo teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to disclose predicting a physical location within the IoT environment where the predicted at least one possible physical accident may occur during the current user activity based on a detection of a user's location within the IoT environment …; and mitigating the hazard based on the predicted location (Molapo, paragraphs 52, 88 and Figs. 7 and 8.) in order to provide “a system enables a user to assess the safety of the home or other environment for a specific individual.” (Molapo, paragraph 52.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the health condition monitoring system of Lu/Cook to include predicting physical location of physical accident and mitigating it, as taught by Molapo, in order to provide a health condition monitoring system provide “a system enables a user to assess the safety of the home or other environment for a specific individual.” (Molapo, paragraph 52.).
Lu, Cook, Cadavid and Molapo are all directed to the electronic processing of patient healthcare data and specifically to the processing of monitored patient data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 3-5, 7, 8, 10, 12, 18, 19 and 21, Lu/Cook/Cadavid/Molapo discloses claims 1 and 13, discussed above. Lu/Cook/Cadavid/Molapo also discloses:
3. wherein the at least one cognitive health index indicates at least one of a physical fitness, a mental fitness, and vulnerability of the user to perform an activity (Lu, a planned activity ability (cognitive health index), see paragraphs 26-28.);
4. wherein the change in the at least one cognitive ability of the user corresponds to a change in cognitive health of the user to perform a task comprising at least one of a physical activity and a mental activity (System uses monitored data to determine that the user has an issue or has a deficiency with respect to a population of patients, see paragraphs 26 and 27. A user’s sleep quality rating would comprise a cognitive ability index. Cook discloses a change in cognitive health, as disclosed above at Fig. 4.);
5. wherein the determined at least one corrective action corresponds to an action that, when performed by the user, results in a mitigation of the predicted at least one possible physical accident that may occur during the current user activity (System determines that user should be made aware of their deficiency and sends a corresponding message to a relevant individual, see paragraphs 28-30. Message can include a text requesting assistance for the user. Molapo discloses a physical accident, as shown above.);
7. monitoring, over the first time period, at least one user activity of the user in the IoT environment (IoT device monitors a plurality of user interactions, such as sleep patterns, social and health habits and/or medical treatments, see paragraphs 20 and 22-24.); and
generating the multi-modal cognitive data corresponding to the user based on the monitored multi-modal input data and the monitored at least one user activity (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28.),
8. wherein the monitored at least one user activity of the user is a location-based activity corresponding to different locations within the IoT environment (Lu discloses a fitness tracker that the user wears all the time and monitors the user in any environment, including at home and while sleeping in a bed, see paragraph 15. Lu also discloses trackers that monitor relevant individual’s locations with respect to the user, see paragraphs 30.);
10. wherein the determining the at least one corrective action comprises:
predicting a capability of the user to handle the predicted at least one possible physical accident associated with the current user activity based on the cognitive ability index of the user (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication), see paragraph 28. Molapo discloses a physical accident, as shown above.);
predicting a type of the predicted at least one possible physical accident based on the predicted capability of the user to handle the predicted at least one possible physical risk (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication), see paragraph 28. A physical risk would comprise a type of physical risk.); and
determining the at least one corrective action based on the predicted capability of the user to handle the predicted at least one possible physical accident and the predicted type of the at least one possible physical risk (System predicts a physical risk based on current planned activity based on the user ability index (user issue indication), see paragraph 28. A physical risk would comprise a type of physical risk.);
12. wherein the multi-modal input data comprises information regarding cognitive health parameters associated with the user (IoT device monitors a plurality of user interactions, such as sleep patterns, social and health habits and/or medical treatments, see paragraphs 20 and 22-24.), and wherein the cognitive health parameters correspond to at least one of Montreal Cognitive Assessment (MoCA), circadian rhythm disruption computation (CRDC), a Blood alcohol concentration (BAC) value, a percentage of water in a user's body, chronic illness including arthritis, vitamin B-12 deficiency, underactive thyroid gland, and diabetic condition, a level of each of Dementia, Alzheimer's, Parkinson's, and cardiovascular diseases, and a level of blood sugar during at least one of pre-meal, post-meal, fasting (It is old and well known that sleep related parameters correspond to all of the listed elements, as sleep patterns could be disrupted by all of the listed elements.); and
18,19. wherein the chronic illness comprises at least one of arthritis, vitamin B-12 deficiency, underactive thyroid gland, and diabetic condition, a level of each of Dementia, Alzheimer's, Parkinson's, and cardiovascular diseases (As shown above, Cook discloses illnesses related to dementia and Alzheimer’s, see paragraphs 67 and 243.);
21. wherein the multi-modal cognitive data are converted based on background metadata, and wherein the background metadata comprises predefined cognitive decline information related to the user and associated cognitive decline labels for accidents and an inability of the user (System creates the background metadata composed of identifying sleep patterns of the user that correspond to a sleep deficiency, see paragraphs 22-24. System predicts a physical risk based on current planned activity based on the user ability index (user issue indication) based on comparing the ability index of the user (sleep quality rating, see Lu, paragraphs 26 and 27), with a planned activity ability (cognitive health index), see paragraph 28. Sleep deficiency would comprise a label.).
As per claim 20, Lu/Cook/Cadavid/Molapo discloses claim 1, discussed above. Lu/Cook/Cadavid/Molapo discloses predicting the physical accident and consideration of cognitive indexes of a user, as shown above. Lu fails to explicitly disclose using and training a machine learning model.
However, Cook discloses using and training a machine learning model (Cook, paragraph 26.) in order to provide a process to “transform smart home based sensor data into activity performance features and statistical activity features which are then processing through a machine learning engine to predict clinical cognitive assessment values” (Cook, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the health condition monitoring system of Lu/Cook/Cadavid/Molapo to include training a machine learning model, as taught by Cook, in order to provide a health condition monitoring system that can “transform smart home based sensor data into activity performance features and statistical activity features which are then processing through a machine learning engine to predict clinical cognitive assessment values” (Cook, Abstract.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Response to Arguments
Applicant’s arguments filed 17 April 2026 concerning the rejection of all claims under 35 U.S.C. 101 and 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 11-13 that the amended material to the claims clearly indicates that the IoT device is a particular machine that is integral to the steps recited in claim 1.
The Office respectfully disagrees. Please see the statutory rejection of the claims, issued above, wherein the claims are shown to be directed to an abstract idea without significantly more.
Regarding A., 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 Applicant’s argued problem is not a technological problem caused by the IoT environment. The problem of identifying physical accidents was not a problem cause by the IoT environment, is it a problem that existed and/or exists regardless of whether a IoT environment is involved in the process. At best, Applicant’s identified problem is a hazard problem. Because no technological problem is present, the claims do not provide a practical application.
Accordingly, the rejection is upheld.
With regard to the rejection of the independent claims under 35 USC 103, the Applicant argues on pages 14-17 that the Molapo fails to disclose identifying a type of current user activity.
The Office respectfully disagrees. Please see the updated rejection of the claims, as shown above, where Lu is disclosed as identifying a type of current user activity and Molapo discloses predicting a physical location within the IoT environment where the predicted at least one possible physical accident may occur during the current user activity based on a detection of a user's location within the IoT environment …; and mitigating the hazard based on the predicted location. Further, the Office notes that a user’s presence in a location comprises a type of current user activity, i.e., a presence, as disclosed by Molapo.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of reference, such as the combination of Lu and Molapo, as shown above. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The remainder of Applicant's arguments have been fully considered but are moot because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Lu, Cook Cadavid, Molapo and Hong, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Actions (17 February 2026, 15 December 2025, 26 June 2025, 4 March 2025), and incorporated herein.
Conclusion
Unused but cited relevant prior art includes:
Barak et al. (U.S. PG-Pub 2020/0168333 A1), which discloses a system and method for improving process safety in an industrial environment.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
7 May 2026
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