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 response filed 12 August 2026, on an application filed 26 May 2023, which is a continuation of an application that claims foreign priority to an application filed 13 June 2022.
Claims 1, 13 and 15 are amended.
Claims 22 and 23 have been added by amendment.
Claims 1, 3-5, 7, 8, 10, 12, 13, 15 and 18-23 are currently pending and have been examined.
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:
wherein the monitoring the multi-modal input data comprises receiving sensor data indicating at least one of eye blinking of the user and a touch of the user that are associated with one or more control operations,
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 wherein the monitoring the multi-modal input data comprises receiving sensor data indicating at least one of eye blinking of the user and a touch of the user that are associated with one or more control operations (See the touchscreen operations of Cadavid at paragraph 332.) and wherein 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 a touchscreen and 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).
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being obvious over Lu/Cook/Cadavid/Molapo, further in view of Asikainen et al. (U.S. PG-Pub 2022/0296966 A1), hereinafter Asikainen.
As per claims 22 and 23, Lu/Cook/Cadavid/Molapo discloses claims 1 and 13, discussed above. Lu/Cook/Cadavid/Molapo fails to explicitly disclose, but Asikainen 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:
22. receiving at least one of a blood sugar level, a hydration level, and an alcohol consumption level from a wearable device (Asikainen discloses monitoring glucose levels at least, see paragraph 72.); and
23. receiving sensor data from one or more optical sensors, the one or more optical sensors comprising a charge-coupled device (CCD) or complementary metal-oxide- semiconductor (CMOS) phototransistors (Asikainen, paragraph 60.).
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 collecting glucose data and data from CMOS/CCD sensors, as taught by Asikainen, in order to provide a health condition monitoring system provide that monitored a wider variety of user data from a wider variety of sensors types. 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).
Lu and Aikainen are both directed to the electronic processing of patient healthcare data and specifically to the processing of monitored patient data.
Response to Arguments
Applicant’s arguments filed 12 August 2026 concerning the rejection of all claims under 35 U.S.C. 101 have been fully considered and are persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 12-15 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 agrees. Applicant’s claimed invention is patent-eligible because of comparison to elements of Examples 37, 39 and 42 from the 2019 PEG. A Method of mitigating physical accidents in an IOT environment wherein the system estimates a user’s cognitive ability and provides a corrective action to prevent a predicted potential physical accident would be a practical application of an abstract idea that otherwise would be defined as an abstract idea overall. Alternatively, the limitations amount to “significantly more’ than the abstract idea. For at least these reasons, the claims are patent eligible under 35 U.S.C. §101.
Accordingly, the statutory rejections have been removed.
Applicant’s arguments filed 12 August 2026 concerning the rejection of all claims under 35 U.S.C. 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the independent claims under 35 USC 103, the Applicant argues on pages 16-17 that the cited references fail to disclose wherein the monitoring the multi-modal input data comprises receiving sensor data indicating at least one of eye blinking of the user and a touch of the user that are associated with one or more control operations,
The Office respectfully disagrees. Please see the updated rejection of the claims, as shown above, where Cadavid is shown to disclose use of a touchscreen.
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, Asikainen 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 (12 May 2026, 17 February 2026, 15 December 2025, 26 June 2025, 4 March 2025), and incorporated herein.
Conclusion
Unused but cited relevant prior art includes:
Patterson (U.S. PG-Pub 2005/0215889 A1) discloses methods for using pet measured metabolism to determine cognitive impairment.
THIS ACTION IS MADE FINAL. 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 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
17 September 2026
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