Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/02/25 has been considered by the examiner.
Amendment Entered
In response to the amendment filed on March 31, 2026, amended claims 1, 2 and 7 have been entered. New claim 13 has been added.
Response to Arguments
Applicant’s arguments and amendments filed with respect to the claim objections raised in the previous office action were fully considered and were persuasive. Therefore, these objections have been withdrawn.
Applicant’s arguments and amendments filed with respect to the 112 rejections raised in the previous office action were fully considered and were persuasive. Therefore, these rejections have been withdrawn.
Applicant's remarks and amendments with respect to the rejections under
U.S.C. 101 have been fully considered. While Examiner agrees that the claimed invention does not explicitly recite mathematical calculations, after considering the amendments, Examiner argues that nothing from the claims, accompanying specification, and/or drawings suggest that the method steps cannot be practically performed mentally, or using pen/paper. Applicant argues the invention is not an abstract idea. Examiner notes that although the claims include a wearable device comprising an IMU, a pedometer, and a memory, no physical aspect of the wearable device mentioned in the claims is novel. The claims merely recite data gathering/outputting steps. Applicant further argues the claims integrate into a practical application. Examiner notes that according to MPEP 2106.04(d)(2), the practical application consists of administering a specific medication in response to the collected data. Alternately, a practical application would consist of incorporating additional structure to the detection system. Lastly, Applicant argues the claims provide an inventive concept. Examiner notes the previously cited references teach all the components (i.e. IMU, pedometer, machine learning for detection of a fall) of the present application. Therefore, as currently claimed, the invention is not an improvement in technology. Accordingly, Examiner maintains that the identified judicial exception recites a mental process that is not integrated into a practical application. As such, the 35 USC 101 rejections are maintained. Examiner suggests incorporating more structure to the claim or a medication administration step. Please see corresponding rejection heading below for more detailed analysis.
Applicant’s remaining arguments filed with respect to the prior art rejections raised in the previous office action were fully considered, but are moot in view of the current combination of references that were necessitated by amendment. Please see prior art section below for more detail, updated citations (Filatov reference), and updated obviousness rationale
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding claim 1, the claim recites a device for determining a glucose level of a patient. Thus, the claim is directed to a machine, which is one of the statutory categories of invention
The claim is then analyzed to determine whether it is directed to any judicial exception. The following limitations set forth a judicial exception:
“wherein the memory stores program instructions configured to: retrieve motion data of the user, process the retrieved motion data after the retrieved motion data has exceeded a pre-defined threshold; predict fall risk of the user using a first trained ML model, wherein the processed motion data is provided as input for the implementation of the first trained ML model; determine a confidence score of fall of the user based on analysis of the predicted fall risk; process successive data frames using a second trained ML model to confirm the fall of the user if the confidence score is above a pre-defined level, wherein the fall is confirmed by analyzing post-fall motion behavior indicated by the successive data frames, the post-fall motion behavior including characteristic movement and body positioning of the user after the fall; and transmit a distress signal if fall of the user is confirmed; wherein the second trained ML model is trained on labelled frame data, including post-fall motion behavior”
These limitations describe a mental process as the skilled artisan is capable of performing the judicial exception mentally, or using pen and paper. Furthermore, nothing from the claims or applicant’s accompanying specification shows that the skilled artisan would not be able to perform the judicial exception mentally, or using pen and paper.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application.
For this part of the 101 analysis, the following additional limitations are considered:
“A wearable device; a processor; a memory; an IMU (Inertial Measurement Unit) and a pedometer”
These additional limitations do not integrate the judicial exception into a practical application. Rather, the additional limitations are each recited at a high level of generality such that it amounts to insignificant pre-solution and post-solution activity e.g., mere generic data collection device, receiving data, outputting data.
Furthermore, the additional limitations recite well-known structural limitations (generically recited wearable device, a processor, etc.) and as such, do not amount to significantly more than the identified judicial exception. Examiner takes official notice that the additional limitations are conventional components in prior analyte monitoring systems. Cheng (applied in this office action) teaches these additional elements, showing these additional elements are well known and conventional [par. 87, 93, 95, 99, 102].
Dependent claims 2-13 also fail to add something more to the abstract independent claims as they merely further limit the abstract idea.
Therefore, claims 1-13 are not patent eligible under 35 USC 101.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-8, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng (U.S. Patent Application Document 2024/0203228) and in further view of Junker (U.S. Patent Application Document 2023/0148960) and Filatov (U.S. Patent Application Document 2025/0391559)
Cheng was submitted in Applicant’s IDS
Cheng and Junker were applied in the previous office action
Regarding claim 1, Cheng teaches a wearable device [fig. 1, element 100; par. 99] for monitoring and analysis of user movements for predicting fall risk [par. 6], the wearable device comprising: a processor [fig. 2, element 116; par. 87]; and a memory coupled with the processor [par. 93], wherein the memory stores program instructions configured to: retrieve motion data of the user, wherein the motion data is recorded by an IMU (Inertial Measurement Unit) [par. 102 “obtaining a series of inertial measurement unit (IMU) data from at least a first IMU (e.g. IMU 114) worn on the subject's foot over the time window”] and a pedometer [par. 95 “various supplemental sensors may be included in the insole 100, for generating supplemental measurements. Such sensors may include…a pedometer”]; process the retrieved motion data after the retrieved motion data has exceeded a pre-defined threshold [par. 125 “BOS may only be measured when certain activities are performed by the subject (e.g. walking and running)”, par. 129 “For example, for the elderly population at risk of falls, walking and sliding may be considered important activities where falls may occur. The activity classification algorithm may be used to separate the chosen activities from other activities, such as standing, sitting, jumping, running, or other activities”]; predict fall risk of the user using a first trained ML model, wherein the processed motion data is provided as input for the implementation of the first trained ML model [par. 125 “The four values may be part of the input data that is input into the machine learning model”, par. 142 “While performing the ambulatory activity or other chosen activity, training data is recorded from the sensors and other devices”]; determine an accuracy of fall of the user based on analysis of the predicted fall risk [par. 180 “After training the machine learning model, the inputs may be evaluated to determine their value in predicting accurate fall prediction data”]; process successive data frames using a second trained ML model to confirm the fall of the user, based on additional data [par. 193 “A separate machine learning model may be used for retrospectively detecting a fall”], wherein the fall is confirmed by analyzing post-fall motion indicated by the successive data frames [par. 193 “The system may also have the capability to detect when a fall has occurred (i.e. the system may retrospectively detect a fall in addition to prospectively predicting a fall)”]; and transmit a distress signal if fall of the user is confirmed [par. 193 “The system may also alert a caretaker and/or third party when a fall has occurred using different visual, tactile and or auditory alerts”]
However, Cheng does not teach determine a confidence score of fall of the user based on analysis of the predicted fall risk
Junker teaches determine a confidence score of fall of the user based on analysis of the predicted fall risk [par. 174 “In some embodiments, the criteria include determining whether a future fall is predicted with a sufficient level of confidence”, 185 “the machine learning system may determine whether a fall is predicted to occur, the generated confidence in the possible future fall, and the like”]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, to incorporate determine a confidence score of fall of the user based on analysis of the predicted fall risk, for determining if interventions should be initiated, as evidence by Junker [par. 185].
However, Cheng does not teach wherein the fall is confirmed by analyzing post-fall motion behavior indicated by the successive data frames, the post-fall motion behavior including characteristic movement and body positioning of the user after the fall; and wherein the second trained ML model is trained on labelled frame data, including post-fall motion behavior.
Filatov teaches wherein the fall is confirmed by analyzing post-fall motion behavior indicated by the successive data frames, the post-fall motion behavior including characteristic movement and body positioning of the user after the fall [par. 123, 141]; and wherein the second trained ML model is trained on labelled frame data, including post-fall motion behavior [par. 56, 82, 123]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, to incorporate wherein the fall is confirmed by analyzing post-fall motion behavior indicated by the successive data frames, the post-fall motion behavior including characteristic movement and body positioning of the user after the fall; and wherein the second trained ML model is trained on labelled frame data, including post-fall motion behavior, for determining if the user has suffered an injury, as evidence by Filatov [par. 141]
Regarding claim 5, Cheng further teaches wherein the first trained ML model is trained using a reduced data obtained from processing of labelled frame data of multiple users [par. 151], wherein the first trained data model is a neural-network based classifier [par. 8, 103].
Regarding claim 6, Cheng further teaches the labelled frame data of each user comprises fall taken class of data frames and non-fall taken class of data frames [par. 128 “which uses certain statistics and features to classify between fallers and non-fallers”]
Regarding claim 7, Cheng further teaches the processing of the labelled frame data involves the extraction of frequency and time domain features of the labelled frame data to reduce data dimensionality [par. 126]
Regarding claim 8, Cheng further teaches the IMU is used for recording 6-axis motion data comprising acceleration and angular velocity along three axes (x, y, z) [par. 115], and the pedometer is used for determining step count of the user [par. 95, 198]
Regarding claim 10, Cheng further teaches wherein the distress signal is transmitted by a device connected to the wearable device [par. 183], and includes fall and non-fall taken frames [par. 90, 146].
Regarding claim 11, Cheng further teaches wherein the second trained ML model is pre-trained based on data belonging specifically to the first trained ML model [par. 151 “The first version of parameters and outputs may be used as the starting point for training the second version of parameters and outputs”].
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker and Filatov and in further view of Zheng (U.S. Patent Application Document 2020/0205697) and Huang (U.S. Patent Application Document 2023/0186784)
Zheng and Huang were applied in the previous office action
Regarding claim 2, Cheng further teaches the program instructions for predicting the fall risk of the user is further configured to: calculate a stride length of the user from data obtained from the IMU during a walking session [par. 128], assess balance of the user by analyzing variability in acceleration and angular velocity data obtained from the IMU [par. 115, 127,136]; assess walking symmetry by analyzing deviation of consistency of forward and backward movement of upper body obtained from the IMU [par. 118, 128 “The critical statistics and features may be designated as more important for predicting falls and may include the statistics and features indicating symmetry”, 132 “general asymmetry between left and right sides of the body, or on the upper or lower part of the body”]; calculate steadiness of the user’s walking pattern based on variability of the stride length, balance, and walking symmetry of the user [par. 128, 136]; and calculate the fall risk based on the user’s walking symmetry and steadiness of the user’s walking pattern [par. 128 “The critical statistics and features may be designated as more important for predicting falls and may include the statistics and features indicating symmetry, stability, and/or motion patterns”].
However, Cheng, Junker and Filatov do not teach wherein the walking session is identified based on step counts above a threshold value; assess walking symmetry by analyzing deviation of consistency of forward and backward movement of hands
Zheng teaches the walking session is identified based on step counts above a threshold value [par. 173]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker and Filatov, to incorporate the walking session is identified based on step counts above a threshold value, for indicating fall risk in order to generate a high-fall risk warning, as evidence by Zheng [par. 173].
Huang teaches assess walking symmetry by analyzing deviation of consistency of forward and backward movement of hands [par. 70].
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker and Filatov, to incorporate assess walking symmetry by analyzing deviation of consistency of forward and backward movement of hands, for error detection, as evidence by Huang [par. 70].
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker, Filatov, Zheng and Huang and in further view of Sheikh (U.S. Patent Application Document 2013/0040656)
Sheikh was applied in the previous office action
Regarding claim 3, Cheng, Junker, Filatov, Zheng and Huang teach a wearable device for monitoring and analysis of user movements for predicting fall risk, as disclosed above
Cheng further teaches wherein the data obtained from the IMU to calculate the stride length includes acceleration data, vertical acceleration component, horizontal acceleration component [par. 120]
However, Cheng, Junker, Filatov, Zheng and Huang do not teach vertical displacement and horizontal displacement
Sheikh teaches vertical displacement and horizontal displacement [par. 54 “The vertical distance between B and D will correspond to the stride length in the vertical direction and the horizontal distance will correspond to the horizontal displacement in the Y-Z plane”]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker, Filatov, Zheng and Huang, to incorporate vertical displacement and horizontal displacement, as are common techniques for determining stride length, as evidence by Sheikh [par. 54].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker, Filatov, Zheng and Huang and in further view of Denton (U.S. Patent Number 12020511)
Denton was applied in the previous office action
Regarding claim 4, Cheng, Junker, Filatov, Zheng and Huang teach a wearable device for monitoring and analysis of user movements for predicting fall risk, as disclosed above
However, Cheng, Junker, Filatov, Zheng and Huang do not teach wherein the walking session is identified if step count of the user is above the threshold without significant pauses.
Denton teaches wherein the walking session is identified if step count of the user is above the threshold without significant pauses [col. 3: lines 15-18]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker, Filatov, Zheng and Huang, to incorporate the walking session is identified if step count of the user is above the threshold without significant pauses, for preventing false positives, as evidence by Denton [col. 3: lines 15-18].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker and Filatov and in further view of Hochman (U.S. Patent Application Document 2023/0414129)
Hochman was applied in the previous office action
Regarding claim 9, Cheng, Junker and Filatov teach a wearable device for monitoring and analysis of user movements for predicting fall risk, as disclosed above
However, Cheng, Junker and Filatov do not teach wherein the processing of retrieved motion data involves at least one of formatting, cleaning, or arranging the motion data in a suitable format.
Hochman teaches wherein the processing of retrieved motion data involves at least one of formatting, cleaning, or arranging the motion data in a suitable format [par. 127]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker and Filatov, to incorporate wherein the processing of retrieved motion data involves at least one of formatting, cleaning, or arranging the motion data in a suitable format, for transforming data to a desired format, as evidence by Hochman [par. 127]
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker and Filatov and in further view of Denton
Regarding claim 12, Cheng, Junker and Filatov teach a wearable device for monitoring and analysis of user movements for predicting fall risk, as disclosed above
However, Cheng, Junker and Filatov do not teach wherein the wearable device is a smart ring
Denton teaches wherein the wearable device is a smart ring [col. 2: lines 45-52]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker and Filatov, to incorporate wherein the wearable device is a smart ring, as a means tracking data of a user from accelerometers, GPS and step counter, as evidence by Denton [col. 2: lines 43-56].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Junker and Filatov and in further view of Ankem (U.S. Patent Application Document 2023/0045099)
Regarding claim 13, Cheng, Junker and Filatov teach a wearable device for monitoring and analysis of user movements for predicting fall risk, as disclosed above
However, Cheng, Junker and Filatov do not teach wherein both the first trained ML model and the second trained ML model employ binary classification and use the threshold value to distinguish between fall-related events and normal activities
Ankem teaches wherein both the first trained ML model and the second trained ML model employ binary classification and use the threshold value to distinguish between fall-related events and normal activities [par. 76, 93]
Therefore, it would have been prima facie obvious to a person having ordinary skill in the art when the invention was filed to modify the method as taught by Cheng, Junker and Filatov, to incorporate wherein both the first trained ML model and the second trained ML model employ binary classification and use the threshold value to distinguish between fall-related events and normal activities, for more accurately detecting a fall of the user, as evidence by Ankem [par. 74]
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE ROZANSKI whose telephone number is (571)272-7067. The examiner can normally be reached M-F 8 AM - 5 PM.
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/GRACE L ROZANSKI/Examiner, Art Unit 3791
/ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791
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