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 .
Response to Amendment
The amendment filed 12/04/2025 has been entered. Claims 1, 2, 4 - 8, and 10 - 12 are pending. Claims 3 and 9 are cancelled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 4 - 8, and 10 - 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to claim 1, claim 1 is directed towards “a system” but only positively claims the structure of “at least one processor”. A single element does not constitute a system, making the metes and bounds of what the Applicant is attempting to claim as the invention unclear. Additionally, claim 1 recites, “wherein a result of the first to fourth geriatric syndrome prediction models is digitized and summed”. However, as written, it is unclear if the limitation refers to a single result generated from any of the four geriatric syndrome prediction models or a first, second, third, and fourth result generated from the first, second, third, and fourth geriatric syndrome prediction models. Further, if the claim limitation only requires a single result, it is unclear how a single result can be summed. Examiner is interpreting the claim to require a first, second, third, and fourth result generated from the first, second, third, and fourth geriatric syndrome prediction models. Claims 2, and 4 - 6 are rejected by virtue of dependence on claim 1.
In regard to claim 5, lines 5 - 6 recite, “the second learning model is the machine-trained artificial neural network model to predict the risk degree of geriatric syndrome…” but “the machine-trained artificial neural network model to predict the risk degree of geriatric syndrome…” lacks antecedent basis.
In regard to claim 7, claim 7 recites, “wherein a result of the first to fourth geriatric syndrome prediction models is digitized and summed”. However, as written, it is unclear if the limitation refers to a single result generated from any of the four geriatric syndrome prediction models or a first, second, third, and fourth result generated from the first, second, third, and fourth geriatric syndrome prediction models. Further, if the claim limitation only requires a single result, it is unclear how a single result can be summed. Examiner is interpreting the claim to require a first, second, third, and fourth result generated from the first, second, third, and fourth geriatric syndrome prediction models. Claims 8, 10 - 12 are rejected by virtue of dependence on claim 7.
In regard to claim 10, lines 5 - 6 recite, “the second learning model is the machine-trained artificial neural network model to predict the risk degree of geriatric syndrome…” but “the machine-trained artificial neural network model to predict the risk degree of geriatric syndrome…” lacks antecedent basis.
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, 2, and 4 - 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system for predicting geriatric syndrome, which is within a statutory category of invention, to “generate foot characteristic information… gait characteristic information… and balance characteristic information of the subject…” and “predict a risk degree of geriatric syndrome of the subject based on the foot characteristic information of the subject, gait characteristic information of the subject, and the balance characteristic information of the subject, by using a second learning model,” which falls into the category of a mental process. This judicial exception is not integrated into a practical application because with regard to Revised step 2A, prong 1, an exception is present as noted above and with regard to Revised step 2A, prong 2, the claim does not recite additional elements that integrate the judicial exception into practical application. Further, with regard to Revised step 2B, the claim does not recite additional elements that integrate the judicial exception into practical application. In particular, while claim 1 includes the limitation of using a processor to “acquire a foot depth image from a scanner… and acquire plantar pressure data from a pressure sensor…”, claim 1 fails to positively claim the step of using the “scanner” and “pressure sensor” or the physical structures of the “scanner” and “pressure sensor” as part of the system, such that the step is directed towards a generalized data gathering step with no specific structure required to “acquire” the data and is not sufficient to integrate the judicial exception into practical application. While claim 1 includes the limitation of “at least one processor,” a processor is considered a general structure that does not impose meaningful limitations or sufficient structure to integrate the judicial exception into practical application. Additionally, “using a first learning model” and “using a second learning model,” both of which are not positively claimed as a part of the system, to generate a result is directed towards a generic method of processing data and is not sufficient to integrate the judicial exception into practical application. Claim 2 is directed towards the type of data acquired and is not sufficient to integrate the judicial exception into practical application. Claim 4 is directed towards a database for storing the first and second learning models and would be considered a general structure that does not impose meaningful limitations or sufficient structure to integrate the judicial exception into practical application. Claim 5 is directed towards further details of the first and second learning model, which are not positively claimed in independent claim 1 from which claim 5 depends, and thus do not impose meaningful limitations or sufficient structure to integrate the judicial exception into practical application. Claim 6 is directed towards further details on the acquisition of data which would be considered a generalized data gathering step as the physical structures used to collect the data and the actual step of collecting said data are not positively claimed in the system limitations and is not sufficient to integrate the judicial exception into practical application.
Response to Arguments
Applicant’s arguments, see Remarks, filed 12/04/2025, with respect to the rejections of claim 1 & 6 under 35 U.S.C. 112(a) have been fully considered and are persuasive. The rejections of claim 1 & 6 under 35 U.S.C. 112(a) have been withdrawn.
Applicant’s arguments, see Remarks, filed 12/04/2025, with respect to the rejections of claims 3 & 9 under 35 U.S.C. 112(b) have been fully considered and are persuasive as claims 3 & 9 have been cancelled.
Applicant's arguments, see Remarks, filed 12/04/2025, with respect to the rejections of claims 1, 2, 4 - 8, & 10 - 12 under 35 U.S.C. 101 have been fully considered and are partially persuasive.
In regard to claims 1, 2, & 4 - 6, Examiner notes that features of independent claim 1 are directed towards a mental process, specifically the generating of foot, gait, and balance characteristic information and the prediction of a risk degree of geriatric syndrome as described above in the 35 U.S.C. 101 rejection of claims 1, 2, & 4 - 6. While Applicant alleges that claim 1 is “rooted in specific physical hardware configured with posture-dependent measurement conditions,” claim 1 merely includes the physical structure of “at least one processor,” which is directed towards a generalized structure that does not impose meaningful limitations or sufficient structure to integrate the judicial exception into practical application. Applicant argues that claim 1 includes “a scanner installed beneath the footrest” and “a pressure sensor installed under a corner of the footrest”, but neither structure are positively claimed as a part of the system nor the steps of using the “scanner” and “pressure sensor” to actually collect the data used in analysis such that the processing step is directed towards a generalized data gathering step with no specific structure required to “acquire” the data and is not sufficient to integrate the judicial exception into practical application. Additionally, as described above in the 35 U.S.C. 101 rejection of claims 1, 2, & 4 - 6, “using a first learning model” and “using a second learning model,” as a part of the system to generate a result regardless of the number of distinct prediction models included in the “second learning model” is directed towards a generic method of processing data and is not sufficient to integrate the judicial exception into practical application. Examiner notes that the 35 U.S.C. 101 rejections of claims 1, 2, & 4 - 6 could be overcome by including the details of the “scanner” and “pressure sensors” as a part of the system and positively claiming the data collection step using the specific elements.
In regard to claims 7, 8, & 10 - 12, sufficient details pertaining to the acquisition of the foot depth and plantar pressure data and connected to the structure of the scanner and pressure sensor have been provided such that the judicial exception has been integrated into practical application. As such, the rejections of claims 7, 8, & 10 - 12 under 35 U.S.C. 101 have been withdrawn.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: while CN Pub. No. CN112401834A (hereinafter ‘834 - Previously Cited) discloses a system and method for predicting geriatric syndrome comprising at least one processor configured to collect depth information and plantar pressure information of the test person (paragraph [0012], lines 1 - 2) and generate foot characteristic information (paragraph [0022], line 136 - 138), gait characteristic information by using a first learning model (paragraph [0020] - [0022]), and plantar pressure information of the subject (paragraph [0046]); FIG. 3), and further predict a risk degree of geriatric syndrome of the subject using a second learning model based on generated information on gait and sole pressure (paragraph [0034]), they do not suggest that the second learning model comprises four different geriatric syndrome prediction models that use foot characteristic information, gait characteristic information, and balance characteristic information to determine a degree of frailty, cognitive impairment, muscle loss, and depression respectively and sum the results to generate a score or grade associated with the geriatric syndrome prediction. The use of the four different geriatric syndrome prediction models is additionally not suggested or obvious when ‘834 is modified by US20210279900A1 (hereinafter Schwartz; Previously Cited), which teaches that balance characteristic information can be determined from plantar pressure data over time (FIG. 3B, components 352 & 354; paragraph [0037]) or when ‘834 is modified by Wei et al., “The Static Standing Postural Stability Measured by Average Entropy” 10 December 2019, Entropy, 21, 1 - 10 (hereinafter Wei et al.; Previously Cited) that further teaches that balance characteristic information of a subject can be determined from plantar pressure data obtained in a state in which the subject’s posture is unstable (section, “Abstract”).
While WO2022067189A1 (hereinafter Pascual-Leone; Previously Cited) teaches a depression prediction model and cognitive impairment prediction model based on input data, such as data collected when a subject performs a variety of tasks and/or assessments (paragraphs [0107] & [0113]), Pascual-Leone does not suggest that the data collected and used to assess depression and cognitive impairment includes foot characteristic information, gait characteristic information, and balance characteristic information or that the prediction models can be combined with prediction models for determining a degree of frailty or muscle loss to predict geriatric syndrome. Similarly, US2020052230A1 (hereinafter Hyun; Previously Cited) teaches a geriatric syndrome prediction model that determines a degree of frailty or a physical frailty index based on input data (paragraph [0071]) and a degree of cognitive impairment (paragraph [0071]), but does not use includes foot characteristic information, gait characteristic information, and balance characteristic information to determine the degree of frailty or cognitive impairment or suggest that the models can be combined with other geriatric syndrome prediction models that determine a degree of depression or muscle loss to generate a score representing a risk of geriatric syndrome.
Claims 7, 8, & 10 - 12 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Conclusion
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/ERIC F WINAKUR/Primary Examiner, Art Unit 3791
/S.C.P./Examiner, Art Unit 3791
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