Notice of Pre-AIA or AIA Status
The present application 18/704,318, filed on 4/24/2024 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
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.
This application is a 371 of PCT/EP2022/080160 filed 10/28/2022
DETAILED ACTION
Claims 1-16 are pending in this application.
Examiner acknowledges applicant’s preliminary amendment filed on 4/24/2024
Drawings
The Drawings filed on 4/24/2024 are acceptable for examination purpose.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Priority
Acknowledgment is made of applicant’s claim for SWEDEN foreign priority under
35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. SWEDEN application # 2151327-0 filed 10/29/2021.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims 12-13,16 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim 12-13,16, limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 12, the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information
the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information
Claim 13, the fall assessment environment comprises a machine learning model, comprising the trained machine learning model being trained in accordance with the computer-implemented method, and/or being trained in accordance with a computer-implemented method comprising the preparation of training data, of claim 1, a user, sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information
Claim 16, the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information;
Because this/these claim 12-13,16 limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification page 20-26 as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claims 14-15 depend on claim 13 are rejected on that basis.
Claim 12-13,16 is/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
Claim 12, the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information
Claim 13, the fall assessment environment comprises a machine learning model, comprising the trained machine learning model being trained in accordance with the computer-implemented method, and/or being trained in accordance with a computer-implemented method comprising the preparation of training data, of claim 1, a user, sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information
Claim 16, the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information;, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In view of specification page 20-26, is devoid of adequate structure to perform the claimed function. In particular, the specification state the claimed function predetermined preparation of training data, and trained using machine learning model. There is no disclosure of any particular structure, either explicitly or inherently, to perform calculating plurality of real storage costs. The use of the term “calculating” is not adequate structure for performing the predetermined number of fragments and sub fragments because it does not describe a particular structure for performing the function for example sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information
As would be recognized by those of ordinary skill in the art, refers to mere routine(s) can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function(s). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 14-15 depend on claim 13 are rejected on that basis.
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.
Claim 4-5,7-9-13,15-16 is/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
Claims 4-5,8-9-13,15-16, it is unclear what is meant by “and/or”, that makes the claim indefinite
Claim 7, it is unclear what is meant by “majority voting scheme”, particularly the term “majority” is a relative term that makes the claim indefinite
Claim 8, it is unclear what is meant by “more precise” dynamic segmentation”, particularly the term “more precise” is a relative term that makes the claim indefinite
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Claim 1-16 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, Federal Register (84 FR 50) on January 7, 2019 hereinafter 2019 PEG
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method of claim 1,13, directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A prong one of the 2019 PEG, the limitations reciting the abstract idea are highlighted, and the limitations directed to additional elements are highlighted, as set forth in exemplary claim 1
Claim 1, A computer-implemented method for training of a machine learning model in fall assessment comprising fall detection, for a fall assessment training environment; the method comprising implementing the machine learning model, and preparing training data, where preparing training data comprises
obtaining and automatically annotating sensor data generated from sensors
collecting data from subjects and coupling values of each subject to their specifically obtained, and annotated, sensor data, respectively,
using the sensor data for generating a time series that comprises data
points,
automatically annotating the data points such that each data point is
annotated reflecting the corresponding sensor data, respectively
using the annotated data points, dynamically segmenting said time series
into at least event segments associated with fall events and event segments
that are not associated with fall events
using the event segments to construct time windows along the
time series such that the time series is discretized, each time window
comprising a plurality of time steps and
automatically annotating the constructed time windows where
the automatic annotation of the time windows comprises information that identifies if the data points associated with the time steps window belong to a fall event or to a non-fall event”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, fall assessment, annotating, event segments, data points, time-series and like, mere data collection
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea
With respect to Step 2A prong two of the 2019 PEG, the judicial exception is not integrated into a practical application. The additional elements are directed to method steps, however, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular data structure of, to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Furthermore, although these elements have been fully considered, they are directed to the use of generic computing elements (page 20-26 of the instant specification make it clear that the disclosed functionality is implemented on well-known computing systems and general purpose computing devices) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the 2019 PEG) and is amount to simply saying "apply it" using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment computer based operating environment) by using the computer as a tool to perform the abstract idea.
Since the analysis of Step 2A prong one and prong two results in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional method limitations are directed to a generic computer, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition page 20-26 of the instant specification describe generic off-the-shelf computer-based elements for implementing the claimed invention which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc.
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
As to claim 2, further elaborates “The computer-implemented method according to claim 1, wherein the automatic annotation of the time windows comprises
- information that identifies the position of the time-steps belonging to the fall segment in each time window and
- information regarding an assigned weight to each time window where the assigned weight has been determined in dependence of said position”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 3, further elaborates , The computer-implemented method according to claim 1 further comprising
“communicating information comprising the prepared training data to the machine
learning model and
training the machine learning model using the training data comprising the prepared training data in fall assessment”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 4, further elaborates , The computer-implemented method according to claim 1, “wherein the obtained, and annotated, sensor data comprise sensor data generated during doings of the trainer subjects said doings comprising activities of daily living (ADLs), including real fall events, and/or simulated falls”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 5, further elaborates , The computer-implemented method according to claim l, “wherein the sensors comprise at least one of motion sensors, 3D sensors, accelerometers, gyroscopes and/or cameras”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 6, further elaborates , The computer-implemented method according to claim 1, “wherein the preparation of training data further comprises setting fall threshold values for sensor data and assignment of fall event segments if relevant sensor data are more than said fall threshold values”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 7, further elaborates , The computer-implemented method according to claim 1,, “wherein the preparation of training data further comprises utilization of a majority voting scheme for determining said corresponding sensor data to each annotated data point and for determining said dynamic segmentations into event segments”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 8, further elaborates The computer-implemented method according to claim 1, “wherein profile properties of the trainer subject profiles comprise profile properties, such as, height, weight and/or Body Mass Index (BMI), and wherein the values of the profile properties enable
- correlation of trainer subject profiles to corresponding user profiles, and
- more precise dynamic segmentation”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 9, further elaborates “A computer program comprising computer readable instructions for applying the computer-implemented method, and/or the preparation of training data, according to claim 1, and/or a computer readable medium comprising said computer program”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 10, further elaborates “A computer program comprising computer readable instructions for a machine learning model according to claim 1 and/or a computer readable medium comprising said computer program”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 11, further elaborates “A control unit arrangement for training of a machine learning model, adapted to control at least, the implementing the machine learning model according to Claim 1 and/or the preparation of training data”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 12, further elaborates “A system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1, and for enabling the preparation of the training data
wherein the fall assessment comprises fall detection;
the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information;
wherein the system utilizes a computer program comprising computer readable instructions for applying the computer implemented method, and/or the preparation of training data, and/or a computer readable medium comprising said computer program”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
claim 13, further elaborates “A computer-implemented method for fall assessment, and for communication of information from the fall assessment, for a fall assessment environment;
wherein the fall assessment comprises fall detection;
the fall assessment environment comprises a machine learning model, comprising the trained machine learning model being trained in accordance with the computer-implemented method, and/or being trained in accordance with a computer-implemented method comprising the preparation of training data, of claim 1, a user, sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information;
wherein the computer-implemented method comprises implementing the machine learning model comprising obtaining, and annotating, user sensor data generated from sensors collecting data from a user, wherein the user has a user profile, and the computer-implemented method further comprises obtaining values of the user profile properties, coupling to the user sensor data, and processing the coupled user sensor data by means of the machine learning model comprising the trained machine learning model, and/or being trained, and
wherein the computer-implemented method comprises the fall assessment and the communication of information from the fall assessment, wherein the fall assessment comprises fall detection”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
.
Claim 14, further elaborates “A computer program comprising computer readable instructions for applying the computer-implemented method according to claim 13, and/or a computer readable medium comprising said computer program”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 15, further elaborates “A control unit arrangement for fall assessment, adapted to control at least, enablement of the computer-implemented method for fall assessment according to claim 13, and/or the implementing of the machine learning model, comprising the trained machine learning model”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
claim 16, further elaborates “A system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1, and for enabling the preparation of training data;
wherein the fall assessment comprises fall detection;
the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information;
wherein the system utilizes a computer program comprising computer readable instructions for the machine learning model, and/or a computer readable medium comprising said computer program”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
.
Claim Rejections - 35 USC § 103
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 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Forth et al., (hereafter Forth), US Pub. No. 2017/0000387 published Jan 2017 in view of Heaton et al., (hereafter Heaton), US Pub. No. 2018/0000385 published Jan 2018
As to Claim 1, Forth teaches a system which including “a computer-implemented method for training of a machine learning model in fall assessment comprising fall detection, for a fall assessment training environment; the method comprising implementing the machine learning model, and preparing training data, where preparing training data comprises” (Forth: Abstract, fig 3, element 330-340- Forth teaches fall risk data monitoring from the load sensors , and fall risk is estimated using machine learning process)
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“obtaining and automatically sensor data generated from sensors collecting data from subjects and coupling values of each subject to their specifically obtained, and, sensor data, respectively” (Forth: 0041, 0059 – Forth teaches data analysis module that automatically receives sensor data in determining fall risk, further dynamic sensor data collected from different sources of postures used in data analysis for classifying fall risk levels and maintains metrics),
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“using the sensor data for generating a time series that comprises data
Points” (Forth: fig 2, 0042, 0044,0048 - Forth teaches sensor data from the postural states both static, dynamic states processed using punctuated equilibrium model or PEM that including equilibria identified in a “time-series”, further load sensor inputs generate the timeseries of center of pressure or COP data or COP data series)
“automatically the data points such that each data point is reflecting the corresponding sensor data, respectively” (Forth: fig 1-3, element 110, 310, 0045 – Forth teaches automatically generating not only time-series data processing but also acquiring load data points from multiple load sensors over a period of time in estimating fall risk)
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“using the data points, dynamically segmenting said time series into at least event segments associated with fall events and event segments that are not associated with fall events” (Forth: fig 3, 5A-5B, 0010, 0045, 0050-0051,0055-0056 – Forth teaches processing data points from the time series associated with fall risk particularly using center of pressure data, while time series data represents postural stability and performing additional processing the sensor data to present balance and fall risk data but not representing fall events, as such it represents trend with specific fall risk data associated with respective scores)
“using the event segments to construct time windows along the time series such that the time series is discretized, each time window comprising a plurality of time steps” (Forth: fig 5-7, fig 9, 0049-0053 – Forth teaches metrics of IPM and PEM model data classified with respect to fall risk event(s) as time series data, Forth’s fig 7 describes level of fall risk with respect to scores, and these scores are derived from the above model constructed based on time series and Forth’s fig 9 teaches graph data of particular classified activity representing not only fall risk, but also trend of changes in fall risk based on event segments of respective time series data)
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“automatically the constructed time windows where the automatic of the time windows comprises information that identifies if the data points associated with the time steps window belong to a fall event” (Forth: fig 9, 0055 – Forth teaches data classified in COP and PEM model calculating postural stability, scores representing time-series data and these time series represents increase and reduced balance and fall risk of particular activity)
It is however, noted that Forth does not teach “automatically annotating sensor data”, automatic annotation, non-fall event, although Forth teaches processing sensors data, computing and estimate[ing] fall risk using machine learning algorithms and like (Forth: Abstract). On the other hand, Heaton disclosed “automatically annotating sensor data, automatic annotation” (Heaton: 0057,0082,0088-0089 – Heaton teaches fall event incidents being automatically labeled in the corpus of sensor data received from the wearable device, i.e., device is responsive to detected fall event, further it is noted that Heaton teaches automatically label “detected fall event” via incident report because Heaton’s fall detection model includes an artificial neural network as detailed in 0089 , this automatically annotating sensor data is the process of using machine learning algorithms that annotates and/or tags or labels the sensor data, as such machine learning technique(s) to automatically generate annotations or labels for data. The prior art of Heaton teaches “non-fall event”(Heaton: 0046, fig 4-5 – Heaton teaches fall detection model where wearable device senses, transfer the data contents processed in training the fall detection model on data correlated with “non-fall events” and “fall events”)
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It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention responding to falls by residents in a facility including sensor integrated into wearable device particularly using fall detection model of Heaton et al., into identifying fall risk using machine learning particularly fall risk detection model of Forth et al., because both Forth, Heaton teaches fall detection, fall risk using machine learning model (Forth: Abstract, fig 3; Heaton: fig 1, Abstract) and both Forth, Heaton teaches processing sensor data to the fall detection model (Forth: fig 3; Heaton: fig 1, 0010), and they both Forth, Heaton are from the same field of endeavor. Because both Forth, Heaton teaches processing sensor data using machine learning model particularly training sensor data for fall detection, it would have been obvious to one skilled in the art to substitute and/or modify one method for the other particularly automatically labelling sensor data that allows to add annotation and /or labels such as activity types of the users recorded from the wearable devices, processing sensor data to train fall detection model(s) (Heaton: fig 1) thereby teaching models more accurate, further allows remotely processing wearable device sensor data into the complete fall detection model that allows care provider to assist the users (Heaton: fig 1-4, 0011-0012), thus improves quality and reliability of the fall detection system.
As to claim 2, the combination of Forth, Heaton disclosed:
- “information that identifies the position of the time-steps belonging to the fall segment in each time window” (Forth: fig 5-7, fig 9, 0049-0053) and
-“ information regarding an assigned weight to each time window where the assigned weight has been determined in dependence of said position” (Forth: 0011, 0014,0048). On the other hand, Heaton disclosed “automatic annotation” (Heaton: 0057,0082,0088-0089)
As to Claim 3, the combination of Forth, Heaton disclosed:
“ communicating information comprising the prepared training data to the machine learning model” (Forth: Abstract, 0045-0046, fig 3) and
“training the machine learning model using the training data comprising the prepared training data in fall assessment” (Forth: fig 3, 0046).
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As to Claim 4, the combination of Forth, Heaton disclosed: “wherein the obtained, and annotated, sensor data comprise sensor data generated during doings of the trainer subjects said doings comprising activities of daily living (ADLs), including real fall events, and/or simulated falls” (Heaton: 0096-0097 – Heaton teaches fall detection models tailored to identify and handle “activity of daily living” or ADL.
As to Claim 5, the combination of Forth, Heaton disclosed “wherein the sensors comprise at least one of motion sensors, 3D sensors, accelerometers, gyroscopes and/or cameras” (Forth: Abstract, fig 2, element 110 – Forth teaches load sensors used in punctuated equilibrium model in track changes in fall risk and changes in health status; Heaton teaches motion sensors-fig 3, 0013; accelerometers, gyroscopes – fig 1, 0032,0040).
As to Claim 6, the combination of Forth, Heaton disclosed “wherein the preparation of training data further comprises setting fall threshold values for sensor data and assignment of fall event segments if relevant sensor data are more than said fall threshold values” (Forth: 0011,0045,0053 – Forth teaches fall risk measurement and analysis defining the threshold parameters value range assigned to the score for classifying patients)
As to Claim 7, the combination of Forth, Heaton disclosed “wherein the preparation of training data further comprises utilization of a majority voting scheme for determining said corresponding sensor data to each data point and for determining said dynamic segmentations into event segments” (Forth: fig 4-5, 0046-0047, 0049-0051 – Forth teaches multiple individual models are trained in dynamically generating respective metrics of data points including training data in a machine learning environment). On the other hand, Heaton disclosed “annotated data point” (0057,0082,0088-0089
As to Claim 8, the combination of Forth, Heaton disclosed “wherein profile properties of the trainer subject profiles comprise profile properties, such as, height, weight and/or Body Mass Index (BMI), and wherein the values of the profile properties enable (Forth: fig 3, 0013, 0045, 0072),
- “correlation of trainer subject profiles to corresponding user profiles” (Abstract, 0039), and
- more precise dynamic segmentation (Forth: 0041-0042).
As to Claim 9, the combination of Forth, Heaton disclosed “A computer program comprising computer readable instructions for applying the computer-implemented method, and/or the preparation of training data, according to claim 1, and/or a computer readable medium comprising said computer program” (Forth: 0060, 0075,0077; Heaton: 0104, computer system element 130)
As to Claim 10, the combination of Forth, Heaton disclosed “a computer program comprising computer readable instructions for a machine learning model according to claim 1 and/or a computer readable medium comprising said computer program” (Forth: Abstract, fig 3, 0060, 0075,0077; Heaton: 0104, computer system element 130, page 10, machine learning )
.
As to Claim 11, the combination of Forth, Heaton disclosed “a control unit arrangement for training of a machine learning model, adapted to control at least, the implementing the machine learning model according to Claim 1 and/or the preparation of training data” (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning ).
As to Claim 12, the combination of Forth, Heaton disclosed “a system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1, and for enabling the preparation of the training data (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning).
“wherein the fall assessment comprises fall detection” (Forth: Abstract, fig 4; Heaton: Abstract, fig 1);
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“the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information” (Forth: Abstract, 0041,0046 0059);
“wherein the system utilizes a computer program comprising computer readable instructions for applying the computer implemented method, and/or the preparation of training data, and/or a computer readable medium comprising said computer program” (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning)
As to claim 13, the combination of Forth, Heaton disclosed “a computer-implemented method for fall assessment, and for
communication of information from the fall assessment, for a fall assessment environment” (Forth: Abstract, fig 3; Heaton: Abstract, fig 1-3);
“wherein the fall assessment comprises fall detection (Forth: Abstract, fig 3; Heaton: Abstract, fig 1-3);;
the fall assessment environment comprises a machine learning model, comprising the trained machine learning model being trained in accordance with the computer-implemented method, and/or being trained in accordance with a computer-implemented method comprising the preparation of training data, of claim 1, (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning) a user, sensors collecting data from the user, means for obtaining data, means for processing data and means for communication of information (Forth: Abstract, 0041,0046 0059);
“wherein the computer-implemented method comprises implementing the machine learning model comprising obtaining, and, user sensor data generated from sensors collecting data from a user, wherein the user has a user profile,(Forth: 0056-0057,0059, fig 11; ; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning) and the computer-implemented method further comprises obtaining values of the user profile properties, coupling to the user sensor data, and processing the coupled user sensor data by means of the machine learning model comprising the trained machine learning model, and/or being trained” (Forth: Abstract, 0041,0046 0059; Heaton: fig 2-3), and
“wherein the computer-implemented method comprises the fall assessment and the communication of information from the fall assessment, wherein the fall assessment comprises fall detection” (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning, fig 1-4).On the other hand, Heaton disclosed annotating data (Heaton: 0057,0082,0088-0089)
As to Claim 14, A computer program comprising computer readable instructions for applying the computer-implemented method according to claim 13, and/or a computer readable medium comprising said computer program.
Claim 15, the combination of Forth, Heaton disclosed “a control unit arrangement for fall assessment, adapted to control at least, enablement of the computer-implemented method for fall assessment according to claim 13, and/or the implementing of the machine learning model, comprising the trained machine learning model (Forth: Abstract, fig 3; Heaton: Abstract, fig 1-4, page 10, 0071-0072);
As to claim 16, the combination of Forth, Heaton disclosed “a system for training of a machine learning model in fall assessment, for communication of information, for enabling implementing the machine learning model according to claim 1, and for enabling the preparation of training data” (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning).
wherein the fall assessment comprises fall detection (Forth: Abstract, fig 3; Heaton: Abstract, fig 1-3);
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“the system comprises a machine learning model, trainer subjects, sensors collecting data from the trainer subjects, means for obtaining data, means for processing data and means for communication of information” (Forth: Abstract, 0041,0046 0059);
“wherein the system utilizes a computer program comprising computer readable instructions for the machine learning model, and/or a computer readable medium comprising said computer program” (Forth: Abstract, fig 3, 0046, 0060, 0075,0077; Heaton: 0071, 0073, 0104, computer system element 130, page 10, machine learning)
Conclusion
The prior art made of record
a. US Pub. No. 2017/0000387
b. US Pub. No. 2018/0000385
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201,73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154
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