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 .
Election/Restrictions
Applicant’s election without traverse of Group I: Claims 1 - 18 in the reply filed on 10 FEBRUARY 2026 is acknowledged.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Information Disclosure Statement
Regarding the IDS filed on 5/09/24:
US Patent Application Publication Reference 12 (Morris Bamberg) appears to have an incorrect publication date of 2017-03-14. This has been crossed out and corrected on the IDS to be 2009-09-24.
It appears that NPL Reference 13 (Duda) has not been provided. This reference has been accordingly crossed-out and has not been considered.
NPL References 5 (Hannink) and 8 (Zihajahzadeh) do not appear to have been supplied with the IDS on 05/09/2024 and were instead provided with the IDS on 10/30/2024. They have been accordingly crossed-out on the 05/09/2024 IDS to remove the redundancy in the record.
Claim Interpretation
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 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 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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“wearable device” in claim 1 to “access a database”.
The claim limitation is interpreted according to paragraph [0011] with “…insole module for placement in a shoe of a user. The insole module itself may include a piezoresistive sensor, an inertial sensor, a logic unit communicatively coupled to the piezoresistive sensor and to the inertial sensor, and a transmission unit. The insole module may also interface with the database and a computing unit adapted to implement the aforementioned method.”
“reference device” in claim 1 to “access a database”.
The claim limitation is interpreted according to paragraph [0011] with “the identified subset of previously collected observations of gait data from the wearable motion capture device or from the reference device”. There is no particular structure provided for the reference device, such that it can potentially also collect “observations of gait data” and that it is “more accurate” than the wearable motion capture device, whether this is another wearable device, standardized gait measurement equipment, or another processor that has a document with notes about gait parameters (such as a researcher’s accurate notes of the number of steps they observed a subject take in a given about of time.) It is unclear what structure of the reference device is intended to access a database. It is not shown in a figure. There is insufficient disclosure present of the corresponding structure that performs the claimed function “access a database”.
Because this/these claim 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 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.
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 – 18 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 1 recites the term “a wearable motion capture device” in line 4. It is unclear if this wearable motion capture device is supposed to be the same or different than the previously-recited wearable motion capture device. For the purposes of examination, the term “a wearable motion capture device” is deemed to claim “the wearable motion capture device”. Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
Claim 1 recites the term “said wearable device” in lines 10 – 11, 13 – 14, and 17. It is unclear if this is intended to be the same or different than the previously-recited wearable motion capture device. For the purposes of examination, the term “said wearable device” is deemed to claim “said wearable motion capture device”. Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
The term “accessing a database containing previously collected observations of gait data from said user or from other users, said accessing step being performed using said wearable device and a more accurate reference device” in line 10 of claim 1 is a relative term which renders the claim indefinite. The term “more accurate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear if the reference device is a different kind of device, or how the accuracy is being measured. It is also unclear if the comparison is no longer valid if steps are taken to make the original wearable motion capture device is made to be more accurate than the reference device by processing steps. It appears from [0010] that this is possibly intended to be a validation step with non-wearable, industry-standard equipment, such that data from two kinds of device are obtained. It is also suggested that the “step” language could be omitted without changing the meaning of the claim. For the purposes of examination, the term “accessing a database containing previously collected observations of gait data from said user or from other users, said accessing step being performed using said wearable device and a more accurate reference device” is deemed to claim “accessing a database containing previously collected observations of gait data from said user or from other users using said wearable device and a reference gait data measurement device.” Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
Claim limitation “reference device” in Claim 1 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. Looking to the specification, paragraph [0011] with “the identified subset of previously collected observations of gait data from the wearable motion capture device or from the reference device”. There is no particular structure provided for the reference device. It is described given a potential function to also collect “observations of gait data”, and that it is “more accurate” than the wearable motion capture device. It is unclear whether this is another wearable device, standardized gait measurement equipment, or another processor that has a document with notes about gait parameters (such as a researcher’s accurate notes of the number of steps they observed a subject take in a given about of time.) It is unclear what structure of the reference device is intended to access a database. It is not shown in a figure. There is insufficient disclosure present of the corresponding structure that performs the claimed function “access a database”. 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.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
Claim 1 recites the term “an individualized machine learning inference model” in line 15. It is unclear if this is intended to be the same or different than the previously-recited individualized machine learning inference model. For the purposes of examination, the term “an individualized machine learning inference model” is deemed to claim “the individualized machine learning inference model”. Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
Claim 1 recites the term “said reference device” in line 17. It is unclear if this is intended to be the same or different than the previously-recited more accurate reference device. For the purposes of examination, the term “said reference device” is deemed to claim “said more accurate reference device”. Claims 2 – 18 are similarly rejected due to their dependence on Claim 1.
Claim 2 recites the term “the step of applying said individualized machine learning inference model to said measurements” in lines 1 – 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited step of applying said individualized machine learning inference model to said measurements. It is also suggested that the “step of” language could be omitted without changing the meaning of the claim. For the purposes of examination, the term “the step of applying said individualized machine learning inference model to said measurements” is deemed to claim “applying said individualized machine learning inference model to said measurements”.
Claim 7 recites the term “the group consisting of” in line 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited group. For the purposes of examination, the term “the group consisting of” is deemed to claim “a group consisting of”.
Claim 8 recites the term “the step of generating dynamic plantar pressure maps” in lines 1 – 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited step of generating dynamic plantar pressure maps. It is also suggested that the “step of” language could be omitted without changing the meaning of the claim. For the purposes of examination, the term “the step of generating dynamic plantar pressure maps” is deemed to claim “generating dynamic plantar pressure maps”.
Claim 9 recites the term “the step of classifying activities” in line 1. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited step of classifying activities. It is also suggested that the “step of” language could be omitted without changing the meaning of the claim. For the purposes of examination, the term “the step of classifying activities” is deemed to claim “classifying activities”.
Claim 12 recites the term “the step of providing gait and/or balance rehabilitation” in lines 1 - 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited step of providing gait and/or balance rehabilitation. It is also suggested that the “step of” language could be omitted without changing the meaning of the claim. For the purposes of examination, the term “the step of providing gait and/or balance rehabilitation” is deemed to claim “providing gait and/or balance rehabilitation”
Claim 13 recites the term “the use of said individualized machine learning inference model to diagnose” in lines 1 – 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited use of said individualized machine learning inference model to diagnose. For the purposes of examination, the term “the use of said individualized machine learning inference model to diagnose” is deemed to claim “using said individualized machine learning inference model to diagnose”.
Claim 13 recites the term “the risk of musculoskeletal injuries” in line 3. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited risk of musculoskeletal injuries. For the purposes of examination, the term “the risk of musculoskeletal injuries” is deemed to claim “a risk of musculoskeletal injuries”.
Claim 16 recites the term “one or more of the techniques from the group consisting of” in line 2. There is insufficient antecedent basis for this limitation in the claim. There is no previously-recited technique or group. For the purposes of examination, the term “one or more of the techniques from the group consisting of” is deemed to claim “one or more techniques from a group consisting of.”
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 - 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1, the claim recites "an act or step, or series of acts or steps" and is therefore a process, which is a statutory category of invention (Step 1). The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong 1).
Each of claims 1 – 18 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 – 18 recites at least one step or instruction for observations, evaluations, judgments, and opinions, which are grouped as a mental process under the 2019 PEG. The claimed invention involves making observations, evaluations, judgments, and opinions, which are concepts performed in the human mind under the 2019 PEG.
Accordingly, each of Claims 1 – 18 recites an abstract idea.
Specifically, Claims 1 – 18 recite (underlined are observations, judgments, evaluations, or opinions, which are grouped as a mental process under the 2019 PEG) (additional elements bolded, see Step 2A, prong 2);
Claim
A method for creating an individualized machine learning inference model to augment validity and reliability of a wearable motion capture device, comprising the steps of:
providing a user with a wearable motion capture device, which includes a piezoresistive sensor and an inertial sensor;
acquiring measurements using said wearable motion capture device via said piezoresistive sensor and said inertial sensor;
computing a first estimate of one or more gait parameters using said measurements;
accessing a database containing previously collected observations of gait data from said user or from other users, said accessing step being performed using said wearable device and a more accurate reference device;
applying transductive learning to identify an optimal set of input features and parameters using a subset of said previously collected observations of gait data from said wearable device that is most informative for said user, based on a similarity score; and
developing an individualized machine learning inference model using said optimal set of input features and parameters from said subset of previously collected observations of gait data from said wearable device and said reference device.
(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
These underlined limitations describe a mathematical calculation and/or a mental process, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a human with the aid of a pen and paper, or by using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps. For example, in Independent Claim 1, these limitations include:
Observation and judgment to provide a user with a wearable motion capture device, which includes a piezoresistive sensor and an inertial sensor;
Observation and judgment to acquire measurements using said wearable motion capture device via said piezoresistive sensor and said inertial sensor;
Observation and judgment to evaluate a first estimate of one or more gait parameters using said measurements;
Observation and judgment to access a database containing previously collected observations of gait data from said user or from other users,
Observation and judgment to apply transductive learning to Observe and judge an optimal set of input features and parameters using a subset of said previously collected observations of gait data from said wearable device that is most informative for said user, based on a similarity score;
Observation and judgment to develop an individualized machine learning inference model using said optimal set of input features and parameters from said subset of previously collected observations of gait data from said wearable device and said reference device.
all of which are grouped as mental processes or mathematical calculations under the 2019 PEG.
Similarly, Dependent Claims 2 – 18 include the following abstract limitations, in addition the aforementioned limitations in Independent Claim 1 (underlined observation, judgment or evaluation, which is grouped as a mental process and certain methods of organizing human activity under the 2019 PEG):
applying said individualized machine learning inference model to said measurements to obtain a second estimate of one or more gait parameters.
Observation and judgment to apply said individualized machine learning inference model to said measurements to Observe and judge a second estimate of one or more gait parameters.
generating dynamic plantar pressure maps and/or center of pressure trajectories based on said measurements.
Observation and judgment to generate dynamic plantar pressure maps and/or center of pressure trajectories based on said measurements.
classifying activities of daily living based on said measurements.
Observation and judgment to classify activities of daily living based on said measurements.
wherein walking and/or balance exercises are monitored and/or administered, either remotely or in person, using said measurements.
wherein walking and/or balance exercises are observed and judged either remotely or in person, using said measurements.
diagnose medical conditions affecting human gait and balance, or predict the risk of musculoskeletal injuries.
Observation and judgment to diagnose medical conditions affecting human gait and balance, or Observation and judgment to predict the risk of musculoskeletal injuries.
all of which are grouped as mental processes or mathematical calculations under the 2019 PEG. Further abstract ideas include
wherein walking and/or balance exercises are monitored and/or administered, either remotely or in person, using said measurements.
wherein walking and/or balance exercises are administered, either remotely or in person, using said measurements.
which are grouped as methods of organizing human activity under the 2019 PEG
Accordingly, as indicated above, each of the above-identified claims recite an abstract idea.
Step 2A, Prong 2
The above-identified abstract ideas in each of Independent Claim 1 (and their respective Dependent Claims) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in Independent Claim 1), either alone or in combination, generally link the use of the above-identified abstract ideas to a particular technological environment or field of use. More specifically, the additional elements of:
“piezoresistive sensor”
“inertial sensor”
“Wearable motion capture device”, ”wearable device”
“More accurate reference device”
“Database”
“mobile device”
“one or more single-board computers”
“at least one wireless connection module”
“multiple wireless sensors”
“micro-SD card”
“cloud service”
Additional elements recited include an “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” in the Independent Claim 1, and its dependent claims. These component are recited at a high level of generality, , i.e., as a generic piezoresistive sensor performing a generic function of sensing pressure data. These generic hardware component limitations for “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” are no more than mere instructions to apply the exception using generic computer and hardware components. As such, these additional elements do not impose any meaningful limits on practicing the abstract idea.
Further additional elements from Independent Claim 1 and the dependent claims include pre-solution activity limitations, such as:
providing a user with a wearable motion capture device, which includes a piezoresistive sensor and an inertial sensor;
wherein said one or more gait parameters of said first estimate are the same as said one or more gait parameters of said second estimate.
wherein said one or more gait parameters of said first estimate are different from said one or more gait parameters of said second estimate.
wherein said measurements involve center of pressure and/or dynamic margin of stability.
wherein said measurements involve inter-limb parameters.
wherein said one or more gait parameters of said first estimate are selected from the group consisting of stride length, foot-ground clearance, foot trajectory, cadence, double support time, single support time, walking or running speed, center of pressure, stride width, and margin of stability.
wherein said method is implemented by a mobile device having GPS in order to realize a portable navigation system.
wherein said method is performed by one or more single-board computers running a Linux distribution with a real-time kernel operating in headless mode.
wherein each single-board computer uses at least one wireless connection module to synchronize said measurements from multiple wireless sensors, each single-board computer also being configured to write said measurements to a micro-SD card.
wherein said individualized machine learning inference model involves one or more of the techniques from the group consisting of: Support Vector Regression; Gaussian Mixture Models; Gaussian Process Regression; and Support Vector Machines.
wherein said individualized machine learning inference model is adapted to be implemented through a cloud service or a mobile device.
These pre-solution measurement elements are insignificant extra-solution activity, setting up the parameters of the system, and serve as data-gathering for the subsequent steps.
The “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” as recited in Independent Claim 1 and its dependent claims are generically recited computer and hardware elements which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract ideas identified above in Independent Claim 1 (and its respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer processor as claimed. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in Independent Claim 1 (and its respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, Independent Claim 1 (and its respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B –
None of Claims 1 – 18 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” as recited in Independent Claim 1 and its dependent claims.
The additional elements of the “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” Claims 1 - 18, as discussed with respect to Step 2A Prong Two, amounts to no more than mere instructions to apply the exception using generic computer and hardware components. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the “piezoresistive sensor” is described on generically at [0028] as pressure sensors, “a footwear module 10 includes a piezoresistive sensor 12, an inertial sensor 14, and a custom- made logic unit 16 (not shown). The piezoresistive sensor 12 and logic unit 16 are embedded, inlaid or otherwise attached to the sole of the footwear module 10.” The “piezoresistive sensor” is shown with an arrow “12” to the bottom (sole) of a “Prior Art” sandal in Figure 1.
Per Applicant’s specification, the “inertial sensor” is defined generically at [0028] as “a footwear module 10 includes a piezoresistive sensor 12, an inertial sensor 14, and a custom- made logic unit 16 (not shown)…“the inertial sensor 14 can be located, for instance, along the midline of the foot. It is shown as arrow “14” in a box on the back of the sandal in “Prior Art” Fig. 1.
Per Applicant’s specification, the “Wearable motion capture device” and “wearable device” is generically described at [0011] with “A user is provided with a wearable motion capture device. Then measurements are acquired using the wearable motion capture device.“ There are wearable devices shown as shoes in “Prior Art Figure 1” and Figure 3.
Per Applicant’s specification and as described in the 112f interpretation above, the “More accurate reference device” is described generically at [0011] as “a database is accessed that contains previously collected observations of gait data from the user or from other users obtained with the wearable motion capture device or from a more accurate reference device.” There is no particular structure provided for the reference device, such that it can potentially also collect “observations of gait data” and that it is “more accurate” than the wearable motion capture device, whether this is another wearable device, standardized gait measurement equipment, or another processor that has a document with notes about gait parameters (such as a researcher’s accurate notes of the number of steps they observed a subject take in a given about of time.) It is not shown in a figure.
Per Applicant’s specification, the “Database” is discussed generically as being “accessed” in [0011] and that an insole module may interface with it and a computing unit. It is not clearly shown in a figure.
Per Applicant’s specification, the “mobile device” is defined generically at [0012] with “off- the-shelf mobile devices such as a mobile phone and a wrist-worn device.” It is not clearly shown in a figure.
Per Applicant’s specification, the “one or more single-board computers” is defined at [0027] as an embodiment from patent publication 2017/0055880 that is “a battery-powered single-board computer (or mobile device) running a data- logger. In an embodiment, the single-board computer fit inside a running belt that can be worn by the user or can be optionally located offboard within a 30-meter range from the user.” It is potentially shown as “logic unit 166” in Figure 3.
Per Applicant’s specification, the “at least one wireless connection module” is defined generically at [0063] as a “wireless connection module” that helps synchronize the overall system. It is shown as “Wi-Fi router 118” in Figure 3.
Per Applicant’s specification, the “multiple wireless sensors” are generically described at [0028] with the “piezoresistive sensor 12, an inertial sensor 14”, from which [0029] “all the data are sampled at 500 Hz and sent through UDP over WLAN to the single-board computer by means of a Wi-Fi module.“ The wireless sensors are arrow “12” to the bottom (sole) and arrow “14” in a box on the back of a “Prior Art” sandal in Figure 1.
Per Applicant’s specification, the “micro-SD card” is defined generically as a micro-SD card in [0030] on which data is written. It is not clearly shown in a figure.
Per Applicant’s specification, the “cloud service” is defined generically at [0036] as a “cloud server”. It is not clearly shown in a figure.
Accordingly, in light of Applicant’s specification, the claimed terms “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service” are reasonably construed as a generic computing and hardware devices. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “piezoresistive sensor”, “inertial sensor”, “Wearable motion capture device”, ”wearable device”, “More accurate reference device”, “Database”, “mobile device”, “one or more single-board computers”, “at least one wireless connection module”, “multiple wireless sensors”, “micro-SD card”, and “cloud service”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1 – 18 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method of Claims 1 - 18 is directed to applying an abstract idea as identified above on a general-purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1 - 18 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements for Step 2A Prong 2 in Independent Claim 1 (and its dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1 - 18 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1 - 18 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1 - 18 are not patent eligible and are rejected under 35 U.S.C. 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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 4, 6 – 7, 11, and 16 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et. al., (“Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models”, Ref U on PTO-892), hereinafter Zhang 2020, in view of Kim et. al., (“Adaptive Calibration of Soft Sensors Using Optimal Transportation Transfer Learning for Mass Production and Long-Term Usage”, Ref V on PTO-892), hereinafter Kim 2020, further in view of Kim, et. al., (US 2011/0054358 A1), hereinafter Kim 2011.
Regarding Claim 1, Zhang 2020 discloses A method for creating an individualized machine learning inference model to augment validity and reliability of a wearable motion capture device ([Abstract]), comprising the steps of:
providing a user with a wearable motion capture device (Fig 4, including “subject is running…while wearing the SportSole…”; Fig 1, Fig 4), which includes a piezoresistive sensor and an inertial sensor (Fig 1, Page 193, Left Column, “A. System Description” Section] “Each insole module consists of a multi-cell piezo resistive sensor, an IMU…”) ;
acquiring measurements using said wearable motion capture device via said piezoresistive sensor and said inertial sensor (Fig 4, [Page 193, Left Column, “A. System Description” Section] “Each insole module consists of a multi-cell piezo resistive sensor, an IMU…”, Fig 5);
computing a first estimate of one or more gait parameters using said measurements ([Page 194, Right Column, 2nd Full Paragraph] “…raw estimates of SL, SV, and FC”, [Page 194, Left Column, Bottom] “stride length (SL)”, “stride velocity (SV), “foot clearance (FC)”)
accessing previously collected observations of gait data from said user or from other users (Fig 6, VICON + force plates vs SportSole; [Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject, using data from all the other subjects…”; Fig 6); said accessing step being performed using said wearable device and a more accurate reference device (Fig 6, VICON + force plates vs SportSole; [Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject, using data from all the other subjects…”; [Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject, using data from all the other subjects…”; Fig 6)(Examiner notes that the analysis of the data occurs after is in is measured (or collected), so the analysis of the data is broadly on previously collected observations of gait data. ;
developing an individualized machine learning inference model ([Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject…”) using said optimal set of input features and parameters from said subset of previously collected observations of gait data from said wearable device and said reference device [Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject, using data from all the other subjects…”; Fig 6)(Examiner notes that the analysis of the data occurs after is in is measured (or collected), so the analysis of the data is broadly on previously collected observations of gait data. ;
Zhang does not disclose applying transductive learning to identify an optimal set of input features and parameters using a subset of said previously collected observations of gait data from said wearable device that is most informative for said user. Zhang does broadly disclose using machine learning techniques to analyze gait parameters from the observations of gait data collected for the user and other users.
Kim 2020 teaches an adaptive calibration method for soft sensors, such as human gait sensors, using transductive machine learning to process the sensor measurements. Specifically for Claim 1, Kim 2020 teaches applying transductive learning ([Page 3, Left Column, 3rd Full Paragraph] “Transfer learning…a transductive process…focus on transductive transfer learning in this article”) to identify an optimal set of input features and parameters using a subset of said previously collected observations of gait data from said wearable device that is most informative for said user ([Page 3, Left Column, 3rd Full Paragraph] – [Page 3, Right Column, 1st Paragraph] “Transductive transfer learning…solves a problem when the source and the target domain distributions are different…task labeling of the target is not necessary…”,“…finds the mapping…domain adaptation…optimal transportation theory”, Fig 6, [Page 6, Right Column, Paragraph 3], Fig 4) (Examiner notes that most informative is broadly interpreted as pertaining relative a similarity score. It can also be broadly interpreted as data from the user, as that would be more informative regarding the user than data that is not from the user.) based on a similarity score ([Page 3, Right Column, “2.3. Domain Adaptation by the Optimal Transportation Theory” Section] “The Wasserstein metric…the distance between two probabilistic distributions…optimal cost for the optimal transportation mapping is equivalent to the 1-Wasserstein metric.”; Fig 6)
Zhang 2020 and Kim 2020 both disclose and teach on-body sensors for human gait detection from which data is processed using machine learning: Zhang 2020 with the SportSole sensors and LASSO and SVR machine learning models, and Kim 2020 with the on-leg soft sensors (Kim 2020 Figure 4) with transductive transfer machine learning processing of the sensor data. Kim 2020 provides a motivation to combine at [Page 8, Left Column] with “These results strongly suggest that use of transfer learning (notably the improved algorithm) can prevent performance loss caused by sensor drift.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using transductive machine learning for sensor data would be useful for increasing performance of gait sensors to accurately measure gait parameters.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and machine learning data processing of gait data disclosed in Zhang 2020 with the transfer machine learning processing of gait sensor data taught by Kim 2020, creating a single gait sensor system incorporating transductive machine learning in order to increase the accuracy of the gait sensors’ measurements.
Kim 2020 does not teach accessing a database.
Kim 2011 teaches a gait and posture analysis method that analyzes sensor data from a shoe including pressure sensors, and the data and analyzed from the sensors is stored in a database. Specifically for Claim 1, Kim 2011 teaches accessing a database ([0055] “database 26 serves to store data required for analyzing the walker's gait/posture and the gait/posture analysis result data….database 26 may store all kinds of information related to the gait/posture analysis, including the plurality of base foot pressures measured at arbitrary intervals…”)
Kim 2011 provides a motivation to combine at [0055] with “…the database 26 may store all kinds of information related to the gait/posture analysis…” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using a database for storing and accessing data from sensors would be a routine, convenient way of storing and using large quantities of a multiple-sensor data.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and machine learning data processing of gait data disclosed in Zhang 2020 with the database for data storage and analysis taught by Kim 2011, creating a single gait sensor system that accesses a database with stored sensor data, increasing the ability to effectively process large quantities of data.
Regarding Claim 2, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 2, Zhang discloses further comprising the step of applying said individualized machine learning inference model to said measurements to obtain a second estimate of one or more gait parameters ([Page 196, Left Column, 2nd Full Paragraph] “the same gait parameters were computed again, this time by applying models trained using Session 1 data to raw data collected in Session 2. Then, the ANOVA models were run on the new estimates.”).
Regarding Claim 3, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 3, Zhang discloses wherein said one or more gait parameters of said first estimate ([Page 194, Right Column, 2nd Full Paragraph] “…raw estimates of SL, SV, and FC”, [Page 194, Left Column, Bottom] “stride length (SL)”, “stride velocity (SV), “foot clearance (FC)”) are the same as said one or more gait parameters of said second estimate ([Page 196, Left Column, 2nd Full Paragraph] “the same gait parameters were computed again…”)
Regarding Claim 4, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 4, Zhang discloses wherein said one or more gait parameters of said first estimate ([Page 194, Right Column, 2nd Full Paragraph] “…raw estimates of SL, SV, and FC”, [Page 194, Left Column, Bottom] “stride length (SL)”, “stride velocity (SV), “foot clearance (FC)”) are different from said one or more gait parameters of said second estimate ([Page 196, Left Column, 2nd Full Paragraph] “the same gait parameters were computed again…”)(Examiner notes that since there are at least three parameters that are calculated again, then one or more would be different than or one more of the other set. For example, when “stride length” is calculated both times, it is a different parameter than both “stride velocity” and “foot clearance”.)
Regarding Claim 6, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 6, Zhang discloses wherein said measurements involve inter-limb parameters (Fig 5, “left (blue) and right (red) foot trajectories…SL and FC”).
Regarding Claim 7, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 7, Zhang discloses wherein said one or more gait parameters of said first estimate are selected from the group consisting of stride length ([Page 194, Right Column, 2nd Full Paragraph] “…raw estimates of SL, SV, and FC”, [Page 194, Left Column, Bottom] “stride length (SL)”…) foot-ground clearance ([Page 194, Right Column, 2nd Full Paragraph] “…raw estimates of…FC”, [Page 194, Left Column, Bottom] …“foot clearance (FC)”), foot trajectory, cadence, double support time, single support time, walking or running speed, center of pressure, stride width, and margin of stability.
Regarding Claim 11, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 11, Zhang discloses wherein walking and/or balance exercises are monitored and/or administered ([Page 193, Right Column, 1st Full Paragraph] “subjects were instructed to complete a 6-minute walking task on the treadmill…”), either remotely or in person (Figure 4; [Page 193, Right Column, 1st Full Paragraph] “subjects were instructed…”)(Examiner notes that the alternative recitation of remotely or in person appears to apply to all conditions, since if it isn’t “in person” it is broadly “remote”), using said measurements (Fig 4, [Page 193, Left Column, “A. System Description” Section] “Each insole module consists of a multi-cell piezo resistive sensor, an IMU…”, Fig 5)(Examiner notes that the measurements are broadly used by being collected.)
Regarding Claim 16, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 16, Zhang discloses wherein said individualized machine learning inference model involves one or more of the techniques from the group consisting of: Support Vector Regression ([Page 195, Left Column, 1st Full Paragraph] “Two alternative training methods were implemented to train LASSO and SVR models…”); Gaussian Mixture Models; Gaussian Process Regression ([Page 192, Left Column, 2nd Full Paragraph] “…authors of [32] applied Gaussian process regression to estimate walking speed from IMUs”), and Support Vector Machines.
Regarding Claim 17, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 17, Zhang discloses wherein said first estimate of one or more gait parameters is obtained by using conventional data processing techniques ([Page 192, Right Column, 1st full Paragraph] “…conventional methods (ZUPT and velocity drift compensation, VDC) in the double integration process, to obtain drift-free stride-to-stride raw estimates of fundamental gait parameters.”) to obtain spatiotemporal, kinematic or kinetic gait parameters ([Page 192, Right Column, 1st full Paragraph] “…fundamental gait parameters…”; [Page 194, Right Column, 1st Full Paragraph] “…estimates of temporal gait parameters…Raw stride-to-stride estimates of SL [stride length], SV [stride velocity], and FC [foot clearance]…from the accelerometer readings…”)
Claims 5, 8, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang 2020 (“Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models”, Ref U on PTO-892) in view of Kim 2020 and Kim 11, further in view of Zhang et. al., (“Estimating CoP Trajectories and Kinematic Gait Parameters in Walking and Running Using Instrumented Insoles”), hereinafter Zhang 2017.
Regarding Claim 5, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 5, Zhang does not specifically disclose wherein said measurements involve center of pressure and/or dynamic margin of stability.
Zhang 2017 teaches the estimation of center of pressure trajectories using the SportSole insole sensor system. Specifically for Claim 5, Zhang 2017 teaches wherein said measurements involve center of pressure ([Abstract] “a fully portable system that can measure spatiotemporal gait parameters and center of pressure (CoP) trajectories.“; [Page 2160, Left Column, 3rd Full Paragraph] “…SportSole, instrumented insoles capable of measuring…CoP trajectories during walking and running tasks.”) and/or dynamic margin of stability.
Zhang 2017 provides a motivation to combine at [Page 2165, Left Column, 1st Full Paragraph] with “SportSole, the first fully-portable instrumented insoles capable of measuring spatiotemporal gait parameters and CoP trajectories during running tasks.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that it would be possible to measure CoP trajectories using the SportSole sensor system in any system or method that incorporates the SportSole, an additional useful parameter for human gait analysis.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and gait parameter calculations disclosed in Zhang 2020 with the CoP trajectory gait calculation from SportSole sensor data taught by Zhang 2017, creating a single gait sensor system that calculates CoP trajectory for additional useful gait parameter information.
Regarding Claim 8, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 8, Zhang does not specifically disclose further comprising the step of generating dynamic plantar pressure maps and/or center of pressure trajectories based on said measurements. Zhang broadly discloses that other researchers have generated plantar pressure maps from instrument measurements at ([Page 192, Left Column, 1st Full Paragraph] “linear regression models have been applied to calibrate plantar pressure maps…”)
Zhang 2017 teaches generating dynamic plantar pressure maps ([Page 2159, Right Column, 1st Full Paragraph] “Quantitative analysis of the plantar pressure map during walking and running can help identify abnormal foot loading patterns before such injuries occur.”) and/or center of pressure trajectories [Abstract] “a fully portable system that can measure spatiotemporal gait parameters and center of pressure (CoP) trajectories.“; [Page 2160, Left Column, 3rd Full Paragraph] “…SportSole, instrumented insoles capable of measuring…CoP trajectories during walking and running tasks.”) based on said measurements ([Page 2160, Left Column, 3rd Full Paragraph] “measuring…CoP trajectories…”).
The motivation for Claim 8 to combine Zhang 2020 with Zhang 2017 is similar to that described in more detail in Claim 5. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and gait parameter calculations disclosed in Zhang 2020 with the CoP trajectory gait calculation and plantar pressure map generation from SportSole sensor data taught by Zhang 2017, creating a single gait sensor system that calculates CoP trajectory and plantar pressure maps for additional useful gait parameter information.
Regarding Claim 13, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 13, Zhang 2020 discloses the use of said individualized machine learning inference model ([Page 195, Left Column, 3rd Full Paragraph] “…LASSO and SVR models were trained, subject by subject…”)
Zhang 2020 does not specifically disclose to diagnose medical conditions affecting human gait and balance, or predict the risk of musculoskeletal injuries.
Specifically for Claim 13, Zhang 2017 teaches comprising the use of said individualized machine learning inference model ([Page 2162, Right Column, 2nd Full Paragraph] “1) Subject-Specific Calibration…robust linear regression (with automatic outlier rejection through bi-square weighting functions)…)(Examiner notes that the machine learning regression is broadly used as part of the process that facilitates diagnosis of foot and ankle dysfunctions.) to diagnose medical conditions affecting human gait and balance ([Abstract] “can facilitate diagnosis of foot and ankle dysfunctions…instrumented insoles…is a promising approach…“; [Page 2159, Right Column, 2nd Full Paragraph] “…diagnosis and treatment of impairments associated with various musculoskeletal and neurological disorders”), or predict the risk of musculoskeletal injuries
Zhang 2017 provides a motivation to combine at [Page 2159, Right Column, 2nd Full Paragraph] with “analyzing the distribution of normal forces between the foot and the support surface during…locomotion can inform the diagnosis and treatment of impairments associated with various musculoskeletal and neurological disorders”. A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that it applying the results measurements of gait data would be useful for diagnosis and treatment of conditions associated with particular gait pattern results.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and gait parameter analysis disclosed in Zhang 2020 with the application of using gait data analysis to diagnose and treat impairments taught by Zhang 2017, creating a single gait sensor system that can be used to diagnose various musculoskeletal and neurological disorders and improve patient outcomes.
Claims 9 - 10, 12, and 14 - 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang 2020 (“Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models”, Ref U on PTO-892) in view of Kim 2020 and Kim 2011, further in view of Zanotto et. al., (US 2020/0000375 A1).
Regarding Claim 9, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 9, Zhang does not disclose further comprising the step of classifying activities of daily living based on said measurements.
Zanotto teaches a quantitative gait training and analysis system that performs analysis on data from its footwear sensor module. Specifically regarding Claim 9, Zanotto teaches further comprising the step of classifying activities of daily living based on said measurements ([0017] “machine learning models to automatically classify activities of daily living based on the signals recorded by the system.”)
Zanotto provides a motivation to combine at [0018] with “Another application for the data collected by the system is activity monitoring/classification. This can be realized with machine learning models to automatically classify activities of daily living based on the signals recorded by the system.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that activity monitoring and classification would be useful information to a user that could be provided by a gait sensor and analysis system, allowing for between understanding of one’s day-to-day activities.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors and machine learning parameter calculations disclosed in Zhang 2020 with the application of classifying activities of daily living based on the signals of an insole sensor system taught by Zanotto, creating a single gait sensor system that classifies activities of daily living based on the gait data, allowing for users to have better understanding of their day-to-day activities.
Regarding Claim 10, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 10, Zhang does not disclose wherein said method is implemented by a mobile device having GPS in order to realize a portable navigation system.
Zanotto teaches wherein said method is implemented by a mobile device having GPS in order to realize a portable navigation system ([0017] “the system…used with a smartphone equipped with GPS to realize a portable navigation system.”)
Zanotto provides a motivation to combine at [0017] with “Additionally, the system can potentially be used with a smartphone equipped with GPS to realize a portable navigation system,” and [0026] “…sample data can be streamed…an easy-to-use user interface running on the user's laptop or mobile phone”. A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that gait sensor data that can be transmitted wirelessly (such as that of Zhang 2020) could be transmitted to a mobile phone, which would also include a GPS system that could pair location data with the gait data to provide a navigation system to be used for more comprehensive data collection.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors that can transmit data wirelessly disclosed in Zhang 2020 with Zanotto’s system that collects gait data from sensors with a smartphone equipped with GPS, creating a single gait sensor system that can also serve as a portable navigation system with the gait data collection.
Regarding Claim 12, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 12, Zhang does not specifically disclose further comprising the step of providing gait and/or balance rehabilitation to said user.
Zanotto teaches further comprising the step of providing gait and/or balance rehabilitation to said user ([0015] “objects of the invention include…offering gait or balance rehabilitation with real-time augmented feedback…”; [0017] “…mid-level sensors…vibro-tactile feedback that can be utilized by a user for gait rehabilitation”).
Zanotto provides a motivation to combine at [0004] with “Compared to traditional laboratory equipment for gait analysis, instrumented footwear systems are more affordable and versatile. These devices can be used to assess the wearers' gait in unrestricted environments, in diverse motor tasks, and over extended time periods…” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that an instrumented footwear system (like the SmartSole-associated system disclosed by Zhang 2020) would be useful for gait or balance rehabilitation by being portable to use over extended time periods in a physical therapy setting (such as at-home physical therapy).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the portable SportSole sensors and machine learning parameter calculations disclosed in Zhang 2020 with the application of providing gait and/or balance rehabilitation to a user taught by Zanotto, creating a single gait sensor system that can be used to increase patient rehabilitation outcomes in a more convenient way (such as at-home physical therapy).
Regarding Claim 14, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 14, Zhang wherein said method is performed by one or more single-board computers ([Page 193, Left Column, “A. System Description” Section] “the single-board computer running the data logger software. “)
Zhang does not specifically disclose running a Linux distribution with a real-time kernel operating in headless mode.
Zanotto teaches running a Linux distribution with a real-time kernel operating in headless mode ([0026] “the single-board computer runs a Linux distribution with a real-time kernel operating in headless mode.”) A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that using Linux distribution with a real-time kernel operating in headless mode would be an option for remote data acquisition from sensors.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors operating by a single-board computer disclosed in Zhang 2020 with using Linux distribution with a real-time kernel operating in headless mode for a gait sensor single-board computer taught by Zanotto, creating a single gait sensor system that can successfully acquire remote gait data using a single-board system.
Regarding Claim 15, Zhang 2020 in view of Kim 2020 and Kim 2011, further in view of Zanotto discloses as described above, The method of Claim 14. For the remainder of Claim 15, Zhang discloses wherein each single-board computer ([Page 193, Left Column, “A. System Description” Section] “the single-board computer running the data logger software. “) uses at least one wireless connection module ([Page 193, Left Column, “A. System Description” Section] “…send through UDP over WLAN“) to synchronize said measurements from multiple wireless sensors ([Page 193, Left Column, “A. System Description” Section] “Data measured by each insole…sent through UDP over WLAN…synchronizing the overall system…”).
Zhang does not specifically disclose each single-board computer also being configured to write said measurements to a micro-SD card.
Zanotto teaches each single-board computer also being configured to write said measurements to a micro-SD card ([0026] “single-board computer… computer synchronizes the data incoming from the insole modules and writes them to a micro-SD card.”)
A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that writing measurements to a micro-SD card would be beneficial to transport stored data to another location in a common physical format for storing data.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors operating by a single-board computer disclosed in Zhang 2020 with writing sensor measurements to an SD card from a single-board computer taught by Zanotto, creating a single gait sensor system that can write data to a common physical format for storing and transporting data, a micro-SD card.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang 2020 (“Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models”, Ref U on PTO-892) in view of Kim 2020 and Kim 2011, further in view of Huang et. al., (US 2018/0279915 A1).
Regarding Claim 18, Zhang 2020 in view of Kim 2020, further in view of Kim 2011 discloses as described above, The method of Claim 1. For the remainder of Claim 18, Zhang does not disclose wherein said individualized machine learning inference model is adapted to be implemented through a cloud service or a mobile device.
Huang teaches systems and methods to analyze gait, balance, or posture information gathered by wearable sensors that can be processed on a mobile computing device with machine learning. Specifically for Claim 18, Huang teaches machine learning inference model is adapted to be implemented through a cloud service or a mobile device (Fig 1, [0015] “The wearable device…send the data related to the subject's gate wirelessly to the mobile computing device for analysis…portable computing device…apply a…machine learning-based classification…”)
Huang provides a motivation to combine at [0015] “The wearable device can provide a portable, user friendly solution for detecting data related to a subject's gait, balance or posture in the subject's natural environment.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that gait sensor data that can be transmitted wirelessly (such as that of Zhang 2020) could be transmitted to a mobile computing device, which would be useful for a user to obtain analyzed gait sensor data at home or on the go, rather than a specific laboratory environment.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the SportSole sensors that can transmit data wirelessly disclosed in Zhang 2020 with Huang’s system that collects gait data from sensors to analyze with machine learning on a mobile computing device, creating a single gait sensor system that can be used conveniently outside of a laboratory environment.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELISSA J MONTGOMERY whose telephone number is (571)272-2305. The examiner can normally be reached Monday - Friday 7:30 - 5:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at (571) 272 - 4233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
Prosecution Insights