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 .
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.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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: “storage” in Claim 1.
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. The specification provides the structure for the “storage device” limitation to include “a cloud, a server, or the like”.
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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 5, and 8-11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1, the limitation “the estimation model is trained using experimental feature amount data extracted from experimental sensor data” is not recited within the instant application’s specification. Instead, the specification discloses sensor data that results from a manufacturer (Paragraph [0075] - The estimation model may be stored in the storage unit 132 at the time of factory shipment of a product, calibration before the user uses the estimation system) as well as data obtained from a sensor in a shoe worn by a subject performing various tasks (Paragraph [0079] - In the following verification, a subject wearing a smart apparel and a shoe on which the measurement device 10 is mounted was caused to make a round trip by walk twice on a straight path of 5 m…The prediction value is an estimation value estimated using sensor data measured by the measurement device 10 mounted in the shoe worn by the subject at the same time when the measured value was measured). The recited limitation in Claim 1 is not an obvious variant of those disclosed in the instant application’s specification.
Claims not explicitly rejected above are rejected due to their dependence on the above claims.
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 2 and 5 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.
Regarding Claim 2, it is unclear what the limitations “obtained in the gait measurements experiments” entails as “the gait measurement experiments” are not addressed within the instant application’s specification. This limitation is being interpreted to mean “obtained in gait measurements”.
Claims not explicitly rejected above are rejected due to their dependence on the above claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5, and 8-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of Claim 9 follows.
STEP 1
Regarding Claim 9, the claim recites a series of steps or acts, including receiving sensor data related to a motion of a foot of the user; extracting gait waveform data for one gait cycle; normalizing the extracted gait waveform data; extracting a feature amount; generating feature amount data; inputting generated feature amount data; estimating, as the index value relating to the knee flexion angle of the user, an output obtained by inputting the generated feature amount data to the estimation model; and outputting information related to the estimated index value related to the knee flexion angle of the user. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of estimating, as the index value relating to the knee flexion angle of the user, an output sets forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 9 fails to recite any application of estimating, as the index value indicating the knee state of the user, an output in a manner that imposes a meaningful limitation on the Abstract Idea. The Abstract Idea alone does not provide an improvement to the technological field, the method does not affect a particular treatment or effect a particular change based on an estimated index value indicating a knee state of the user or outputting information regarding that index value, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, Claim 9 recites additional steps of receiving sensor data related to a motion of a foot of the user; extracting gait waveform data for one gait cycle; normalizing the extracted gait waveform data; extracting a feature amount; generating feature amount data; inputting generated feature amount data; and outputting information related to the estimated index value related to the knee flexion angle of the user. The receiving, extracting, normalizing, generating, and inputting steps are recited at a high level of generality such that they amount to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering activity engaged in by medical professionals prior to Applicant's invention. Additionally, the outputting step is merely insignificant post-solution activity that does not affect a particular change or treatment. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the receiving, extracting, normalizing, generating, inputting, and outputting steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding Claim 1, the claim recites a series of components, including a storage device configured to store an estimation model that outputs an index value related to a knee flexion angle, a memory configured to store instructions, and a processor configured to execute instructions to receive sensor data related to a motion of a foot of a user, extract gait waveform data for one gait cycle, normalize the extracted gait waveform data, extract a feature amount from the normalized gait waveform data, generate feature amount data for one gait cycle, input generated feature amount data, estimate an output, and output information related to the estimated index value related to the knee flexion angle of the user. Thus, the claim is directed to a machine, which is one of the statutory categories of invention. The steps of estimating an output set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgement, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. Additionally, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited “index value estimation device” is a generic device configured to perform receive sensor data, extract gait waveform data, normalize extracted gait waveform data, generate feature amount data, input generated feature amount data, and estimate an output as mere pre-solution data gathering; outputting information related to an estimated index value as mere post-solution data gathering; and the “storage device”, the “memory”, and the “processor” are generic computer programs configured to perform storing an estimation model that outputs data, storing instructions, and executing instructions as well as perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Regarding Claim 10, the claim recites a non-transitory recording medium as a component configured to store a program that causes a computer to receive sensor data, extract gait waveform data, normalize gait waveform data, extract feature amount from the normalized gait waveform data, generate feature amount data for one gait cycle, input generated feature amount data to an estimation model, estimate an output, and output information related to the estimated index value related to the knee flexion angle of the user. Thus, the claim is directed to a machine, which is one of the statutory categories of invention. The step of estimating an output sets forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgement, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea. Additionally, the claim recites a series of steps or acts, including receiving sensor data, extracting gait waveform data, normalizing gait waveform data, extracting feature amount from the normalized gait waveform data, generating feature amount data for one gait cycle, inputting generated feature amount data to an estimation model, estimating an output, and outputting information related to the estimated index value related to the knee flexion angle of the user. Thus, the claim is directed to a process, which is one of the statutory categories of invention. The device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited “non-transitory recording medium” is a generic computer device with a generic computer program that is configured to perform receiving sensor data, extracting gait waveform data, normalizing gait waveform data, extracting feature amount from the normalized gait waveform data, generating feature amount data for one gait cycle, inputting generated feature amount data to an estimation model, and as mere pre-solution data gathering; and outputting information related to the estimated index value related to the knee flexion angle of the user as mere post-solution data gathering. The generic computer and its program are configured to perform the above as well as the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Dependent Claims 2, 5, 8, and 11 fail to add something more to the abstract independent claims as they generally recite steps pertaining to data gathering and processing. Regarding Claim 2, an estimation model are recited at a high level of generality that they amount to a generic computer and generic computer program. Regarding Claim 8, a “measurement device” containing a sensor is a generic computer device that performs mere pre-solution data gathering by measuring spatial acceleration and angular velocity; normalizing waveform data; extracting a feature amount; generating feature amount data; and outputting the generated feature amount. Additionally, “memory” and “processor” are generic computer programs. Regarding Claim 11, “machine learning” is a generic computer program.
The receiving, extracting, normalizing, generating, inputting, estimating, and outputting steps recited in the independent claims, Claims 1 and 9-10, maintain a high level of generality even when considered in combination with the dependent claims.
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.
Claims 1-2, 5, and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Marcus et. al.'779 (U.S. Patent Publication 20220051779 – previously cited) in view of Zhang et. al.'290 (CN Patent Application 112107290 – previously cited), and further in view of Farris et. al.’522 (U.S. Patent Publication 20240148522).
Regarding Claim 1, Marcus et. al.'779 discloses a storage device storing an estimation model that outputs an index value related to a knee flexion angle according to an input of feature amount data (Paragraph [0043] - the pose module 221 may estimate one or more internal forces of the lower-extremity musculoskeletal system, and in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time; Paragraph [0053] - In some embodiments, the numerical value that estimates knee health of the wearer may be a knee index that characterizes articular cartilage loading at the tibio-femoral interface, represented by the resultant knee adduction moments (KAM) and knee flexion moments (KFM); Paragraph [0054] - Using an implementation of the foot-wearable apparatus described herein, the micro-interventions provided by the foot-wearable apparatus may provide more than a 20% reduction in total damage per stride (when compared to no intervention), calculated using an integral of a total knee moment as calculated by the knee index);
the estimation model is trained using feature amount data extracted from sensor data obtained in gait measurements for estimating the knee flexion angle (Paragraph [0038] - In some embodiments, sensor signals 230 from the one or more sensors 214 are applied to a model to determine the values. In some embodiments, the model comprises a machine learning model 224, which may be trained using sensor data from the wearer and/or one or more other wearers; Paragraph [0045] - measures a knee angle (θ.sub.2) and may be combined with a femur length (f) to provide an estimate of the position of the pelvis and a center of mass);
at least one memory storing instructions; and at least one processor connected to the at least one memory and configured to execute the instructions (Paragraph [0122] - The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention) to:
receive sensor data related to a motion of a foot of a use, the sensor data being measured by a measurement device installed in footwear worn by the user (Paragraph [0041] – entire paragraph; Paragraph [0043] - the pose module 221 may estimate one or more internal forces of the lower-extremity musculoskeletal system, and in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time; Paragraph [0053] - the numerical value that estimates knee health of the wearer may be a knee index; Paragraph [0068] - the foot-wearable apparatus 300 may be integrated into an article of footwear);
extract gait waveform data for one gait cycle between consecutive heel contacts from the sensor data (Paragraph [0088] - An aft sensor 610 (e.g., a “heel” sensor); Paragraph [0089] – entire paragraph - which transitions from the swing phase 625-4 to a heel strike phase 625-1 of the gait cycle 660-2; Paragraph [0095] - In other embodiments, the computer processor(s) may predict one or more poses associated with the heel strike phase 625-1 as an alternative to detecting the heel strike);
normalize the extracted gait waveform data based on a detected toe off timing to generate normalized gait waveform data (Paragraph [0092] - During the push-off phase 625-3 (or “late-stance phase”), a second transition 655-2 of the sensor signal 645 occurs as the toe is lifted from the ground surface 620. At time t4, the sensor signal 645 is reduced below the threshold value 650, which transitions from the push-off phase 625-3 to the swing phase 625-4 of the gait cycle 660-2; Paragraph [0094] - For example, the computer processor(s) may determine a timing (e.g., a periodicity and/or duration) of different phases of the gait cycles 660-1, 660-2 using the sensor signals 630, 645, and may predict various poses of the leg 605 and/or foot based on the timing);
extract a feature amount from the normalized gait waveform data (Paragraph [0042] - In some embodiments, the pose module 221 applies the sensor signals 230 to a biomechanical model representing the wearer to determine and/or predict the poses);
generate feature amount data for one gait cycle based on the extracted feature amount (Paragraph [0043] - in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time);
input the generated feature amount data extracted from the normalized gait waveform data to the estimation model (Paragraph [0043] - In some embodiments, the pose module 221 may estimate one or more internal forces of the lower-extremity musculoskeletal system, and in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time; Paragraph [0053] - the numerical value that estimates knee health of the wearer may be a knee index… the KFM may be an accepted predictor);
estimate, as the index value related to the knee flexion angle of the user, an output obtained by inputting the generated feature amount data to the estimation model (Paragraph [0043] - the pose module 221 may estimate one or more internal forces of the lower-extremity musculoskeletal system, and in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time; Paragraph [0053] - the numerical value that estimates knee health of the wearer may be a knee index); and
output information related to the estimated index value related to the knee flexion angle of the user (Paragraph [0056] - poses may be displayed on the mobile computing device 125 and the wearer may graphically adjust the target poses (e.g., rotating a graphical representation of the leg to adjust an angle of the target poses); Paragraph [0063] - The application 266 may adapt the user commands 270 based on the user configuration information 268, and may communicate the user commands 270 to the MCU 242. Other types of user interactions 274 are also contemplated, such as user jumping, running, and/or hopping motions).
Marcus et. al.’779 fails to disclose the index value being a parameter associated with two peaks appearing in time series of data of the knee flexion angle for one gait cycle. Zhang et. al.’290 teaches obtaining a value indicative of a knee flexion angle of a user by analyzing peaks obtained from a user’s foot motion within a user’s gait cycle (Page 10 Paragraph 11 - indicating four peak values in two gait periods. As is known in the art, peaks 71 and 75 indicate the heel landing, and the peaks 73 and 77 represent the toe-to-ground portions of the subject gait cycle. The segment 78a between the reference numerals 71 and 73 and the segment 78b between the reference numerals 75 and 77 are the standing periods of each gait cycle, and are periods useful for calculating KAM). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Marcus et. al.’779 to include a model that considers a relationship between two peaks in a user’s gait cycle caused by the user’s foot motion in order to gather information regarding a knee’s position at various time points within a gait and discard time periods that are not preferred as seen in Zhang et. al.’290 (Page 9 Paragraph 12 - As will be understood by those skilled in the art, KAM is present only between the leg and the ground contact (i.e., standing period). In order to eliminate the unnecessary swing period part (if it contains these parts, it may cause abnormal prediction and additional calculation burden), and also in order to provide clear and accurate feedback to the object, implementing the real-time segmentation algorithm).
Marcus et. al.’779 further discloses identifying stance and swing phases and adjusting thresholds based on those values (Paragraph [0091] - During the push-off phase 625-3 (or “late-stance phase”), a second transition 655-2 of the sensor signal 645 occurs as the toe is lifted from the ground surface 620. At time t4, the sensor signal 645 is reduced below the threshold value 650, which transitions from the push-off phase 625-3 to the swing phase 625-4 of the gait cycle 660-2), but fails to disclose normalizing extracted gait waveform data in which a stance phase and a swing phase have a predetermine ratio. Farris et. al.’522 teaches a stance and swing phase being set at a certain ratio (Paragraph [0067] - The expected ratio between stance and swing can be set and programmed into the electronic control system and tuned for an individual user so that the user is comfortable walking with a variable cadence and having the electronic control system adapt the swing substate duration to the user's performance). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Marcus et. al.’779 in view of Zhang et. al.'290 to include normalizing data based on a predetermined stance to swing ratio in order to account for a user’s personal cadence and person variations within their cadence as seen in Farris et. al.’522 (Paragraph [0067] - As the stance duration changes with continued walking, the swing substate duration adjusts in accordance with the ratio… individual users have varying ratios of stance to swing time, depending upon the gait pattern).
Regarding Claims 9 and 10, the sections of Marcus et. al.’779 in view of Zhang et. al.'290 and further in view of Farris et. al.’522 cited above disclose a method comprising the steps set forth in the claims. Additionally, Marcus et. al.’779 discloses a non-transitory recording medium (Paragraph [0122] - The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention; Paragraph [0123] - A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM)) as recited in Claim 10.
Regarding Claim 2, Marcus et. al.’779 in view of Zhang et. al.'290 and further in view of Farris et. al.’522 discloses the device outlined in Claim 1 above. Marcus et. al.’779 further discloses wherein the estimation model is trained using training data having, as an explanatory variable, a feature amount extracted from the sensor data obtained in gait measurements regarding a gait of each of a plurality of subjects, and having, as an objective variable, a measured value of the index value related to the knee flection angle actually measured in the gait measurement device regarding the gait of each of the plurality of subjects (Paragraph [0038] - In some embodiments, the model comprises a machine learning model 224, which may be trained using sensor data from the wearer and/or one or more other wearers. The values may be represented in any suitable forms, such as pressure set points, dimensions or sizes of the one or more actuatable components 216 (e.g., height values), and so forth. In some embodiments, the sensor device 205 transmits sensor signal(s) 230 to the mobile computing device 125 via the communicative link 228; Paragraph [0043] - the pose module 221 may estimate one or more internal forces of the lower-extremity musculoskeletal system, and in some cases may combine those force estimate(s) into a single numerical value that estimates knee health of the wearer in real-time; Paragraph [0053] - the numerical value that estimates knee health of the wearer may be a knee index; Paragraph [0063] - The user interactions 274 with the foot-worn sensor device 120 may further include performing user walking motions 278, causing the pressure sensors 248 to transmit sensor signals to the MCU 242…The database 264 may store information associated with the wearer, as well as one or more other wearers. One or more ML and/or artificial intelligence (AI) algorithms 262 of the cloud-based server 260 may generate the user commands 270 based on the database 264, and may communicate the user commands 270 to the application 266).
Regarding Claim 5, Marcus et. al.’779 in view of Zhang et. al.'290 and further in view of Farris et. al.’522 discloses the device outlined in Claim 2 above. Marcus et. al.’779 further discloses obtaining gait information of a user based on foot motion that involves identifying “push off” – also known as “toe off” - and “swing” moments (Paragraph [0092] – entire paragraph; Paragraph [0106] – entire paragraph; Figure 6), wherein the at least one processor is configured to execute the instructions to input the feature amount data acquired according to a gait of the user to the estimation model (Paragraph [0105] - For each of the phases, the computer processor(s) 206, 218 may access threshold values and/or set point parameters for the body-worn actuatable components, and in some cases may include transition set points that specify behavior of the body-worn actuatable components during transition between the phases. In some embodiments, the threshold values and/or set point parameters may be dynamically adjusted for subsequent gait cycles based on the sensor signals).
Marcus et. al.'779 fails to disclose wherein the index value related to the knee flexion angle includes a temporal relationship between a timing of a peak appearing in the swing phase of the two peaks appearing in the time series date of the knee flexion angle for one gait cycle and the detected toe off timing. Zhang et. al.'290 teaches estimating a knee angle of a user by analyzing peaks obtained from a user’s foot motion within a user’s gait cycle (Page 10 Paragraph 11 - indicating four peak values in two gait periods. As is known in the art, peaks 71 and 75 indicate the heel landing, and the peaks 73 and 77 represent the toe-to-ground portions of the subject gait cycle. The segment 78a between the reference numerals 71 and 73 and the segment 78b between the reference numerals 75 and 77 are the standing periods of each gait cycle, and are periods useful for calculating KAM). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Marcus et. al.’779 to include a model that considers a relationship between two peaks in a user’s gait cycle caused by the user’s foot motion in order to gather information regarding a knee’s position at various time points within a gait and discard time periods that are not preferred as seen in Zhang et. al.’290 (Page 9 Paragraph 12 - As will be understood by those skilled in the art, KAM is present only between the leg and the ground contact (i.e., standing period). In order to eliminate the unnecessary swing period part (if it contains these parts, it may cause abnormal prediction and additional calculation burden), and also in order to provide clear and accurate feedback to the object, implementing the real-time segmentation algorithm). Although this reference, Zhang et. al.’290, considers data that is not in the “swing” phase, it would have been obvious to one of ordinary skill in the art to also consider data within the “swing” phase as it would have been obvious to try by choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success; see as reference KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
Regarding Claim 8, Marcus et. al.’779 in view of Zhang et. al.'290 and further in view of Farris et. al.’522 discloses the system outlined in Claim 1 above. Marcus et. al.’779 further discloses the measurement device installed in the footwear of the user for whom the index value related to the knee flexion angle is to be estimated (Paragraph [0053] - In some embodiments, the numerical value that estimates knee health of the wearer may be a knee index that characterizes articular cartilage loading at the tibio-femoral interface, represented by the resultant knee adduction moments (KAM) and knee flexion moments (KFM); Paragraph [0054] - Using an implementation of the foot-wearable apparatus described herein, the micro-interventions provided by the foot-wearable apparatus may provide more than a 20% reduction in total damage per stride (when compared to no intervention), calculated using an integral of a total knee moment as calculated by the knee index), wherein
the measurement device includes a sensor that measures a spatial acceleration and a spatial angular velocity, generates sensor data related to a motion of a foot using the measured spatial acceleration and the measured spatial angular velocity, and outputs the generated sensor data (Paragraph [0027] - Measurement devices used may include inertial measurement units (IMU) (measuring acceleration and rotation to estimate angle in space) and/or goniometers (measuring joint angle). The IMU typically senses some combination of acceleration (in up to six axes), gyroscopes (rotational velocity));
at least one memory storing instructions, and at least one processor connected to the at least one memory (Paragraph [0122] - The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention), and wherein
the at least one processor is configured to execute the instructions to acquire time series data of the sensor data including a feature of a gait (Paragraph [0106] - In some cases, the set point parameters for the heel strike phase 625-1 may also be applied between times t2, t3, and the set point parameters for the push-off phase 625-3 may also be applied between time t4 and a time associated with a first transition 640-1 of the next gait cycle),
extract gait waveform data for one gait cycle from the time series data of the sensor data (Paragraph [0031] - detects a phase of the gait cycle from the sensor signals, and wirelessly transmits control signals to the plurality of sensor devices based on the detected phase);
normalize the extracted gait waveform data; extract a feature amount used for estimating the index value related to the knee flexion angle from the normalized gait waveform data from a gait phase cluster constituted by at least one temporally continuous gait phase (Paragraph [0045] - a knee angle (θ.sub.2) and may be combined with a femur length (f) to provide an estimate of the position of the pelvis and a center of mass; Paragraph [0046] – entire paragraph; Paragraph [0055] - Returning to FIG. 2A, in one example the pose module 221 determines and/or predicts a pose (e.g., for the leg and/or foot of the wearer) based on the one or more sensor signals 230, and compares the pose with a target (or desired) pose. In some cases, the target pose corresponds to a phase or portion of a gait cycle, or of another predefined movement. Based on a determined difference between the pose and the target pose, the component geometry module 222 alters the external geometry of the one or more actuatable components 216 to adjust the leg of the wearer toward the target pose during the motion);
generate feature amount data including the extracted feature amount (Paragraph [0058] - In one simplified, non-limiting example of operation of the pose module 221, the pose module 221 determines an overpronation of the wearer's foot in a mid-stance phase of a gait cycle. The overpronation tends to cause the wearer's ankle to roll inward during the mid-stance phase);
output the generated feature amount data to the index value estimation device (Paragraph [0058] - Based on a target pose stored in the memory 208 for the mid-stance phase (e.g., a neutral position), the pose module 221 may communicate with the component geometry module 222).
Regarding Claim 11, Marcus et. al.'779 discloses wherein the output is estimated by machine learning (Paragraph [0038] - In some embodiments, the model comprises a machine learning model 224, which may be trained using sensor data from the wearer and/or one or more other wearers), and
the information is used for decision making to address a knee state related to the knee flexion angle of the user (Paragraph [0045] - a knee angle (θ.sub.2) and may be combined with a femur length (f) to provide an estimate of the position of the pelvis and a center of mass; Paragraph [0053] - the numerical value that estimates knee health of the wearer may be a knee index that characterizes articular cartilage loading at the tibio-femoral interface, represented by the resultant knee adduction moments (KAM) and knee flexion moments (KFM); Paragraph [0131] - For example, the component geometry application could execute on a computing system in the cloud, receive data from body-worn actuatable components of sensor devices, and generate values for a desired external geometry using a machine-learning algorithm. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet)).
Response to Arguments
Applicant's arguments filed 23 January 2026 have been fully considered and they are not entirely persuasive.
Applicant’s amendments have overcome the prior claim objections.
Applicant’s amendments have not overcome the prior 35 U.S.C. 112f interpretations. It is noted by the examiner that the applicant does not provide rationale as to why they believe the amended limitations overcome the current 112f interpretations beyond stating that the limitation does not include "means-plus-function" language. The examiner does not find these arguments to be persuasive and further notes that "storage device" is a means and "storing an estimation model that outputs an index value related to a knee flexion angle" is a function.
Applicant’s amendments have created a new 35 U.S.C. 112a rejection that has been addressed in Paragraph 6 above.
Applicant’s amendments have overcome the prior 35 U.S.C. 112b rejections, but a new 112b rejection has been addressed in Paragraph 7 above.
Application’s amendments and reasons regarding overcoming the prior 35 U.S.C. 101 rejections were considered, but were found not to be persuasive. The examiner notes that a "computer" is a generic device well known in the art. The steps of extracting gait waveform data, normalizing the extracted gait waveform data, extracting feature amount from the normalized gait waveform data, and generating feature amount data are recited at a high level of generality such that they amount to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. It is further noted by the examiner that Enfish, as recited by the applicant, requires that improvements have been made in processing computer data, but the recited limitations of the instant application do not provide improvements to how data is processed. Rather, they rely on "extracting", "normalizing", and "generating" functions all of which are common to computers and processors. The analysis of the 101 rejection is in Paragraph 8 above.
Claims 1-2, 5, and 8-11 are rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraph 9 above. It is noted that Claims 1-2 and 8-11 that were once rejected under 35 U.S.C. 102 are now rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraph 9 above.
Given that the rest of the arguments were based on the amendments, these concerns were addressed by the examiner in Paragraph 9 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH ANN WESTFALL whose telephone number is (571) 272-3845. The examiner can normally be reached Monday-Friday 7:30am-4:30pm EST.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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