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 .
Status of Claims
This office action is in response to the patent application 18/514,133 originally filed on November 20, 2023. Claims 1-20 are presented for examination. Claims 1 and 11 are independent.
Information Disclosure Statement
The Information Disclosure Statement filed on November 20, 2023 has been considered. Copies of the cited foreign patent documents and non-patent literature documents were obtained from parent application 17/723,512. However, non-patent literature document Cite No 2 could not be considered because it is illegible. An initialed copy of the Form 1449 is enclosed herewith.
Priority
This application is a continuation-in-part of US application 18/461,144 (filed 9/5/2023), which is a continuation of US application 17/723,512 (filed 4/19/2022), which is a continuation of US application 16/859,005 (filed 4/27/2020), which has a US provisional application 62/842,668 (filed 5/3/2019).
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 2 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2, and substantially similar limitations in claim 12, recites “wherein the first user is the same as the second user.” It is unclear how two distinctly claimed users can actually be the same user.
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 1-20 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.
Claim 1 is directed to “a method” (i.e. a process) and claim 11 is directed to “a system” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
However, the claims are drawn to an abstract idea of “guiding a physical movement of a subject,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
“receiving a request to generate a movement signature;
receiving first sensor data… associated with a first user…
associating the first sensor data with the movement signature and storing the sensor data;
receiving second sensor data… associated with a second user…
analyzing the second sensor data in comparison to the first sensor data; and
based on the analysis:
generating a notification, or
modifying the movement signature.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “one or more sensors,” “a first wearable device,” “a second wearable device,” “an accelerometer,” “a gyroscope,” “a magnetometer,” “a physiological sensor,” “a touch screen,” “a memory,” and “one or more processors” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “guiding a physical movement of a subject,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g., “one or more sensors,” “a first wearable device,” “a second wearable device,” “an accelerometer,” “a gyroscope,” “a magnetometer,” “a physiological sensor,” “a touch screen,” “a memory,” and “one or more processors” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them 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), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “one or more sensors,” “an accelerometer,” “a gyroscope,” “a magnetometer,” “a physiological sensor,” “a touch screen,” “a memory,” and “one or more processors” are intended to be part of “a first wearable device” and “a second wearable device.” Wearable devices are described in paragraph [0054] as including “any form factor designed or intended to be worn by a person (e.g., personal equipment such as helmets, face guards, apparel, or the like; personal devices such as head-mounted electronic devices, wrist mounted devices, body-mounted devices, devices worn around the neck; or any form factor that, while not necessarily designed or intended to be worn by a person, may be adapted to be worn by a person (e.g., smartphones, tablet computers, and/or other digital processing devices).” (emphasis added)
That is, the wearable devices as claimed can be reasonably interpreted as being generic computing devices such as smartphones or tablet computers, which provide no details of anything beyond ubiquitous standard equipment. As such, the claimed limitations are reasonably understood as not providing anything significantly more than the judicial exception. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-10 and 12-20 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-10 and 12-20 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 1 and 11.
Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 8, 10-14, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Chang et al. (hereinafter “Chang,” US 2017/0344919).
Regarding claim 1, and substantially similar limitations in claim 11, Chang discloses a method for generating or modifying movement signatures (Chang Abstract, “generating an ergonomic model that associates task properties and kinematic activity”), the method comprising:
receiving a request to generate a movement signature (Chang [0062], “which includes collecting kinematic data, functions to sense, detect, or otherwise obtain sensor data relating to motion of a worker. The kinematic data can be collected with an inertial measurement system that may include an accelerometer system and/or a gyroscope system. Preferably, the inertial measurement system includes a three-axis accelerometer and gyroscope. The kinematic data is preferably a stream of kinematic data collected over periods of time when a task is performed. The kinematic data may be collected continuously but may alternatively be selectively activated prior to a task”);
receiving first sensor data from one or more sensors of a first wearable device associated with a first user, the first sensor data being generated with one or more of an accelerometer, a gyroscope, or a magnetometer of the wearable device (see Chang Fig. 3G, showing two sensors on a user’s leg; also Chang [0065], “a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”);
associating the first sensor data with the movement signature and storing the sensor data (Chang [0067-0068], “processing the kinematic data and thereby generating at least one biomechanical measurement of a task associated kinematic activity, functions to produce a metric used in assessing worker ergonomics and biomechanics during a task. A biomechanical measurement is a metric or signal that preferably characterizes at least one aspect of how a user is moving or positioned. One or more different biomechanical measurements may be produced depending on the use case and/or the current operating state… The biomechanical measurements may characterize the motion patterns of a particular body part.”);
receiving second sensor data from one or more sensors of a second wearable device associated with a second user, the second sensor data being generated with one or more of an accelerometer, a gyroscope, or a magnetometer of the second wearable device (see Chang Fig. 3G, showing two sensors on a user’s leg; also Chang [0027], “In the multiple participants embodiment, multiple activity monitor configurations may be supported. A second individual may wear a first and second activity monitor about the knee to measure knee mobility biomechanical signals,” showing that more than one user may wear the sensors; also Chang [0065], “a sensing device provides acceleration as detected by an accelerometer and angular velocity as detected by a gyroscope along three orthonormal axes”);
analyzing the second sensor data in comparison to the first sensor data (Chang [0068], “The biomechanical measurements may alternatively characterize the biomechanical relationship of multiple body parts. For example, the biomechanical measurements can show imbalances in a user's gait or a comparison of mobility between two joints.”; also Chang [0116], “Each lift event can be compared to all other previous lifts of the user or against an average good lift across the entire population. A risk profile can be generated to highlight if the user is fatiguing or potentially susceptible to injury. The ergonomic model can also compare specific lifting qualities across the entire workforce,” comparing different motions of the same user, or comparing one user’s motions to the motions of others in the workforce); and
based on the analysis:
generating a notification (Chang [0038], “The worker user application 132 can additionally display alerts, provide active feedback (e.g., vibrate or audio cues), or other forms of feedback to notify or warn a worker. For example, an audio or haptic warning cue may be played if a worker is improperly lifting an object, twisting in a non-ergonomic manner, and/or climbing down a ladder too quickly. Such feedback can be predictive, real-time, or as a post-performance review. The predictive feedback can be used to alert a worker to potential performance risk, which may be based on personal performance or group performance.”), or
modifying the movement signature (Chang [0137], “Various forms of analysis can be used for updating and/or generating the various ergonomic models”; also Chang [0024], “The biomechanical signal can additionally be a real-time signal, wherein each data point is a value that corresponds to some instance of time. Real-time values can be for lifting motions, posture, walking gait strides, and/or any suitable type of biomechanical signals. The real-time value can be a current value for the biomechanical property or a running average. For example real-time stride time can be averaged over a window spanning a defined set of steps (e.g., 4 steps). Averaging, smoothing, and other error correcting processes may be applied to a real-time signal,” wherein averaging previous movements is a form of modifying the movement signature).
Regarding claim 2, and substantially similar limitations in claim 12, Chang discloses wherein the first user is the same as the second user (see Chang Fig. 3H, showing multiple sensors at different locations of a single user’s body, indicating multiple wearable devices with sensors).
Regarding claim 3, and substantially similar limitations in claim 13, Chang discloses wherein the first user and the second user are different users (see Chang Fig. 3G, showing two sensors on a user’s leg; also Chang [0027], “In the multiple participants embodiment, multiple activity monitor configurations may be supported. A second individual may wear a first and second activity monitor about the knee to measure knee mobility biomechanical signals,” showing that more than one user may wear the sensors).
Regarding claim 4, and substantially similar limitations in claim 14, Chang discloses wherein the movement signature includes physiological data measured with a physiological sensor (Chang [0050], “biomechanical sensing is used as the primary biometric property, but additional biometric signals could be collected, processed and used in combination with the biomechanical. For example, biometric sensing such as heart rate, respiratory rate, blood chemistry and/or other biometric properties can additionally be collected and used”).
Regarding claim 8, and substantially similar limitations in claim 18, Chang discloses further including applying a filtering technique to the first sensor data such that filtered first sensor data is associated with the movement signature (Chang [0063], “data of the kinematic data is raw, unprocessed sensor data as detected from a sensor device. Raw sensor data can be collected directly from the sensing device, but the raw sensor data may alternatively be collected from an intermediary data source. In another variation, the data can be pre-processed. For example, data can be filtered, error corrected, or otherwise transformed. In one variation, in-hardware sensor fusion is performed by an on-device processor of the inertial measurement unit.”).
Regarding claim 10, and substantially similar limitations in claim 20, Chang discloses wherein the request to generate the movement signature is a movement of the wearable device that is measured with the first sensor data (Chang [0061], “Kinematic activity tracking can be triggered based on digitally signaled work activity”; also Chang [0072], “Associations of locations and kinematic activities may alternatively be dynamically determined based on an existing ergonomic model. Regions with higher ergonomic risk (e.g., where previously measured kinematic activity was indicative of bad or concerning ergonomics) can trigger tracking of kinematic activity.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chang in view of French (US 2013/0171596).
Regarding claim 5, and substantially similar limitations in claim 15, Chang does not explicitly teach wherein the request to generate the movement signature is received via an interaction with a touch screen of the first wearable device.
However, French discloses wherein the request to generate the movement signature is received via an interaction with a touch screen of the first wearable device (see French Fig. 8, showing a user interface for initiating data reading on the devices; also French [0050], “If the user presses a key or touches a screen, the computing device 102 then returns to block 510 and the system is reinitialized”).
French is analogous to Chang, as both are drawn to the art of motion tracking. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include wherein the request to generate the movement signature is received via an interaction with a touch screen of the first wearable device, as taught by French, in order to apply a known graphical user interface technique to a known wearable device method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Claims 6, 7, 9, 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang in view of Lee et al. (hereinafter “Lee,” US 2017/0227995).
Regarding claim 6, and substantially similar limitations in claim 16, Chang does not explicitly teach further including analyzing the second sensor data using a supervised, an unsupervised, or a reinforcement learning algorithm.
However, Lee discloses further including analyzing the second sensor data using a supervised, an unsupervised, or a reinforcement learning algorithm (Lee [0009], “the sensors may include motion and/or non-notion sensors, including but not limited to accelerometers, gyroscopes, magnetometers, heart rate monitors, pressure sensors, or light sensors, which may be located in different devices, including smartphones, wearable devices (including hut not limited to smartwatches and smartglasses), implantable devices, and other sensors accessible via an internet of things cloT) system… Unsupervised machine learning algorithms and Deep Learning algorithms can also be used.”).
Lee is analogous to Chang, as both are drawn to the art of movement tracking. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include further including analyzing the second sensor data using a supervised, an unsupervised, or a reinforcement learning algorithm, as taught by Lee, since it applies a known machine learning technique to a known method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 7, and substantially similar limitations in claim 17, Chang does not explicitly teach further including analyzing the second sensor data using a Naïve Bayes, decision tree, random forest, or K-nearest neighbors technique.
However, Lee discloses further including analyzing the second sensor data using a Naïve Bayes, decision tree, random forest, or K-nearest neighbors technique (Lee [0009], “the sensors may include motion and/or non-notion sensors, including but not limited to accelerometers, gyroscopes, magnetometers, heart rate monitors, pressure sensors, or light sensors, which may be located in different devices, including smartphones, wearable devices (including hut not limited to smartwatches and smartglasses), implantable devices, and other sensors accessible via an internet of things cloT) system… The machine learning technique that is utilized can include, but is not limited to, decision trees, kernel ridge regression, support vector machine algorithms, random forest, naïve Bayesian, k-nearest neighbors (K-NN), and least absolute shrinkage and selection operator (LASSO).”).
Lee is analogous to Chang, as both are drawn to the art of movement tracking. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include further including analyzing the second sensor data using a Naïve Bayes, decision tree, random forest, or K-nearest neighbors technique, as taught by Lee, since it applies a known machine learning technique to a known method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Regarding claim 9, and substantially similar limitations in claim 19, Chang does not teach further including using the movement signature to authenticate the second user.
However, Lee discloses further including using the movement signature to authenticate the second user (Lee [0009], “the sensors may include motion and/or non-notion sensors, including but not limited to accelerometers, gyroscopes, magnetometers, heart rate monitors, pressure sensors, or light sensors, which may be located in different devices, including smartphones, wearable devices (including hut not limited to smartwatches and smartglasses), implantable devices, and other sensors accessible via an internet of things cloT) system… the method can include continuously testing the user's behavior patterns and environment characteristics, and allowing authentication without interrupting the user's other interactions with a given device or requiring user input.”).
Lee is analogous to Chang, as both are drawn to the art of movement tracking. It would be obvious to try by one of ordinary skill in the art at the time of filing to have modified the method as taught by Chang, to include further including using the movement signature to authenticate the second user, as taught by Lee, since it applies a known authentication technique to a known method ready for improvement to yield predictable results. Doing so is a predictable solution that one of ordinary skill in the art could have pursued with a reasonable expectation of success.
Conclusion
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/STEPHEN ALVESTEFFER/Examiner, Art Unit 3715