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 .
Drawings
The drawings filed on 12/19/22 are accepted.
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 1-15 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.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a method for detecting validity of human body movement, which is a process. Independent claim 14 is directed to a non-transitory computer-readable medium, which is a manufacture. Independent claim 15 is directed to a system, which is a machine. All other claims depend on independent claims 1 and 14-15. As such, claims 1-15 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
A method for detecting validity of human body movement (As seen below, the claimed method recites abstract mathematical concepts and mental processes.)
vectorizing the user movement dataset to obtain a plurality of eigenvectors of the user movement dataset (This limitation recites mathematical calculations in the form of vectorizing data to obtain a plurality of eigenvectors. It recites an abstract mathematical concept.)
inputting the plurality of eigenvectors of the user movement dataset into a validity detection model to determine whether the human body movement corresponding to the user movement dataset is valid (This limitation recites abstract mathematical relationships in the form of eigenvectors that are input into a validity detection model.)
wherein the validity detection model comprises a baseline center and a baseline Mahalanobis distance threshold (This limitation recites abstract mathematical relationships in the form of disclosing variables of a model.)
and wherein determining whether the human body movement corresponding to the user movement dataset is valid comprises (The claimed determination recites an abstract mental process, when the determination is merely an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. The claimed determination recites an abstract mathematical concept when it discloses mathematical relationships, mathematical formulas or equations, and/or mathematical calculations.):
determining a proportion, out of the plurality of eigenvectors of the user movement dataset, of eigenvectors having a Mahalanobis distance from the baseline center that does not exceed the baseline Mahalanobis distance threshold (This limitation recites an abstract mathematical concept in the form of disclosing specific mathematical relationships and calculations.)
determining whether the proportion is greater than a proportion threshold (This limitation recites an abstract mathematical concept in the form of a mathematical relationship between two variables. Making an observation, evaluation, judgment, and/or opinion about whether one numerical variable is greater than another numerical variable is also an abstract mental process that can be performed in the human mind.)
determining that the human body movement corresponding to the user movement dataset is valid in response to the proportion being greater than the proportion threshold (This limitation recites an abstract mathematical concept in the form of a mathematical relationship between different variables (i.e. a proportion and a proportion threshold). Making an observation, evaluation, judgment, and/or opinion about whether one numerical variable is greater than another numerical variable and then tying that outcome to some sort of meaning, such as validity of a dataset, is an abstract mental process that can be performed in the human mind.)
determining that the human body movement corresponding to the user movement dataset is invalid in response to the proportion being not greater than the proportion threshold (This limitation recites an abstract mathematical concept in the form of a mathematical relationship between different variables (i.e. a proportion and a proportion threshold). Making an observation, evaluation, judgment, and/or opinion about whether one numerical variable is greater than another numerical variable and then tying that outcome to some sort of meaning, such as validity of a dataset, is an abstract mental process that can be performed in the human mind.)
Independent claims 14-15 represent variations of claim 1 above and recite similar abstract ideas under step 2A, prong one. All dependent claims depend on independent claims 1 and 14-15 and also recite abstract limitations, by virtue of their dependence. In addition, the dependent claims also recite their own abstract mathematical concepts and/or mental processes.
Claims 2-4, 10, and 12 further disclose various mathematical relationships and calculations.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
obtaining a user movement dataset (This limitation is not indicative of integration into a practical application because a general and generic obtaining of data for data processing merely adds insignificant extra-solution activity to the judicial exception. (see MPEP 2106.05(g)).)
the user movement dataset comprising a plurality of n-dimensional movement data items corresponding to a human body movement (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
Claim 14
A non-transitory computer-readable medium having storage content, wherein the storage content causes a computing system to perform automated operations (This limitation is not indicative of integration into a practical application because it merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
obtaining a user movement dataset (This limitation is not indicative of integration into a practical application because a general and generic obtaining of data for data processing merely adds insignificant extra-solution activity to the judicial exception. (see MPEP 2106.05(g)).)
the user movement dataset comprising a plurality of n-dimensional movement data items corresponding to a human body movement (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
Claim 15
one or more processors (This limitation is not indicative of integration into a practical application because it merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
a wireless communication module configured to obtain a user movement dataset (This limitation is not indicative of integration into a practical application because it merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
and at least one memory having stored instructions that, when executed by at least one of the one or more processors, cause the system to perform automated operations (This limitation is not indicative of integration into a practical application because it merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
obtaining a user movement dataset (This limitation is not indicative of integration into a practical application because a general and generic obtaining of data for data processing merely adds insignificant extra-solution activity to the judicial exception. (see MPEP 2106.05(g)).)
the user movement dataset comprising a plurality of n-dimensional movement data items corresponding to human body movement (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
All dependent claims depend on independent claims 1 and 14-15 and also recite limitations that are not indicative of integration into a practical application, by virtue of their dependence. In addition, the dependent claims also recite their own limitations that are not indicative of integration into a practical application.
Claims 5-7 link each of the plurality of n-dimensional movement data items to specific parameters. This merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. It is not indicative of integration into a practical application.
Claims 8-9 are directed to collecting movement data items by using at least one sensor in a mobile device or by invoking at least one of a GPS positioning service, an application program, a gyroscope of a smart device, or a combination thereof. Collecting data merely adds insignificant extra-solution activity to the judicial exception. Mentioning that the data is collected using a generic recitation of some sort of device or program merely serves to generally link the use of the judicial exception to a particular technological environment or field of use, or it merely uses a computer as a tool to perform an abstract idea. The claimed functional result(s) of constructing the user movement dataset or the training dataset merely uses a computer as a tool to perform an abstract idea.
Claim 11 is directed to tagging movement data items and removing tagged movement data items. These operations are computer processing operations that merely use a computer as a tool to perform an abstract idea.
Claim 13 discloses that the model is deployed on a device terminal with edge computing capability, and the method is executed by the device terminal. This limitation merely uses a computer as a tool to perform an abstract idea.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Examiner’s Note - Allowable Subject Matter
With respect to independent claims 1 and 14-15, the following limitations, when considered as a whole, are distinguished from the prior art, in that they were not found, taught, suggested, or disclosed in the prior art. However, the claims cannot be allowed until the above 101 rejection is overcome.
inputting the plurality of eigenvectors of the user movement dataset into a validity detection model to determine whether the human body movement corresponding to the user movement dataset is valid; wherein the validity detection model comprises a baseline center and a baseline Mahalanobis distance threshold, and wherein determining whether the human body movement corresponding to the user movement dataset is valid comprises:
determining a proportion, out of the plurality of eigenvectors of the user movement dataset, of eigenvectors having a Mahalanobis distance from the baseline center that does not exceed the baseline Mahalanobis distance threshold; and
determining whether the proportion is greater than a proportion threshold; determining that the human body movement corresponding to the user movement dataset is valid in response to the proportion being greater than the proportion threshold; determining that the human body movement corresponding to the user movement dataset is invalid in response to the proportion being not greater than the proportion threshold
The closest art found was Meir et al (US PgPub 20090187112). On a general level, it discloses many of the same elements as the claimed invention.
For example, it discloses detection of body movement (abstract; paragraphs 0005-0006 and 0008). It also discloses eigenvectors (figure 5, reference S5-5; paragraphs 0057, 0067, 0093, 0099, and 0113). It also discloses Mahalanobis distance (paragraphs 0069, 0102, and claim 3).
However, Meir et al does not disclose, “determining a proportion, out of the plurality of eigenvectors of the user movement dataset, of eigenvectors having a Mahalanobis distance from the baseline center that does not exceed the baseline Mahalanobis distance threshold.”
As stated in paragraph 0069 of Meir et al, “A plausible fit measure D is therefore calculated as the Mahalanobis distance between the fitted sectioned breathing signal model and the sectioned breathing signal model’s mean.” Here, Mahalanobis distance is not used in the context of the eigenvectors, which are disclosed in the context of principal component analysis (see paragraph 0057 of Meir). Although there are areas of overlap between the claimed invention and Meir et al, Meir et al does not teach, suggest, or disclose the specific claimed limitations. The examiner also could not find sufficient art, with proper motivation, to combine with Meir et al to arrive at the claimed limitations.
Another relevant reference that was found was Takiguchi (US PgPub 20060158173). Takiguchi discloses Mahalanobis in the context of tracking human body movement (see figures 11-12; paragraphs 0095-0097 and 0104 for Mahalanobis teachings of Takiguchi).
Takiguchi also teaches a proportion/ratio in relation to the Mahalanobis distance (figure 11, references SP24-SP26). However, this is different than the claimed “proportion of eigenvectors having a Mahalanobis distance from the baseline center that does not exceed the baseline Mahalanobis distance threshold.” Takiguchi mentions vectors (paragraph 0095) but does not use the term “eigenvector.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marsden et al (US Pat 11854308) discloses hand initialization for machine learning based gesture recognition.
Duke et al (US PgPub 20200245953) discloses automatic recognition of known patterns in physiological measurement data.
Toews et al (US PgPub 20230284968) discloses system and method for automatic personalized assessment of human body surface conditions.
Berman et al (EP2535835A2) discloses real-time user identification by hand motion signatures.
Mulligan et al (US PgPub 20200205747) discloses device-based maneuver and activity state-based physiologic status monitoring.
Black et al (US PgPub 20160203361) discloses a method and apparatus for estimating body shape.
Metaxas et al (US PgPub 20080187174) discloses a system and method for detecting and tracking features in images.
Allahdadian et al (US PgPub 20220156578) discloses a statistical confidence metric for reconstructive anomaly detection models.
Chen et al (US PgPub 20170046510) discloses methods and systems of building classifier models in computing devices.
Ogawa et al (US PgPub 20150049908) discloses a subject change detection system and subject change detection method.
Shusterman (US PgPub 20110004110) discloses personalized monitoring and healthcare information management using physiological basis functions.
Ozer et al (US PgPub 20040120581) discloses a method and apparatus for automated video activity analysis.
Cham et al (US Pat 6618490) discloses a method for efficiently registering object models in images via dynamic ordering of features.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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/LEONARD S LIANG/Examiner, Art Unit 2857 09/24/25