DETAILED ACTION
Claims 1-6 are presented for examination.
This office action is in response to submission of application on 04-DECEMBER-2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04-DECEMBER-2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-6 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
MPEP 2106.04(a)(2)(I) “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations.”
Regarding claim 1:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
wherein each of the plurality of semantic trajectories is annotated with geographical locations or higher semantic locations associated with GPS locations present in each of the plurality of GPS trajectories
Annotation of the trajectories with geographical locations would be association of data with other, which is performable in the human mind as an evaluation.
generating a single training semantic trajectory by concatenating trajectories amongst the plurality of trajectories associated with plurality of subjects
This limitation describes a process of arranging data i.e. concatenation that would be performable with the aid of a pen and paper, for example by graphing concurrent trajectories or graphing multiple subjects’ trajectories on the same plot over time.
classifying, via a cohort classifier executed by the one or more hardware processors, the unknown subject to a cohort from amongst the plurality of cohorts based on meta data acquired for the unknown subject
Classifying is a form of sorting, which may be performed by the human mind based on a variety of factors.
identifying, via the one or more hardware processors, the trained ensemble classifier associated with the cohort of the unknown subject, from among the plurality of trained ensemble classifiers
Identification described herein would be a form of selection as it choses the classifier from a plurality of classifiers.
A judicial exception is recited in this claim as it recites a mathematical concept:
generating, via one or more hardware processors, a plurality of semantic trajectories of each of a plurality of subjects in a cohort amongst a plurality of cohorts from associated plurality of Global Positioning System (GPS) trajectories obtained for each of the plurality of subjects across a plurality of time periods
Generating a trajectory from a series of points collected over time would be a mathematical concept as it describes calculating the trajectory from that data.
generating a plurality of training samples by splitting the single training semantic trajectory into a plurality of sub- trajectories in accordance with a predefined timestep using a sliding window approach
Generating a plurality of samples via a sliding window approach would be a mathematical concept as it relies upon the use of the sliding window approach, which is itself a mathematical algorithm.
imputing, via the trained ensemble classifier executed by the one or more hardware processors, the mobility data of the unknown subject
Imputation is a mathematical process to predict missing values.
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
training, via the one or more hardware processors, a plurality of ensemble classifiers for each of the plurality of cohorts for imputation of mobility data for a subject using the plurality of semantic trajectories of each of the plurality of subjects in the each of the plurality of cohorts, the training of an ensemble classifier from among of the plurality of ensemble classifiers comprising
The training of a plurality of ensemble classifiers using a particular sort of data ultimately describes a generic machine learning model for classification, wherein the training of that generic machine learning model would be a general computer function.
training the ensemble classifier based using the plurality of training samples
This limitation describes a generic training step for a generic machine learning model for classification.
are interpreted as a general purpose computer under MPEP 2106.05(f)
Furthermore, MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
The following steps are mere data gathering:
receiving a request during an inferencing phase, by the one or more hardware processors, for imputing the mobility data of an unknown subject, wherein historical mobility data of the unknown subject is scarce or unavailable
Receiving a request would be a form of data gathering as it is accepting data regarding a subject.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 2 which depends upon claim 1”
This claim describes a mental process:
wherein the cohort for the unknown subject are identified using the cohort classifier, pre-trained using the metadata of the subject
Identification of the cohort for the unknown subject would be evaluation based on the metadata of the subject.
wherein the plurality of cohorts are identified using clustering techniques.
Clustering techniques describe a process of evaluation for identifying similar groupings.
This claim is rejected for incorporating the parent claim in full.
This claim is ineligible.
Claims 3-4 recite a system that parallels the method of claims 1-2 respectively. Therefore, the analysis discussed above with respect to claims 1-2 also applies to claims 3-4 respectively. Accordingly, claims 3-4 are rejected based on substantially the same rationale as set forth above with respect to claims 1-2 respectively.
Claims 5-6 recite a non-transitory computer readable storage medium that parallels the method of claims 1-2 respectively. Therefore, the analysis discussed above with respect to claims 1-2 also applies to claims 3-4 respectively. Accordingly, claims 3-4 are rejected based on substantially the same rationale as set forth above with respect to claims 1-2 respectively.
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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Pub. No. US 20200107163 A1, filed February 14th 2018, hereinafter Li) in view of Myung et al. (Pub. No. KR 20190092217 A, published August 7th 2019, hereinafter Myung) further in view of Sanders et al. (Pub. No. WO 2023039591 A1, filed September 13th 2022, hereinafter Sanders).
Regarding claim 1:
Claim 1 recites:
A processor implemented method for imputation of mobility data, the method comprising: generating, via one or more hardware processors, a plurality of semantic trajectories of each of a plurality of subjects in a cohort amongst a plurality of cohorts from associated plurality of Global Positioning System (GPS) trajectories obtained for each of the plurality of subjects across a plurality of time periods, wherein each of the plurality of semantic trajectories is annotated with geographical locations or higher semantic locations associated with GPS locations present in each of the plurality of GPS trajectories; training, via the one or more hardware processors, a plurality of ensemble classifiers for each of the plurality of cohorts for imputation of mobility data for a subject using the plurality of semantic trajectories of each of the plurality of subjects in the each of the plurality of cohorts, the training of an ensemble classifier from among of the plurality of ensemble classifiers comprising: generating a single training semantic trajectory by concatenating trajectories amongst the plurality of trajectories associated with plurality of subjects; generating a plurality of training samples by splitting the single training semantic trajectory into a plurality of sub- trajectories in accordance with a predefined timestep using a sliding window approach; and training the ensemble classifier based using the plurality of training samples; receiving a request during an inferencing phase, by the one or more hardware processors, for imputing the mobility data of an unknown subject, wherein historical mobility data of the unknown subject is scarce or unavailable; classifying, via a cohort classifier executed by the one or more hardware processors, the unknown subject to a cohort from amongst the plurality of cohorts based on meta data acquired for the unknown subject; identifying, via the one or more hardware processors, the trained ensemble classifier associated with the cohort of the unknown subject, from among the plurality of trained ensemble classifiers; and imputing, via the trained ensemble classifier executed by the one or more hardware processors, the mobility data of the unknown subject.
Li discloses processor implemented method for imputation of mobility data, the method comprising: generating, via one or more hardware processors, a plurality of semantic trajectories of each of a plurality of subjects in a cohort amongst a plurality of cohorts from associated plurality of Global Positioning System (GPS) trajectories obtained for each of the plurality of subjects across a plurality of time periods:
Li teaches machine learning techniques for a trajectory of users based on a sequence of user locations (Paragraph 9) wherein user locations may be provided by a GPS receiver (Paragraph 56) wherein users may be grouped by mode of transportation (Paragraph 9). The users grouped by mode of transportation would be a plurality of subjects in a cohort amongst a plurality of cohorts wherein their mode of transportation would be their cohort, and the GPS trajectories are obtained from a sequence of locations, providing a plurality of semantic trajectories of each of a plurality of subjects across a plurality of time periods i.e. the points the data was gathered at.
Li discloses wherein each of the plurality of semantic trajectories is annotated with geographical locations or higher semantic locations associated with GPS locations present in each of the plurality of GPS trajectories:
Li teaches that for each GPS location corresponding broader location data may be obtained (Paragraph 78). This would result in the trajectories using these points being annotated with geographical locations associated with GPS locations present in each of the plurality of GPS trajectories.
Li discloses generating a single training semantic trajectory by concatenating trajectories amongst the plurality of trajectories associated with plurality of subjects:
Li teaches generating a training set by having a set of users move through a city using various transportation modes, and capturing transportation mode for each journey (Paragraph 87) wherein each journey would be an individual trajectory. Therefore, the full user trajectory would be a single trajectory amongst the plurality of trajectories associated with plurality of subjects, with all the users’ trajectories generating the training data.
Li discloses training [the ensemble] classifier based using the plurality of training samples:
Li teaches training a classifier using a training set, which would be a plurality of training samples (Paragraph 237).
However, Li does not disclose an ensemble classifier, which is taught by Myung below.
Li discloses receiving a request during an inferencing phase, by the one or more hardware processors, for imputing the mobility data of an unknown subject, wherein historical mobility data of the unknown subject is scarce or unavailable;
Li teaches that GPS may be unavailable at times, in which case location should be calculated in another manner (Paragraph 76). This would be imputation of mobility data of an unknown subject, wherein historical mobility data may be scarce as a result of occurring early in trajectory calculation, wherein location data is requested through a cell network (Paragraph 146).
However, Li does not disclose:
training, via the one or more hardware processors, a plurality of ensemble classifiers for each of the plurality of cohorts for imputation of mobility data for a subject using the plurality of semantic trajectories of each of the plurality of subjects in the each of the plurality of cohorts
classifying, via a cohort classifier executed by the one or more hardware processors, the unknown subject to a cohort from amongst the plurality of cohorts based on meta data acquired for the unknown subject;
identifying, via the one or more hardware processors, the trained ensemble classifier associated with the cohort of the unknown subject, from among the plurality of trained ensemble classifiers; and imputing, via the trained ensemble classifier executed by the one or more hardware processors, the mobility data of the unknown subject
Instead, these limitations are disclosed by Myung in the same field of endeavor of machine learning.
Myung discloses training, via the one or more hardware processors, a plurality of ensemble classifiers for each of the plurality of cohorts for imputation of mobility data for a subject using the plurality of semantic trajectories of each of the plurality of subjects in the each of the plurality of cohorts:
Myung recites:
“The ensemble prediction apparatus 130 may generate an ensemble model […] The ensemble prediction apparatus 130 may generate and train the target relationship model by classifying the ensemble training data 32 by feature”
“As a result, the first to n-th health prediction apparatuses 111 to 11n may generate the first to n-th prediction result data by individual prediction models”
Myung teaches training an ensemble model which classifies, which would be a plurality of ensemble classifiers wherein this model may be used with the plurality of cohorts of Li as previously described for advantages as described further below.
Generating the first to n-th prediction results data as seen above would be imputation of data for a subject wherein the data may be the plurality of semantic trajectories of each of the plurality of subjects in the each of the plurality of cohorts as described by Li.
Myung and the present application are analogous art because they are in the same field of endeavor.
Myung discloses classifying, via a cohort classifier executed by the one or more hardware processors, the unknown subject to a cohort from amongst the plurality of cohorts based on meta data acquired for the unknown subject;
Myung recites:
“The ensemble model learner 134 may cluster the meta information into one or more groups according to the similarity between the meta information and select one representative for each clustered group”
Myung teaches clustering via the meta data, which would be a form of classifying a subject to a cohort from amongst the plurality of cohorts based on meta data acquired for the unknown subject.
Myung discloses identifying, via the one or more hardware processors, the trained ensemble classifier associated with the cohort of the unknown subject, from among the plurality of trained ensemble classifiers; and imputing, via the trained ensemble classifier executed by the one or more hardware processors, the mobility data of the unknown subject
Myung recites:
“Different medical institutions or public institutions can individually predict the predictive models and apply the user's time series medical data to the predictive models constructed according to the learning to predict the state of health for the future time points of the user”
Myung teaches predicting which predictive model to use for a user’s data, which would be identifying the trained ensemble classifier associated with the cohort of the unknown subject since Myung has previously used ensemble learning. Furthermore, Myung teaches that via this predictive model further data is generated, which would be imputing, via the trained ensemble classifier, data of the unknown subject wherein the data may be the mobility data of Li.
However, neither Li nor Myung discloses generating a plurality of training samples by splitting the single training semantic trajectory into a plurality of sub- trajectories in accordance with a predefined timestep using a sliding window approach. Rather, this limitation is disclosed by Sanders in the same field of endeavor of machine learning:
Sanders teaches the use of a sliding window technique (Paragraph 30) which would split data into a plurality of pieces of that data for a predefined timestep, wherein the data may be the training samples with a single training semantic trajectory into a plurality of sub-trajectories.
Sanders and the present application are analogous art because they are in the same field of endeavor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Li, the teachings of Myung, and the teachings of Sanders. This would have provided the improvements of privacy of sensitive user data (Myung, “sharing data among various medical institutions can be virtually difficult due to a variety of reasons, including ethical, legal, and personal privacy issues […] instead of constructing a single predictor for multi-organized integrated big data, the individual predictive models are trained with data that are individually constructed from various medical institutions”] as well as processing segments of data separately (Sanders, Paragraph 31).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method of claim 1, wherein the cohort for the unknown subject are identified using the cohort classifier, pre-trained using the metadata of the subject, and wherein the plurality of cohorts are identified using clustering techniques.
Li in view of Myung further in view of Sanders discloses the method of claim 1 upon which claim 2 depends. However, Li does not disclose the limitations of claim 1. Instead, these limitations are taught by Myung:
Myung recites:
“As described above, the ensemble prediction apparatus 130 receives the meta information and the learning result data in response to the transmission of the raw training data […] The ensemble model learner 134 may cluster the meta information into one or more groups according to the similarity between the meta information and select one representative for each clustered group”
Myung teaches clustering techniques wherein the metadata forms the initial clusters hence pretraining the classifier, wherein this process identifies the clusters of cohorts by identifying similarity in the metadata.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Li, the teachings of Myung, and the teachings of Sanders. This would have provided the improvements of privacy of sensitive user data (Myung, “sharing data among various medical institutions can be virtually difficult due to a variety of reasons, including ethical, legal, and personal privacy issues […] instead of constructing a single predictor for multi-organized integrated big data, the individual predictive models are trained with data that are individually constructed from various medical institutions”] as well as processing segments of data separately (Sanders, Paragraph 31).
Claims 3-4 recite a system that parallels the method of claims 1-2 respectively. Therefore, the analysis discussed above with respect to claims 1-2 also applies to claims 3-4 respectively. Accordingly, claims 3-4 are rejected based on substantially the same rationale as set forth above with respect to claims 1-2 respectively.
Claims 5-6 recite a non-transitory computer readable storage medium that parallels the method of claims 1-2 respectively. Therefore, the analysis discussed above with respect to claims 1-2 also applies to claims 3-4 respectively. Accordingly, claims 3-4 are rejected based on substantially the same rationale as set forth above with respect to claims 1-2 respectively.
Conclusion
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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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