DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11 November 2025 [hereinafter Response] has been entered, where:
Claims 1, 2, 10, 11, 19, and 21 have been amended.
Claims 3, 12, and 20 have been cancelled.
New claim 22 is presented for examination.
Claims 1, 2, 4-11, and 13-19, 21, and 22 are pending.
Claims 1, 2, 4-11, and 13-19, 21, and 22 are rejected.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1, 2, 4-11, and 13-19, 21 and 22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a “machine learning system,” which is a machine, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites “[(a)1 a spatio-temporal action recognition module configured] to [(a.1)] recognize an action taken by a subject from the plurality of human key-point coordinates of the plurality of video frames,” “[(a) the spatio-temporal action recognition module] being configured to [(a.2)] generate a plurality of attention weights,” “[(a) the spatio-temporal action recognition module] . . . configured to [(a.5)] identify an engagement of a human key-point coordinate in a respective time frame for the recognized action,” and “[(b) a fuzzy intensity index calculation module] . . . to [(b.2)] produce an intensity index associated with the recognized action.” The limitations of “process,” “recognize,” “generate,” “identify,” and “produce,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are a mental process, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The claim recites more details or specifics to the abstract idea of “[(a.5)] to identify an engagement of a human key-point coordinate,” where “[(a.6)] the human key-point coordinate being associated with at least one of the plurality of attention weights,” and accordingly, is merely more specific to the abstract idea. The claim also recites more details or specifics to the abstract idea of “produce an intensity index,” where the “[(b.2.1) intensity index being produced based at least in part on] inputting the plurality of attention weights to a first fuzzier and inputting an initial intensity score into a second fuzzifier,” in which a fuzzifier operates to convert “crisp values” to “fuzzy values” to regulate the degree of membership, the activity of “converting” being a mental process, (MPEP § 2106.04(a)(2) sub III). Thus claim 1 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of the claim is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a processor configured to perform an integrated model,” “a memory device in communication with the processor,” “[(a)] a spatio-temporal action recognition module,” “[(b)] a fuzzy intensity index calculation module,” and a “video source connected via a video connection.” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not serve to integrate the abstract idea into a practical application. The claim also recites a “machine learning system” and an “integrated model,” which are recited at a high-level of generality and are thus generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not integrate the abstract idea into a practical application. Also, the claim recites more details or specifics to the additional element of the “[(a)] spatio-temporal action recognition module,” comprising “[(a.3)] a spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which is recited at a high-level of generality, and is a generic computer component used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that does not integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “a processor configured to perform an integrated model,” “a memory device in communication with the processor,” “[(a)] a spatio-temporal action recognition module,” “[(b)] a fuzzy intensity index calculation module,” and a “video source connected via a video connection.” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. The claim also recites a “machine learning system” and an “integrated model,” which are recited at a high-level of generality, and thus, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. Also, the claim recites more details or specifics to the additional element of the “[(a)] spatio-temporal action recognition module,” comprising “[(a.3)] a spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which is recited at a high-level of generality, and is a generic computer component used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claim 10 recites a “machine learning method,” which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites “[(a)] performing a transformation of an input of raw data for a plurality of video frames into a plurality of human key-point coordinates,” “[(b)] performing a spatio-temporal action recognition algorithm [(b.1)] recognizes an action taken by a subject from the plurality of human key-point coordinates of the plurality of video frames,” “[(b) the spatio-temporal action recognition algorithm] being configured to [(b.2)] generate a plurality of attention weights,” “[(b.4) the spatio-temporal action recognition algorithm] having a first attention mechanism over time frames and a second attention mechanism over human key-points that are configured to [(b.5)] identify an engagement of a human key-point coordinate in a respective time frame for the recognized action,” and “[(c) performing a fuzzy intensity index calculation algorithm] [(c.1)] that produces an intensity index associated with the recognized action.” The limitations of “[(a), (b)] performing,” “[(b.1)] recognize,” “[(b.2)] generate,” “[(b.5)] identify,” and “[(c.1)] produce,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are a mental process, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of “[(b.5)] to identify an engagement of a human key-point coordinate,” where “[(b.5.1)] the human key-point coordinate being associated with at least one of the plurality of attention weights,” and accordingly, is merely more specific to the abstract idea. The claim also recites more details or specifics to the abstract idea of “[(c.1)] produces an intensity index,” where the “[(c.2.1) intensity index being produced based at least in part on] inputting the plurality of attention weights to a first fuzzier and inputting an initial intensity score into a second fuzzifier,” in which a fuzzifier operates to convert “crisp values” to “fuzzy values” to regulate the degree of membership, the activity of “converting” being a mental process, (MPEP § 2106.04(a)(2) sub III). Thus claim 10 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of the claim is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “one or more processors” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not serve to integrate the abstract idea into a practical application. Also, the claim recites more details or specifics to the additional element of the “[(b)] spatio-temporal action recognition algorithm,” comprising “[(b.3)] a trained spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which is recited at a high-level of generality, and thus is a generic computer component used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that does not integrate the abstract idea into a practical application. Therefore, claim 10 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include “one or more processors.” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. Also, the claim recites more details or specifics to the additional element of the “[(b)] spatio-temporal action recognition algorithm,” comprising “[(b.3)] a trained spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which is recited at a high-level of generality, and thus is a generic computer component used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Therefore, claim 10 is subject-matter ineligible.
Claim 19 recites a “machine learning computer program embodied on a non-transitory computer-readable medium,” which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites [(a)] a pre-processing algorithm transforms an input of raw data for a plurality of video frames into a plurality of human key-point coordinates,” [(b)] a spatio-temporal action recognition algorithm that [(b.1)] recognizes an action taken by the subject from the plurality of human key-point coordinates of the plurality of video frames,” “[(b)] the spatio-temporal action recognition algorithm being configured [(b.2)] to generate a plurality of attention weights,” and “[(c)] a fuzzy intensity index calculation algorithm that produces an intensity index associated with the recognized action.” The limitations of “[(a)] transforms,” “[(b.1)] recognize,” “[(b.2)] generate,” and “[(c)] produce,” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are a mental process, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus claim 19 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of the claim is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “machine learning computer program embodied on a non-transitory computer-readable medium for recognizing actions performed by a subject and estimating an intensity of the recognized action.” ” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not serve to integrate the abstract idea into a practical application. Also, the claim recites more details or specifics to the additional element of the “[(a)] a pre-processing algorithm,” [(b)] spatio-temporal action recognition algorithm,” comprising “[(b.3)] a trained spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that does not integrate the abstract idea into a practical application.
The claim also recites more details or specifics to the additional element of [(a)] . . . a plurality of video frames,” where “[(b.3.1)] the video frames input being obtained from a video source connected via a video connection or more a memory device,” and accordingly, are merely more specific to the additional element. Therefore, claim 19 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a “machine learning computer program embodied on a non-transitory computer-readable medium for recognizing actions performed by a subject and estimating an intensity of the recognized action.” ” These are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not amount to significantly more than the abstract idea. Also, the claim recites more details or specifics to the additional element of the “[(a)] a pre-processing algorithm,” [(b)] spatio-temporal action recognition algorithm,” comprising “[(b.3)] a trained spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea into a practical application, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea.
The claim also recites more details or specifics to the additional element of [(a)] . . . a plurality of video frames,” where “[(b.3.1)] the video frames input being obtained from a video source connected via a video connection or more a memory device,” and accordingly, are merely more specific to the additional element. Therefore, claim 19 is subject-matter ineligible.
Claim 21 recites a “machine learning-based method,” which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites limitations of “b) extracting the pose of at least one person,” “c) recognizing the performed action using . . . an LSTM module,” “[(c.1)] . . . to generate a plurality of attention weights,” “[(c.2)] . . . having a first attention mechanism and a second attention mechanism that are configured to identify an engagement of a human key-point coordinate in a respective time frame for the recognized action,” and “d) recognizing an action intensity using the spatio-temporal distribution of the attention weights, fuzzy entropy measures and dynamically learned fuzzy logic rules.” The limitations of “b) extracting,” “c)recognizing,” “[(c.1)] generate,” and [(c.2)] identify” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are a mental process,” (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP ¶ 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of “[(c.2)] . . . identify an engagement,” where “[(c.3)] the human key-point coordinate being associated with at least one of the plurality of attention weights,” and the abstract idea of “d) recognizing an action intensity,” where “[(d.1)] the action intensity being recognized based at least in part on an intensity index,” and accordingly, are merely more specific to the respective abstract idea. The claim also recites more details or specifics to the abstract idea of “[d) recognizing an action intensity” where “[(d.2)] the intensity index being generated based at least in part on inputting the plurality of attention weights to a first fuzzifier and inputting an initial intensity score into a second fuzzifier,” in which a fuzzifier operates to convert “crisp values” to “fuzzy values” to regulate the degree of membership, the activity of “converting” being a mental process, (MPEP § 2106.04(a)(2) sub III). Thus, claim 21 is directed to an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 21 is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a spatio-temporal action recognition module that comprises an LSTM module,” which is an additional element of a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites “a) preparing a streaming video of at least one person in the group,” which such “preparing” is a pre-process insignificant extra-solution activity of data processing preparation, (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application. The claim also recites “e) dynamically updating the spatio-temporal action recognition module as well as the fuzzy logic rules for further adaptation to a unique way an action intensity is performed,” which are post-processing insignificant extra-solution activities of data updating, (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application. Thus, claim 21 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself, which include “a spatio-temporal action recognition module that comprises an LSTM module,” which is an additional element of a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites “a) preparing a streaming video of at least one person in the group,” which such “preparing” is a pre-processing, well-understood and conventional activity , (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application. The claim also recites “e) dynamically updating the spatio-temporal action recognition module as well as the fuzzy logic rules for further adaptation to a unique way an action intensity is performed,” which are post-processing insignificant extra-solution activities of data updating, (MPEP § 2106.05(g)), that does not amount to significantly more than the abstract idea. The claim also recites “a) preparing a streaming video of at least one person in the group,” which such “preparing” is a pre-processing well-understood, routine, and conventional activity of selecting information based on types of information for analysis, (MPEP § 2106.05(d); see Electric Power Group LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)), that does not amount to significantly more than the abstract idea. The claim also recites “e) dynamically updating the spatio-temporal action recognition module as well as the fuzzy logic rules for further adaptation to a unique way an action intensity is performed,” which are post-processing, well-understood, routine, and conventional activities of updating fields in memory, (MPEP § 2106.05(d) sub II.iv), which does not amount to significantly more than the abstract idea. Thus, claim 21 is subject-matter ineligible.
Claim 2 depends from claim 1. The claim further recites the limitation of “[(d)] a pre-processing module configured [(d.1)] to perform the transformation of the raw data . . . into the plurality of human key-point coordinates using a pose estimation technique,” which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly is a mental process,” (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP ¶ 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claim 2 is subject-matter ineligible.
Claim 4 depends directly or indirectly from claim 1. Claim 13 depends directly or indirectly from claim 10. The claims recite (claims 4 and 13: [wherein] . . . [(b.2.1)] performs a kinetic fuzzy intensity analysis that processes the attention weights to calculate a fuzzy entropy associated with the recognized action”), which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly is a mental process,” (MPEP § 2106.04(a)(2) sub III), and also, is a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract idea. (MPEP § 2106.04(a)(2) sub I).
The claims also recite more details or specifics of the additional element of the “[(b)] fuzzy intensity calculation module,” (claims 4 and 13: [wherein] . . . includes [(b.2)] a kinetic fuzzy intensity analysis module that performs . . .”), and accordingly, is merely more specific to the additional element, and is a generic computer component used to implement the abstract idea of “calculate a fuzzy entropy” (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application, nor does it amount to significantly more than the abstract idea. Thus, claims 4 and 13 are subject-matter ineligible.
Claims 5 and 6 depend directly or indirectly from claim 1. Claims 14 and 15 depend directly or indirectly from claim 10. The claims recite (claims 5 and 14: “[(b.3.1)] calculates the intensity index based at least in part on the calculated fuzzy entropy”; claims 6 and 15: comprises [(a.4.1)] a first attention mechanism over time frames that calculates attention over time of the video frames and [(1.4.2)] a second attention mechanism over human key-points that calculates attention over at least some of the key-point coordinates to produce first and second sets of the attention weights, respectively”), which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly is a mental process,” (MPEP § 2106.04(a)(2) sub III), and also, is a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2) sub I).
The claims also recite more details or specifics of the additional element of the “[(b)] fuzzy intensity index calculation module,” (claims 5 and 14: [(b)] fuzzy intensity index calculation module includes [(b.3)] a fuzzy inference module”), and the “[(a)] spatio-temporal action recognition module,” (claims 6 and 14: “wherein [(a)] the spatio-temporal action recognition module comprises [(a.4.1)] a first attention mechanism . . . and [(a.4.2)] a second attention mechanism . . . .”), and accordingly, are merely more specific to the additional element, and are generic computer components used to implement the abstract idea of “calculates the intensity index” and “calculates attention,” respectively, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application, nor does it amount to significantly more than the abstract idea. Thus, claims 5, 6, 14, and 15 are subject-matter ineligible.
Claims 7 and 8 depend directly or indirectly from claim 1. Claims 16 and 17 depend directly or indirectly from claim 10. The claims recite more details or specifics of the abstract idea of “calculating the fuzzy entropy,” (claims 7 and 16: “[(b.2.1)] the fuzzy entropy associated with the recognized action is calculated using the first and second sets of attention weights”; claims 8 and 17: “[(b.2)] wherein the kinetic fuzzy intensity analysis module [(b.2.2)] computes the initial intensity score based on the fuzzy entropy, and [(b.3)] wherein the fuzzy inference module [(b.3.2)] converts the initial intensity score and the first and second sets of attention weights into fuzzy sets using an adaptive membership function”), and thus, are merely more specific to the abstract idea in which generic computer components are used to implement. (MPEP § 2106.05(g)). Thus, claims 7, 8, 16, and 17 are subject-matter ineligible.
Claim 9 depends directly or indirectly from claim 1. Claim 18 depends directly or indirectly from claim 10. The claims recite limitations directed to a “mental process,” (claims 9 and 18: [(b)] wherein the kinetic fuzzy intensity index calculation module [(b.3.2)] uses truth values of the fuzzy sets to define fuzzy rules through which a final intensity index is determined by the fuzzy inference module), which is a grouping of abstract ideas. (MPEP § 2106.04(a)(2) sub III). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claims 9 and 18 are subject-matter ineligible.
Claim 11 depends from claim 10. The claims recite a “mental process” (claim 11: “[(d) performing a pre-processing algorithm] . . . [(d.2)] transforms the received video frames into the key-point coordinates over time”), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2) sub III). Also, the claim recites an additional element, (claim 11: [(d) a pre-processing module] . . . to receive video frames input to the machine learning system . . .”), which is an insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(g)), which does not integrate the abstract idea into a practical application, and also, is a well-understood, routine, and conventional activity of receiving and transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Thus, claim 11 is subject-matter ineligible.
Claim 22 depends from claim 19. The claim recites more details or specifics of the abstract idea of “[(a)] a pre-processing algorithm,” “[(a.1)] wherein the plurality of human key-point coordinates representing a point on a human body of the subject,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Thus, claim 22 is subject-matter ineligible.
Response to Arguments
5. Examiner has fully considered Applicant’s arguments and/or amendments, and responds below, accordingly.
Claim Rejections – 35 U.S.C. § 101
6. Applicant submits that, under Step 2A Prong Two, that “[c]laims 1, 2, 4-11, 13-19 and 21 integrate the alleged abstract idea into a practical application because the claims are directed to a technological improvement for training machine learning models for human action recognition and identifying an intensity level for the recognized action, which is an improvement in the field of computer vision.” (Response at p. 13).
Examiner’s Response:
Applicant submits that the instant claims are directed to “a technological improvement for a technological improvement for training machine learning models for human action recognition and identifying an intensity level for the recognized action, which is an improvement in the field of computer vision.” (Response at p. 13). Applicant points to the specification as a “drawback to the supervised deep learning approach of action recognition is that less focus is given to predicting the intensity of the action than to detect the action itself.” (Response at p. 12 (Specification ¶ 0003 (“Background”)). Also, Applicant points to the instant claims as reflecting the improvement.
Under Step 2A Prong Two, “integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
Examiner respectfully submits that the disclosure Background sets out an intended result, but is not directed to the specific improvement. The additional elements of the claim is directed to generic computer components (processor, memory device, a trained spatio-temporal LSTM) and the use of these components to implement the abstract idea, (MPEP § 2106.04(f)), that does not serve to integrate the abstract idea into a practical application, as is set out above in detail.
Also, the disclosure confirms that these components are not required to be specialized computing components. (Specification ¶ 0029 (“A ‘processor’ or ‘processing logic,’ as those terms are used herein, encompass an electronic component that is able to execute a computer program, portions of a computer program or computer instructions.”); Specification ¶ 0028 (“References herein to ‘memory’ or ‘memory device’ should be interpreted as one or more memories or memory devices. The memory may, for example, be multiple memories within the same computer system. The memory may also be multiple memories distributed amongst multiple computer systems or computing devices”); Specification ¶ 0037 (“For supervised deep learning, there are various known LSTM models that have developed for action detection”)).
The specification does, however, disclose an integrated model for action intensity determination:
[t]he integrated model is analyzed herein by applying it to videos of human actions with different action intensities to demonstrate that it is able to achieve an accuracy of 89.16% on an intensity indexing generated dataset. The integrated model demonstrates the ability of a neuro-fuzzy inference module to effectively estimate the intensity index of human actions.”
(Specification ¶ 0015). Portions of the Specification also set out in relation to known fuzzy entropy methods, in which there are sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” such as Specification relating to
adding it to the attention distribution as fuzzy membership weights and computing their fuzzy entropy. The weights are the change of the coordinates' locations from the last frame multiplied by their corresponding attention weights. Using known fuzzy entropy methods, the fuzzy entropy of the attention vector can be calculated, which is indirectly related to intensity.”
(Specification ¶ 0030 (emphasis added by Examiner)).
However, under the second leg of the evaluation, if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. In this respect, the claims simply recite, generally, the use of a known fuzzy entropy method in relation to attention weights, which does not “reflect the disclosed improvement” under the Specification.
Applicant submits “Claims 1, 2, 4-11, 13-19, and 21 integrate the alleged abstract idea into a practical application because the claims are directed to a technological improvement for training machine learning models for human action recognition and identifying an intensity level for the recognized action, which is an improvement in the field of computer vision.” (Response at p. 15).
Applicant sets out “a technological improvement for a technological improvement for training machine learning models for human action recognition and identifying an intensity level for the recognized action, which is an improvement in the field of computer vision.” (Response at p. 13).
With respect to model training, the disclosure recites
Before the raw data can be input into the action recognition module, human key-point coordinates are generated using the pose estimation technique. Using human key-point coordinates to train the action recognition module helps reduce the background clutter. It also reduces the computational complexity as compared to using the entire image/video to train the module.
(Specification ¶ 0027 (emphasis added by Examiner)). With respect to the fuzzy intensity index calculation model, the disclosure sets out
the final intensity index output is inferred based on fuzzy logic principles on the input sets . . . . Each rule Rmld/int refers to the corresponding joint’s individual decision on the aggregated categorization whose role is weighted by aj. Next, we combine the inferences of these rules using the linear combination of their output fuzzy membership functions to compute the overall membership function of the intermediate output set. This process is an adaptive filter as ajs are adaptively learned during the training session on the intension indexing dataset [21].
(Specification ¶ 0042 (emphasis added by Examiner)).
However, should the disclosure one of ordinary skill in the art would recognize the claimed invention as providing an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (MPEP § 2106.04(d)(1)). Regarding “training,” for example, the instant claims simply recite regarding a spatio-temporal LSTM that:
* * *
[(a)] the spatio-temporal action recognition module comprising [(a.3)] a spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,
(see claim 1, lines 14-16). That is, without more, the claim would not reflect the disclosed improvement.
Accordingly, Applicant’s arguments under Step 2A Prong Two, directed to “integration of the abstract idea,” are unpersuasive.
7. Applicant submits that “[i]n the MPEP, section 2106.04 (d)(1) states that "first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement." Further, in Ex parte Desjardins, Appeal 2024-000567, Application 16/319,040 (September 26, 2025), the USPTO Director states on page 9:
Categorically excluding Al innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology .. the panel essentially equated any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation . . . most troubling [the Panel] eschewed the clear teachings of Enfish, and instead substituted only a cursory analysis that ignored this well-settled precedent. Panels should treat such precedent with more care, especially when acting sua sponte.
Further, the USPTO Director further states on Page 10:
This case demonstrates that §§ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope. These statutory provisions should be the focus of examination.
Here, the specification of the present application describes the improvement machine learning models for computer vision relating to analyzing video. For example, paragraph [003] of the present application states ‘[o]ne drawback to the supervised deep learning approach of action recognition is that less focus is given to predicting the intensity of the action than to detect the action itself.’" (Response at p. 16).
Examiner’s Response:
With regard to MPEP § 2106.04(d)(1), Examiner discussed this aspect above.
With regard to Ex parte Desjardins, the fact pattern pertains to a reversal of a sua sponte new ground of rejection under Section 101 by the Board. Nevertheless, in regards to Enfish, Desjardins recites:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes." 822 F.3d at 1339. Moreover, because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determination should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336
(Ex parte Desjardins at p. 8). In contrast to the instant claims, the Board held that “we are persuaded that the claims reflect such an improvement.” (Id.). In other words, the SME Analysis under MPEP § 2106.04(d)(1) applies, as set out above.
8. Applicant also submits that “[f]urther, Applicant asserts that the claim 1 has been amended to recite a ‘processor configured to transform an input of raw data for a plurality of video frames into a plurality of human key-point coordinates, the plurality video frames being input from a video connection or from a memory device.’ This limitation relates to various technical problems described in the present application.” (Response at p. 16)
Examiner’s Response:
The “transformation” of data from one form to another can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process. (MPEP § 2106.04(a)(2) sub III; see Hawk Technology Systems, LLC v. Castle Retail LLC, 60 F.4th 1349 (Fed. Cir. 2023)). Moreover, the “processor” is a generic computer component that does not serve to integrate the abstract idea into a practical application, nor amounts to significantly more than the abstract idea. (MPEP § 2106.05(f)).
9. Applicant also submits that “the claims are analogous to Example 39 of Subject Matter Eligibility Examples.” (Response at p. 15).
Examiner’s Response:
Examiner respectfully disagrees because “Example 39,” pertains to an exemplar claim that lacks an “abstract idea” under Step 2A Prong One.
Example 39 recites, recites, inter alia, "creating a first training set comprising [(a)] the collected set of digital facial images, [(b)] the modified set of digital facial images, and [(c)] a set of digital non-facial images;" and "creating a second training set for a second stage of training comprising [(a)] the first training set and [(b)] digital non-facial images that are incorrectly detected as facial images after the first stage of training." (Example 39 (example claim)). The analysis explains that “[t]he claim [of Example 39] does not recite any of the judicial exceptions enumerated in the 2019 PEG. . . . Thus, the claim is eligible [under Step 2A Prong One] because it does not recite a judicial exception.” (Example 39 (analysis)). In other words, the claim of Example 39 recites “additional elements,” but no “abstract ideas,” and the evaluation ends.
As explained in the 2024 SME Guidance, “[t]here is no need to move to Step 2A, Prong Two if the claim does not recite a judicial exception in the first instance.” (2024 SME Guidance, 89 Fed. Reg. 137 at p. 58134 (17 July 2024)).
Applicant submits that “[similar to Example 39], the claims of the present application recite a combination of functionality that are not practically performed in the human mind, as stated in page 9 of Subject Matter Eligibility Examples.” (Response at p. 18).However, Applicant’s claims are not limited to additional elements, such as the claim of Example 39. Instead, Applicant’s claims recite abstract ideas (as well as “additional elements”) including activities of “process,” “recognize,” “generate,” “identify,” and “produce,” that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP § 2106.04(a)(2) sub III), as set out above in detail.
Accordingly, the Applicant’s instant claims have a fact pattern that differs from that of Example 39, and Applicant’s arguments are unpersuasive.
10. “Additionally, Applicant asserts that the claims do not recite generic computer components as alleged page 3 of the Office Action.
For example, Applicant asserts that at least the elements such as
‘[(a.3)] a spatio-temporal Long Short-Term Memory (LSTM) model that has been trained using a dataset to recognize a plurality of user actions of a plurality of different user action intensities in a respective video sequence,’
‘[(a)] the spatiotemporal action recognition module having [(a.4)] a first attention mechanism over time frames and a second attention mechanism over human key-points,’
‘[(b.1.1)] the intensity index being produced based at least in part on inputting the plurality of attention weights to a first fuzzifier and inputting an initial intensity score into a second fuzzifier’
of claim 1 are not related to generic computer components because not of computer-related systems in the field of computer vision use these components.” (Response at pp. 18-19).
Examiner’s Response:
Examiner respectfully disagrees. The generic components of Applicant’s instant claims is the “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to “process,” “recognize,” “generate,” “identify,” and/or “produce”) or simply adding a general purpose computer or computer components after the fact to an abstract idea . . .does not . . . provide significantly more.” (MPEP § 2106.05(f)(2)).
With regard to the term “module,” these components are not defined by the Applicant’s disclosure. However, the plain meaning “module” is self-contained unit with distinct functionality, which under a broadest reasonable interpretation covers execution of a function with a generic computer component including a computer and/or a processor and a memory, which is not inconsistent with the Applicant’s disclosure. Figure 11 recites a “block diagram of the machine system 100 in accordance with a representative embodiment:”
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(see Specification ¶ 0082 & Fig. 11). Further, Applicant’s disclosure submits that “known” components are used in the implementation of the abstract idea. (see, e.g., Specification ¶ 0035 (“known model can be used that is known to achieve state-of-the-art results”); Specification ¶ 0037 (“known LSTM models”); Specification ¶ 0040 (“known fuzzy entropy methods”)). That is, the claims do not require specialized computing components.
Accordingly, the instant claims are not rendered patent eligible by stating an abstract idea and instructing “apply it on a computer.” (MPEP § 2106.05(f)),
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Karan Sikka, “Facial Expression Analysis for Estimating Pain in Clinical Settings,” ACM (2014)) teaches use facial expression information to objectify the process of both detecting and measuring pain intensity in clinical settings. Since pain is a complex signal such a system should be able to capture both the appearance variation and temporal dynamics of pain expression.
(US Published Application 20170255832 to Jones et al.) teaches RNNs have been used for action recognition. A 3D convolutional neural network followed by a Long Short-Term Memory (LSTM) classifier can be used for action recognition. LSTMs can improve performance over a two-stream network for action recognition. Bi-directional LSTMs have been used to recognize actions from a sequence of three-dimensional human joint coordinates.
12. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 Reference markers added to the claims for the limited purpose of the subject matter eligibility evaluation under Office guidance.
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