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 .
2. Applicant's submission filed on 26 May 2026 [hereinafter Response] has been entered, where:
Claims 1, 10, 19, and 21 have been amended.
Claims 3, 12, and 20 have been cancelled.
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 spatio-temporal action recognition module configured] to recognize an action taken by a subject from the plurality of human key-point coordinates of the plurality of video frames,” “[the spatio-temporal action recognition module] being configured to generate a plurality of attention weights,” “[the spatio-temporal action recognition module] . . . configured to identify an engagement of a human key-point coordinate in a respective time frame for the recognized action,” and “[a fuzzy intensity index calculation module] . . . to 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 “to identify an engagement of a human key-point coordinate,” where “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,” “wherein the fuzzy intensity index calculation module applies, for each human key-point coordinate, intermediate fuzzy rules,” “wherein each of the intermediate fuzzy rules are weighted by a respective weight,” “wherein the fuzzy intensity index calculation module combines inferences of the intermediate fuzzy rules using a linear combination of output fuzzy membership functions to compute an overall membership function,” “wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset,” and “[the 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,” and accordingly, are merely more specific to the abstract idea. Also, 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 spatio-temporal action recognition module,” “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 “spatio-temporal action recognition module,” comprising “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 spatio-temporal action recognition module,” “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 “spatio-temporal action recognition module,” comprising “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 “performing a transformation of an input of raw data for a plurality of video frames into a plurality of human key-point coordinates,” “performing a spatio-temporal action recognition algorithm recognizes an action taken by a subject from the plurality of human key-point coordinates of the plurality of video frames,” “[the spatio-temporal action recognition algorithm] being configured to generate a plurality of attention weights,” “[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 identify an engagement of a human key-point coordinate in a respective time frame for the recognized action,” and “[performing a fuzzy intensity index calculation algorithm] that produces an intensity index associated with the recognized action.” The limitations of “performing,” “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 “to identify an engagement of a human key-point coordinate,” where “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 “produces an intensity index,” “wherein the fuzzy intensity index calculation module applies, for each human key-point coordinate, intermediate fuzzy rules,” “wherein each of the intermediate fuzzy rules are weighted by a respective weight,” “wherein the fuzzy intensity index calculation module combines inferences of the intermediate fuzzy rules using a linear combination of output fuzzy membership functions to compute an overall membership function,” “wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset,” and “[the 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,” and accordingly, are merely more specific to the abstract idea. Also, 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 “spatio-temporal action recognition algorithm,” comprising “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 “spatio-temporal action recognition algorithm,” comprising “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 pre-processing algorithm transforms an input of raw data for a plurality of video frames into a plurality of human key-point coordinates,” a spatio-temporal action recognition algorithm that recognizes an action taken by the subject from the plurality of human key-point coordinates of the plurality of video frames,” “the spatio-temporal action recognition algorithm being configured to generate a plurality of attention weights,” and “a fuzzy intensity index calculation algorithm that produces an intensity index associated with the recognized action.” The limitations of “transforms,” “recognize,” “generate,” 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 “produces an intensity index,” “wherein the fuzzy intensity index calculation module applies, for each human key-point coordinate, intermediate fuzzy rules,” “wherein each of the intermediate fuzzy rules are weighted by a respective weight,” “wherein the fuzzy intensity index calculation module combines inferences of the intermediate fuzzy rules using a linear combination of output fuzzy membership functions to compute an overall membership function,” “wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset,” and “[the 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,” and accordingly, are merely more specific to the abstract idea. Also, 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 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 pre-processing algorithm,” spatio-temporal action recognition algorithm,” comprising “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 plurality of video frames,” where “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 pre-processing algorithm,” “spatio-temporal action recognition algorithm,” comprising “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 plurality of video frames,” where “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,” “. . . to generate a plurality of attention weights,”. . . 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,” “generate,” and “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 “. . . identify an engagement,” where “the human key-point coordinate being associated with at least one of the plurality of attention weights,” and accordingly, are merely more specific to the respective abstract idea.
The claim also recites more details or specifics of the abstract idea of “d) recognizing an action intensity,” where “the action intensity being recognized based at least in part on an intensity index,” “the intensity index being generated based at least in part on intermediate fuzzy rules applied for each human key-point coordinate,” “wherein each of the intermediate fuzzy rules are weighted by a respective weight, and inferences of the intermediate fuzzy rules are combined using a linear combination of output fuzzy membership functions to compute an overall membership function,” and “wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset and inputting the plurality of attention weights to a first fuzzifier and inputting an initial intensity score into a second fuzzifier,” and accordingly, are merely more specific to the abstract idea. Also, the activity of “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 “a pre-processing module configured 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] . . . 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 “fuzzy intensity calculation module,” (claims 4 and 13: [wherein] . . . includes 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: “calculates the intensity index based at least in part on the calculated fuzzy entropy”; claims 6 and 15: comprises a first attention mechanism over time frames that calculates attention over time of the video frames and 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 “fuzzy intensity index calculation module,” (claims 5 and 14: “fuzzy intensity index calculation module includes a fuzzy inference module”), and the “spatio-temporal action recognition module,” (claims 6 and 14: “wherein the spatio-temporal action recognition module comprises a first attention mechanism . . . and 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: “the fuzzy entropy associated with the recognized action is calculated using the first and second sets of attention weights”; claims 8 and 17: “wherein the kinetic fuzzy intensity analysis module computes the initial intensity score based on the fuzzy entropy, and wherein the fuzzy inference module 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: wherein the kinetic fuzzy intensity index calculation module 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: “[performing a pre-processing algorithm] . . . 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: [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 pre-processing algorithm,” “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 respectfully submits that claim 1 cannot recite a ‘mental process’ because claim 1 ‘cannot practically be performed in the human mind.’ See MPEP § 2106.04(a)(2). For example, the human mind cannot practically ‘produce an intensity index associated with the recognized action . . . wherein the fuzzy intensity index calculation module applies, for each human key-point coordinate, intermediate fuzzy rules, and wherein each of the intermediate fuzzy rules are weighted by a respective weight, and wherein the fuzzy intensity index calculation module combines inferences of the intermediate fuzzy rules using a linear combination of output fuzzy membership functions to compute an overall membership function, and wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset.’ (Emphasis added).” (Response at p. 16).
Examiner Response:
Examiner respectfully disagrees because the claim recites limitations that are a mental process. For Step 2A Prong One, the rejection identifies the judicial exception (that is, abstract idea) by referring to what is recited in the claim and explain why it is considered an exception. For example, if the claim is directed to an abstract idea, the rejection identifies the abstract idea as it is recited in the claim and explains why it is an abstract idea. (MPEP §2106.07(a)).
Exemplar claim 1 recites:
* * *
a fuzzy intensity index calculation module configured to produce an intensity index associated with the recognized action,
wherein the fuzzy intensity index calculation module applies, for each human key-point coordinate, intermediate fuzzy rules, and
wherein each of the intermediate fuzzy rules are weighted by a respective weight, and
wherein the fuzzy intensity index calculation module combines inferences of the intermediate fuzzy rules using a linear combination of output fuzzy membership functions to compute an overall membership function, and
wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset,
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; and
the memory device in communication with the processor.
(claim 1, lines 21-33 (emphasis added by Examiner showing amended claim language)).
In the claim, the activity of “produce an intensity index associated with the recognized action” includes a limitation that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions, and accordingly, is a mental process. The amended claim language identified above provides more details or specifics to the abstract idea of “produce an intensity index,” and accordingly, are merely more specific to the abstract idea, as is set out above in detail.
With regard to the claim language emphasized by Applicant, “wherein the respective weight is adaptively learned during a training session on an intensity indexing dataset,” the claim does not put any limits on how the “training session” is conducted and whether an additional element is intended to be a recipient of the training. That is, “adaptive learning” can practically be performed in the human mind, and accordingly, is a mental process.
Thus, the claims recite an abstract idea, as set out above in detail.
7. Applicant submits “Claims 1, 2, 4-11, 13-19, 21, and 22 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. 17).
Applicant submits that “[t]he Office Action (pp. 11 and 12), in its Response to Arguments section, states the following regarding Applicant's claims
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 . . . 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)).
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 Rmid/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 αjs are adaptively learned during the training session on the intensity indexing dataset [21 ].
(Specification [0042] (emphasis added by Examiner)).
Applicant has amended claim 1 to include the disclosed improvements as identified by the Office Action.” (Response at pp. 17-18).
“Further, Applicant's specification discusses how claims 1, 2, 4-11, 13-19, 21, and 22 are directed to a technical solution to a computer vision problem for how to train machine learning models for human action recognition and identifying an intensity level for the recognized action.” (Response at p. 18).
Examiner Response:
Examiner respectfully submits that for Step 2A Prong Two, the rejection identifies any additional elements recited in the claim beyond the identified judicial exception; and evaluates the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)- (c) and (e)- (h). (MPEP § 2106.07(a)).
Under Step 2A, Prong Two, the analysis considers the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application. (MPEP § 2106.04(d) sub III).
The additional elements identified in the claim include a “processor,” a “memory device,” a “machine learning system,” and an “integrated model,” which are all recited at a high-level of generality, and accordingly, are generic computer components used to implement the abstract idea. (MPEP § 2106.05(f)). That is, the claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of discerning action intensity (for example, intense / mild), as opposed to claims directed to an improvement in the functioning of a computer to a technical field. Also, the Specification provides no detail suggesting that the claimed additional elements are unconventional or operate in an unconventional manner.
Conclusion
8. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Jawad et al., “Improving Disturbance Storm Time Index Prediction using Linear and Nonlinear Parametric Models: A Comprehensive Analysis,” IEEE (2018)) teaches linear as well as nonlinear parametric techniques to model the complex magnetosphere dynamics. Neural network (NN)-based modeling techniques, such as feed-forward NN (FFNN), NN integrated with nonlinear autoregression with exogenous (NARX) inputs, adaptive neuro-fuzzy inference system (ANFIS), and recurrent NN are developed for the prediction of the magnetic storm.
(Yeganejou et al., “Interpretable Deep Convolutional Fuzzy Classifier,” IEEE (2019)) teaches comprehensible deep networks by hybridizing them with fuzzy logic. Our proposed architecture first employs a convolutional neural network as an automated feature extractor and then performs a fuzzy clustering in the derived feature space. After hardening the clusters, we employ Rocchio’s algorithm to classify the datapoints.
(US Published Application 20120290131 to Khoukhi et al.) teaches parallel kinematic machine (PKM) trajectory planning method is operable via a data-driven neuro-fuzzy multistage-based system. Offline planning based on robot kinematic and dynamic models, including actuators, is performed to generate a large dataset of trajectories, covering most of the robot workspace and minimizing time and energy, while avoiding singularities and limits on joint angles, rates, accelerations and torques.
10. 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.
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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.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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