DETAILED ACTION
1. This office action is in response to the Application No. 19412842 filed on 12/08/2025. Claims 1-18 are presented for examination and are currently pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
3. Claims 1-18 are rejected under 35 U.S.C 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1
Independent claim 1 is directed to a system, and falls into one of the four statutory categories.
Step 2A, Prong 1
Claim 1 recites the following abstract ideas:
initialize a persistent cognitive state with language and reasoning capabilities
(Mental process directed to initializing a cognitive state language and reasoning capabilities which can be done with the use of pen and paper);
monitor for external stimuli or internal thought triggers (Mental process directed to monitoring stimuli or thought triggers which is done by observing the external stimuli and internal thought triggers);
analyze incoming stimuli by comparing with existing thought patterns in memory (Mental process directed to analyze incoming stimuli by comparing with existing thought patterns in memory which can be done by observing incoming stimuli and the existing thought, then making a judgement on the comparison);
regulate an elastic temporal manifold whose curvature varies in response to cognitive load (Mental process directed to regulating the elastic temporal manifold that varies in response to cognitive load which can be done by observing the curvature of the elastic temporal manifold and making a judgement on the regulation);
organize stored thoughts based on semantic relationships and temporal context (Mental process directed to organizing stored thoughts which can be done by observing the stored thoughts and making a judgement on how to organize based on semantic relationships and temporal context);
maintain a hierarchical pulse structure including fast, medium, and slow pulse layers that operate at distinct characteristic frequencies and are coupled through adjustable coupling coefficients to sustain coherent temporal rhythm (Mental process directed to maintaining a hierarchical pulse structure that includes fast, medium, and slow pulse layers which can be done with the aid of pen and paper);
determine a global order parameter representing phase alignment among the pulse layers (Mental process directed to determining the global order parameter representing phase alignment among the pulse layers which can be done by observing the phase alignment among the pulse layers) and
adjust coupling strengths to maintain spectral coherence based on curvature feedback (Mental process directed to adjusting the coupling strengths which can be done by observing coupling strengths and making a judgement on when to make the adjustment to maintain the spectral coherence);
monitor spectral entropy to classify operating states of coherence, adaptation, or desynchronization (Mental process directed to monitoring the spectral entropy which can be done by observing the spectral entropy and making a judgement on the classification) and
detect temporal pathologies comprising starvation, storm, and desynchronization (Mental process directed to detecting temporal pathologies which can be done by observing the starvation, storm, and desynchronization and making a judgement on the detection) and
initiate corrective control actions that restore equilibrium of the temporal manifold (Mental process directed to initializing corrective control actions which can be done by observing the corrective control actions and making a judgement on when to initialize) ;
control temporal curvature through a feedback loop including a curvature sensor, comparator, controller, and actuator that maintain the curvature within a reference profile for stable operation (Mental process directed to controlling temporal curvature which can be done by observation and making a judgement on when to control);
align temporal curvature among multiple persistent cognitive machine instances by diffusing curvature information across a communication manifold to achieve federated synchronization (Mental process directed to aligning temporal curvature which can be done by observing the temporal curvature by diffusing curvature information).
Step 2A, Prong 2
a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that (This limitation is directed to a memory which is recited at a high level generic computer component. This does not integrate the abstract idea into a practical application. See MPEP 2106.05 (f)):
retrieve relevant thoughts from a thought cache based on conceptual similarity to current context (This limitation is directed to insignificant extra solution activity of data transmission. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
generate responses using integrated language and reasoning models informed by retrieved thoughts (This limitation is directed to insignificant extra solution activity of data gathering. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
store new thoughts created during processing as vector representations in the thought cache (This limitation is directed to insignificant extra solution activity of data gathering. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
automatically adjust pulse parameters to remain within a stability corridor (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h));
in distributed configurations (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)),
Step 2B
a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that (This limitation is directed to a memory which is recited at a high level of generic computer component. This does not amount to significantly more than the judicial exception. See MPEP 2106.05(h)):
retrieve relevant thoughts from a thought cache based on conceptual similarity to current context (This limitation is directed to insignificant extra solution activity of data gathering and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
generate responses using integrated language and reasoning models informed by retrieved thoughts (This limitation is directed to insignificant extra solution activity of data gathering and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
store new thoughts created during processing as vector representations in the thought cache (This limitation is directed to insignificant extra solution activity of data gathering and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
automatically adjust pulse parameters to remain within a stability corridor (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h));
in distributed configurations (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h)),
4. Dependent claim 2 is directed to a system, and falls into one of the four statutory categories.
Claim 2 do not recite any abstract ideas.
Claim 2 recites the following additional elements:
wherein the hierarchical pulse structure comprises: a fast-pulse layer implementing short-duration operator cycles (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h));
a medium-pulse layer implementing reflexive cycles coupling a metacognitive core and an adaptive edge (These are mere instructions to implement the abstract ideas. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)); and
a slow-pulse layer implementing long-term architectural foliations that consolidate experience into persistent schemas (These are mere instructions to implement the abstract ideas. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)).
Claim 2 recites the following additional elements:
wherein the hierarchical pulse structure comprises: a fast-pulse layer implementing short-duration operator cycles (This limitation is directed to a particular type or source of data, which is field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h));
a medium-pulse layer implementing reflexive cycles coupling a metacognitive core and an adaptive edge (These are mere instructions to implement the abstract ideas. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)); and
a slow-pulse layer implementing long-term architectural foliations that consolidate experience into persistent schemas (These are mere instructions to implement the abstract ideas. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)).
5. Dependent claim 3 is directed to a system, and falls into one of the four statutory categories.
Claim 3 recite the following abstract ideas:
wherein a metacognitive core monitors an uncertainty metric and, upon exceeding a defined threshold, initiates a reflex pulse that reconfigures an adaptive edge layer to restore curvature balance and cognitive coherence (Mental process directed to monitoring the uncertainty metric which can be done by observing the uncertainty metric and making a judgement on when to initiates a reflex pulse).
Claim 3 do not recite any additional limitations.
6. Dependent claim 4 is directed to a system, and falls into one of the four statutory categories.
Claim 4 do not recite any abstract ideas.
Claim 4 recites the following additional elements:
wherein the temporal manifold possesses an elastic metric that shortens or lengthens internal time intervals in response to variations in cognitive load so that total curvature exchanged between cognition and time remains conserved (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 4 recites the following additional elements:
wherein the temporal manifold possesses an elastic metric that shortens or lengthens internal time intervals in response to variations in cognitive load so that total curvature exchanged between cognition and time remains conserved (This limitation is directed to a particular type or source of data, which is field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)).
7. Dependent claim 5 is directed to a system, and falls into one of the four statutory categories.
Claim 5 do not recite any abstract ideas.
Claim 5 recite the following additional elements:
wherein coupling strengths among the pulse layers are dynamically modulated to maximize a global order parameter representing rhythmic alignment (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)) and
to minimize spectral irregularity, thereby sustaining synchronization among the fast, medium, and slow pulse layers (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 5 recite the following additional elements:
wherein coupling strengths among the pulse layers are dynamically modulated to maximize a global order parameter representing rhythmic alignment (This limitation is directed to a particular type or source of data, which is field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h)) and
to minimize spectral irregularity, thereby sustaining synchronization among the fast, medium, and slow pulse layers (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
8. Dependent claim 6 is directed to a system, and falls into one of the four statutory categories.
Claim 6 recite the following abstract ideas:
further configured to determine spectral entropy from the temporal-power distribution of pulses (Mental process directed to determining spectral entropy by observing the temporal-power distribution of pulses and making a judgement on the entropy) and
to compare the spectral entropy with the order parameter to identify regimes of coherent stability, adaptive flexibility, and desynchronization (Mental process directed to comparing the spectral entropy with the order parameter by observing the spectral entropy and the order parameter and making a judgement on the identification).
Claim 6 do not recite any additional elements.
9. Dependent claim 7 is directed to a system, and falls into one of the four statutory categories.
Claim 7 recite the following abstract ideas:
wherein the feedback controller detects and corrects temporal pathologies by: increasing pulse excitation during starvation (Mental process directed to detecting and correcting temporal pathologies which can be done by observing the temporal pathologies and making a judgement on when to increase the pulse excitation during starvation);
applying damping when curvature amplitude exceeds a stability limit during storm conditions; and re-synchronizing pulse phases during desynchronization (Mental process directed to applying damping by observing when the curvature amplitude exceeds a stability limit and making a judgement on when to apply the damping and re-synchronizing pulse phases).
Claim 7 do not recite any additional elements.
10. Dependent claim 8 is directed to a system, and falls into one of the four statutory categories.
Claim 8 recites the following abstract ideas:
measures current curvature (Mental process directed to measuring current curvature by observing the current curvature and making a judgement on when to measure),
that determines deviation from a reference curvature (Mental process directed to determining deviation from a reference curvature by observing the curvature and making a judgement on the deviation),
that modifies pulse frequency to return the temporal curvature to equilibrium (Mental process directed to modifying pulse frequency by observing the pulse frequency and making a judgement on when the pulse frequency returns the temporal curvature to equilibrium).
Claim 8 recite the following additional elements:
wherein the feedback loop comprises a curvature sensor (This limitation is directed to a sensor which is recited at a high level of generality of computer component. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)) that
a comparator (This limitation is directed to a computer component to implement the judicial exception/abstract ideas. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f))
a controller that generates corrective commands based on the deviation and cognitive load (This limitation is directed to using a computer component to implement the judicial exception/abstract ideas. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)), and
an actuator (This limitation is directed to using a computer component to implement the judicial exception/abstract ideas. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f))
Claim 8 recite the following additional elements:
wherein the feedback loop comprises a curvature sensor (This limitation is directed to a sensor which is recited at a high level of generality of computer component. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)) that
a comparator (This limitation is directed to a computer component to implement the judicial exception/abstract ideas. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f))
a controller that generates corrective commands based on the deviation and cognitive load (This limitation is directed to using a computer component to implement the judicial exception/abstract ideas. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)), and
an actuator (This limitation is directed to using a computer component to implement the judicial exception/abstract ideas. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f))
11. Dependent claim 9 is directed to a system, and falls into one of the four statutory categories.
Claim 9 do not recites any the abstract ideas.
Claim 9 recite the following additional elements:
wherein multiple persistent cognitive machine instances communicate curvature information across a shared communication manifold (This limitation is directed to insignificant extra solution activity of data transmission. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g)),
each instance adjusting its local temporal curvature toward a collective average to maintain synchronized operation across the federation (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This limitation does not integrate the abstract idea into a practical application, see MPEP 2106.05 (h)).
Claim 9 recite the following additional elements:
wherein multiple persistent cognitive machine instances communicate curvature information across a shared communication manifold (This limitation is directed to insignificant extra solution activity of data transmission. This limitation does not amount to significantly more than the judicial exception. See MPEP 2106.05(g)),
each instance adjusting its local temporal curvature toward a collective average to maintain synchronized operation across the federation (This limitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (h)).
12. Independent claim 10 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 10, it is substantially similar to claim 1, and is rejected in the same manner and reasoning applying.
13. Dependent claim 11 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 11, it is substantially similar to claim 2, and is rejected in the same manner and reasoning applying.
14. Dependent claim 12 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 12, it is substantially similar to claim 3, and is rejected in the same manner and reasoning applying.
15. Dependent claim 13 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 13, it is substantially similar to claim 4, and is rejected in the same manner and reasoning applying.
16. Dependent claim 14 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 14, it is substantially similar to claim 5, and is rejected in the same manner and reasoning applying.
17. Dependent claim 15 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 15, it is substantially similar to claim 6, and is rejected in the same manner and reasoning applying.
18. Dependent claim 16 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 16, it is substantially similar to claim 7, and is rejected in the same manner and reasoning applying.
19. Dependent claim 17 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 17, it is substantially similar to claim 8, and is rejected in the same manner and reasoning applying.
20. Dependent claim 18 is directed to a method, and falls into one of the four statutory categories.
With regards to claim 18, it is substantially similar to claim 9, and is rejected in the same manner and reasoning applying.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
21. Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 10 recites “persistent”. It is unclear what the Applicant means by persistent. It is unclear if persistent means something that occurs consecutively or that is infinitive.
Claims 1 and 10 recites “reasoning capabilities”. It is unclear what the Applicant means by reasoning capabilities. It is unclear if reasoning capabilities refers to artificial intelligence.
It unclear in claims 1 and 10 what the Applicant means by “initialize a persistent cognitive state with language and reasoning capabilities”. It is unclear if the cognitive state is a variable that is initialized. It is also unclear how the initialization occurs.
It is unclear in claims 1 and 10 what the Applicant means by “monitor for external stimuli or internal thought triggers”. It is unclear what the Applicant means by “internal thought” since machine learning systems do not think but produce responses based on mathematical and statistical computations.
It is unclear where the internal thought is occurring in the computer system and how the internal thought can be triggered.
It is unclear in claims 1 and 10 what the Applicant mean by “starvation”, “storm” and “diffusing curvature” information. There is no definition for these terms in the instant specification.
Claim 7 and 16 recites “applying damping”. It is unclear what damping means. There is no definition for the term damping in the instant specification. It is also unclear how damping is applied
Claims 2-6 and 11-15, 17 and 18 that are not specifically mentioned are rejected due to dependency.
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.
22. Claims 1, 3, 4, 8, 9, 10, 12, 13, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Recanatesi et al. ("Predictive learning as a network mechanism for extracting low-dimensional latent space representations." Nature communications 12.1 (2021): 1417) in view of Ju et al. ("Graph neural networks on SPD manifolds for motor imagery classification: A perspective from the time–frequency analysis." IEEE Transactions on Neural Networks and Learning Systems 35.12 (2023): 17701-17715) and further in view of Rengifo et al. ("An affordable set of control system laboratories using a low-cost robotic platform." IEEE/ASME Transactions on Mechatronics 23.4 (2018): 1705-1715).
Regarding claim 1, Recanatesi teaches a computer system (We cast this system into predictive learning by generating randomly correlated binary input pulses (pg. 7. Left col., second to the last para.); Furthermore, our contribution shall also be seen in light of computational models (pg. 10, right col., first para.); we show that the computation of predicting future inputs can contribute to this process, giving rise to low-dimensional neural representation of the underlying latent spaces in artificial neural networks, pg. 9, right col., last para.)
initialize a persistent cognitive state (At each time t an agent observes the state of a system ot and takes an action at out of a set of possible actions (pg. 2, right col., first para.); The connectivity matrix of the recurrent network was initialized to the identity, while input and output connectivity matrices were initialized to be random matrices, pg. 11, left col., fourth para.) with language and reasoning capabilities (From an algorithmic and computational perspective, our proposal is motivated by ... predictive models in machine-learning tasks that require vector representations reflecting semantic relationships in the data, pg. 10, right col., first para. The Examiner notes semantic indicates language connected to meaning);
monitor for external stimuli or internal thought triggers (To each state is associated a unique set of five random cards that the agent observes whenever it is in that state, pg. 2, right col., first para. The Examiner notes agent observations in a state are internal thoughts);
analyze incoming stimuli (While the agent moves in the environment it collects a stream of observations, pg. 4, left col., second para.) by comparing with existing thought patterns in memory (the agent learns to predict the upcoming sensory observation, Fig. 2c. It achieves this by minimizing the difference between its prediction yt at time t and the upcoming observation, Fig. 2d, pg. 4, left col., last para.);
retrieve relevant thoughts from a thought cache based on conceptual similarity to current context ( ... to verify that the network is actually making predictions we first ask whether the network’s output is most similar to the upcoming observation rather than current or previous ones (pg. 5, left col., first para.); We train the predictive recurrent network to predict future (x, y) locations of both the elbow and the wrist given their current locations and the input to the six muscles, pg. 7, left col., last para.);
generate responses using integrated language and reasoning models informed by retrieved thoughts (neural networks are able to extract latent semantic characteristics from linguistic corpora when trained to predict the con text in which a given word appears, pg. 2, left col., second para.);
store new thoughts created during processing as vector representations in the thought cache (Thus it recorded, for each sensor, four variables at every timestep: ... This information was represented by a vector ot of size 5 × 4 =20. Such a vector, together with the action represented as a one-hot representation, pg. 11, left col., third to the last para.);
organize stored thoughts based on semantic relationships and temporal context (an underlying semantic organization, is through learning to predict observations about the world (abstract); This is a predictive framework in the temporal domain, where the prediction is along the time axis, pg. 2, right col., first para.);
maintain a hierarchical pulse structure including fast, medium, and slow pulse layers that operate at distinct characteristic frequencies (The activation signals for the muscles were used as actions in our model. For each of the six muscles, we used a pulsed binary signal where at each instant in time the pulse can be turned on or off (pg. 11, right col., fourth para.); The Examiner notes a pulsed binary signal represents a hierarchical structure because binary pulse occurs when a signal changes from one logic level to the other for a short period) and are coupled through adjustable coupling coefficients to sustain coherent temporal rhythm (To define the latent signal transfer metric, at each stage of learning we compute the average of the canonical correlation (CC) coefficients between the representation projected into its PCs, and latent space variables x, y, θ, pg. 5, right col., first full para.);
regulate an elastic temporal manifold whose curvature varies in response to cognitive load (measuring the dimensionality of the neural representation with nonlinear techniques sensitive to the local curvature of the representation manifold (pg. 6, left col., third para.); this allows information from the stream of sensory observations to be integrated over time, pg. 4, left col., first para.);
determine a global order parameter (measuring a global property of the representation manifold, and the nonlinear dimensionality ID, pg. 6, left col., third to the last para.) representing phase alignment among the pulse layers and adjust coupling strengths to maintain spectral coherence based on curvature feedback (The agent then attempted a move to the cell, among the eight adjacent ones, that was best aligned to θ ... To ensure coherence between updates in the direction θ and the cell towards which the agent just moved, we required each update in dθ to be towards the direction of the agent’s last movement da, pg. 11, left col., section: Description of the spatial environment. The Examiner notes spectral images in Fig. 5);
and initiate corrective control actions (Again, up to nonlinear corrections, this gives the condition:
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pg. 8, right col., second to the last para.) that restore equilibrium of the temporal manifold (We train 50 networks of 100 neurons in each of the predictive and non-predictive conditions and equalize the learning axis between the two to highlight the trends of the different measures (pg. 7, Fig. 4); In predictive learning a neural network learns to minimize the errors between its output at the present time and a stream of future observations, pg. 2, right col., first para.);
control temporal curvature through a feedback loop (Diagram of the predictive recurrent neural network: the network receives actions and observations as inputs and is trained to output the upcoming sensory observation (Fig. 2, pg. 4); we describe a series of 12 other control networks that show how results on the role of prediction are robust against a number of factors (pg. 6, right col., second para.) the Examiner notes RNN in Fig. 2c shows a feedback loop) including a curvature sensor (The network’s task is to predict the next sensory observation. By learning to do so it recovers information regarding the underlying hidden latent space. b Illustration of the agent with sensors in the square environment where the walls have been colored (cfr. Methods). The sensors span a 90o degree angle and register the color and distance of the wall along their respective directions, pg. 4, Fig. 2),
Recanatesi does not explicitly teach a computer system comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that: monitor spectral entropy to classify operating states of coherence, adaptation, or desynchronization and automatically adjust pulse parameters to remain within a stability corridor; detect temporal pathologies comprising starvation, storm, and desynchronization, comparator, controller, and actuator that maintain the curvature within a reference profile for stable operation; in distributed configurations, align temporal curvature among multiple persistent cognitive machine instances by diffusing curvature information across a communication manifold to achieve federated synchronization.
Ju teaches monitor spectral entropy to classify operating states of coherence, adaptation, or desynchronization and automatically adjust pulse parameters to remain within a stability corridor ( ... specialized first-order Riemannian adaptive optimization methods have been developed to optimize networks with manifold-valued data ... These two operators, within the manifold domain, enable neural networks to perform gradient descent with improved precision in each update iteration (pg. 12, second to the last para.));
detect temporal pathologies comprising starvation, storm, and desynchronization (During the planning and execution of movement, the sensorimotor rhythms exhibit changes in amplitude that are referred to as event-related desynchronization (ERD) (pg. 1, left col., second para.); Expressly, we assume that brain activities generate a time evolution effect on the power spectrum of the EEG signals along the time axis, pg. 4, right col., last para.),
in distributed configurations (novel time–frequency distribution consisting of SPD matrices derived from EEG segments, pg. 4, left col., last para.),
align temporal curvature among multiple persistent cognitive machine instances by diffusing curvature information across a communication manifold to achieve federated synchronization (For instance, the abstract formulation of SPD manifolds provides a natural framework for interpolating diffusion tensors (pg. 3, left col., second para.); The determination of a segmentation plan
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relies on changes in ongoing EEG activity, as evidenced by the appearance of the ERD/ERS (event related synchronization) effect that is induced by cognitive and motor processing, pg. 4, left col., last para.).
Since Recanatesi as primary reference teaches a neural network (abstract) involving representation manifolds, that capture the dynamical and geometrical properties of the representation manifold (pg. 4, right col., last para.), while Ju, as secondary reference, teaches a novel geometric deep learning-based approach on SPD manifolds (pg. 3. left col., second full para.), then,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Recanatesi to incorporate the teachings of Ju for the benefit of novel geometric deep-learning-based approach on SPD manifolds (pg. 3, left col., second full para.) achieving near-optimal classification accuracies (Ju, abstract)
Modified Recanatesi does not explicitly teach a computer system comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that,comparator, controller, and actuator that maintain the curvature within a reference profile for stable operation;
Rengifo teaches a computer system comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that (The embedded computing is powered by an Atmega168A microcontroller, which has an 8 b architecture, running at 8 MHz with 16 kB of memory, pg. 1707, right col., last para.),
comparator, controller, and actuator that maintain the curvature within a reference profile for stable operation (The implementation of navigation strategies requires control over the robot’s heading, so the linear velocity is maintained constant and the controller adjusts the angular speed w. However, the final actuation is applied to motor’s angular velocity using PWM (pg. 1709, left col., first full para.); To solve the task of safely navigation, we introduce classic PID and fuzzy logic techniques (pg. 1708, right col., last para.); Particularly, we use the PID controller ... to drive the go to goal behavior (pg. 1709, left col., last para.). The Examiner notes a PID controller comprises a comparator);
Since Recanatesi as primary reference, desires control networks that show how results on the role of prediction are robust against a number of factors (pg. 6, left col., second para.), an environment the agent navigates is a discrete grid of 64 x 64 locations (pg. 4, left col., first para) and task-related maps useful for goal directed behavior (pg. 2, left col., second para.), while Rengifo, as secondary reference teaches single-agent techniques that ensure safe and predictable navigation are shown (abstract) and “Go to goal” behavior (pg. 1709, right col., first para.), then,
It would have been obvious to a person of ordinary skill before the effective filling date to have modified the method of Modified Recanatesi to incorporate the teachings of Rengifo for the benefit of implementing control strategies, such as PID, neural networks, and synchronization (pg. 1713, right col., first para.) to provide a complete low cost to illustrate control system methods (Rengifo, abstract)
Regarding claim 3, Modified Recanatesi teaches the computer system of claim 1, Recanatesi teaches wherein a metacognitive core monitors an uncertainty metric (It will also be exciting to adapt and test these ideas for the analysis of large-scale population recordings of in vivo neural data—ideally longitudinally, so that the evolution of learned neural representations can be tracked with metrics such as the emergence of a low-D neural representation manifold, predictive error, pg. 10, right col., second para.) and,
upon exceeding a defined threshold, initiates a reflex pulse that reconfigures an adaptive edge layer to restore curvature balance and cognitive coherence (measuring the dimensionality of the neural representation with nonlinear techniques sensitive to the local curvature of the representation manifold (pg. 6, left col., third para.); To ensure coherence between updates in the direction θ and the cell towards which the agent just moved, we required each update in dθ to be towards the direction of the agent’s last movement da, pg. 11, left col., section: Description of the spatial environment).
Regarding claim 4, Modified Recanatesi teaches the computer system of claim 1, Recanatesi teaches wherein the temporal manifold possesses an elastic metric that shortens or lengthens internal time intervals (when assessed through nonlinear metrics sensitive to the dimensionality of curved manifolds, the dimensionality will be lower, in the ideal case tending to the number of independent latent variables, pg. 10, left col., second para.) in response to variations in cognitive load so that total curvature exchanged between cognition and time remains conserved (Thus, through making predictions about future observations, the network still learns to bind states that occur nearby in time together, extracting the latent space, pg. 3, right col., second para.).
Regarding claim 8, Modified Recanatesi teaches the computer system of claim 1, Rengifo teaches wherein the feedback loop comprises a curvature sensor that measures current curvature (In this sense, the robot describes a curvature motion, where the heading is changing through the controller output w, pg. 1709, left col., third para.),
a comparator that determines deviation from a reference curvature, a controller that generates corrective commands based on the deviation and cognitive load (Particularly, we use the PID controller ... to drive the go to goal behavior and the fuzzy logic controller in Section III-D to avoid obstacles. Fig. 4(a) shows the obstacle distances to the robot center, measured by each IR proximity sensor, pg. 1709, left col., section B. Navigation Strategies), and
an actuator that modifies pulse frequency to return the temporal curvature to equilibrium (Also, an experiment using an extra gripper or actuator that gives the robot the possibility to interact with objects, and transport a set of little balls (resources) from one place (environment) to another (nest) in an optimal way, pg. 1713, right col., second para.).
The same motivation to modify independent claim 1 applies here.
Regarding claim 9, Modified Recanatesi teaches the computer system of claim 1, Recanatesi teaches wherein multiple persistent cognitive machine instances communicate curvature information across a shared communication manifold (Illustration of the task and information flow diagram: the neural representation receives state observations and actions and extracts the latent space structure by means of predicting upcoming observations, Fig. 1b, pg. 3), each instance adjusting its local temporal curvature toward a collective average to maintain synchronized operation across the federation (For each of the six muscles, we used a pulsed binary signal where at each instant in time the pulse can be turned on or off, pg. 11, right col., fourth para.).
Regarding claim 10, claim 10 is similar to claim 1. It is rejected in the same manner and reasoning applying.
Regarding claim 12, claim 12 is similar to claim 3. It is rejected in the same manner and reasoning applying.
Regarding claim 13, claim 13 is similar to claim 4. It is rejected in the same manner and reasoning applying.
Regarding claim 17, claim 17 is similar to claim 8. It is rejected in the same manner and reasoning applying.
Regarding claim 18, claim 18 is similar to claim 9. It is rejected in the same manner and reasoning applying.
23. Claims 2, 5-7, 11 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Recanatesi et al. ("Predictive learning as a network mechanism for extracting low-dimensional latent space representations." Nature communications 12.1 (2021): 1417) in view of Ju et al. ("Graph neural networks on SPD manifolds for motor imagery classification: A perspective from the time–frequency analysis." IEEE Transactions on Neural Networks and Learning Systems 35.12 (2023): 17701-17715) in view of Rengifo et al. ("An affordable set of control system laboratories using a low-cost robotic platform." IEEE/ASME Transactions on Mechatronics 23.4 (2018): 1705-1715) and further in view of Gao et al. ("A pulse-based integrated communication and control design for decentralized collective motion coordination." IEEE Transactions on Automatic Control 63.6 (2017): 1858-1864).
Regarding claim 2, Modified Recanatesi teaches the computer system of claim 1, Modified Recanatesi does not explicitly teach the limitation of claim 2.
Gao teaches wherein the hierarchical pulse structure comprises: a fast-pulse layer implementing short-duration operator cycles; a medium-pulse layer implementing reflexive cycles coupling a metacognitive core and an adaptive edge; and a slow-pulse layer implementing long-term architectural foliations that consolidate experience into persistent schemas (As the pulses are content-free, they can be implemented at the low layer of the protocol stack (even exclusively at the physical layer) with very short message lengths, which significantly reduces the high-layer processing latencies and channel communication delays, pg. 3, left col., second para.).
It would have been obvious to a person of ordinary skill before the effective filling date to have modified the method of Modified Recanatesi to incorporate the teachings of Gao for the benefit of an integrated communication and control approach for the motion coordination (Gao, abstract)
Regarding claim 5, Modified Recanatesi teaches the computer system of claim 1, Modified Recanatesi does not explicitly teach the limitation of claim 5.
Gao teaches wherein coupling strengths among the pulse layers are dynamically modulated to maximize a global order parameter representing rhythmic alignment (we design a pulse based integrated communication and control approach for motion coordination by exploiting the close relationship between phase dynamics in pulse coupled oscillators (pg. 1, right col., last para. to pg. left col., first para.); relative estimation of global parameters can also be exchanged, then collective motion can be achieved for general communication patterns, pg. 1, left col., second to the last) and
to minimize spectral irregularity, thereby sustaining synchronization among the fast, medium, and slow pulse layers (Restricting to synchronized collective motion (aligning headings to the same value) (pg. 1, left col., first para.); As the pulses are content-free, they can be implemented at the low layer of the protocol stack (even exclusively at the physical layer) with very short message lengths, which significantly reduces the high-layer processing latencies and channel communication delays, pg. 3, left col., second para.).
It would have been obvious to a person of ordinary skill before the effective filling date to have modified the method of Modified Recanatesi to incorporate the teachings of Gao for the benefit of an integrated communication and control approach for the motion coordination (Gao, abstract)
Regarding claim 6, Modified Recanatesi teaches the computer system of claim 1, Modified Recanatesi does not explicitly teach the limitation of claim 6.
Gao teaches further configured to determine spectral entropy from the temporal-power distribution of pulses and to compare the spectral entropy with the order parameter to identify regimes of coherent stability, adaptive flexibility, and desynchronization (In this framework, the time instants for pulse exchanging are determined ... so communication and control are integrated (pg. 3, left col., second para.); pulse-coupled oscillators with guaranteed phase continuity, we can use a continuous-heading-implementation mechanism to spread the needed heading adjustment in a certain time interval without affecting the convergence stability (pg. 5, right col., second para.); So taking inspiration from results on pulse-coupled synchronization and desynchronization, we propose the following heading control strategy for the synchronized-state and splay-state collective motions, respectively, pg. 4, right col., second to the para.).
It would have been obvious to a person of ordinary skill before the effective filling date to have modified the method of Modified Recanatesi to incorporate the teachings of Gao for the benefit of an integrated communication and control approach for the motion coordination (Gao, abstract)
Regarding claim 7, Modified Recanatesi teaches the computer system of claim 1, Modified Recanatesi does not explicitly teach the limitation of claim 7.
Gao teaches wherein the feedback controller detects and corrects temporal pathologies by: increasing pulse excitation during starvation (Motivated by biological pulse-coupled oscillators (e.g., flashing fireflies and firing neurons) which can achieve synchronization with remarkable robustness and simplicity through exchanging simple identical pulses at discrete-time instants, we design a pulse based integrated communication and control approach, pg. 1, right col., last para. to pg. 2 left col., first sentence);
applying damping when curvature amplitude exceeds a stability limit during storm conditions (In the proposed integrated communication and control framework, ωo is a design parameter controlling communication frequency. A larger ωo leads to a higher communication frequency, pg. 4, left col., second to the last para.); and
re-synchronizing pulse phases during desynchronization (Pulse-coupled oscillators can synchronize/desynchronize oscillating phases via exchanging simple identical pulses, pg. 3, left col., second para.).
It would have been obvious to a person of ordinary skill before the effective filling date to have modified the method of Modified Recanatesi to incorporate the teachings of Gao for the benefit of an integrated communication and control approach for the motion coordination (Gao, abstract)
Regarding claim 11, claim 11 is similar to claim 2. It is rejected in the same manner and reasoning applying.
Regarding claim 14, claim 14 is similar to claim 5. It is rejected in the same manner and reasoning applying.
Regarding claim 15, claim 15 is similar to claim 6. It is rejected in the same manner and reasoning applying.
Regarding claim 16, claim 16 is similar to claim 7. It is rejected in the same manner and reasoning applying.
Conclusion
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/M.G./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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