DETAILED ACTION
This final action is in response to the amendment and remarks filed on 08/13/2025 for application 17/570,113.
Claims 1, 3, 5-13, 15-18, 20-21 and 23 have been amended. Claims 2, 4, and 14 are cancelled. Claims 24-26 are newly added claims.
Claims 1, 3, 5-13, and 15-26 are pending in the application. Claims 1 and 20 are independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) filed 09/25/2025 has been considered by the examiner.
Response to Amendment
The amendment filed 08/13/2025 has been entered.
Applicant’s amendment to the claims with respect to resolving claim objections and indefiniteness rejections under 35 U.S.C. 112(b) has been considered, and overcomes the objections and 112(b) rejections set forth in the office action mailed 04/24/2025. Consequently, the previous objections and rejections have been withdrawn.
Applicant’s amendment to the claims, particularly the claim limitations that were
previously interpreted in accordance with 35 U.S.C. 112(f) in the office action mailed
04/24/2025, results in them no longer invoking an interpretation under 35 U.S.C. 112(f).
Consequently, the interpretations have been withdrawn.
In light of the amendments made to both the instant application (17/570,113) and
the co-pending applications at issue (17/555,577 and 17/563,480), the examiner has
withdrawn the previous provisional nonstatutory double patenting rejection of the
instant application in view of co-pending application 17/563,480.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 6-9, 11-12, 20, and 22-26 of the instant application are provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claims 10 and 12-15 of co-pending application 17/555,577 in view of Kasabov et al., (“NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data”, published May 2013, cited in IDS dated 09/25/2025), hereinafter Kasabov.
Claims 5 and 10 of the instant application are provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 10 of co-pending application 17/555,577 in view of Kasabov, further in view of Pecevski et al., (“Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons”, published 2011), hereinafter Pecevski.
Claims 15-16 of the instant application are provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 10 of co-pending application 17/555,577 in view of Kasabov, further in view of Fan et al., (“On Applications of Spiking Neural P Systems”, published 2020), hereinafter Fan.
Claim 19 of the instant application are provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 10 of co-pending application 17/555,577 in view of Kasabov, further in view of Yu et al. (“Traffic Scheduling based on Spiking Neural Network in Hybrid E/O Switching Intra-Datacenter Networks”, published 2020), hereinafter Yu.
Claim 21 of the instant application are provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 10 of co-pending application 17/555,577 in view of Kasabov, further in view of Kammerer et al., (“Process-Driven and Flow-Based Processing of Industrial Sensor Data”, published 2020), hereinafter Kammerer.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are largely covered in scope by the claims of the co-pending application, such that a person of ordinary skill in the art would deem the claims of the instant application to be an obvious variation.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The table below shows similarities and differences between the instant application and the co-pending application; the left side contains claims 1, 3, 5-12, 15-16, and 19-26 of the instant application, while the right side contains portions of claims 1, 10, and 12-15 of the co-pending application 17/555,577. Differences that amount to more than minor variations in language and punctuation are further discussed below.
Instant Application (17/570,113)
Co-pending Application (17/555,577)
Claim 1. Neuromorphic hardware
for storing or for both storing and processing a knowledge graph that comprises observed triple statements,
said neuromorphic hardware comprising a hardware component, a learning component, and a control component,
wherein the learning component comprises an input layer,
wherein the input layer comprises a first node embedding population and a second node embedding population,
wherein the first node embedding population comprises N first neurons and represents a first node n1 in the knowledge graph, wherein the first node n1 is characterized by N sequentially ordered first spike times t11, ..., t1N of the N first neurons during a recurring time interval, wherein N is at least 2, wherein the second node embedding population comprises N second neurons and represents a second node n2 in the knowledge graph, wherein the second node n2 is characterized by N sequentially ordered second spike times t21, ..., t2N of the N second neurons during the recurring time interval, and
wherein each triple statement Sp of P triple statements has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein each relation p connects the first node n1 and the second node n2 to each other in accordance with a representation as n1 - p - n2 of the triple statement Sp, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, and wherein rp is a specified spike time difference associated with relation p.
(Claim 3, depending from claim 1)
wherein the learning component comprises an output layer,
wherein the output layer comprises output neurons,
wherein each relation is stored in a respective output neuron that is connected to the first neurons and to the second neurons, and
wherein each relation is specified by vector components that are stored in dendrites of the respective output neuron.
(Claim 5, depending from claim 1)
wherein each node embedding population of the first and second node embedding populations is connected to an inhibiting neuron, and is selectable by inhibition of the inhibiting neuron.
(Claim 6, depending from claim 1)
wherein the first neurons are connected to a monitoring neuron,
wherein each first neuron is connected to a corresponding parrot neuron,
wherein the corresponding parrot neurons are connected to the output neurons, and
wherein the corresponding parrot neurons are connected to an inhibiting neuron.
(Claim 7, depending from claim 1)
wherein the first neurons and the second neurons are non-leaky integrate-and-fire neurons or current-based leaky integrate-and-fire neurons.
(Claim 8, depending from claim 1)
wherein each of the first neurons and each of the second neurons spikes only once during the recurring time interval, or wherein only a first spike during the recurring time interval is counted.
(Claim 9, depending from claim 1)
wherein the learning component comprises an output layer,
wherein the output layer comprises output neurons,
and wherein all relations in the knowledge graph are stored in the
output neurons.
(Claim 10, depending from claim 1)
wherein the neuromorphic hardware implements: a recommendation system, a digital twin, a semantic feature selector, or an anomaly detector.
(Claim 11, depending from claim 1)
wherein the hardware component is an application specific integrated circuit, a field-programmable gate array, a wafer-scale integration, a hardware with mixed-mode VLSI neurons, a neural processing unit, or a mixed-signal neuromorphic processor.
(Claim 12, depending from claim 1)
wherein a learning component comprises an output layer,
wherein the output layer comprises output neurons,
wherein the output neurons represent a likelihood for each triple statement generated in the sampling mode of the learning component.
(Claim 15, depending from claim 1)
An industrial device, comprising the neuromorphic hardware
(Claim 16, depending from claim 15)
The industrial device, wherein the industrial device is a field device, an edge device, a sensor device, a PLC controller, an industrial PC implementing a SCADA system, a network hub, an industrial ethernet switch, or an industrial gateway connecting an automation system to cloud computing resources.
(Claim 19, depending from claim 1)
A server, with the neuromorphic hardware
Claim 20. A method for storing, processing, or both storing and processing a knowledge graph that comprises triple statements,
wherein a neural network comprises N first neurons, N second neurons and output neurons that include a first output neuron, said method comprising:
training the neural network, wherein said training the neural network comprises:
encoding a representation of a first node ni in the knowledge graph comprising triple statements into N sequentially ordered first spike times tii, ..., tiN of the N first neurons during a recurring time interval, wherein N is at least 2,
encoding a representation of a second node n2 in the knowledge graph into N sequentially ordered second spike times t21, ..., t2N of the N second neurons during the recurring time interval,
wherein each triple statement Sp of P triple statements has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein each relation p connects the first node n1 and the second node n2 to each other in accordance with a representation as n1 - p - n2 of the triple statement Sp, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (t1n – t2n -rp) or II t1n – t2n – rp II, wherein Δtn=t1n - t2n. and wherein rp is a specified spike time difference associated with relation p, and
ranking each triple statement Sp of P triple statements with a rank θ1,2.p with respect to each relation p of P relations, wherein P is at least 2, wherein each relation p connects the first node n1 and the second node n2 to each other in accordance with a representation as n1 -p - n2 of the triple statement Sp,
wherein for p = 1, ..., P, said ranking comprises: (i) decoding, by the first output neuron, Δtn for n = 1..., N wherein Δtn = t1n - t2n; and (ii) determining θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, and wherein rp is a specified spike time difference associated with relation p.
(Claim 21, depending from claim 20)
wherein the knowledge graph is an industrial knowledge graph describing parts of an industrial system,
wherein nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors industrial controllers, robots, drives, manufactured objects, tools, elements of a bill of materials, or combinations thereof, and
wherein nodes of the knowledge graph representing abstract entities include sensor measurements, attributes, configurations or skills of the physical objects, production schedules, and plans.
(Claim 22, depending from claim 20)
A computer-readable storage media having stored thereon: instructions executable by one or more processors of a computer system, wherein execution of the instructions causes the computer system to perform the method
(Claim 23, depending from claim 20)
A computer program product, comprising a non-transitory computer readable storage medium having instructions stored thereon, said instructions upon being executed by one or more processors of a computer system perform the method
(Claim 24, depending from claim 1)
wherein the control component sequentially switches the learning component: (i) into a data-driven learning mode, (ii) into a sampling mode of the learning component, and (iii) into a model-driven learning mode.
(Claim 25, depending from claim 24)
wherein the control component switches the learning component into the data-driven learning mode in which the learning component is trained with a maximum likelihood learning algorithm minimizing energy in a probabilistic, sampling-based model, using only observed triple statements,
wherein the control component switches the learning component into the sampling mode of the learning component, wherein triple statements are generated in the sampling mode, and
wherein the control component switches the learning component into the model-driven learning mode that trains the learning component with the maximum likelihood learning algorithm using only the generated triple statements.
(Claim 26, depending from claim 25) wherein during said training the learning component in the data-driven learning mode: the observed triple statements are assigned low energy values, the probabilistic, sampling-based model is derived from an energy function, and the observed triple statements have minimal energy, and
wherein during said training the learning component in the model-driven learning mode, the learning component learns to assign high energy values to the generated triple statements.
Claim 1. A neuromorphic hardware for processing a knowledge graph comprising observed triple statements,
wherein the neuromorphic hardware comprises a hardware component, a learning component, and a control component,
wherein each triple statement in the knowledge graph is structured according to s - p - o, wherein s and o are different entities and p is a relation between s and o and is directed from s to o,
wherein the hardware component is selected from the group consisting of an application specific integrated circuit, a field-programmable gate array, a wafer-scale integration, a hardware with mixed-mode VLSI neurons, a neural processing unit, or a mixed-signal neuromorphic processor,
wherein the learning component comprises an input layer and an output layer, wherein the input layer comprises a plurality of node embedding populations, wherein each node embedding population comprises a plurality of neurons and represents an entity contained in the observed triple statements,
wherein the output layer comprises output neurons representing a likelihood for each triple statement generated in a sampling mode of the learning component, and wherein the control component is configured for sequentially switching the learning component: (i) into a data-driven learning mode in which the learning component is trained with the maximum likelihood learning algorithm minimizing energy in a probabilistic, sampling-based model, using only the observed triple statements which are assigned low energy values, wherein the probabilistic, sampling-based model is derived from an energy function, and wherein the observed triple statements have minimal energy, ii) into the sampling mode, and (iii) into a model-driven learning mode configured for training the component with the maximum likelihood learning algorithm using only the generated triple statements, with the learning component learning to assign high energy values to the generated triple statements.
(Claim 10, depending from claim 1)
The neuromorphic hardware,
(repeated from claim 1)
for processing a knowledge graph comprising observed triple statements,
wherein the neuromorphic hardware comprises a hardware component, a learning component, and a control component,
wherein the learning component comprises an input layer and an output layer, wherein the input layer comprises a plurality of node embedding populations,
(continuing from claim 10)
wherein the plurality of node embedding populations comprise a first node embedding population and a second node embedding population,
wherein the first node embedding population comprises first neurons representing a first entity contained in the observed triple statements, and wherein the first node embedding population is characterized by first spike times of the first neurons during a recurring time interval,
wherein the second node embedding population comprises second neurons representing a second entity contained in the observed triple statements, and wherein the second node embedding population is characterized by second spike times of the second neurons during the recurring time interval, and
wherein a relation between the first entity and the second entity is represented as the differences between the first spike times and the second spike times.
(Claim 12, depending from claim 10)
(repeated from claim 1)
wherein the learning component comprises an input layer and an output layer,
wherein the output layer comprises output neurons
(repeated from claim 10)
first neurons representing a first entity,
second neurons representing a second entity,
a relation between the first entity and the second entity,
(continued from claim 12)
wherein the relation is stored in one of the output neurons, and
wherein the relation is given by vector components that are stored in dendrites of the output neurons.
(claim 10, as above)
(Claim 13, depending from claim 10)
wherein the first neurons are connected to a monitoring neuron,
wherein each first neuron is connected to a corresponding parrot neuron,
wherein the corresponding parrot neurons are connected to the output neurons, and
wherein the corresponding parrot neurons are connected to an inhibiting neuron.
(Claim 14, depending from claim 10)
wherein the first neurons and the second neurons are spiking neurons, that are non-leaky integrate-and-fire neurons or current-based leaky integrate-and-fire neurons.
(Claim 15, depending from claim 10)
wherein each of the first neurons and each of the second neurons spikes only once during the recurring time interval, or wherein only a first spike during the recurring time interval is counted.
(claim 12, as above)
(repeated from claim 1)
wherein the learning component comprises an input layer and an output layer,
wherein the output layer comprises output neurons
(repeated from claim 1)
wherein each triple statement in the knowledge graph is structured according to s - p - o, wherein s and o are different entities and p is a relation between s and o
(repeated from claim 10)
first neurons representing a first entity,
second neurons representing a second entity,
a relation between the first entity and the second entity,
(repeated from claim 12)
wherein the relation is stored in one of the output neurons
(claim 10, as above)
(claim 10, as above)
(repeated from claim 1)
wherein the hardware component is selected from the group consisting of an application specific integrated circuit, a field-programmable gate array, a wafer-scale integration. a hardware with mixed-mode VLSI neurons, a neural processing unit, or a mixed-signal neuromorphic processor,
(claim 10, as above)
(repeated from claim 1)
wherein the learning component comprises an input layer and an output layer,
wherein the output layer comprises output neurons representing a likelihood for each triple statement generated in a sampling mode of the learning component.
(claim 10, as above)
(claim 10, as above)
(claim 10, as above)
(claim 10, as above)
(repeated from claim 1)
A neuromorphic hardware for processing a knowledge graph comprising observed triple statements,
wherein the neuromorphic hardware comprises a hardware component, a learning component, and a control component,
wherein the learning component comprises an input layer and an output layer,
wherein the input layer comprises a plurality of node embedding populations, wherein each node embedding population comprises a plurality of neurons
(repeated from claim 10)
wherein the plurality of node embedding populations comprise a first node embedding population and a second node embedding population,
wherein the first node embedding population comprises first neurons,
wherein the second node embedding population comprises second neurons
(repeated from claim 1)
wherein the output layer comprises output neurons
wherein the control component is configured for sequentially switching the learning component: (i) into [modes] in which the learning component is trained
(repeated from claim 10)
wherein the first node embedding population comprises first neurons representing a first entity contained in the observed triple statements, and wherein the first node embedding population is characterized by first spike times of the first neurons during a recurring time interval,
wherein the second node embedding population comprises second neurons representing a second entity contained in the observed triple statements, and wherein the second node embedding population is characterized by second spike times of the second neurons during the recurring time interval, and
wherein a relation between the first entity and the second entity is represented as the differences between the first spike times and the second spike times.
(repeated from claim 1)
wherein each triple statement in the knowledge graph is structured according to s - p - o, wherein s and o are different entities and p is a relation between s and o and is directed from s to o,
(claim 10, as above)
(claim 10, as above)
(Examiner Note: Merely implementing the claimed method via a generic computer system is an obvious variation)
(claim 10, as above)
(Examiner Note: Merely implementing the claimed system via a generic computer-readable storage medium is an obvious variation)
(claim 10, as above)
(repeated from claim 1)
wherein the control component is configured for sequentially switching the learning component: (i) into a data-driven learning mode, ii) into the sampling mode, and (iii) into a model-driven learning mode.
(claim 10, as above)
(repeated from claim 1)
wherein the control component is configured for sequentially switching the learning component: (i) into a data-driven learning mode in which the learning component is trained with the maximum likelihood learning algorithm minimizing energy in a probabilistic, sampling-based model, using only the observed triple statements which are assigned low energy values, wherein the probabilistic, sampling-based model is derived from an energy function, and wherein the observed triple statements have minimal energy, ii) into the sampling mode, and (iii) into a model-driven learning mode configured for training the component with the maximum likelihood learning algorithm using only the generated triple statements, with the learning component learning to assign high energy values to the generated triple statements.
(claim 10, as above)
(repeated from claim 1)
wherein the control component is configured for sequentially switching the learning component: (i) into a data-driven learning mode in which the learning component is trained with the maximum likelihood learning algorithm minimizing energy in a probabilistic, sampling-based model, using only the observed triple statements which are assigned low energy values, wherein the probabilistic, sampling-based model is derived from an energy function, and wherein the observed triple statements have minimal energy
and (iii) into a model-driven learning mode configured for training the component with the maximum likelihood learning algorithm using only the generated triple statements, with the learning component learning to assign high energy values to the generated triple statements.
Claim 10 of the co-pending application does not expressly teach wherein each triple statement Sp of P triple statements has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, and wherein rp is a specified spike time difference associated with relation p, or ranking each triple statement Sp of P triple statements with a rank θ1,2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P, said ranking comprises: (i) decoding, by the first output neuron, Δtn for n = 1..., N wherein Δtn = t1n - t2n; and (ii) determining θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, as recited in claims 1 and 20 of the instant application.
In the same field of endeavor, Kasabov teaches a means of modeling temporal relations via a spiking neural network architecture framework (“This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD…The Neu- Cube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships ‘hidden’ in the data” [Kasabov Abstract]) wherein each pair of spiking neuron populations has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, wherein rp is a specified spike time difference associated with relation p (“The process of creating a NeuCube model for a given STBD takes the following steps: a. Encode the STBD into spike sequences: continuous value input information is encoded into trains of spikes; b. Construct and train in an unsupervised mode a recurrent 3D SNN reservoir, SNNr, to learn the spike sequences that represent individual input patterns; c. Construct and train in a supervised mode an evolving SNN classifier to learn to classify different dynamic patterns of the SNNr activities that represent different input patterns from SSTD that belong to different classes; d. Optimize the model through several iterations of steps (a)–(c) above for different parameter values until maximum accuracy is achieved. e. Recall the model on new data” [Kasabov page 66 The NeuCube Architecture]; “Continuous value input data can be transformed into spikes so that the current value of each input variable (e.g. pixel, EEG channel, fMRI voxel) is entered into a population of neurons that emit spikes based on how much the input value belongs to their receptive fields…The transformed input data into spike series is entered (mapped) into spatially located neurons from the SNNr” [Kasabov pages 66-67 Input data encoding module]; “In a current implementation, the SNNr has a 3D structure connecting leaky-integrate and fire model (LIFM) spiking neurons with recurrent connections…The neuronal connections are adapted and the SNNr learns to generate specific trajectories of spiking activities when a particular input pattern is entered…It can be seen that as a result of training new connections have been created that represent spatio-temporal interaction between input variables captured in the SNNr from the data. The connectivity can be dynamically visualized for every new pattern submitted” [Kasabov page 67 The SNNr module]; “After the SNNR is trained on the STBD in an unsupervised model, the same input data is propagated again through the SNNr, pattern by pattern, the state of the SNNr is measured for each pattern and an output classifier is trained to recognize this state in a predefined output class for this input pattern. For fast learning, we use evolving SNN classifiers (eSNN). All neurons from the SNNr are connected to each of the evolved LIFM neurons of the eSNN classifier…The recall procedure can be performed using different recall algorithms applying different methods:… b) The second method implies a creation of a new output neuron in the eSNN for each new input pattern from the SNNr, in the same way as the output neurons were created during the learning phase in the eSNN, and then—comparing the connection weight vector of the new one to the already existing neurons using Euclidean distance. The closest output neuron in terms of synaptic connection weights is the ‘winner’. This method uses the principle of transductive reasoning and nearest neighbor classification in the connection weight space. It compares spatially distributed synaptic weight vectors of a new neuron that captures a new input pattern and existing ones. This method is called eSNNs (deSNNs)” [Kasabov page 67 Evolving output classification module]; The disclosed NeuCube model teaches encoding values of input variables into spikes of corresponding neuron populations (e.g., encoding a first variable into first spike times t11, ..., t1N and a second variable into second spike times t21,..,t2N), wherein connection weights between neurons represent spatio-temporal interaction patterns (i.e., relations) between input variables, and said connection weights, by definition under the spike-timing dependent plasticity (STDP) learning rule [see Kasabov pages 65-66 Models of spiking neurons and methods of learning in SNN further discussing STDP learning rule, including equation 2 for Δwj,i(t)], are a function of differences in spike times between the pre-synaptic and post-synaptic neuron (i.e., Δtn = t1n – t2n). Additionally, an output classifier is further trained to recognize and classify input patterns of spatio-temporal interaction between neurons (i.e., relations) by, e.g., comparing connection weight vectors (functions of Δtn) to connections of already existing neurons (i.e., spike time differences rp corresponding to each relation type) via Euclidean distance (i.e., function of Δtn – rp), wherein the closest output neuron based on synaptic connection (i.e., corresponding neuron with best rank (e.g., distance f(Δtn – rp)) is determined to represent the correct input pattern (i.e., type of relation))
, and ranking each triple statement Sp of P triple statements with a rank θ1,2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P, said ranking comprises: (i) decoding, by the first output neuron t node Δtn for n = 1..., N wherein Δtn = t1n - t2n; and (ii) determining θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II ([Kasabov page 66 The NeuCube Architecture] and [Kasabov pages 66-67 Input data encoding module] and [Kasabov page 67 The SNNr module] and [Kasabov page 67 Evolving output classification module], as detailed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Kasabov into the co-pending application because Kasabov is similarly directed towards a means of modeling temporal relations via a spiking neural network architecture framework. Incorporating the teachings of Kasabov would provide an inexpensive means of further boosting accuracy of modeling temporal relations through spike times and enabling early prediction of temporal events (“The main advantage of the eSNN, when compared with other supervised or unsupervised learning and classification SNN models, is that it is computationally inexpensive and boosts the importance of the order in which input spikes arrive, thus making the eSNN suitable for on-line learning and early prediction of temporal events” [Kasabov page 68 Evolving output classification module]).
Claim 10 of the co-pending application does not explicitly teach wherein each node embedding population of the first and second node embedding populations is connected to an inhibiting neuron, and is selectable by inhibition of the inhibiting neuron or wherein the neuromorphic hardware implements: a recommendation system, a digital twin, a semantic feature selector, or an anomaly detector as recited in claims 5 and 10 of the instant application.
In the same field of endeavor, Pecevski teaches a neuromorphic hardware for processing a knowledge graph represented by triple statements ("We will focus in this article on Bayesian networks, a common type of graphical model for probability distributions...A Bayesian network is a directed graph (without directed cycles), whose nodes represent RVs z1, . . . ,zK. Its graph structure indicates that p(z1, . . . ,zK) admits a factorization of the form [equation 7] where pa(zk) is the set of all (direct) parents of the node indexed by zk...We show that the complexity of the resulting network of spiking neurons for carrying out probabilistic inference for p can be bounded in terms of the graph complexity of the Bayesian network that gives rise to the factorization (7)…Altogether, our computer simulations and our theoretical analyses demonstrate that networks of spiking neurons can emulate probabilistic inference for general Bayesian networks. Hence we propose to view probabilistic inference in graphical models as a generic computational paradigm, that can help us to understand the computational organization of networks of neurons in the brain, and in particular the computational role of precisely structured cortical microcircuit motifs" [Pecevski page 5 Introduction]; Pecevski discloses a spiking neural network system implemented on a cortical microcircuit structure (i.e. neuromorphic hardware) that performs probabilistic inference on (i.e., learns from) a Bayesian network (i.e., knowledge graph). A Bayesian network takes the form of a directed acyclic graph, wherein nodes representing variables (RVs) are connected to other nodes in a parent-child (i.e. subject-object) relationship. In light of the instant specification [page 2 lines 1-5], two connected nodes of a Bayesian network, representing probabilistic relations between variables, are thereby interpretable as a triple, or triple statement) wherein each node embedding population of the first and second node embedding populations is connected to an inhibiting neuron, and is selectable by inhibition of the inhibiting neuron (“This distribution requires knowledge about when exactly a neuron nk with zk(t)~1 had fired. Therefore auxiliary RVs f1, . . . ,fK with multinomial or analog values were introduced in [1], that keep track of when exactly in the preceding time interval of length t a neuron nk had fired" [Pecevski page 3]; "The inputs connect to the auxiliary neuron akv either with a direct strong excitatory connection, or through an inhibitory interneuron ikv that connects to the auxiliary neuron. The inhibitory interneuron ikv fires whenever any of the principal neurons of the RVs zBk that connect to it fires" [Pecevski page 20]) and wherein the neuromorphic hardware implements: a recommendation system, a digital twin, a semantic feature selector, or an anomaly detector ("We have tested the viability of the previously described approach for neural sampling by satisfying the NCC also on two larger and more complex Bayesian networks: the well-known ASIA-network [24]...Computer Simulation II: ASIA Bayesian network. The ASIA-network is an example for a larger class of Bayesian networks…Networks of this type, that consist of 3 types of RVs (context information, true causes, observable symptoms) with directed edges only from one class to the next...A typical example for probabilistic inference in this network arises when one enters as evidence the facts that the patient visited Asia (A= 1) and has Dyspnoea (D = 1), and asks what is the likelihood of each of the RVs T, C, B that represent the diseases, and how the result of a positive x-ray test would affects these likelihoods. We tested this probabilistic inference in a network of spiking neurons" [Pecevski section Probabilistic Inference through Neural Sampling in Larger and More Complex Bayesian Networks on page 11]; The inference system can predict likelihood of (i.e., detect) diseases (i.e., anomalies) based on symptom variables)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Pecevski into the co-pending application because they are both directed towards neuromorphic hardware for processing a knowledge graph represented by triple statements. Incorporating the teachings of Pecevski would enable further functionalities of the neuromorphic hardware with respect to managing neuronal spikes (e.g., absolute refractory periods) (“Each firing of one of these auxiliary neurons should cause an immediately subsequent firing of the principal neuron n1. Lateral inhibition among these auxiliary neurons can make sure that after a firing of an auxiliary neuron no other auxiliary neuron fires during the subsequent time interval of length t, thereby implementing the required absolute refractory period of the theoretical model from [1]” [Pecevski page 7 Implementation with auxiliary neurons (Implementation 2)]).
Claim 10 of the co-pending application does not explicitly teach An industrial device comprising the neuromorphic hardware, wherein the industrial device is a field device, an edge device, a sensor device, a PLC controller, an industrial PC implementing a SCADA system, a network hub, an industrial ethernet switch, or an industrial gateway connecting an automation system to cloud computing resources, as recited in claims 15 and 16 of the instant application.
In the same field of endeavor, Fan teaches a spiking neural networks framework hosted on neuromorphic hardware (“Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware” [Fan Abstract]) that discloses An industrial device ("Many variants of SNPS have been introduced by incorporating features from the biological neurons...These models have also been used in solving problems related to real life applications such as...programming for logic controllers [8], etc." [Fan page 2]; "The main contributions of this work are as follows: (1) Listing a majority of the SNPS models used for solving problems...Additionally, their use in solving computationally hard problems, the construction of u-fluidic biochip design and programming for PLC (programmable logic controller);" [Fan pages 2-3]; In light of the specification [page 5 lines 15-22] and the described “real life applications”, PLCs are industrial devices) comprising neuromorphic hardware ("In Figure 6, we presented a comparison of all SNPS models performing arithmetic and logical operations and also their hardware implementation [Figure 6]" [Fan page 18]) wherein the industrial device is a field device, an edge device, a sensor device, a PLC controller, an industrial PC implementing a SCADA system, a network hub, an industrial ethernet switch, or an industrial gateway connecting an automation system to cloud computing resources ([Fan pages 2-3] as detailed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Fan into the co-pending application because they are both directed towards a spiking neural networks framework hosted on neuromorphic hardware. Incorporating the teachings of Fan would further expand the scope of application of the SNN framework, as well as its potential implementations in hardware (“SNNs are hardware friendly and energy efficient...The main motivation to prepare this survey is as follows: (3) Study implementations of these models in hardware; (4) Introduce some new ideas to expand the scope of SNPS models" [Fan page 2]).
Claim 10 of the instant application does not expressly teach A server with the neuromorphic hardware, as recited in claim 19 of the instant application.
In the same field of endeavor, Yu teaches an application of a spiking neural networks framework for predictive inference (“However, the low accuracy of existing deep learning-based prediction approaches, which cannot fully extract the features of burst traffic, directly restricts the efficiency of traffic scheduling. In view of this, this study considers the spiking neural networks that can predict high burstiness and heterogeneous traffic to further improve the efficiency of traffic scheduling. We first propose a supervised spiking neural network (s-SNN) framework for high accuracy traffic prediction in HS-IDCNs” [Yu Abstract]) that discloses a server with neuromorphic hardware (“With the overwhelming growth of emerging cloud applications, such as VR/AR and streaming video, the hybrid E/O switching intra-datacenter networks (HS-IDCNs) have been built to interconnect massive servers and to cope with the rich traffic types…with the development of computer technologies, SNNs can be trained in neuromorphic hardware, such as IBM’s TrueNorth chip [7] and Intel’s Loihi processor [8]… To the best of our knowledge, no study has discussed the use of this biology-based neural network for traffic prediction in HS-IDCNs” [Yu Introduction page 1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Yu into the co-pending application because both of these systems are directed towards applications of a spiking neural networks framework for predictive inference. Incorporating the teachings of Yu would further expand the scope of the SNN framework to cloud computing applications (particularly, e.g., for improving efficiency of datacenter networks (e.g., predicting path blocking probability)) (“The simulation results demonstrate that the s-SNN framework can significantly improve the traffic prediction accuracy, and the proposed TP-TS algorithm can improve the resource utilization and decrease blocking probability of HS-IDCNs” [Yu Introduction page 1]).
Claim 10 of the co-pending application does not expressly teach wherein the knowledge graph is an industrial knowledge graph describing parts of an industrial system, wherein nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors industrial controllers, robots, drives, manufactured objects, tools, elements of a bill of materials, or combinations thereof, and wherein nodes of the knowledge graph representing abstract entities include sensor measurements, attributes, configurations or skills of the physical objects, production schedules, and plans, as recited in claim 21 of the instant application.
In the same field of endeavor, Kammerer teaches a system of inference on an industrial knowledge graph describing parts of an industrial system, (“In industrial machines, sensors and actors are typically controlled by a programmable logic controller (see Figure 7a)” [Kammerer page 12]; [Kammerer Figure 7 Schematic overview of context-aware process execution framework on page 13]; "Execution contexts, in turn, can be mapped to a context graph, which is a direct acyclic graph and represents the logical structure of a cyber-physical system. Therefore, each node in a context graph has predefined context types and can be used as a basis for the concept called context-aware process family, which is introduced in the following" [Kammerer page 13]) with nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors, industrial controllers, robots, drives, manufactured objects, and either tools or both the tools and elements of a bill of materials, ([Kammerer page 13]; A cyber-physical system [see Figure 4 Information flow processing schema on page 10 and Figure 7 Schematic overview of context-aware process execution framework on page 13] comprises physical objects including sensors) and with nodes of the knowledge graph representing abstract entities including sensor measurements, sensor attributes, configurations of the physical objects or skills of the physical objects, production schedules and plans ([Kammerer page 13]; A cyber-physical system [see Figure 4 Information flow processing schema on page 10 and Figure 7 Schematic overview of context-aware process execution framework on page 13] comprises abstract entities including sensor data/measurements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Kammerer into the co-pending application because they are both directed towards performing inference on a knowledge graph. Incorporating the teachings of Kammerer would expand the scope of the co-pending application to industrial applications such as predictive maintenance (“For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance” [Kammerer Abstract]).
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.
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter (see MPEP § 2106.03) because it is directed to a signal per se.
The claim recites “A computer-readable storage media having stored thereon: instructions”. The specification does not provide a definition of what is considered “computer-readable storage media”. At best, the specification provides an example for “non-transitory computer-readable storage media”, which is described as distinct from “computer-readable storage media” (“The instructions for implementing processes or methods described herein may be provided on non-transitory computer-readable storage media or memories, such as a cache, buffer, RAM, FLASH, removable media, hard drive, or other computer readable storage media. Computer readable storage media include various types of volatile and non-volatile storage media. The functions, acts, or tasks illustrated in the figures or described herein may be executed in response to one or more sets of instructions stored in or on computer readable storage media” [page 40 lines 30-36]). Therefore, under broadest reasonable interpretation, the term “computer-readable storage media” includes non-statutory, transitory forms of signal transmission.
As such, claim 22 is directed to non-statutory subject matter. Applicant may amend the claim to fall within a statutory category (e.g., “A non-transitory computer- readable storage media”) to overcome this rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, 7-13, 20, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Pecevski (“Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons”, published 2011) in view of Pecevski and Maass (“Learning Probabilistic Inference through Spike-Timing Dependent Plasticity”, published 25 Mar 2016), hereinafter Pecevski-Maass, and Jiang et al., (“Encoding Temporal Information for Time-Aware Link Prediction”, published 2016), hereinafter Jiang, and Kasabov (“NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data”, published May 2013, cited in IDS dated 09/25/2025).
Regarding claim 1, Pecevski teaches Neuromorphic hardware for storing or for both storing and processing a knowledge graph that comprises observed triple statements, ("We will focus in this article on Bayesian networks, a common type of graphical model for probability distributions...A Bayesian network is a directed graph (without directed cycles), whose nodes represent RVs z1, . . . ,zK. Its graph structure indicates that p(z1, . . . ,zK) admits a factorization of the form [equation 7] where pa(zk) is the set of all (direct) parents of the node indexed by zk...We show that the complexity of the resulting network of spiking neurons for carrying out probabilistic inference for p can be bounded in terms of the graph complexity of the Bayesian network that gives rise to the factorization (7)." [Pecevski Introduction page 5]; "Altogether, our computer simulations and our theoretical analyses demonstrate that networks of spiking neurons can emulate probabilistic inference for general Bayesian networks. Hence we propose to view probabilistic inference in graphical models as a generic computational paradigm, that can help us to understand the computational organization of networks of neurons in the brain, and in particular the computational role of precisely structured cortical microcircuit motifs" [Pecevski Introduction page 5]; The examiner notes that mathematical notation quoted from prior art references is not copied with identical formatting, and applicant is advised to refer to quoted sections in this office action via the attached copies for further clarity. Pecevski discloses a spiking neural network system implemented on a cortical microcircuit structure (i.e. neuromorphic hardware) that performs probabilistic inference on (i.e., learns from) a Bayesian network. A Bayesian network takes the form of a directed acyclic graph, wherein nodes representing variables (RVs) are connected to other nodes in a parent-child (i.e. subject-object) relationship. In light of the instant specification [page 2 lines 1-5], two connected nodes can be interpreted as a triple, or triple statement) said neuromorphic hardware comprising a hardware component, a learning component, and a control component, ("We demonstrate in computer simulations that the precisely structured neuronal microcircuits enable networks of spiking neurons to solve through their inherent stochastic dynamics a variety of complex probabilistic inference tasks" [Pecevski page 2 Author Summary]; The structured neuronal microcircuits (i.e., neuromorphic hardware) are inherently a hardware component, and further enable (i.e., control) a spiking neural network framework (i.e. learning component) capable of performing a variety of probabilistic inference tasks) and
neurons corresponding to a first node n1 in the knowledge graph, and a second node n2 in the knowledge graph , wherein the first node n1 is characterized by N sequentially ordered first spike times tii, ..., tiN during a recurring time interval,, wherein the second node n2 is characterized by N sequentially ordered second spike times t21, ..., t2N during a recurring time interval, ("...in this approach the spike times, rather than the firing rate, of the neuron nk carry relevant information as they define the value of the RV zk at a particular moment in time t according to (2)” [Pecevski Introduction page 3]), and
wherein each relation p connects a first node n1 and a second node n2 to each other in accordance with a representation as n1 - p - n2 of a triple statement ([Pecevski page 5 Introduction] as detailed above; As explained above, two nodes (i.e., entities) of a Bayesian network connected by a directed edge are interpretable as a triple statement of a knowledge graph – e.g., structured as n1 – edge – n2, wherein the edge is directed from node n1 to node n2).
However, Pecevski does not expressly teach wherein the learning component comprises an input layer, wherein the input layer comprises a first node embedding population and a second node embedding population, wherein the first node embedding population comprises N first neurons. wherein N is at least 2, wherein the second node embedding population comprises N second neurons.
In the same field of endeavor, Pecevski-Maass teaches a system of performing probabilistic inference on triple statements of a Bayesian network using neuromorphic hardware (“The learning model that is presented in this article ties in to this second approach, and shows that stochastic networks of neurons are able to automatically absorb the relevant statistical information from examples that they receive. As a result, we have now one first complete theory for the emergence of probabilistic inference in networks of spiking neurons through learning… We first show how an extension of an ubiquitous network motif of cortical microcircuits, interconnected populations of pyramidal cells with lateral inhibition (Winner- Take-All (WTA) circuits; Douglas and Martin, 2004; Nessler et al., 2013), gives rise to the basic building block for absorbing probabilistic information from examples” [Pecevski-Maass page 2 Introduction]; “We show that the underlying distribution p* can be learnt (approximately) from examples for this visual perception task, and that the network N which learns this approximation learns simultaneously to deal with the explaining away effect as an emergent phenomenon. The structure of the neural network N suitable for learning this target probability distribution p* is given in Figure 6C. It consists of four interconnected learning modules, where the connections between the learning modules reflect the dependencies between the RVs in the Bayesian network in Figure 6B” [Pecevski-Maass page 12]; [see Figure 6 Description of the perceptual explaining away example on page 10]) wherein the learning component comprises an input layer, wherein the input layer comprises a first node population and a second node population representing nodes in the knowledge graph, wherein the first node population comprises N first neurons. wherein N is at least 2, wherein the second node population comprises N second neurons ((“This network motif is a three-layer feedforward network of excitatory spiking neurons with lateral inhibition on the hidden layer (see Fig. 2). We show that this simple microcircuit motif can be viewed as an atomic learning module, that extracts via STDP and intrinsic plasticity from examples probabilistic associations between input variables x and output variable z that are encoded through population coding on its input and output layer” [Pecevski-Maass page 3 Results]; see Figure 2 including input neurons layer comprising populations of neurons xi – “Figure 2. Structure of a stochastic association module that is able to learn probabilistic associations between multinomial variables x = (x1,…xl) and z through STDP. Populations of neurons xi (i = 1, . . . , I) on the first layer encode the values of input variables xi.” [Pecevski-Maass page 4]; Each input variable xi (which can be drawn from RVs comprising nodes of a Bayesian network, as explained above) is encoded by a respective population of neurons (i.e., node population)))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the learning component comprises an input layer, wherein the input layer comprises a first node embedding population and a second node embedding population, wherein the first node embedding population comprises N first neurons. wherein N is at least 2, wherein the second node embedding population comprises N second neurons as taught by Pecevski-Maass into Pecevski because they are both directed towards performing probabilistic inference on triple statements of a Bayesian network using neuromorphic hardware. Pecevski-Maass explicitly discloses similarity to the framework taught in Pecevski, and expands upon a similar base structure (“The structure of the network N in Figure 5 is very similar to the structure of a constructed network of spiking neurons that directly mimics a representation of p* by a Bayesian network according to Pecevski et al. (2011)” [Pecevski-Maass page 11]). Incorporating the teachings of Pecevski-Maass would thereby enable the recognized improved computational efficiencies (e.g., requiring a smaller number of hidden neurons) (“However, there the number of hidden neurons αk for a random variable yk was required to be exponentially large in the number of variables in the Markov blanket of yk. In contrast, in the learning approach of this article, one can employ in principle any number, also a very small number, of hidden neurons in αk” [Pecevski-Maass page 11]).
However, the combination does not expressly teach nodes and edges of the knowledge graph (i.e., entities and relations of a triple statement) being representable via vector embeddings, such that the neuron populations of Pecevski-Maass are node embedding populations.
In the same field of endeavor, Jiang further teaches a method of performing inference on a knowledge graph comprising triple statements, (“Knowledge bases (KBs) such as Freebase (Bollacker et al., 2008) and YAGO (Fabian et al., 2007) play a pivotal role in many NLP related applications. KBs consist of facts in the form of triplets (ei, r, ej), indicating that head entity ei and tail entity ej is linked by relation r. Although KBs are large, they are far from complete. Link prediction is to predict relations between entities based on existing triplets, which can alleviate the incompleteness of current KBs…This paper mainly focuses on incorporating the temporal order information and proposes a time-aware link prediction model” [Jiang Introduction page 1]) wherein nodes and edges of the knowledge graph (i.e., entities and relations of a triple statement) are representable via vector embeddings ("Recently a promising approach for this task called knowledge base embedding (Nickel et al., 2011; Bordes et al., 2011; Socher et al., 2013) aims to embed entities and relations into a continuous vector space while preserving certain information of the KB graph. TransE (Bordes et al., 2013) is a typical model considering relation vector as translating operations between head and tail vector" [Jiang Introduction page 1]; "To make the embedding space compatible with the observed triples, we make use of the triple set △ and follow the same strategy adopted in previous methods such as TransE" [Jiang page 2 Time-Aware KB Embedding]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated nodes and edges of the knowledge graph being representable via vector embeddings as taught by Jiang into the combination because both Pecevski and Jiang are directed towards performing inference on a knowledge graph comprising triple statements.
TransE, which forms the base upon which the method of Jiang expands [Jiang Introduction page 1] is known in the art as a method of modelling large-scale, multi-relational datasets/knowledge bases [see Bordes (cited in IDS) Introduction pages 1-2]; it is further known that multi-relational data can be modeled by learning/operating on vector representations (i.e., embeddings) of entities and relationships via "Bayesian clustering frameworks or energy-based frameworks" [Bordes Introduction page 2], wherein TransE is an energy-based framework. Pecevski further indicates that the disclosed system of probabilistic inference can be applied to a variety of graphical models, including both energy-based models (e.g., a Boltzmann machine) ("The results of [21] and numerous related results suggest that the brain is able to carry out probabilistic inference for more complex distributions than the 2nd order Boltzmann distribution" [Pecevski Introduction page 4]) and Bayesian models ("We will focus in this article on Bayesian networks, a common type of graphical model for probability distributions. But our results can also be applied for other types of graphical models" [Pecevski Introduction page 5]). A person of ordinary skill would have thereby been able to combine the teachings of the probabilistic inference systems of Pecevski and Pecevski-Maass and the Trans-E energy-based model of Jiang to arrive at the claimed invention with a reasonable expectation of success.
Jiang further discloses an improvement over TransE by incorporating an awareness of time and temporal order into the embedding space ("Traditional KB embedding models such as TransE often confuse relations such as wasBornIn and diedIn when predicting (person,?,location) because TransE learns only from time-unknown facts and cannot distinguish relations with similar semantic meaning. To make more accurate predictions, it is non-trivial for existing KB embedding methods to incorporate temporal order information. This paper mainly focuses on incorporating the temporal order information and proposes a time-aware link prediction model" [Jiang Introduction page 1]). Given that the spiking neural network system of Pecevski is heavily interrelated with temporal dynamics, including temporal order of neuron spikes, by nature ("But it is in conflict with basic features of networks of spiking neurons, where each action potential (spike) of a neuron triggers inherent temporal processes in the neuron itself (e.g. refractory processes), and postsynaptic potentials of specific durations in other neurons to which it is synaptically connected" [Pecevski Introduction page 3]), one of ordinary skill would recognize potential for the system of Jiang to improve the system of Pecevski by improving accuracy in modelling temporal dynamics ("In this paper, we propose a general time-aware KB embedding, which incorporates creation time of entities and imposes temporal order constraints on the geometric structure of the embedding space and enforce it to be temporally consistent and accurate" [Jiang Conclusion page 4]).
However, the combination does not expressly teach wherein each triple statement Sp of P triple statements has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, and wherein rp is a specified spike time difference associated with relation p.
In the same field of endeavor, Kasabov teaches a means of modeling temporal relations via a spiking neural network architecture framework (“This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD…The Neu- Cube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships ‘hidden’ in the data” [Kasabov Abstract]) wherein each pair of spiking neuron populations has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, wherein rp is a specified spike time difference associated with relation p (“The process of creating a NeuCube model for a given STBD takes the following steps: a. Encode the STBD into spike sequences: continuous value input information is encoded into trains of spikes; b. Construct and train in an unsupervised mode a recurrent 3D SNN reservoir, SNNr, to learn the spike sequences that represent individual input patterns; c. Construct and train in a supervised mode an evolving SNN classifier to learn to classify different dynamic patterns of the SNNr activities that represent different input patterns from SSTD that belong to different classes; d. Optimize the model through several iterations of steps (a)–(c) above for different parameter values until maximum accuracy is achieved. e. Recall the model on new data” [Kasabov page 66 The NeuCube Architecture]; “Continuous value input data can be transformed into spikes so that the current value of each input variable (e.g. pixel, EEG channel, fMRI voxel) is entered into a population of neurons that emit spikes based on how much the input value belongs to their receptive fields…The transformed input data into spike series is entered (mapped) into spatially located neurons from the SNNr” [Kasabov pages 66-67 Input data encoding module]; “In a current implementation, the SNNr has a 3D structure connecting leaky-integrate and fire model (LIFM) spiking neurons with recurrent connections…The neuronal connections are adapted and the SNNr learns to generate specific trajectories of spiking activities when a particular input pattern is entered…It can be seen that as a result of training new connections have been created that represent spatio-temporal interaction between input variables captured in the SNNr from the data. The connectivity can be dynamically visualized for every new pattern submitted” [Kasabov page 67 The SNNr module]; “After the SNNR is trained on the STBD in an unsupervised model, the same input data is propagated again through the SNNr, pattern by pattern, the state of the SNNr is measured for each pattern and an output classifier is trained to recognize this state in a predefined output class for this input pattern. For fast learning, we use evolving SNN classifiers (eSNN). All neurons from the SNNr are connected to each of the evolved LIFM neurons of the eSNN classifier…The recall procedure can be performed using different recall algorithms applying different methods:… b) The second method implies a creation of a new output neuron in the eSNN for each new input pattern from the SNNr, in the same way as the output neurons were created during the learning phase in the eSNN, and then—comparing the connection weight vector of the new one to the already existing neurons using Euclidean distance. The closest output neuron in terms of synaptic connection weights is the ‘winner’. This method uses the principle of transductive reasoning and nearest neighbor classification in the connection weight space. It compares spatially distributed synaptic weight vectors of a new neuron that captures a new input pattern and existing ones. This method is called eSNNs (deSNNs)” [Kasabov page 67 Evolving output classification module]; The disclosed NeuCube model teaches encoding values of input variables into spikes of corresponding neuron populations (e.g., encoding a first variable into first spike times t11, ..., t1N and a second variable into second spike times t21,..,t2N), wherein connection weights between neurons represent spatio-temporal interaction patterns (i.e., relations) between input variables, and said connection weights, by definition under the spike-timing dependent plasticity (STDP) learning rule [see Kasabov pages 65-66 Models of spiking neurons and methods of learning in SNN further discussing STDP learning rule, including equation 2 for Δwj,i(t)], are a function of differences in spike times between the pre-synaptic and post-synaptic neuron (i.e., Δtn = t1n – t2n). Additionally, an output classifier is further trained to recognize and classify input patterns of spatio-temporal interaction between neurons (i.e., relations) by, e.g., comparing connection weight vectors (functions of Δtn) to connections of already existing neurons (i.e., spike time differences rp corresponding to each relation type) via Euclidean distance (i.e., function of Δtn – rp), wherein the closest output neuron based on synaptic connection (i.e., corresponding neuron with best rank (e.g., distance f(Δtn – rp)) is determined to represent the correct input pattern (i.e., type of relation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein each pair of spiking neuron populations has a rank θ1.2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P: θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II, wherein Δtn=t1n - t2n, wherein rp is a specified spike time difference associated with relation p as taught by Kasabov into the combination because both Pecevski and Kasabov are directed towards a means of modeling temporal relations via a spiking neural network architecture framework. Incorporating the teachings of Kasabov would provide an inexpensive means of further boosting accuracy of modeling temporal relations through spike times and enabling early prediction of temporal events (“The main advantage of the eSNN, when compared with other supervised or unsupervised learning and classification SNN models, is that it is computationally inexpensive and boosts the importance of the order in which input spikes arrive, thus making the eSNN suitable for on-line learning and early prediction of temporal events” [Kasabov page 68 Evolving output classification module]).
Regarding claim 3, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein each relation is specified by vector components that are stored in dendrites of a respective output neuron ("Our models postulate that knowledge is encoded in the brain in the form of probability distributions p, that are not required to be of the restricted form of 2nd order Boltzmann distributions (5). Furthermore they postulate that these distributions are encoded through synaptic weights and neuronal excitabilities, and possibly also through the strength of dendritic branches. Finally, our approach postulates that these learnt and stored probability distributions p are activated through the inherent stochastic dynamics of networks of spiking neurons, using nonlinear features of network motifs and neurons to represent higher order dependencies between RVs" [Pecevski Experimentally Testable Predictions of our Models page 11]; "The alternative model that only uses dendritic computation (Implementation 5) will have groups of dendritic branches corresponding to the different factors. The number of auxiliary neurons that connect to nk in Implementation 4 (and the corresponding number of dendritic branches in Implementation 5) is equal to the sum of the exponents of the sizes of factors that depend on zk: [summation]. where D(zc\k) denotes the number of RVs in the vector zc\ k." [Pecevski page 9]). Pecevski-Maass further teaches wherein the learning component comprises an output layer, wherein the output layer comprises output neurons (see Figure 2 including output neurons layer– “The population of neurons on the third layer encodes the value of z… STDP applied to the weights wim, j l of synaptic connections from the first layer to the neurons α on the hidden layer enables the network to approximate for any network input x through the firing probability of neurons on the third layer the distribution of values z that were associated with x in previously processed examples <x, z>” [Pecevski-Maass page 4]; “Note that in general the same input x will occur in combination with different values z(1), z(2), . . . of z in the training examples, and the goal of learning is to learn for each value z(i) the probability that it occurs for input x” [Pecevski-Maass page 4 A network module for learning stochastic associations]; “The other approach for emulating probabilistic inference in networks of spiking neurons is based on the assumption that a network of neurons can “embody” a distribution p in such a way that it can generate samples from p. Probabilistic inference for p can then be performed through simple operations on these samples” [Pecevski-Maass page 2 Introduction]; “We provide in this article a proof of principle that these parameters of p do not have to be programmed into the network: they can be learnt by a network of spiking neurons via simple local plasticity rules from examples y˜ that are generated by p” [Pecevski-Maass page 2 Results]) connected to the first neurons and to the second neurons ([see Figure 4 on page 8]; “Figure 4. Recursive combination of learning modules. The learning module for the RV yk at the bottom has the same structure as the modules shown in Figures 2 and 3. For learning complex distributions p* its input variables x1, . . . , xl form a Markov blanket of yk. Each variable xi is encoded by the same population coding as the output variables of learning modules, and can therefore be produced by the output of another learning module (as shown for the RV xl). As here yk is in the Markov blanket of xl, yk appears among the input variables of the upper module, and its corresponding input neurons are the same as the output neurons of the lower module” [Pecevski-Maass page 8]; [see Figure 6 on page 10, components B through D]; “Figure 6. Description of the perceptual explaining away example…B, The “explaining away” Bayesian network proposed by Kersten and Yuille (2003) that models the effect from A. It consists of four RVs y1, y2, y3, and y4…C, The structure of the neural network N that corresponds to the Bayesian network in B. For each RV yk in the Bayesian network there is a learning module Nk composed of a population of neurons that outputs yk in population coding, and a population of hidden neurons αk. The learning modules are interconnected according to the Markov blankets of the RVs in the Bayesian network as indicated in Figure 4. For example, the RVs in the Markov blanket of y1 are y2 and y3, and therefore the learning module N1 receives connections from N2 and N3. D, Structure of the learning module N1 for the RV y1 in the neural network in C.” [Pecevski-Maass page 10]; RVs and their associated learning modules are connected via the Markov blanket, wherein for any given learning module, it can both receive (be connected to) input neurons from other modules, and also output neurons to other modules).
Regarding claim 5, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein each node embedding population of the first and second node embedding populations is connected to an inhibiting neuron, and is selectable by inhibition of the inhibiting neuron (“This distribution requires knowledge about when exactly a neuron nk with zk(t)~1 had fired. Therefore auxiliary RVs f1, . . . ,fK with multinomial or analog values were introduced in [1], that keep track of when exactly in the preceding time interval of length t a neuron nk had fired" [Pecevski page 3]; "The inputs connect to the auxiliary neuron akv either with a direct strong excitatory connection, or through an inhibitory interneuron ikv that connects to the auxiliary neuron. The inhibitory interneuron ikv fires whenever any of the principal neurons of the RVs zBk that connect to it fires" [Pecevski page 20]).
Regarding claim 7, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein the first neurons and the second neurons are non-leaky integrate-and-fire neurons or current-based leaky integrate-and-fire neurons ("We consider in this article two types of models for spiking neurons (see Methods for details):...stochastic leaky integrate –and –fire neurons with absolute and relative refractory periods, formalized in the spike–response framework of [16] (as in [1])," [Pecevski page 3]; "The evidence about known RVs in the neural network was introduced by injected constant current in the corresponding principal neurons of amplitude Az~50 if the value of the RV is 1 and A{~{30 if the value of the RV is 0." [Pecevski page 23]).
Regarding claim 8, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein each of the first neurons and each of the second neurons spikes only once during the recurring time interval, or wherein only a first spike during the recurring time interval is counted (The NCC requires that for each RV zk the firing probability density rk(t) of its corresponding neuron nk at time t satisfies, if the neuron is not in a refractory period, [equation 3]…In the simpler version of this neuron model one assumes that it has an absolute refractory period of length t, and that the instantaneous firing probability pk(t) satisfies outside of its refractory period..." [Pecevski page 3]; The neuron will only fire once within an absolute refractory period (recurring time interval)).
Regarding claim 9, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski-Maass further teaches wherein the learning component comprises an output layer, wherein the output layer comprises output neurons (see Figure 2 including output neurons layer– “The population of neurons on the third layer encodes the value of z… STDP applied to the weights wim, j l of synaptic connections from the first layer to the neurons α on the hidden layer enables the network to approximate for any network input x through the firing probability of neurons on the third layer the distribution of values z that were associated with x in previously processed examples <x, z>” [Pecevski-Maass page 4]; “Note that in general the same input x will occur in combination with different values z(1), z(2), . . . of z in the training examples, and the goal of learning is to learn for each value z(i) the probability that it occurs for input x” [Pecevski-Maass page 4 A network module for learning stochastic associations]; “The other approach for emulating probabilistic inference in networks of spiking neurons is based on the assumption that a network of neurons can “embody” a distribution p in such a way that it can generate samples from p. Probabilistic inference for p can then be performed through simple operations on these samples” [Pecevski-Maass page 2 Introduction]; “We provide in this article a proof of principle that these parameters of p do not have to be programmed into the network: they can be learnt by a network of spiking neurons via simple local plasticity rules from examples y˜ that are generated by p” [Pecevski-Maass page 2 Results]), wherein all relations in the knowledge graph are stored in the output neurons (see Figure 6 including components B through D [Pecevski-Maass page 10]; The learning modules Nk each represent RVs (i.e., nodes) in the Bayesian network, and represent relations between nodes through connections between modules, wherein the modules are interconnected via their respective populations of output neurons).
Regarding claim 10, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein the neuromorphic hardware implements a recommendation system, a digital twin, a semantic feature selector, or an anomaly detector ("We have tested the viability of the previously described approach for neural sampling by satisfying the NCC also on two larger and more complex Bayesian networks: the well-known ASIA-network [24]...Computer Simulation II: ASIA Bayesian network. The ASIA-network is an example for a larger class of Bayesian networks…Networks of this type, that consist of 3 types of RVs (context information, true causes, observable symptoms) with directed edges only from one class to the next...A typical example for probabilistic inference in this network arises when one enters as evidence the facts that the patient visited Asia (A= 1) and has Dyspnoea (D = 1), and asks what is the likelihood of each of the RVs T, C, B that represent the diseases, and how the result of a positive x-ray test would affects these likelihoods. We tested this probabilistic inference in a network of spiking neurons" [Pecevski section Probabilistic Inference through Neural Sampling in Larger and More Complex Bayesian Networks on page 11]; The inference system can predict likelihood of (i.e., detect) diseases (i.e., anomalies) based on symptom variables).
Regarding claim 11, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein the hardware component is an application specific integrated circuit, a field- programmable gate array, a wafer-scale integration, a hardware with mixed-mode VLSI neurons, er a neural processing unit: or a mixed-signal neuromorphic processor (“Altogether, our computer simulations and our theoretical analyses demonstrate that networks of spiking neurons can emulate probabilistic inference for general Bayesian networks. Hence we propose to view probabilistic inference in graphical models as a generic computational paradigm, that can help us to understand the computational organization of networks of neurons in the brain, and in particular the computational role of precisely structured cortical microcircuit motifs" [Pecevski Introduction page 5]; The underlying neuromorphic hardware is a precise cortical microcircuit structure (i.e., application specific integrated circuit)).
Regarding claim 12, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski-Maass further teaches wherein a learning component comprises an output layer, wherein the output layer comprises output neurons, wherein the output neurons represent a likelihood for each triple statement generated in the sampling mode of the learning component (see Figure 2 including output neurons layer– “The population of neurons on the third layer encodes the value of z… STDP applied to the weights wim, j l of synaptic connections from the first layer to the neurons α on the hidden layer enables the network to approximate for any network input x through the firing probability of neurons on the third layer the distribution of values z that were associated with x in previously processed examples <x, z>” [Pecevski-Maass page 4]; “Note that in general the same input x will occur in combination with different values z(1), z(2), . . . of z in the training examples, and the goal of learning is to learn for each value z(i) the probability that it occurs for input x” [Pecevski-Maass page 4 A network module for learning stochastic associations]; “The other approach for emulating probabilistic inference in networks of spiking neurons is based on the assumption that a network of neurons can “embody” a distribution p in such a way that it can generate samples from p. Probabilistic inference for p can then be performed through simple operations on these samples” [Pecevski-Maass page 2 Introduction]; “We provide in this article a proof of principle that these parameters of p do not have to be programmed into the network: they can be learnt by a network of spiking neurons via simple local plasticity rules from examples y˜ that are generated by p” [Pecevski-Maass page 2 Results]).
Regarding claim 13, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 12, and Pecevski-Maass further teaches wherein the control component: presents inputs to the learning component by selectively activating subject and object populations among the node embedding populations (“The network learns from presented examples y˜0, y˜1, ..., y˜n, . . . drawn from the target probability distribution p y. In the examples the RV yk assumes an integer value drawn from the set 1, 2, . . . , Myk . An example is presented to the network in form of injected currents in the neurons. These injected currents are assumed to originate from external neurons. The neurons in k are driven by the injected currents such that their firing reflects correctly the values of the RVs yk in the current example y˜n. More precisely, for y˜ k n l the neuron kl receives strong positive current and fires with a high firing rate, whereas the other neurons in the population k receive a strong negative current, which prevents them from firing” [Pecevski-Maass page 26 Theoretical properties of networks of recursively interconnected basic learning modules])
sets hyperparameters of the learning component, in particular a factor (η) that modulates learning updates of the learning component, (We use a simple STDP rule, which has the advantage of being theoretically tractable. Let w be the weight of the synapse at the connection from some presynaptic neuron vpre to a postsynaptic neuron vpost. At each postsynaptic spike of neuron vpost at time t this weight undergoes an update: w [Wingdings font/0xDF] w + ηΔw, where η is the learning rate” [Pecevski-Maass page 3 Results])
reads output of the learning component, ([Pecevski-Maass page 3 Results] as detailed above; The postsynaptic spikes of neurons are processed (i.e., read)) and
uses output of the learning component as feedback to the learning component ([Pecevski-Maass page 3 Results] as detailed above; The postsynaptic spikes are used as feedback to update hyperparameters).
Regarding claim 20, it is a method claim that largely corresponds to the system/apparatus of claim 1, which is already taught by the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov as detailed above. Kasabov further teaches ranking each triple statement Sp of P triple statements with a rank θ1,2.p with respect to each relation p of P relations, wherein P is at least 2, wherein for p = 1, ..., P, said ranking comprises: (i) decoding, by the first output neuron, Δtn for n = 1..., N wherein Δtn = t1n - t2n; and (ii) determining θ1.2.p = ∑Nn-1Δn, wherein Δn is either (Δtn-rp) or II Δtn – rp II ([Kasabov page 66 The NeuCube Architecture] and [Kasabov pages 66-67 Input data encoding module] and [Kasabov page 67 The SNNr module] and [Kasabov page 67 Evolving output classification module], as detailed in claim 1 above). Consequently, claim 20 is rejected for the same reasons as claim 1.
Regarding claim 24, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Jiang further teaches sequentially switch[ing] the learning component: (i) into a data-driven learning mode (“Triple classification aims to judge whether an unseen triple is correct or not…To create labeled data for classification, for each triple in the test and validation sets, we construct a corresponding negative triple…During triple classification a triple is predicted as positive if the score is below a relation-specific threshold; otherwise as negative” [Jiang Triple Classification page 4] ; “…we make use of the triple set Δ and follow the same strategy adopted in previous methods such as TransE [equation 2] For each candidate triple, it requires positive triples to have lower scores than negative triples” [Jiang Time-Aware KB Embedding page 2]; “The optimization is to minimize the joint score function, [equation 3] where x+ is the positive triple (quad), x- is corresponding the negative triple” [Jiang Time-Aware KB Embedding page 2]; Triples in, e.g., the test and validation set (i.e., the observed data), are positive, unlike negative triples which are constructed (i.e. generated). Jiang further discloses training via minimizing a joint score (i.e., energy) function that includes f(x+) as a parameter, wherein f(x+) represents the energy of a positive (i.e., observed) triple)
(ii) into a sampling mode of the learning component, (“To create labeled data for classification, for each triple in the test and validation sets, we construct a corresponding negative triple by randomly corrupting the entities” [Jiang Triple Classification page 4]; Negative triples are artificially constructed (i.e. generated). The model generates a corresponding negative triple for each positive triple, and further alternates (i.e., sequentially switches) between two modes of training: e.g., using only the positive (i.e., observed) triples (i.e., data-driven mode) versus using only the negative (i.e., generated) triples (i.e., model-driven mode)) and
(iii) into a model-driven learning mode (The optimization is to minimize the joint score function, [equation 3] where x+ is the positive triple (quad), x- is corresponding the negative triple” [Jiang Time-Aware KB Embedding page 2]; Jiang discloses training via minimizing a joint score (i.e., energy) function that includes -f(x-) as a parameter, wherein f(x-) represents the energy of a negative (i.e., generated) triple).
Regarding claim 25, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 24, and Jiang further teaches wherein the control component switches the learning component into the data-driven learning mode in which the learning component is trained with a maximum likelihood learning algorithm minimizing energy in a probabilistic, sampling-based model, using only observed triple statements, ([Jiang Triple Classification page 4] and [Jiang Time-Aware KB Embedding page 2] as detailed in claim 24 above)
wherein the control component switches the learning component into the sampling mode of the learning component, wherein triple statements are generated in the sampling mode ([Jiang Triple Classification page 4] as detailed in claim 24 above), and
wherein the control component switches the learning component into the model-driven learning mode that trains the learning component with the maximum likelihood learning algorithm using only the generated triple statements ([Jiang Time-Aware KB Embedding page 2] as detailed in claim 24 above).
Regarding claim 26, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 25, and Jiang further teaches
wherein during said training the learning component in the data-driven learning mode: the observed triple statements are assigned low energy values, the probabilistic, sampling-based model is derived from an energy function, and the observed triple statements have minimal energy, ([Jiang Triple Classification page 4] and [Jiang Time-Aware KB Embedding page 2] as detailed in claim 24 above) and
wherein during said training the learning component in the model-driven learning mode, the learning component learns to assign high energy values to the generated triple statements ([Jiang Triple Classification page 4] as detailed in claim 24 above).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, and Kasabov, as applied to claim 1 above, further in view of Pfeil (“Exploring the potential of brain-inspired computing”, available heiDOK 2015).
Regarding claim 6, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Pecevski further teaches wherein the first neurons are connected to a monitoring neuron (““This distribution requires knowledge about when exactly a neuron nk with zk(t)~1 had fired. Therefore auxiliary RVs f1, . . . ,fK with multinomial or analog values were introduced in [1], that keep track of when exactly in the preceding time interval of length t a neuron nk had fired" [Pecevski page 3]), wherein the neurons are connected to the output neurons, (“Thus if the principal nk neuron is modelled as a point neuron, we have [equation 8] where bk is the bias of neuron (which regulates its excitability), Wki is the strength of the synaptic connection from neuron vi to vk, and zi (t) approximates the time course of the postsynaptic potential in neuron vk caused by a firing of neuron vi” [Pecevski Results page 5-6]) and wherein the neurons are connected to an inhibiting neuron ("The inputs connect to the auxiliary neuron akv either with a direct strong excitatory connection, or through an inhibitory interneuron ikv that connects to the auxiliary neuron. The inhibitory interneuron ikv fires whenever any of the principal neurons of the RVs zBk that connect to it fires" [Pecevski page 20]).
However, the combination does not explicitly teach neurons including corresponding parrot neurons wherein each first neuron is connected to a corresponding parrot neuron.
In the same field of endeavor, Pfeil teaches a spiking neural networks framework hosted on neuromorphic hardware ("Having a control software that abstracts hardware greatly simplifies modeling on the neuromorphic hardware system. However, modelers are already struggling with multiple incompatible interfaces to software simulators. That is why our neuromorphic hardware system supports PyNN, a widely used application programming interface (API) that strives for a coherent user interface, allowing portability of neural network models between different software simulation frameworks (e.g., NEST or NEURON) and hardware systems (e.g., the Spikey system)" [Pfeil page 23]; [Pfeil section The Spikey neuromorphic system on pages 19-22]) that discloses parrot neurons wherein each neuron is connected to a corresponding parrot neuron ("All simulations involving synapses are simulated with NEST. Spike trains are applied to built-in parrot neurons, that simply repeat their input, in order to control pre- and postsynaptic spike trains to interconnecting synapses." [Pfeil page 68])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated parrot neurons wherein each neuron is connected to a corresponding parrot neuron as taught by Pfeil into the combination because both Pfeil and Pecevski are directed towards a spiking neural networks framework hosted on neuromorphic hardware. Incorporating the teachings of Pfeil would make it easier to precisely control pre- and post- synaptic spike times ([Pfeil page 68]) within the system of Pecevski (“Fig. 8 also suggests that different neurons may have drastically different firing rates, where a few neurons fire a lot, and many others fire rarely. This is a consequence both of different marginal probabilities for different RVs, but also of the quite different computational role and dynamics of neurons that represent RVs (‘‘principal neurons’’), and auxiliary neurons that support the realization of the NCC, and which are only activated by a very specific activation patterns of other presynaptic neurons” [Pecevski page 17]).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, and Kasabov, as applied to claim 1 above, further in view of Fan (“On Applications of Spiking Neural P Systems”, published 2020).
Regarding claim 15, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1.
However, the combination does not explicitly teach An industrial device, comprising the neuromorphic hardware.
In the same field of endeavor, Fan teaches a spiking neural networks framework hosted on neuromorphic hardware (“Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware” [Fan Abstract]) that discloses An industrial device ("Many variants of SNPS have been introduced by incorporating features from the biological neurons...These models have also been used in solving problems related to real life applications such as...programming for logic controllers [8], etc." [Fan page 2]; "The main contributions of this work are as follows: (1) Listing a majority of the SNPS models used for solving problems...Additionally, their use in solving computationally hard problems, the construction of u-fluidic biochip design and programming for PLC (programmable logic controller);" [Fan pages 2-3]; In light of the specification [page 5 lines 15-22] and the described “real life applications”, PLCs are industrial devices) comprising neuromorphic hardware ("In Figure 6, we presented a comparison of all SNPS models performing arithmetic and logical operations and also their hardware implementation [Figure 6]" [Fan page 18]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated An industrial device comprising neuromorphic hardware as taught by Fan into the combination because both Pecevski and Fan are directed towards a spiking neural networks framework hosted on neuromorphic hardware. Incorporating the teachings of Fan would further expand the scope of application of the system of Pecevski as well as its potential implementations in hardware (“SNNs are hardware friendly and energy efficient...The main motivation to prepare this survey is as follows: (3) Study implementations of these models in hardware; (4) Introduce some new ideas to expand the scope of SNPS models" [Fan page 2]).
Regarding claim 16, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1, and Fan further teaches wherein the industrial device is a field device, an edge device, a sensor device, a PLC controller, an industrial PC implementing a SCADA system, a network hub, an industrial ethernet switch, or an industrial gateway connecting an automation system to cloud computing resources. ([Fan pages 2-3] as detailed in claim 15 above).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, Kasabov, and Fan, as applied to claim 15 above, further in view of Kammerer et al., (“Process-Driven and Flow-Based Processing of Industrial Sensor Data”, published 2020), hereinafter Kammerer.
Regarding claim 17, the combination of Pecevski, Pecevski-Maass, Jiang, Kasabov, and Fan teaches the limitations of parent claim 15, and Fan further teaches wherein the neuromorphic hardware is an application specific integrated circuit, a field-programmable gate array, a wafer-scale integration, a hardware with mixed-mode VLSI neurons, a neural processing unit, or a mixed-signal neuromorphic processor, (“Altogether, our computer simulations and our theoretical analyses demonstrate that networks of spiking neurons can emulate probabilistic inference for general Bayesian networks. Hence we propose to view probabilistic inference in graphical models as a generic computational paradigm, that can help us to understand the computational organization of networks of neurons in the brain, and in particular the computational role of precisely structured cortical microcircuit motifs" [Pecevski Introduction page 5]; The underlying neuromorphic hardware is a precise cortical microcircuit structure (i.e., application specific integrated circuit))
a triple store that sotres the triple statements, ("Finally, our approach postulates that these learnt and stored probability distributions p are activated through the inherent stochastic dynamics of networks of spiking neurons, using nonlinear features of network motifs and neurons to represent higher order dependencies between RVs" [Pecevski Experimentally Testable Predictions of our Models page 11]) and wherein the learning component performs an inference in an inference mode of the learning component (“We show that the complexity of the resulting network of spiking neurons for carrying out probabilistic inference for p can be bounded in terms of the graph complexity of the Bayesian network that gives rise to the factorization (7)." [Pecevski Introduction page 5]).
Fan further teaches application of a spiking neural network to an industrial device (e.g., a PLC controller) [Fan pages 2-3], as detailed in claim 15 above. However, the combination does not explicitly teach wherein the industrial device comprises at least one sensor or both the at least one sensor and at least one data source that provides raw data, an ETL component that converts the raw data into the triple statements using mapping rules,
In the same field of endeavor, Kammerer teaches an industrial device, including a PLC controller (“PLC is an example of a complex real-time system, as its output results must be produced in response to input conditions within a limited time period; otherwise, unintended operation may be the result” [Kammerer page 8]) with at least one sensor or both the at least one sensor and at least one data source that provides raw data, and an ETL component that converts the raw data into the triple statements using mapping rules ([Kammerer see Figure 7 on page 13; see Sensor Data Management including Sensor Data Acquisition and Processing. See component PLC (labeled “a”) in Sensor Data Capturing; see Context Management including Context Evaluation and Context Mapping]; "In order to manage the execution context of a cyber-physical system, events from a sensor data management component are received by an event processing agent (see Figure 7k). Events are continuously evaluated by executing queries that are stored in an event query repository (see Figure 7l). Each query can include certain context patterns to map events to entities in a context graph (see Figure 7m). Execution contexts, in turn, can be mapped to a context graph, which is a direct acyclic graph and represents the logical structure of a cyber-physical system. Therefore, each node in a context graph has predefined context types and can be used as a basis for the concept called context-aware process family, which is introduced in the following." [Kammerer page 13]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the industrial device comprises at least one sensor or both the at least one sensor and at least one data source that provides raw data, an ETL component that converts the raw data into the triple statements using mapping rules as taught by Kammerer into the combination because Kammerer and Fan are both directed towards implementing an industrial device that processes data. Given that Fan already teaches applicability of a spiking neural networks framework to programming for logic controllers (PLC controller) [Fan pages 2-3], Kammerer would further develop the system of Fan by disclosing an incorporation of the PLC controller within a larger framework of collecting and processing sensor data ([Kammerer Figure 7 on page 13]; “Typically, a PLC is connected to other information systems, such as industrial PCs (IPCs), equipped with human–machine interfaces (HMI) to configure and to control the PLC execution” [Kammerer page 8]). Therefore, incorporating the teachings of Kammerer would enable the system of Fan to effectively collect, process, and utilize massive amounts of data ("Besides the discussed sensor differences, sensor data are typically delivered from sensor subsystems (e.g., a programmable logic controller, PLC) and continuously streamed to subsequent processing components, which must then cope with massive amounts of data…To tackle the aforementioned challenges, a sensor processing pipeline (SPP) is proposed, which provides solutions for capturing, processing, storing, and visualizing raw sensor data in a continuous processing pipeline" [Kammerer pages 1-2]).
Regarding claim 18, the combination of Pecevski, Pecevski-Maass, Jiang, Kasabov, Fan, and Kammerer teaches the limitations of parent claim 17, and Kammerer further teaches a statement handler, configured for triggering an automated action based on the inference of the learning component ([Kammerer see Figure 7 on page 13; see Context-aware Process Execution]; "Context-aware process execution (CaPE) enables the management of context-aware processes. It supports the modeling of process variants at design time and the automated, controlled adaption of processes at runtime" [Kammerer page 13]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, and Kasabov, as applied to claim 1 above, further in view of Yu (“Traffic Scheduling based on Spiking Neural Network in Hybrid E/O Switching Intra-Datacenter Networks”, published 2020).
Regarding claim 19, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 1.
However, the combination does not explicitly teach A server with the neuromorphic hardware.
In the same field of endeavor, Yu teaches an application of a spiking neural networks framework for predictive inference (“However, the low accuracy of existing deep learning-based prediction approaches, which cannot fully extract the features of burst traffic, directly restricts the efficiency of traffic scheduling. In view of this, this study considers the spiking neural networks that can predict high burstiness and heterogeneous traffic to further improve the efficiency of traffic scheduling. We first propose a supervised spiking neural network (s-SNN) framework for high accuracy traffic prediction in HS-IDCNs” [Yu Abstract]) that discloses a server with neuromorphic hardware (“With the overwhelming growth of emerging cloud applications, such as VR/AR and streaming video, the hybrid E/O switching intra-datacenter networks (HS-IDCNs) have been built to interconnect massive servers and to cope with the rich traffic types…with the development of computer technologies, SNNs can be trained in neuromorphic hardware, such as IBM’s TrueNorth chip [7] and Intel’s Loihi processor [8]… To the best of our knowledge, no study has discussed the use of this biology-based neural network for traffic prediction in HS-IDCNs” [Yu Introduction page 1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated a server with neuromorphic hardware as taught by Yu into the combination because both Pecevski and Yu are directed towards applications of a spiking neural networks framework for predictive inference. Incorporating the teachings of Yu would further expand the scope of the probabilistic inference system of Pecevski to cloud computing applications, particularly for improving efficiency of datacenter networks (e.g., predicting path blocking probability) (“The simulation results demonstrate that the s-SNN framework can significantly improve the traffic prediction accuracy, and the proposed TP-TS algorithm can improve the resource utilization and decrease blocking probability of HS-IDCNs” [Yu Introduction page 1]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, and Kasabov, as applied to claim 20 above, further in view of Kammerer (“Process-Driven and Flow-Based Processing of Industrial Sensor Data”, published 2020).
Regarding claim 21, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 20.
However, the combination does not explicitly teach wherein the knowledge graph is an industrial knowledge graph describing parts of an industrial system, wherein nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors industrial controllers, robots, drives, manufactured objects, tools, elements of a bill of materials, or combinations thereof, and wherein nodes of the knowledge graph representing abstract entities include sensor measurements, attributes, configurations or skills of the physical objects, production schedules, and plans.
In the same field of endeavor, Kammerer teaches a system of inference on a knowledge graph wherein the knowledge graph is an industrial knowledge graph describing parts of an industrial system, (“In industrial machines, sensors and actors are typically controlled by a programmable logic controller (see Figure 7a)” [Kammerer page 12]; [Kammerer Figure 7 Schematic overview of context-aware process execution framework on page 13]; "Execution contexts, in turn, can be mapped to a context graph, which is a direct acyclic graph and represents the logical structure of a cyber-physical system. Therefore, each node in a context graph has predefined context types and can be used as a basis for the concept called context-aware process family, which is introduced in the following" [Kammerer page 13])
wherein nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors industrial controllers, robots, drives, manufactured objects, tools, elements of a bill of materials, or combinations thereof, ([Kammerer page 13]; A cyber-physical system [see Figure 4 Information flow processing schema on page 10 and Figure 7 Schematic overview of context-aware process execution framework on page 13] comprises physical objects including sensors),
and wherein nodes of the knowledge graph representing abstract entities include sensor measurements, attributes, configurations or skills of the physical objects, production schedules, and plans. ([Kammerer page 13]; A cyber-physical system [see Figure 4 Information flow processing schema on page 10 and Figure 7 Schematic overview of context-aware process execution framework on page 13] comprises abstract entities including sensor data/measurements).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the knowledge graph is an industrial knowledge graph describing parts of an industrial system, wherein nodes of the knowledge graph representing physical objects, wherein the physical objects include sensors industrial controllers, robots, drives, manufactured objects, tools, elements of a bill of materials, or combinations thereof, and wherein nodes of the knowledge graph representing abstract entities include sensor measurements, attributes, configurations or skills of the physical objects, production schedules, and plans. as taught by Kammerer into the combination because both Pecevski and Kammerer are directed towards performing inference on a knowledge graph. Incorporating the teachings of Kammerer would expand the scope of the probabilistic inference system of Pecevski to industrial applications such as predictive maintenance (“For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance” [Kammerer Abstract]).
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Pecevski, Pecevski-Maass, Jiang, and Kasabov, as applied to claim 20 above, further in view of Fan (“On Applications of Spiking Neural P Systems”, published 2020).
Regarding claim 22, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 20. Fan further teaches A computer-readable storage media having stored thereon: instructions executable by one or more processors of a computer system, wherein execution of the instructions causes the computer system to perform the method ("One of the major motivations for studying arithmetic and logical operations using SNPS has been the designing of the arithmetic logic unit of CPU under the framework of SNPS which can be useful in constructing novel digital circuits/chips" [Fan page 16]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated A computer-readable storage media having stored thereon: instructions executable by one or more processors of a computer system, wherein execution of the instructions causes the computer system to perform the method as taught by Fan into the combination because both Pecevski and Fan are directed towards a spiking neural networks framework hosted on neuromorphic hardware. Incorporating the teachings of Fan would further enable the system of Pecevski to reap the benefits of improved efficiency of SNPS models (“Along with the advancement of technologies, it has become imperative to construct efficient hardware which can perform complex tasks. SNPS models have some very useful features, such as parallel and distributive architecture, non-determinism, etc., and these features help the models to perform millions of computations very efficiently with minimum time and space resources. These models also can perform basic arithmetic and logic operations which make SNPS an important candidate for designing of CPU” [Fan page 16]).
Regarding claim 23, the combination of Pecevski, Pecevski-Maass, Jiang, and Kasabov teaches the limitations of parent claim 20. Fan further teaches A computer program product, comprising a non-transitory computer readable storage medium having instructions stored thereon, said instructions upon being executed by one or more processors of a computer system perform the method (“Along with the advancement of technologies, it has become imperative to construct efficient hardware which can perform complex tasks. SNPS models have some very useful features, such as parallel and distributive architecture, non-determinism, etc., and these features help the models to perform millions of computations very efficiently with minimum time and space resources. These models also can perform basic arithmetic and logic operations which make SNPS an important candidate for designing of CPU” [Fan page 16]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated A computer program product, comprising a non-transitory computer readable storage medium having instructions stored thereon, said instructions upon being executed by one or more processors of a computer system perform the method as taught by Fan into the combination because both Fan and Pecevski are directed towards a spiking neural networks framework hosted on neuromorphic hardware. Incorporating the teachings of Fan would further enable the system of Pecevski to reap the benefits of improved efficiency of SNPS models ([Fan page 16]).
Response to Arguments
The remarks filed 08/13/2025 have been fully considered.
Applicant’s remarks [Remarks pages 16-19] traversing the provisional nonstatutory double patenting rejections set forth in the office action mailed 04/24/2025 have been considered, but are moot because the amendments made to the co-pending applications at issue (17/555,577 and 17/564,380) have necessitated a new grounds of provisional nonstatutory double patenting rejection (with respect to 17/555,577) and a withdrawal of the previous rejection (with respect to 17/570,113), as explained above.
The examiner respectfully notes that although the remarks state that “Applicant has amended claim 22 in accordance with the proceeding suggestion by the examiner” [Remarks page 21], the language of claim 22 has not actually been amended (see “Original” status), and therefore the non-statutory subject matter rejection of claim 22 under U.S.C. 101 has been maintained.
Applicant’s remarks [Remarks pages 22-34] traversing the anticipation rejections under 35 U.S.C. 102 and obviousness rejections under 35 U.S.C. 103 set forth in the office action mailed 04/24/2025, with respect to claims 1, 3, 5-13, and 15-26 as amended, have been considered.
Although a new grounds of rejection has been applied, the examiner has determined a response necessary for certain portions of the remarks [Remarks page 23], particularly with respect to discussing the broadest reasonable interpretation of claim language.
The remaining remarks, while having been considered, are moot because the new grounds of rejection set forth above does not rely on the reference(s) applied in the prior rejection of record for the subject matter being challenged in applicant's argument.
The examiner respectfully notes that applicant’s argument [Remarks page 23] with respect to claim 24 appears to implicitly suggest that “sequentially switching the learning component” into a data-driven learning mode, into a sampling mode, and into a model-driven learning mode recites an order of switching the learning component into each mode followed one after another without interruption, i.e., a consecutive order. However, as per Cambridge Dictionary, “sequential” is defined as “following a particular order”, and not necessarily a consecutive order. Applicant’s argument thereby appears to follow a narrower interpretation that is not recited in the claim language. Additionally, it is noted that in light of the specification (“It is controlled by a control component CC that can switch between different modes of operation of the learning component LC, either autonomously (e.g., periodically) or based on external stimuli (e.g., a specific system state, or an operator provided input)” [page 15 lines 4-7]), the claimed procedure of a control component “switching modes” of a learning component maintains a broad interpretation – e.g., a processor merely allocating resources or determining an order of tasks in response to external input/instructions, which are conventional operations in the execution of software.
Applicant has not presented further substantive arguments with respect to the dependent claims. As such, claims 1, 3, 5-13, and 15-26 stand rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fukushige et al., (“Representing Probabilistic Relations in RDF”, posted online 03/07/2005) discloses a vocabulary for representing probabilistic relations in an RDF triple format, and an algorithm for transforming a set of probabilistic relations in an RDF graph to a Bayesian network.
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.
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/V.M.B./
Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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