NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application, 18/361,976 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on July 31, 2023 and claims priority to Russian application RU2022122824 filed on August 24, 2022.
The Office mailed a requirement for restriction on February 26, 2026, which Applicant responded to on April 10, 2026 with amendment and election without traverse.
The application was rerouted to a new examiner, who initiated an interview with Applicant’s representative Michael Fainberg (RN 50441) around June 10, 2026 to discuss the application. No final agreement was reached.
Claims 1-16 are pending and all are rejected. Claim 1 is the sole independent claim.
Status of Claims
Claims 1-4 and 7-8 are objected to for what appears to be typographical errors.
Claims 2, 3, 5-9, 11, 12 and 14-15 are rejected under 35 U.S.C. 112(b).
Claims 1, 2-6, 8, 10-11 and 14 are provisionally rejected on the ground of nonstatutory double patenting.
Claims 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 4 and 16 are rejected under 35 USC. § 103 as being unpatentable over Kursun, United States Patent Application Publication 2020/0160170, published on May 21, 2020 in view of Valente et al. (“Valente”), United States Patent Application Publication 2023/0291668, published on Sept. 14, 2023.
Claim 10 is rejected under 35 USC. § 103 as being unpatentable over Kursun, in view of Valente in further view of non-patent literature Mitra et al. (“Mitra”), “Investigation and analysis of hyper and hypo neuron pruning to selectively update neurons during unsupervised adaptation,” published in 2020.
Claim 13 is rejected under 35 USC. § 103 as being unpatentable over Kursun, in view of Valente in further view of Yu, United States Patent Application Publication 2023/0229905, published on July 20, 2023.
Claim 15 is rejected under 35 USC. § 103 as being unpatentable over Kursun, in view of Valente in further view of non-patent literature, Kharlamov et al. (“Kharlamov”) “Neuroninformatics and Semantic Representations,” published in 2020 (inventor-disclosed reference).
Objections
Claims 1-4 and 7-8 are objected to for what appears to be typographical errors resulting in antecedent basis issues:
Claim 1:
“the neurosemantic network” is recited before “a neurosemantic network.”
“at least one episode” should be used consistently. “[G]enerated episodes” is used throughout the claim, which as recited requires more than one episode.
Claim 2: “network” is missing after neurosemantic.
Claim 3: “the optimal coverage,” is recited without antecedent basis.
Claim 4: “the episode” is recited. Presumably, this is supposed to reference “at least one episode,” or the “generated episode?”
Claim 7:
“the network hyperparameter” is recited without antecedent basis.
Additionally, “the allowable number of layers of the neurosemantic network,” is recited without antecedent basis.
“IP” should be corrected to “IS.”
Claim 8:
“configure a neurosemantic network,” appears to be typographical error and should be “the” if referring to claim 1’s network.
“the sleep mode of the system” is recited without antecedent basis.
“the preservation of the state of the neurosemantic network” is recited without antecedent basis.
Correction and clarification are required for these claims.
Claim Rejections -- §112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 5-9, 11, 12 and 14-15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites in part, “the neurons corresponding to events and patterns.” Examiner queries as to what neurons they may be – whether they are the “special neuron-monitor neurons,” or some other neuron in the neurosemantic network. The claim is rendered indefinite.
Claim 3 recites in part, “in which the optimal coverage of all events of the episode is selected according to either a hierarchical principle of the minimum length of the description.” There is no recitation of what “the description” is. Further, there appears to be lack of written description support sufficient to practice the invention for this claim as written.
Claim 5 recites in part, “wherein the structures of the pattern are detected through a hierarchy of layers…” Presumably, the “pattern” refers to claim 1’s “recognizing events and patterns previously learned by the neurosemantic network.” At least three issues arise here: (1) it is unclear to Examiner, even after review of the Specification as filed, how patterns have structure; (2) if the patterns are previously learned, then, how are the patterns now detected? This appears to be circular claiming and is unclear and indefinite. And (3), there are “patterns consisting of patterns of the previous layer thereby resulting in nested patterns.” Examiner queries as to how it is nested – which a plain meaning interpretation requires a demarcation of some sort between patterns to allow for “nesting.” There does not appear to be any written description support for this either in the Specification sufficient to allow a person having ordinary skill in the art to practice the claimed invention.
Claim 6 recites in part, “on the first layer … wherein each input corresponds to the terminal neuron of the channel, different from the channels of terminal neurons of other inputs.” Which channel is “the channel?” In the same claim, the earlier zero layer claim element recites “the system having neurons corresponding to the values of the event fields, grouped by channels corresponding to the event fields.” It also is not clear as to how the zero layer’s channels grouping and the first layer’s input are connected necessarily. Additionally, the claim recites, “an expanding of the pattern to a sequence of events …” Examiner queries as to how a patter may be expanded, particularly in light of the “nested” nature recited in the parent claim 5.
Claim 7 recites in part, “configuration of permissible time deviations in the duration of the pattern, within which the neurosemantic network interprets sequences of events of the same order or nested patterns, but with different total durations of such sequences as the same pattern.” Inasmuch there are “durations” of the sequences, Examiner notes that parent claim 5 recites that there are total duration of the event neurons being … “equal to zero,” Examiner queries as to how there may be “different total durations” as required by the claim.
Claim 8 recites in part, “the optimization including optimizing the structure of patterns and recognizing patterns at long intervals of time.” First, “long” is a relative term that renders the claim indefinite. Second, what optimizing a structure of a pattern is unclear, as parent claim 1 discusses “identifying a structure of patterns by mapping to the patterns of neurons on a hierarchy of layers of the neurosemantic network.” This would appear to be in conflict with the patterns that are mapped – how are they to be optimized? It is also unclear from the Specification as filed as to whether there is sufficient written description support.
Claim 11 recites in part, “wherein a frequency of activation and/or a number of activations are based only on subsequent modification of the attribute in the generated neurons.” Examiner queries as what “subsequent modification” is in relation to.
Claim 14 recites in part, “periodically remove patterns from the neurosemantic network of unused neurons, wherein the use of neurons is determined by statistical properties of the neurons and a hierarchical principle based on a minimum length of description of a functioning of the neurosemantic network.” It is unclear as to what a description of a “functioning” of the neurosemantic network may mean.
Claim 15 recites, “acceptable”, which is a relative term and is indefinite.
No prior art rejection is made as to claims 2, 3, 5-8, 11 and 14. The dependent claims, as they inherit these deficiencies, also include claims 9 and 12. See MPEP 2173.06(II) (“As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. § 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims.”).
Requirement for Restriction/Election Withdrawn
Applicant’s election without traverse of in the reply filed on April 10, 2026 is acknowledged. However, as noted in the June 10, 2026 interview, the basis and original requirement is also withdrawn. Applicant is advised that the original restriction requirement is reversed and may not be relied upon by Applicant in the future.
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, 2-6, 8, 10-11 and 14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the claims noted below of copending Application No. 18/361,999 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Instant Application – 18/361,976
(claims filed April 10, 2026)
Reference Application – 18/361,999
(claims filed May 26, 2026)
1. A system for detecting patterns and anomalies in the flow of events coming from a cyber physical system (CPS) or an information system (IS), comprising:
one or more hardware processors;
one or more memory media;
a storage subsystem configured to:
store a configuration of event fields, episode configurations, system configurations,
to store and load into at least one memory of the neurosemantic network configurations and previous states of the neurosemantic network;
one or more connectors configured to:
get event data that includes a set of field values and an event timestamp for each event;
generate at least one episode consisting of a sequence of events; and
transfer the generated episodes to an event processor; and
an event processor configured to process the episodes using a neurosemantic network, wherein the processing episodes includes:
recognizing events and patterns previously learned by the neurosemantic network;
training the neurosemantic network;
identifying a structure of patterns by mapping to the patterns of neurons on a hierarchy of layers of the neurosemantic network;
attributing events and patterns corresponding to neurons of the neurosemantic network to an anomaly depending on a number of activations of the corresponding neuron; and
storing the state of the neurosemantic network.
1. (Original) A method for detecting patterns and anomalies in the flow of events coming from a cyber physical system (CPS) or an information system (IS), the method comprising:
using at least one connector, getting event data that includes a set of field values and an event timestamp for each event, generating at least one episode consisting of a sequence of events, and
transferring the generated episodes to an event processor; and using the event processor, process episodes using a neurosemantic network, wherein the processing episodes includes recognizing events and patterns previously learned by the neurosemantic network, training the neurosemantic network, identifying a structure of patterns by mapping to the patterns of neurons on a hierarchy of layers of the neurosemantic network, attributing events and patterns corresponding to neurons of the neurosemantic network to an anomaly depending on a number of activations of the corresponding neuron, and storing the state of the neurosemantic network.
2. The system of claim 1, wherein the configuration for processing of the episode further comprises a configuration for:
identifying a set of events and patterns of the neurosemantic that satisfy a predetermined criterion; and
generating output information about the set of events and patterns through the use of special neuron-monitor neurons of the neurosemantic network, which, by activating monitor neurons, monitor the creation and activation of the neurons corresponding to events and patterns,
wherein the predetermined criteria specify at least: values of the fields of individual events and events in the patterns; a sign of an recurrence of the events and patterns; and tracking, based on a sliding time interval, the activations and the number of activations during the sliding time interval.
2. (Original) The method according to claim 1, wherein the processing of the episode further comprises: identifying a set of events and patterns of the neurosemantic that satisfy a predetermined criterion; and generating output information about the set of events and patterns through the use of special neuron-monitor neurons of the neurosemantic network, which, by activating monitor neurons, monitor the creation and activation of the neurons corresponding to events and patterns,
wherein the predetermined criteria specify at least: values of the fields of individual events and events in the patterns; a sign of a recurrence of the events and patterns; and tracking, based on a sliding time interval, the activations and the number of activations during the sliding time interval.
4. The system according to claim 1, wherein the processing of the episode is performed in the neurosemantic network in layers.
7. (Original) The method of claim 6, wherein each episode is sequentially processed on the layers of the neurosemantic network: by matching the values of the event fields of the terminal neurons, by matching the events of the neurons of the first layer, by creating neurons of the top episodes on the second layer, by matching the sequences of events of the neurons of the patterns on the second and above layers up to the layer on which one neuron top neuron is mapped indicating the pattern reaching the maximum layer.
5. The system of claim 4, wherein the structures of the pattern are detected through a hierarchy of layers of the neurosemantic network on which neurons are located, and wherein
on a zero layer there are neurons of event field values grouped by channels corresponding to event fields, the zero layer being a terminal layer,
on the first layer there are event neurons,
on the second layer there are patterns consisting of events, and
on the third and higher layers there are patterns consisting of patterns of the previous layer thereby resulting in nested patterns.
6. The system of claim 5, wherein,
on the zero layer of the neurosemantic network, the system having neurons corresponding to the values of the event fields, grouped by channels corresponding to the event fields, wherein the duration of the terminal neuron being taken as being zero and the zero layer being a terminal layer;
on the first layer of the neurosemantic network, the system having neurons corresponding to events, and having inputs from neurons of the terminal layer, wherein each input corresponds to the terminal neuron of the channel, different from the channels of terminal neurons of other inputs, the time intervals between event inputs from the field values being taken as being equal to zero, and a total duration of the event neuron being respectively taken as being equal to zero thereby indicating that the event has no duration;
on the second layer of the neurosemantic network, the system having neurons corresponding to episodes, the number of inputs of the neuron of the episode being equal to the number of events in the episode, the intervals between events in the episode being exactly stored as intervals between inputs of the neuron of the episode; and
on the second and subsequent layers of the neurosemantic network, the system having neurons corresponding to the sequences of events, with neurons of the third layer and subsequent layers having inputs from neurons of the previous layers and an expanding of the pattern to a sequence of events being carried out through recursive disclosure of all inputs up to the first layer, and the event being expanded to the values of the fields through inputs from terminal neurons.
6. (Original) The method of claim 1, wherein the structure of patterns is revealed through a hierarchy of layers of the neurosemantic network, the structure determining the order of the layers of the neurosemantic network on which neurons are located, and wherein the processing of events using the neurosemantic network comprising: on the zero layer of the neurosemantic network, having neurons corresponding to the values of the event fields, grouped by channels corresponding to the event fields, wherein the duration of the terminal neuron being taken as being zero and the zero layer being a terminal layer; on the first layer of the neurosemantic network, having neurons corresponding to events, and having inputs from neurons of the terminal layer, wherein each input corresponds to the terminal neuron of the channel, different from the channels of terminal neurons of other inputs, the time intervals between event inputs from the field values being taken as being equal to zero, and a total duration of the event neuron being respectively taken as being equal to zero thereby indicating that the event has no duration; on the second layer of the neurosemantic network, having neurons corresponding to episodes, the number of inputs of the neuron of the episode being equal to the number of events in the episode, the intervals between events in the episode being exactly stored as intervals between inputs of the neuron of the episode; and the second and subsequent layers of the neurosemantic network having neurons corresponding to the sequences of events, with neurons of the third layer and subsequent layers having inputs from neurons of the previous layers and an expanding of the pattern to a sequence of events being carried out through recursive disclosure of all inputs up to the first layer, and the event being expanded to the values of the fields through inputs from terminal neurons.
8. The system of claim 5, wherein the event processor is further designed to:
configure neurosemantic network activity monitors through a creation of special monitor neurons that are activated based on subscription to other neurons or layers on which new neurons are created, configure a neurosemantic network, and form a subscription according to criteria specified in terms of event field values, the sliding time interval and the number of activations of neurons to which such a subscription is to be performed;
execute user requests on history of events and patterns;
perform periodic optimization of the neurosemantic network in the sleep mode of the system, the optimization including optimizing the structure of patterns and recognizing patterns at long intervals of time; and
perform the preservation of the state of the neurosemantic network, including information about patterns and statistics of activations on stream of events, and processed information about episodes of events.
15. (Original) The method of claim 11, wherein the neurons have at least the following properties: contain at least one input to which a dendritic tree is attached with nodes implementing logical operations on the outputs attached to them from other neurons or from network layers; contain a dendritic node attached to the root of the dendritic tree with a logical operation "or" and outputs attached to the node from neurons of the zero and subsequent layers used to detect the activation of field values, events and patterns already learned by the network, the criterion for the subscription of the monitor neuron to the activation of such neurons being set through the definition of conditions for field values; contain 0 or more attached to the node "or" other dendritic nodes with a logical operation "and", to which in turn are attached outputs from neurons of the zero layer corresponding to the values of the fields and outputs directly from the zero layer and subsequent layers, which are used to detect the creation of new neurons corresponding, depending on the layer, to the values of the fields, either events or patterns, dendritic nodes "and" set the conditions for what field values the neurons created on the specified layers must have in order to activate this neuron-monitor; have the ability to dynamically change the set of inputs of the monitor neuron from the network neurons to automatically add to the dendritic node "or" new created neurons that meet the criteria of the dendritic node "and"; have the ability to set an attention option on the values of the subscription fields and create child monitor neurons for each unique value or a unique combination of such values of such fields, becoming the parent monitor neuron, child monitor neurons will count the number of activationswith this unique value and the rest of the subscription conditions, as in the parent monitor neuron, and notify the parent monitor about their trigger;have the ability to subscribe only to previously created and activated more than once neurons, and only to new neurons that are activated once, as well as to both together, subscribing only to new neurons being considered as anomalies in the flow of events; andcontain a property for specifying the sliding monitoring interval and the amount of activation of the monitor neuron at the sliding interval, upon reaching which the monitor neuron will generate an alert to the user or other systems.
10. The system of claim 1, wherein the training is partially stopped by forcing the transfer of the neurosemantic network to a mode where the creation of new neurons does not occur, and wherein all training occurs only by changing neurons when they are activated.
3. (Original) The method of claim 1, wherein the training is partially stopped by forcing the transfer of the neurosemantic network to a mode where the creation of new neurons does not occur, and wherein all training occurs only by changing neurons when they are activated.
11. The system according to claim 1, wherein a teacher is used for training a neurosemantic network by submitting, to the input, targeted patterns for training, and wherein a frequency of activation and/or a number of activations are based only on subsequent modification of the attribute in the generated neurons.
4. (Original) The method of claim 1, wherein a teacher is used for training a neurosemantic network by submitting, to the input, targeted patterns for training, and wherein a frequency of activation and/or a number of activations are based only on subsequent modification of the attribute in the generated neurons.
14. The system of claim 1, wherein the event processor is further configured to:
periodically remove patterns from the neurosemantic network of unused neurons, wherein the use of neurons is determined by statistical properties of the neurons and a hierarchical principle based on a minimum length of description of a functioning of the neurosemantic network.
5. (Original) The method of claim 1, wherein patterns are periodically removed from the neurosemantic network of unused neurons, wherein the use of neurons is determined by statistical properties of neurons and a hierarchical principle of a minimum length of description, the minimum length determining a functioning of the neurosemantic network.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections – § 101 Subject Matter Eligibility
Claims 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 1, at step 1, the claim recites a system with hardware components, and therefore is an apparatus, or manufacture, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites a computer-implemented method for multimodal response generation through a virtual agent.
The following limitations are the abstract idea of mental processes. See MPEP § 2106.04(a)(2)(III)(D):
generate at least one episode consisting of a sequence of events; and
recognizing events and patterns previously learned by the neurosemantic network;
identifying a structure of patterns by mapping to the patterns of neurons on a hierarchy of layers of the neurosemantic network;
attributing events and patterns corresponding to neurons of the neurosemantic network to an anomaly depending on a number of activations of the corresponding neuron.
Therefore, the claim recites at least one abstract idea per this part of the analysis.
At step 2A prong 2, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d).
The limitations:
one or more hardware processors;
one or more memory media;
a storage subsystem configured to:
store a configuration of event fields, episode configurations, system configurations, to store and load into at least one memory of the neurosemantic network configurations and previous states of the neurosemantic network;
one or more connectors configured to: get event data that includes a set of field values and an event timestamp for each event; transfer the generated episodes to an event processor; and
an event processor configured to process the episodes using a neurosemantic network, wherein the processing episodes includes: recognizing events and patterns previously learned by the neurosemantic network; training the neurosemantic network; and storing the state of the neurosemantic network.
are additional elements that generally links the use of the judicial exception to a particular technological environment or field of use, specifically artificial intelligence systems. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element do nothing more than linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Therefore, claim 1 is ineligible.
As to dependent claims 2-16, the analysis of the parent claim is incorporated. In the step 2A, prong 2 analysis, the additional limitations are additional element that are steps under broadest reasonable interpretations, are additional elements that generally link the use of the judicial exception to a particular technological application. See MPEP § 2106.05(h). The claims are also ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
A.
Claims 1, 4 and 16 are rejected under 35 USC. § 103 as being unpatentable over Kursun, United States Patent Application Publication 2020/0160170, published on May 21, 2020 in view of Valente et al. (“Valente”), United States Patent Application Publication 2023/0291668, published on Sept. 14, 2023.
As to Claim 1, Kursun teaches: A system for detecting patterns and anomalies in the flow of events coming from a cyber physical system (CPS) or an information system (IS) (Kursun: par. 0003, 0027 a data stream is monitored for cybersecurity) from , comprising:
one or more hardware processors;
one or more memory media (Kursun, par. 00982, processor, program code, memory);
a storage subsystem configured to:
store a configuration of event fields, episode configurations, system configurations,
to store and load into at least one memory of the neurosemantic network configurations and previous states of the neurosemantic network (Kursun: pars. 0049-0050, a data storage [308] may store data related to the system environment, including the configuration of the neural network, other system state information);
one or more connectors configured to:
get event data that includes a set of field values for each event (Kursun: par. 0063, the data stream may include information regarding characteristics of the data itself (i.e. field values));
generate at least one episode consisting of a sequence of events; and
transfer the generated episodes to an event processor (Kursun: par. 0064, the data stream (i.e. sequence of events) is analyzed for patterns that may correspond to a baseline and to identify triggering condition to reconfigure the network (i.e. an episode); and
an event processor configured to process the episodes using a neurosemantic network, wherein the processing episodes includes:
recognizing events and patterns previously learned by the neurosemantic network (Kursun: par. 0061, historical data either alone or in combination with the real=time data may be used for learning and decisioning);
training the neurosemantic network (Kursun: par. 0059, 61, the neural networks can be trained based on predetermined training data and/or new data acquired in realtime);
identifying a structure of patterns by mapping to the patterns of neurons on a hierarchy of layers of the neurosemantic network (Kursun: par. 0077, changes may be made to the neural network, including the layers);
storing the state of the neurosemantic network (Kursun: pars. 0049-0050, a data storage [308] may store data related to the system environment, including the configuration of the neural network, other system state information).
Kursun may not explicitly teach: get event data that includes a set of field values and an event timestamp for each event;
an event processor configured to process the episodes using a neurosemantic network, wherein the processing episodes includes: attributing events and patterns corresponding to neurons of the neurosemantic network to an anomaly depending on a number of activations of the corresponding neuron.
Valente teaches in general concepts related to anomaly detection in time series data produced by devices of a network infrastructure (Valente: Abstract). Specifically, Valente teaches that timeseries data (including timestamps) is obtained (Valente, par. 0020). The time series data is analyzed to determine if there are any anomalous behaviors (Valente, par. 022, a sliding time window is used).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have further modified the Kursun disclosures and teachings by attributing the anomaly to the detected anomalies as taught and suggested by Valente. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the optimization and success of the overall detection system.
As to Claim 4, Kursun and Valente teaches the limitations of claim 1.
Valente further teaches: wherein the processing of the episode is performed in the neurosemantic network in layers (Valente: par. 0037, “In most common applications the neurons
are organized in consecutive layers. In this structure each layer receives a series of values as input and produces an output which is used, with some manipulations, as input for the subsequent layer.”).
As to Claim 16, Kursun and Valente teaches the limitations of claim 1.
Kursun further teaches: wherein events are received from predictive detectors by CPS telemetry and/or directly from the CPS (Kursun: par. 001, the data stream may be transmiitted over a data network [101]).
B.
Claim 10 is rejected under 35 USC. § 103 as being unpatentable over Kursun, United States Patent Application Publication 2020/0160170, published on May 21, 2020 in view of Valente et al. (“Valente”), United States Patent Application Publication 2023/0291668, published on Sept. 14, 2023 in further view of non-patent literature Mitra et al. (“Mitra”), “Investigation and analysis of hyper and hypo neuron pruning to selectively update neurons during unsupervised adaptation,” published in 2020.
As to Claim 10, Kursun and Valente teaches the limitations of claim 1.
Kursun and Valente may not explicitly: wherein the training is partially stopped by forcing the transfer of the neurosemantic network to a mode where the creation of new neurons does not occur, and wherein all training occurs only by changing neurons when they are activated.
Mitra teaches in general on improving performance of a neural networks with selective adaptation (Mitra: Abstract). Specifically, Mitra teaches that instead of node pruning, selective adaptation, meaning training of only certain neurons is a method of improving performance (Mitra: Sec. 8, Conclusion, “We investigated whether instead of pruning the hyper and hypo neurons, we can selectively update those neurons during unsupervised model adaptation and found such strategy to be more successful compared to blind model update.”).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Kurson-Valente disclosures and teachings by selectively adapting the neurons as taught by Mitra. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the improvement of the performance (Mitra: Sec. 8, Conclusion, “We observed that selective model update gave better performance on out-of-domain data and diverged less from the in-domain data, compared to the blind model update approach”).
C.
Claim 13 is rejected under 35 USC. § 103 as being unpatentable over Kursun, United States Patent Application Publication 2020/0160170, published on May 21, 2020 in view of Valente et al. (“Valente”), United States Patent Application Publication 2023/0291668, published on Sept. 14, 2023 in further view of Yu, United States Patent Application Publication 2023/0229905, published on July 20, 2023.
As to Claim 13, Kursun and Valente teaches the limitations of claim 1.
Kursun and Valente may not explicitly teach: wherein the storage subsystem is further configured to restart the operation of the event processor after a shutdown while retaining previously learned events and patterns.
Yu teaches in general concepts related to training machine-learning models with checpoint states stored in local memory (Yu: Abstract). Specifically, checkpoint states include training state information and and may be reinitialize after restarting the training (Yu: cl. 1).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Kursun-Valente disclosures and teachings by allowing for the storage system to restart the training after a failure (or shutdown) with saved information as taught and suggested by Yu. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the successful training of the network and resumption of services.
D.
Claim 15 is rejected under 35 USC. § 103 as being unpatentable over Kursun, United States Patent Application Publication 2020/0160170, published on May 21, 2020 in view of Valente et al. (“Valente”), United States Patent Application Publication 2023/0291668, published on Sept. 14, 2023 in further view of non-patent literature, Kharlamov et al. (“Kharlamov”) “Neuroninformatics and Semantic Representations,” published in 2020 (inventor-disclosed reference).
As to Claim 15, Kursun and Valente teach the limitations of claim 1.
Kursun and Valente may not explicitly teach: wherein the event processor is further configured to:
process episodes consisting of a single event in parallel on the massively parallel hardware architecture of the processors; or
process episodes consisting of a plurality of events sequentially on a single hardware processor, the number of events in the episode being selected based on predetermined requirements to provide information about the monitoring of the CPS or IS in accordance to criteria acceptable to a user of the system.
Kharlamov discusses the use of massively parallel architecture to process episodes in parallel (Kharlamov: p. 239, “If the network runs on a massively parallel hardware architecture, then the episode length can be chosen to be small, so that the episode is quickly processed through all layers of the network.”).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Kursun-Valente disclosures and teachings by allowing for the massive paralle processing as taught and suggested by Kharlamov. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the successful training of the network or in the alternative as an obvious design choice.
Conclusion
Additional relevant prior art:
Hoffman et al. (“Hoffman”), United States Patent 9,311,595 B1 published on Apr. 12, 2016 in view of Lavrentyev, European Patent Application Publication EP 3674946 A1 published on Jan. 7, 2020.
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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