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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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 1 is rejected under 35 U.S.C. 101 because the claimed invention, under the broadest reasonable interpretation, is directed to an abstract idea as well as software per se for not reciting any hardware elements for performing functions recited in the system claim.
Step 1: Statutory Category
Independent claim 1 is drawn to a “cognitive Bayesian reasoning system”. Accordingly,
claim 1 falls under one of the four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A: Prong 1: Judicial Exception
Under the broadest reasonable interpretation (BRI), claim 1 is directed to an abstract idea.
Claim 1 recites:
agents for performing at least one reasoning selected from the group consisting of inference, inductive reasoning, abductive reasoning, deductive reasoning, and causal reasoning; and at least one source for a first data and subsequent data (Mental process, a human security analyst receives first and subsequent observations/log entries, the analyst applies mental inference/inductive/abductive/deductive reasoning and casual analysis to the observed changes)
a plurality of simple logic cells, configured for operably implementing the agents, selected from a group consisting of at least one of "OR", "AND", "NAND (not AND)", "NOR (not OR)", and "X-OR (Exclusive OR)" (Mental process/Mathematical concepts, the analyst uses basic Boolean logic (AND/OR/NAND/NOR/XOR) and probabilistic/Bayesian rules in their head or on paper to determine whether the pattern suggests an intrusion)
wherein the agents, implemented in the simple logic cells, reason from changes in the first data and the subsequent data to identify the intrusion event (Mental process, the analyst reaches a conclusion that an intrusion is present after analyzing the changes seen in the observed changes)
Such reasoning over data to recognize a pattern (intrusion vs. no intrusion) is a mental process and mathematical concepts. See MPEP 2106.04 (a).
With respect to step 2A, prong 2, the additional elements fail to integrate the abstract idea into a practical application. The claim is directed to, or limited to, a technical solution solving a technical problem. They fail to provide an improvement to a technology or the functioning of a computer. See MPEP 2106.04 (d)(1). Instead, the additional elements merely recite, at high level of generality, general purpose computing structure that are used as tools for implementing the abstract idea. The field of uses high-level reasoning, logical operations and unspecified data sources to apply the abstract idea in a particular environment. Thus, the examiner finds the additional elements are mere instructions to implement the judicial exception. See MPEP 2106.05 (a) (e) (f). As, such the examiner must conclude the invention is not integrated into a practical application.
With respect to step 2B, the claim fails to recite significantly more than the abstract idea itself. Similar to the analysis for step 2A, prong 2, the claims fail to provide improvement to a technology of the functioning of a computer. It recites no concrete hardware. All component agents, logic cells, data sources are generic, well-understood, routine, and conventional elements or abstract software constructs. The steps of receiving data, applying Boolean logic, and identifying an intrusion event are abstract processing steps. Thus, examiner finds the additional elements are mere instructions to implement the judicial exception. See MPEP 2106.05(f).
Therefore, the examiner concludes claim 1 is directed to an abstract idea without significantly more and is further directed to software per se.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention, under the broadest reasonable interpretation, is directed to an abstract idea without significantly more.
Step 1: Statutory Category
Independent claim 2 is drawn to a “system having self-learning behaviors.” Accordingly,
claim 2 falls under one of the four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A: Prong 1: Judicial Exception
Under the broadest reasonable interpretation (BRI), claim 2 is directed to an abstract idea.
Claim 2 recites:
one or more sensors for obtaining a first data and subsequent data indicative of an intrusion event (Mental process, a human security analyst receives first and subsequent observations/log entries)
a cognitive Bayesian reasoning system (CBRS) for reasoning probabilistic truths, including: a plurality of simple logic cells, configured with logic for mapping truth, selected from a group consisting of at least one of "OR", "AND", "NAND (not AND)", "NOR (not OR)", and "X-OR (exclusive OR)"; (Mental process/Mathematical concepts, the analyst Bayesian probabilistic evaluation and uses basic Boolean logic (AND/OR/NAND/NOR/XOR) and probabilistic/Bayesian rules in their head or on paper to classify events)
and agents, implemented in the logic cells for performing reasoning in the CBRS, the reasoning comprising at least one reasoning selected from the group consisting of inductive reasoning, abductive reasoning, deductive reasoning, and causal reasoning; (Mental process, a human security analyst receives first and subsequent observations/log entries, the analyst applies mental inference/inductive/abductive/deductive reasoning and casual analysis to the observed changes)
wherein the agents, in response to changes in the first data and the subsequent data, upon reasoning the intrusion event, publish a message indicative of the intrusion event (Organizing human activity, an analyst determines an intrusion and then communicates an alert message verbally or in writing)
Thus, the claim is directed to a mental process and mathematical concepts. See MPEP 2106.04 (a).
With respect to step 2A, prong 2, the additional elements fail to integrate the abstract idea into a practical application. The claim is directed to, or limited to, a technical solution solving a technical problem. They fail to provide an improvement to a technology or the functioning of a computer. See MPEP 2106.04 (d)(1). Instead, the additional elements merely recite, at high level of generality, general purpose computing structure that are used as tools for implementing the abstract idea. The field of uses generic data gathering, generic logical processing, and generic notification output to apply the abstract idea in a particular environment. Thus, the examiner finds the additional elements are mere instructions to implement the judicial exception. See MPEP 2106.05 (a) (e) (f). As, such the examiner must conclude the invention is not integrated into a practical application.
With respect to step 2B, the claim fails to recite significantly more than the abstract idea itself. Similar to the analysis for step 2A, prong 2, the claims fail to provide improvement to a technology of the functioning of a computer. It recites no concrete hardware. All component agents, logic cells, data sources are generic, well-understood, routine, and conventional elements or abstract software constructs. The steps of generic sensors for data collection, high-level reasoning concepts performed by agents, Boolean logic operations and the generic step of publishing a message. Thus, examiner finds the additional elements are mere instructions to implement the judicial exception. See MPEP 2106.05(f).
Therefore, the examiner concludes claim 2 is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 2 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Majumdar (US 20200004752 A1).
Regarding Claim 1, Majumdar teaches:
A cognitive Bayesian reasoning system to identify an intrusion event comprising (Majumdar, para 104, discloses that agents operate using belief networks which are well known in the art as Bayesian probabilistic models; para 118, 126, 127, Majumdar discloses these agents perform abductive, deductive and inductive reasoning cycles to interpret data and generate hypotheses and prediction. The coordinated multi-agent reasoning process constitute a cognitive reasoning system under Broadest reasonable interpretation; para 125, Majumdar discloses the system being used to identify illicit or harmful behaviors, e.g., identifying rogue traders.);
agents for performing at least one reasoning selected from the group consisting of inference, inductive reasoning, abductive reasoning, deductive reasoning, and causal reasoning (Majumdar, para 104 discloses agents are equipped with a reasoning paradigm (belief network, decision tree etc.) or preference function); Majumdar discloses the system utilizes abductive reasoning (para 126) to formulate a working hypothesis based on perceived data, deductive reasoning (para 127) to aggregate and test these hypothesis into a predictive theory; para 118, Fig 9, Majumdar discloses queries can be answered at the lowest layer using inductive, deductive, and abductive reasoning; para 125, Majumdar discloses the causal analysis of identifying rogue traders, the agents are designed to select specific pieces of evidence/evidence signals that are relevant to the user’s requirement of identifying rogue traders. Under the Broadest reasonable interpretation, this process of selecting relevant cause-effect data is fundamental to causal reasoning);
a plurality of simple logic cells, configured for operably implementing the agents, selected from a group consisting of at least one of "OR", "AND", "NAND (not AND)", "NOR (not OR)", and "X-OR (Exclusive OR)" (Majumdar, para 166 discloses the agents/manager use logical
operators like "OR" and "AND" as part of their percept and similarity computation; para 134, Majumdar discloses (pattern vector) PV can be understood as representing percepts from the point of view of the agent relative to the positions of other agents.);
and at least one source for a first data and subsequent data (Majumdar, para 112 discloses agents receive information through respective sensors 1020(1)-(3); agents receive data streams 1020(4) - (N) from other agents; para 125, Majumdar discloses the system starts receiving inputs from sources connected to the data sources (e.g., sensors and data streams));
wherein the agents, implemented in the simple logic cells, reason from changes in the first data and the subsequent data to identify the intrusion event (Majumdar, para 126 discloses agents acts on perceptions in an abductive process to generate hypothesis, and these hypotheses are revised and aggregated into a deductive theory used to form a prediction (para 127);
Majumdar, para 126-129, discloses the agents continuously receive new data and revise hypothesis based on these data changes; para 126, Majumdar discloses agent's task such as "identifying rogue traders" and generates output/prediction (para 129));
Regarding Claim 2, Majumdar teaches:
A system having self-learning behaviors for protection against security intrusions, and comprising (Majumdar, para 125 discloses that its agents continually learn evidential signal schemata “learned earlier from a training period”; Majumdar discloses the system being used to identify illicit or harmful behaviors, e.g., identifying rogue traders);
one or more sensors for obtaining a first data and subsequent data indicative of intrusion event (Majumdar, para 112 discloses sensors 1020(1)-(3) and associated data streams 1020(4) - (N) that continuously capture environmental and cybersecurity-related data (para 84, 200), which the agent network uses as evidential input to detect and reason about anomalous/attack behavior);
a cognitive Bayesian reasoning system (CBRS) for reasoning probabilistic truths, including (Majumdar, para 104, discloses that agents operate using belief networks which are well known in the art as Bayesian probabilistic models; para 118, 126, 127, Majumdar discloses these agents perform abductive, deductive and inductive reasoning cycles to interpret data and generate hypotheses and prediction. The coordinated multi-agent reasoning process constitute a cognitive reasoning system under Broadest reasonable interpretation);
a plurality of simple logic cells, configured with logic for mapping truth, selected from a group consisting of at least one of "OR", "AND", "NAND (not AND)", "NOR (not OR)", and "X-OR (exclusive OR)" (Majumdar, para 166 discloses the agents/manager use logical
operators like "OR" and "AND" as part of their percept and similarity computation; para 134, Majumdar discloses (pattern vector) PV can be understood as representing percepts from the point of view of the agent relative to the positions of other agents.);
and agents, implemented in the logic cells for performing reasoning in the CBRS, the reasoning comprising at least one reasoning selected from the group consisting of inductive reasoning, abductive reasoning, deductive reasoning, and causal reasoning; (Majumdar, para 104 discloses agents are equipped with a reasoning paradigm (belief network, decision tree etc.) or preference function); Majumdar discloses the system utilizes abductive reasoning (para 126) to formulate a working hypothesis based on perceived data, deductive reasoning (para 127) to aggregate and test these hypothesis into a predictive theory; para 118, Fig 9, Majumdar discloses queries can be answered at the lowest layer using inductive, deductive, and abductive reasoning; para 125, Majumdar discloses the causal analysis of identifying rogue traders, the agents are designed to select specific pieces of evidence/evidence signals that are relevant to the user’s requirement of identifying rogue traders. Under the Broadest reasonable interpretation, this process of selecting relevant cause-effect data is fundamental to causal reasoning);
wherein the agents, in response to changes in the first data and the subsequent data, upon reasoning the intrusion event, publish a message indicative of the intrusion event. (Majumdar, para 126 discloses agents acts on perceptions in an abductive process to generate hypothesis, and these hypotheses are revised and aggregated into a deductive theory used to form a prediction (para 127); Majumdar, para 126-129, discloses the agents continuously receive new data and revise hypothesis based on these data changes; para 126, Majumdar discloses agent's task such as "identifying rogue traders" and the response is output as a report to the analyst (para 127));
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIT KHADKA whose telephone number is (703)756-1440. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey L. Nickerson can be reached at (469) 295-9235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMIT KHADKA/Examiner, Art Unit 2432
/SYED A ZAIDI/Primary Examiner, Art Unit 2432
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