DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3.1 Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A- Prong One
The claim(s) recite(s) a landmark-based cognitive system, non-transitory medium, and computer-implemented method for landmark-based cognition in a persistent cognitive machine, comprising the steps to: “maintain a landmark graph on a cognitive manifold, the landmark graph comprising vertices corresponding to landmark points and edges connecting landmark pairs; wherein landmark selection is based on geometric properties of the cognitive manifold”; under the broadest reasonable interpretation fall under a mental process; likewise, the steps to: “update the spectral basis when geometric invariants indicate structural change, while enforcing constraints that limit modification of foundational spectral components more strictly than detailed spectral components; construct edges in the landmark graph with forward and reverse displacement information enabling bidirectional traversal; verify edge reversibility by confirming that forward-then-reverse traversal returns to an origin point within a tolerance; generate cryptographic certificates for verified edges; produce explanations of system outputs by identifying landmarks that contributed to the outputs and mapping the identified landmarks to interpretable labels”, under the broadest reasonable interpretation fall under a mental process and/or otherwise a mathematical concept. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a processor”, “a memory” storing “instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0563]-[0573], and fig.9) which can be of any type, including general-purpose computer (para [0567]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “store edge data and certificates in a data store enabling audit of traversals through the landmark graph”, under the broadest reasonable interpretation, reasonable fall under post-solution activities” that are also well-known, routine and conventional activities to store data in a memory and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a processor”, “a memory” storing “instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0563]-[0573], and fig.9) which can be of any type, including general-purpose computer (para [0567]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “store edge data and certificates in a data store enabling audit of traversals through the landmark graph”, under the broadest reasonable interpretation, reasonable fall under post-solution activities” that are also well-known, routine and conventional activities to store data in a memory and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-10, 12-20 merely include limitations pertaining to further mathematical computations (claims 2, 12), “wherein the landmark graph comprises a hierarchical structure with multiple levels, each level having a different spatial resolution” (mental process). (claims 3, 13); “wherein the instructions further cause the system to establish projections between adjacent levels that map landmarks from finer levels to coarser levels” (mental process or otherwise a mathematical concept); (claims 4, 14); “wherein updating the spectral basis comprises propagating spectral updates across multiple levels while maintaining consistency between levels” (mathematical concept or otherwise mental process); (claims 5, 15); “wherein the geometric properties comprise at least one of curvature or density of cognitive trajectories” (mental process or otherwise mathematical concept); (claims 6, 16) “wherein the constraints that limit modification comprise differential plasticity bounds applied to spectral modes based on frequency, with tighter bounds for lower-frequency modes” (mental process or otherwise mathematical concept); (claims 7, 17); “performing trajectory audit by retrieving stored edge data for a trajectory, verifying cryptographic certificates for edges in the trajectory, and computing residuals confirming trajectory accuracy” (mental process or otherwise mathematical concept); (claims 8, 18) “wherein producing explanations comprises: determining a probability estimate for an outcome by combining a geometric component derived from landmark graph distances, an empirical component derived from simulations, and a historical component derived from archived cases; and generating natural language text describing contributions from landmarks in the landmark graph to the probability estimate” (mathematical concept or otherwise a mental process); (claims 9, 19) “cause the system to assess agreement between the geometric component, empirical component, and historical component, and generate confidence qualifiers based on the assessed agreement” (mental process); (claims 10, 20) “wherein edge weights in the landmark graph are determined by both distance between landmarks and integrated curvature along paths connecting the landmarks” (mathematical concept or otherwise a mental process); all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 102
5. 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 –
5.0 Claim(s) 1-7, 10-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Crabtree et al. (USPG_PUB No. 2026/0148042).
5.1 In considering claims 1 and 11, Crabtree et al. teaches a landmark-based cognitive system, comprising:
a processor and a memory storing instructions that, when executed by the processor (see para [1154] The exemplary computing environment described herein comprises a computing device 10 (further comprising a system bus 11, one or more processors 20, a system memory 30), cause the system to: maintain a landmark graph on a cognitive manifold, the landmark graph comprising vertices corresponding to landmark points and edges connecting landmark pairs (para [0476] FIG. 20 is a block diagram illustrating a multi-hop knowledge graph reasoning integration with discriminative feature extraction for valid/invalid paths, as incorporated within the combined CIF+AEF framework. This sophisticated system represents a significant advancement in knowledge-based AI reasoning, enabling the discovery and validation of complex inference paths across large knowledge graphs while efficiently filtering out spurious or invalid connections. [0478] The knowledge graph and path sampling 2010 section establishes the foundation of the system's reasoning capabilities. The knowledge graph 2011 represents the underlying entity-relation structure that encodes domain knowledge, consisting of entities (such as objects, concepts, or individuals) and the relations that connect them. The path sampling 2012 generates candidate paths for a given query, structuring them as potential multi-hop routes through the knowledge graph. The Adaptive Compositional Graph Engine 2121 dynamically constructs, updates, and manages abstract knowledge graphs that represent complex relationships and hierarchical dependencies within input data across both visual and linguistic domains.); wherein landmark selection is based on geometric properties of the cognitive manifold (para [0366], Once the approximate position is identified, the system assesses the local density i.e. geometric proporties ρ(i) around position i within the funnel structure. This density measurement quantifies the concentration of scenarios in that region, providing an indication of potential computational congestion. [0681], The Manifold Boundary Detection system 3533 employs advanced techniques including one-class support vector machines, isolation forests, and density-based anomaly detection to identify the edges of safe operational manifolds.); derive a spectral basis from the landmark graph (see para [0495] When ontologies differ dramatically in depth or contain many-to-one hierarchical correspondences the Spectral Laplacian Matching kernel becomes advantageous. Each graph is decomposed into its first k Laplacian eigenvectors, which act as smooth structural fingerprints. A Wasserstein barycentric projection then transports the spectral signatures into a common manifold where a Hungarian assignment selects the minimum-cost node pairing. Because eigenvalues capture global shape while the transport step accounts for local distortions, this kernel excels at aligning taxonomies that have grown independently but share latent organisational principles); update the spectral basis when geometric invariants indicate structural change (para [0586] The adaptive learning 2226 continuously updates graph structures based on new information, facilitates graph evolution to reflect changing knowledge, and recognizes emerging patterns across inputs. The neuro-symbolic interface 2225 serves as a critical bridge between neural network representations and symbolic reasoning, enabling bidirectional knowledge flow and aligning representations between the two paradigms. The graph analysis 2224 evaluates potential reasoning paths, verifies consistency across the knowledge graph, and detects anomalies or contradictions that may indicate errors in reasoning or input processing.), while enforcing constraints that limit modification of foundational spectral components more strictly than detailed spectral components (see para [0682] The mathematical framework underlying the entire system centers on a safe evolution constraint formulated as πt+1(a|s)=arg min_π [L_task(π)+λ_KL D_KL(π∥π_baseline)+λ_manifold d_M(π, M_learned)], where L_task(π) represents task-specific performance objectives, λ_KL controls adherence to baseline policy distributions, λ_manifold enforces manifold membership constraints, and d_M(π, M_learned) measures distance from the learned safe manifold. This constraint optimization approach ensures that policy updates simultaneously optimize task performance while maintaining statistical alignment with historical baselines and operational manifold membership. [0699-0701], The Online Knowledge Integration 3733 facilitates real-time knowledge updates through incremental learning protocols that combine new experiences with historical knowledge through experience replay buffers, implementing multi-objective optimization that simultaneously optimizes task performance, maintains appropriate exploration levels, and enforces regularization constraints to prevent overfitting or knowledge degradation. [0670], A constraint satisfaction protocol enforces both hard constraint requirements and soft constraint penalties through multi-level safety checks that ensure mathematical safety bounds ∀constraints: gi(θ, action)≤0 are maintained throughout the knowledge evolution process while preserving system flexibility and adaptation capabilities. This system optimizes change rates while enforcing temporal smoothness constraints ∥θ_t−θ_{t−1}∥≤δ_max that prevent abrupt knowledge changes that could destabilize system performance or introduce inconsistencies.); construct edges in the landmark graph with forward and reverse displacement information enabling bidirectional traversal (para [0036] According to an aspect of an embodiment, the system further comprises an adaptive compositional graph engine (ACGE) that dynamically constructs abstract knowledge graphs representing complex relationships, enables compositional reasoning across visual and linguistic domains, implements cross-domain bridging between different modalities, and provides transparent inference paths for explainable decision-making. [0697], The knowledge graph construction 3713 synthesizes this processed information into dynamic graph G=(E, R, T) where T represents temporal edges that explicitly capture how entity relationships evolve over time, creating comprehensive knowledge dependency graphs that enable sophisticated reasoning about temporal causality and knowledge evolution patterns.); verify edge reversibility by confirming that forward-then-reverse traversal returns to an origin point within a tolerance (see para [0662], The offline annotation process employs a two-stage verification approach, beginning with forward annotation where ŝx←F(x, D) using large language model few-shot learning capabilities, followed by backward verification that provides independent assessment scoring of spans on a scale of {0, 1, 2} and filters out spans receiving a score of 0.); generate cryptographic certificates for verified edges (see para [0339] The cryptographic subsystem further employs forward secrecy protocols that generate ephemeral session keys for each operation, ensuring that compromise of long-term keys does not enable decryption of previously transmitted messages or delegation tokens. [0847] the system initiates 4040, Lazy Weight Materialization, which represents one of the most sophisticated aspects of the plugin activation protocol. This multi-phase initialization process begins with cryptographic manifest signature verification to ensure plugin authenticity and integrity, followed by dependency graph resolution to identify and sequence any prerequisite components.); store edge data and certificates in a data store enabling audit of traversals through the landmark graph (see para [0816], The knowledge representation engine utilizes hierarchical graph structures with weighted edges representing semantic relationships between conceptual nodes, enabling efficient traversal and inference operations with computational complexity of O(log n) for typical query operations.); and produce explanations of system outputs by identifying landmarks that contributed to the outputs and mapping the identified landmarks to interpretable labels (see para [0692], Interpretable decisions generates comprehensive decision explanations, rule trace generation, and causal reasoning pathways through explain(decision).fwdarw.{rules, patterns}, providing transparency mechanisms that enable human operators to understand and validate system decision-making processes, [0717], The structured reasoning system maintains comprehensive consistency through cross-pathway validation ensuring different reasoning approaches produce coherent results, logical contradiction detection that identifies and resolves inconsistencies in reasoning chains, confidence calibration that adjusts certainty scores based on reasoning pathway agreement, and explanation generation providing interpretable reasoning traces that enhance decision transparency and system accountability.).
5.2 As per claims 2 and 12, Crabtree et al. teaches that wherein the landmark graph comprises a hierarchical structure with multiple levels, each level having a different spatial resolution (see para [0408], GISESTO 1260 can be configured to implement a multi-resolution graph representation modeling computational workloads across multiple abstraction levels simultaneously, from fine-grained dataflow representations to coarse transitions between computational phases. The engine may comprise a structural decomposition engine automatically identifying parallelization opportunities through formal analysis of algebraic properties of tensor operations.).
5.3 With regards to claims 3 and 13, Crabtree et al. teaches that wherein the instructions further cause the system to establish projections between adjacent levels that map landmarks from finer levels to coarser levels (see para [0408], GISESTO 1260 can be configured to implement a multi-resolution graph representation modeling computational workloads across multiple abstraction levels simultaneously, from fine-grained dataflow representations to coarse transitions between computational phases.).
5.4 As per claims 4 and 14, Crabtree et al. teaches that wherein updating the spectral basis comprises propagating spectral updates across multiple levels while maintaining consistency between levels (see para [0173], Third, the synergy between AEF's hierarchical search strategies and CIF's agent-parallel processing creates a multi-level optimization framework that efficiently explores solution spaces while maintaining computational tractability. [0324] Secure delegation and authorization handler 410 also connects bidirectionally with operational foundation domain 500, accessing authentication services and audit mechanisms. This connection may enable verification of delegation chains and maintenance of authorization records, potentially implementing Federated Delta Authorization Protocol (FDAP) for efficient propagation of credential updates across distributed systems. The protocol may use asynchronous, bloom-filter-based credential propagation techniques that minimize bandwidth requirements while maintaining security assurances.).
5.5 As per claims 5 and 15, Crabtree et al. teaches that wherein the geometric properties comprise at least one of curvature or density of cognitive trajectories (see para [0366], Once the approximate position is identified, the system assesses the local density ρ(i) around position i within the funnel structure. This density measurement quantifies the concentration of scenarios in that region, providing an indication of potential computational congestion. [0681], The Manifold Boundary Detection system 3533 employs advanced techniques including one-class support vector machines, isolation forests, and density-based anomaly detection to identify the edges of safe operational manifolds.).
5.6 Regarding claims 6 and 16, Crabtree et al. teaches that wherein the constraints that limit modification comprise differential plasticity bounds applied to spectral modes based on frequency, with tighter bounds for lower-frequency modes (see para [0426], Both paths ultimately feed into a reinforcement learning (RL) signals 1424 where the system continuously refines its placement strategies based on real-time performance metrics, access patterns, and insertion/deletion frequencies. [0715], The SEAL framework extends the existing Controlled Temporal Evolution Engine by implementing dynamic neural plasticity mechanisms that enable selective weight modification through learned adaptation functions adapt, where the system intelligently determines which parameters should be modified and by what magnitude based on the specific characteristics of new tasks, domains, or environmental conditions encountered during operation.).
5.7 With regards to claims 7 and 17, Crabtree et al. teaches that wherein the instructions further cause the system to perform trajectory audit by retrieving stored edge data for a trajectory, verifying cryptographic certificates for edges in the trajectory, and computing residuals confirming trajectory accuracy (see para [0342-0343], [0342] Scenario audit and provenance system 520 connects bidirectionally with scenario intelligence domain 200, decision and logic domain 300, and agent orchestration domain 400, receiving event data and provides audit services. In certain embodiments, these connections may involve standardized logging interfaces that normalize how events are recorded across domains, potentially using schema-based validation approaches to ensure consistent and complete audit records. Scenario audit and provenance system 520 may implement real-time monitoring and alerting capabilities that identify abnormal patterns or policy violations during system operation, possibly using anomaly detection techniques or compliance rule engines to flag potential issues for investigation. In some implementations, scenario audit and provenance system 520 may support forensic analysis tools that enable post-hoc investigation of system behavior, potentially using causal inference methods or execution replay capabilities to reconstruct event sequences and understand decision rationales. [0511] A composite distance calculator 2106 aggregates multiple distance components for each active cohort T, including behavior residuals, policy divergence, representation out-of-distribution scores, graph edit distances, and knowledge coverage differentials.).
5.8 As per claims 10 and 20, Crabtree et al. teaches that wherein edge weights in the landmark graph are determined by both distance between landmarks and integrated curvature along paths connecting the landmarks (see para [0881], The highest abstraction layer implements entanglement swapping protocols for establishing long-distance quantum correlations, utilizing linear optical Bell state measurements with success probability of 50% for each swapping operation. Failed swapping attempts trigger automatic rerouting through alternate paths in the quantum network topology, with path selection determined by a modified Dijkstra algorithm that incorporates quantum fidelity as the primary edge weight metric.).
Allowable Subject Matter
6. Claims 8-9, 18-19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
7.1 Kumari et al. (USPG_PUB No. 2021/0327108) teaches systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets, including step to generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks.
8. Claims 1-20 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 July 25, 2026
Prosecution Insights