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 .
Claim Rejections - 35 USC § 101
Claims 1- are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1, Claim 1 recites "a computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media to perform steps. Thus, the claim is system, which are statutory categories of invention under 35 U.S.C. § 101 (Step 1: YES).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim "recites" a judicial exception when the judicial exception is "set forth" or "described" in the claim.
Limitation: “evaluate the stored responses for potential sharing based on content characteristics, access patterns, and privacy classification”. This limitation recites a judicial exception because it encompasses mental processes. The word "evaluate" falls within the definition of mental processes, which includes observations, evaluations, judgments, and opinions.
Limitation: “determine, using a privacy-aware cache selector, a sharing eligibility level for each stored response based on a privacy policy and contextual metadata associated with the response”. This limitation recites a judicial exception because it encompasses mental processes. The action of “determining” a level based on specific criteria functions as a judgment or decision-making step. This is an evaluation process that can practically be performed in the human mind.
Limitation: “apply a privacy mechanism to shared responses, wherein the privacy mechanism comprises at least one of differential privacy, homomorphic encryption, or anonymization of identifying elements”. This limitation recites a judicial exception because it encompasses mathematical concepts. The term “differential privacy” and “homomorphic encryption” are inherently mathematical algorithms involving cryptographic operations.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the mentioned steps fall within the mental process grouping and mathematical concept of abstract ideas and considered as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A Prong Two: The claim recites the additional elements:
receive, from a plurality of distributed nodes, a plurality of codeword-derived representations associated with input data processed through a compression and transformation pipeline;
process the codeword-derived representations through a machine learning model to generate one or more responses;
store the one or more responses in a hierarchical cache comprising a local cache associated with the computer system and a global cache distributed across multiple nodes;
control synchronization of selected cache entries across nodes using an adaptive synchronization controller that dynamically adjusts timing, frequency, and scope of synchronization operations based on network conditions, utility metrics, and privacy budget consumption
aggregate usage statistics from a plurality of nodes into a federated cache learning system configured to compute one or more cache optimization models
distribute selected cache entries to one or more nodes using a federated cache aggregator that manages versioning, deduplication, and replication consistency across the global cache.
MPEP 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field
The limitations recited above, individually and in combination, do not meet MPEP 2106.05(a). "Receive," "process," "store," "aggregate," "control synchronization," and "distribute" are all functions of standard data handling and network transmission operations. They do not reflect an improvement in the functioning of a computer or other technology. The claim does not detail how these steps improve the computer's functionality beyond what is well-understood, routine, and conventional in the field of distributed caching
MPEP 2106.05(b) Particular Machine
The claim relies on a generic computer system comprising hardware memory and executing software instructions. A "computer system" or "hardware memory" as recited are generic components, not a particular machine. Merely specifying a computer environment or generic network nodes does not constitute a particular machine that imposes a meaningful limit on the abstract idea.
MPEP 2106.05(c) Particular Transformation
The steps "receive," "process," "store," and "distribute" describe data transmission and storage, but they do not result in a particular transformation of a physical article into a different state. While data is "transformed" via the machine learning model or processing, the claim does not recite a tangible transformation of an object to a new form, which is required for this consideration to integrate the exception into a practical application.
MPEP 2106.05(e) Other Meaningful Limitations
The claim merely recites generic steps that apply the judicial exceptions to a computer environment. "Apply a privacy mechanism" and "process through a machine learning model" are instructions to implement an abstract idea on a computer, which does not add anything significantly more than the exception itself. The limitations fail to impose a meaningful limit because they are standard data processing functions used in every computer network today.
MPEP 2106.05(g) Insignificant Extra-Solution Activity
The additional limitations recited above are merely extra-solution activity. For instance, "control synchronization" or "distribute selected cache entries" are well-understood functions of a distributed system. They are considered insignificant extra-solution activity that does not integrate the exception into a practical application in a meaningful way.
MPEP 2106.05(h) Field of Use and Technological Environment
The limitations recited above merely links the use of the judicial exception to a particular technological environment (distributed nodes, global cache). Merely reciting a technological environment is insufficient to integrate the exception. This is because the environment is merely used to execute the abstract idea.
Step 2B: Inventive Concept ("Significantly More")
Examine the additional elements individually and as an ordered combination—to see if they provide an inventive concept that adds "significantly more" than the exception itself. The claimed limitations describe a conventional computer system performing generic steps for data caching, privacy classification, and synchronization. None of these steps represent an unconventional use, nor do they describe a specific, non-generic solution to a specific technical problem (Berkheimer v. HP Inc.). If the claim pertains to an improvement to the functioning of a computer or other technology, it must be apparent to one of ordinary skill in the art; here, the limitations only utilize standard computer functions to execute the abstract concept. The ordered combination does not add significantly more than the judicial exception itself because it merely utilizes generic computer components without providing an inventive concept that transforms the claim into patent-eligible subject matter. Thus, the independent claim is patent ineligible.
Claim 2 recites “wherein the privacy-aware cache selector classifies responses into at least a public tier, a group-private tier, and a fully-private tier based on content analysis and contextual metadata." The limitation "classifies responses... based on content analysis" encompasses Mental Processes because evaluating and applying classification rules are examples of observations, evaluations, and judgments that can be performed in the human mind.
Claim 4 recites “wherein the federated cache learning system applies a federated averaging algorithm to derive cache optimization models based on anonymized cache usage statistics received from multiple nodes." The claim encompasses mathematical concepts because the use of specific algorithms like federated averaging and aggregation of statistics involves mathematical relationships and calculations.
Claim 6 recites “wherein the privacy mechanism comprises a differential privacy engine that adds calibrated noise to cache access metrics before aggregation." Merely applying a differential privacy engine to metrics does not cause an improvement in the functioning of a computer or other technology. Applying a known cryptographic/ mathematical technique as an additional element to manage data, which does not provide significantly more than the judicial exception itself.
Claim 7 recites wherein the privacy mechanism to enforce k-anonymity by permitting synchronization only when a cached response is associated with at least k distinct users." This limitation recites mathematical concepts as it requires counting associations and applying a logical threshold condition.
Claim 8 recites wherein the federated cache aggregator maintains a distributed hash table that maps semantic identifiers to cached responses across participating nodes. The limitation does not integrate the abstract idea into a practical application because it simply uses a standard database technique to store and retrieve responses without providing a specific technical improvement or unconventional solution.
Claim 10-11, 13, 15-18 are similar to claims 1-2, 4, 6-9. The claims are rejected based on the same reasons.
Consideration
The combination of claim 1+ 3+ 5 and 10+ 12 + 14
Independent claims 1 and 10 were rejected because they recited functional results at a high level of generality (e.g., "dynamically adjusts timing, frequency, and scope... based on network conditions"). They did not say how they optimized bandwidth and utility. The combination of 1+3+5 and 10+12+14 together sets up a multi-tiered structural mechanism. It uses a selective encryption pipeline (homomorphic encryption assigned exclusively to a group-private tier) intertwined with a performance-optimizing transmission technique (delta synchronization). The paragraphs [0007], [0008], [0010], [0024], [0032], and [0039] disclose shows that combining homomorphic encryption with delta synchronization provides clear benefits. This specific setup updates decentralized caches securely, cuts network bandwidth usage and prevents system lag. By adding these exact rules directly to the claim, the invention does not lock up the broad concept of data encryption or privacy rules. Instead, it is limited to a distinct, practical solution that fixes a real network bottleneck.
Sample
A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
receive, from a plurality of distributed nodes, a plurality of codeword-derived representations associated with input data processed through a compression and transformation pipeline;
process the codeword-derived representations through a machine learning model to generate one or more responses;
store the one or more responses in a hierarchical cache comprising a local cache associated with the computer system and a global cache distributed across multiple nodes;
evaluate the stored responses for potential sharing based on content characteristics, access patterns, and privacy classification;
determine, using a privacy-aware cache selector, a sharing eligibility level for each stored response based on a privacy policy and contextual metadata associated with the response;
aggregate usage statistics from a plurality of nodes into a federated cache learning system configured to compute one or more cache optimization models;
control synchronization of selected cache entries across nodes using an adaptive synchronization controller that dynamically adjusts timing, frequency, and scope of synchronization operations based on network conditions, utility metrics, and privacy budget consumption, wherein the adaptive synchronization controller implements delta synchronization by transmitting only changed portions of cache entries;
apply a privacy mechanism to shared responses, wherein the privacy mechanism comprises at least one of differential privacy, homomorphic encryption, or anonymization of identifying elements, wherein group-private responses are encrypted using homomorphic encryption prior to distribution to a set of authorized nodes; and
distribute selected cache entries to one or more nodes using a federated cache aggregator that manages versioning, deduplication, and replication consistency across the global cache.
Response to Arguments
Section 35 U.S.C 101
Applicant’s argument has been considered. Please see the rejection above
Section 35 U.S.C 103
Applicant’s argument has been considered. Examiner withdraws the rejections.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant s disclosure
U.S. Pub 2025/0378028 A1 – Boue discloses q method, computer program product, and computing system for processing a dataset of query-answer pairs. Synthetic variations of queries are generated and each of the synthetic variations of queries are mapped to a corresponding answer from the dataset. An embedding dataset is generated by transforming the synthetic variations into synthetic query embeddings and the queries into query embeddings. A first set of embeddings is defined for storage in a semantic cache and a second set of embeddings are defined and are not stored in the semantic cache. A separate distance threshold is assigned to each embedding of the first set of embeddings and a pairwise distance between each query and the synthetic variations is determined. Distance thresholds for respective pairwise distances between a query and synthetic variations of the query are generated. Subsequent queries are processed using the semantic cache and the distance thresholds.
U.S. Pub 2024/0311645 A1 – Zhang discloses methods and systems for federated learning using feature normalization are disclosed. A client implements a local model including at least: a feature extraction subnetwork to extract a feature vector from input data, a normalization layer to normalize the feature vector, and a final layer to generate a prediction output from the normalized feature vector. The local model is initialized using a set of global parameters received from a central server. The local model is updated using data sampled from a local dataset. Information about a state of the updated local model is transmitted to the central server.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
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