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 .
Priority
This application claims priority to U.S. Application No. 63/410,560, filed on Sep. 27, 2022, and titled “CAUSAL INFERENCE ON CATEGORY AND GRAPH DATA STORES,” the entire disclosure of which is incorporated herein by reference.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/09/2026 was filed after the mailing date of the Non-Final Office Action on 04/02/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
DETAILED ACTION
This Office Action is in response to an Amendment Application received on 08/19/2026. In the application, claims 1, 4, 8, 10, 13, 15-16, and 20 have been amended. Claims 2, 9, and 18 have been cancelled. Claim 21 has been added as new.
For this Office Action, claims 1, 3-8, 10-17, and 19-21 have been received for consideration and have been examined.
Response to Arguments
Claim Rejections – 35 USC § 112
Applicants’ amendments to claim 1 have been reviewed and amendments have overcome the 35 USC § 112(b) rejection. Therefore, this rejection has been withdrawn.
Claim Rejections – 35 USC § 101
Applicants’ amendments to claims have been reviewed considering the remarks, however, amendments do not overcome the raised 35 USC § 101 rejection reciting an Abstract Idea. Applicant remarks and supported paragraphs from the instant disclosure have been reviewed, however, examiner found these paragraphs to further strengthen Examiner point of view that amended claim language recite an Abstract idea, specifically invoking mathematical concepts and mental processes.
After reviewing the cited instant paragraphs, examiner notes that instant paragraphs describe difference between correlation and causation. The key difference is that Correlation means two variables move together, while causation means one variable directly causes a change in the other. Correlation shows a relationship or pattern where two things happen at the same time (like ice cream sales and sunburns). It can be positive, negative, or zero. Causation proves that an action or variable (A) directly produces an outcome or change in another variable (B).
Considering the above explanation, these claim limitations recite an abstract idea under the United States Patent and Trademark Office (USPTO) Alice/Mayo framework.
The claim focuses on receiving, retrieving, scoring, aggregating, and storing data vectors. Courts consistently rule that collecting, analyzing, and manipulating data using mathematical relationships (like similarity scores and graph links) is an abstract idea.
The steps describe functional software automation (e.g., retrieving records from databases and generating data models) rather than an improvement to the underlying operation of the computer or network itself.
The hardware recited—a computer processor, memory, and a data store—consists of routine, generic computer components performing their ordinary functions.
Breakdown of Abstract Idea Groupings
Mathematical Concepts
The claim explicitly recites mathematical relationships and calculations:
Vector operations: It requires "normalized attribute vectors" comprised of a plurality of attributes.
Calculations: It explicitly mandates "calculating a similarity score" between the vectors and a query.
Mathematical thresholds: It filters these results using a "predetermined similarity score threshold."
Mental Processes / Information Organization
The overarching method outlines a process for "performing causal inference" by searching, comparing, and filtering data based on similarity.
Data collection and filtering: Retrieving data from a database, comparing items, and aggregating them based on a proximity threshold (e.g., "within a predetermined number of links") are foundational concepts of human data organization and logic.
Mental step substitution: While a human might struggle to compute millions of high-dimensional vectors simultaneously, the underlying steps of comparing attributes, checking against thresholds, and grouping closely linked data are things a human can perform using a pen, paper, and logic.
The claim language lacks an "inventive concept" sufficient to transform the abstract idea into a patent-eligible application. Currently, this claim lacks that transformation because:
The data structures (normalized attribute vectors, graph databases) are well-known in computer science.
The aggregation and filtering (using thresholds and link distances) are standard statistical and data-science practices.
The software component simply automates steps that could conceptually be performed through mental processes or manual mathematical modeling, albeit much slower.
To improve eligibility, the claims need to focus on a specific, non-conventional technical improvement in how the graph database operates, or a novel method of processing that fixes a specific computer-centric bottleneck.
Merely executing an abstract mathematical concept or a data aggregation method on a computer does not save it from being categorized as an abstract idea. To be patent-eligible, the claim must integrate the exception into a practical application. Therefore, the Abstract Idea rejection has been maintained.
Claim Rejections - 35 USC § 101 (Abstract Idea)
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more analyzed according to MPEP 2106.
Step 1: The independent claims 1, 8, and 13 do fall into one of the four statutory categories of “a system” and “a method” claims. Nevertheless, the claim still is considered as abstract idea (i.e., combination of Methods Of Organizing Human Activity, Mental process and Mathematical Concepts) for the following prongs and reasons.
Step 2A: Prong 1: The limitations of the independent claims 1, 8, and 13 recite the abstract idea of:
Claim 1. A system to generate and manage data models for causal inference, comprising:
[[a computer processor; a memory configured to store computer executable instructions and computer readable data]]; and
receive a query having attributes normalized in a form of normalized attribute vectors (Mental process: a trained human receives data and tasked to generate models from the data);
retrieve, from a data store, records having data that match the query attributes using a similarity score, wherein the similarity score of the data is within a predetermined threshold (Mental process: the trained human retrieving data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes);
retrieve, from a graph database based on the records retrieved from the data store, records having data in the form of the normalized attribute vectors and matching the data retrieved from the data store, each vector comprised of mathematical model attributes, experiment attributes, and experimental data attributes, wherein the retrieved records are selected based on being within a predetermined number of links in the graph database of the records retrieved from the data store (Mental process & Mathematical concept: the trained human retrieves from a graph database records having data in the form of the normalized attribute vectors and matching the data retrieved from the data store, each vector comprised of various attributes, wherein the records are selected based on a predetermined number of links in the graph database of the records retrieved from the data store);
retrieve, from a category database, category data based on the records retrieved from the data store and graph database (Mental process: the trained human retrieves category data from category database);
aggregate the records retrieved from the data store and graph database along with the retrieved category data; and generate data models from the normalized attribute vectors of the aggregated records and category data (Mental process & Mathematical concept: the trained human gathers and combine the normalized attribute vectors, whose calculated similarity scores meet the predetermined similarity score threshold and the normalized attribute vectors that are stored within the predetermined number of links in the graph database, into a data model comprised of a set of normalized attribute vectors).
Claim 8. A method to perform causal inference, comprising:
receiving, at a data model generator [[software component]], a query (Mental process: a trained human receives data and tasked to generate models from the data);
retrieving data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes (Mental process: the trained human retrieving data from at least one database storing one or more normalized attribute vectors, each normalized attribute vector comprised of a plurality of attributes);
for each normalized attribute vector in the at least one database, calculating a similarity score between the respective normalized attribute vector attributes and the query (Mental process & Mathematical concepts: the trained human performs mathematical calculations and calculate a similarity score from the database between the respective normalized attribute vector attributes and the query);
at the data model generator [[software component]], generating a data model from the normalized attribute vectors whose calculated similarity scores meet a predetermined similarity score threshold (Mental process & Mathematical concept: the trained human generates a data model from the normalized attribute vectors whose calculated similarity scores meet a predetermined similarity score threshold), wherein generating the data model comprises:
retrieving from a graph database storing one or more normalized attribute vectors, one or more normalized attribute vectors that are stored within a predetermined number of links in the graph database from a normalized attribute vector whose calculated similarity scores meet the predetermined similarity score threshold (Mental process: the trained human retrieves from a graph database storing one or more normalized attribute vectors, one or more normalized attribute vectors that are stored within a predetermined number of links in the graph database from a normalized attribute vector whose calculated similarity scores meet the predetermined similarity score threshold); and
aggregating the normalized attribute vectors, whose calculated similarity scores meet the predetermined similarity score threshold and the normalized attribute vectors that are stored within the predetermined number of links in the graph database, into a data model comprised of a set of normalized attribute vectors (Mental process & Mathematical concept: the trained human gathers and combine the normalized attribute vectors, whose calculated similarity scores meet the predetermined similarity score threshold and the normalized attribute vectors that are stored within the predetermined number of links in the graph database, into a data model comprised of a set of normalized attribute vectors).
Claim 13. A method to validate correlations in a causal inference [[engine]], comprising:
[[at a machine learning algorithm software component]], receiving a data model comprised of a plurality of normalized attribute vectors, each normalized attribute vector including a plurality of mathematical model attributes (Mental process & Organizing human activity & Mathematical concepts: a trained human receives a data model comprised of a plurality of normalized attribute vectors, each normalized attribute vector including a plurality of mathematical model attributes);
[[at the machine learning algorithm software component]], generating candidate correlations between two more normalized attribute vectors in the data model based on patterns between attributes of normalized attribute vectors, and a [[machine]] learning model based on the attributes (Mental process & Organizing human activity & Mathematical concepts: the trained human generates candidate correlations between two more normalized attribute vectors in the data model based on patterns between attributes of normalized attribute vectors, and a learning model based on the attributes);
[[at the machine learning algorithm software component]] for each generated candidate correlation, generating a confidence score (Mental process & Organizing human activity & Mathematical concepts: the trained human generates a confidence score for each generated candidate correlation); and
validating each generated candidate correlation based at least on the corresponding generated confidence score, and storing the validated correlations as causal relationships in a causal inference data store (Mental process & Organizing human activity & Mathematical concepts: the trained human validates each generated candidate correlation based at least on the corresponding generated confidence score, and storing the validated correlations as causal relationships in a causal inference data store);
at an inference to mechanism mapping, receiving a set of inference criteria encoded as computational analogues into a rules engine (Mental process & Organizing human activity: the trained human interpret the criteria according the set rules);
at an inference to mechanism mapping, retrieving from the causal inference data store a correlation between at least two normalized attribute vectors (Mental process & Organizing human activity: the trained human compare correlation and causal between the normalized attribute vectors and store this information);
performing the computational analogues of the inference criteria on the retrieved correlation (Mental process, Organizing human activity & Mathematical concept: the trained human performs computational algorithms on the compared correlation and causal); and
if the inference criteria are met within predetermined criteria, storing the correlation in the causal inference data store (Mental process & Organizing human activity: the trained human stores the data of the inference criteria are met within predetermined criteria).
Overall, a data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.
The corresponding professional activity is called generally data modeling or, more specifically, database design. Data models are typically specified by a data expert, data specialist, data scientist, data librarian, or a data scholar. A data modeling language and notation are often represented in graphical form as diagrams.
A data model can be referred to as a data structure, especially in the context of programming languages. Data models are often complemented by function models, especially in the context of enterprise models.
A data model explicitly determines the structure of data; conversely, structured data is data organized according to an explicit data model or data structure.
Step 2A: Prong 2: The judicial exception (i.e., a data model generator software component, a machine leaning algorithm) is not integrated into a practical application. In particular, the claims do not recite any additional element to perform beyond routine steps. To show that the involvement of a computer assists in improving the technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.5(a) II).
In this case, the additional elements of the claim are:
“the system comprising: a computer processor;
a memory configured to store computer executable instructions and computer readable data” (claim 1),
“a data model generator software component” (claim 8), and
“a machine learning algorithm software component” (claim 13).
The analysis of your current claim limitations reveals that it likely fails Prong 2 because:
Generic Software Components: The claim invokes a generic "data model generator software component," a generic "database," and a generic "graph database".
Tool vs. Solution: The technology is being used merely as a tool to execute mathematics. It does not solve an inherent computer hardware or network architecture problem (such as reducing memory usage or speeding up database indexing a structural level).
High-Level Functional Language: Terms like "receiving," "retrieving," "calculating," "generating," and "aggregating" describe what the system accomplishes, not a specific, unconventional technical mechanism of how the computer itself is improved.
The additional elements are recited at a high level of generality (i.e., as generic terms performing generic computer functions (see instant spec. PreGrant-Pub [0065], and [0068-0069]) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed at an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims do not reflect improvement in technology. Further, mere automated instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, the claims are not patent eligible.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements identified above amount to no more than mere instructions to apply the exception using general purpose computer.
To support this factual conclusion, the examiner takes Official Notice that one of the ordinary skill in the art, before the effective filing date of the claimed invention, would have found processors and/or software well-known and routine in technology that involves computers (instant spec. PreGrant-Pub [0065], and [0068-0069] discloses that the functions of the disclosed claims can be implemented using generic computer(s)) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the examiner asserts that the above noted elements, when considered individually or in combination, do not constitute as “significantly more” than the abstract idea.
The dependent claims 3-7, 10-12, 14-17 and 19-21 of respective independent claims 1, 8, and 13 have been analyzed and fall into one of the statutory categories and therefore passes step 1 analysis. However, under step 2, 2A & 2B analysis, the dependent claims recite Mental process, Organizing human activity & Mathematical concepts which can be implemented by one or more human users using pen and paper. Thus, dependent claims also recite abstract ideas and are considered ineligible.
Subject Matter Free of Prior Art
Claim(s) 1, 3-8, 10-17, and 19-21 are allowable over prior art because the prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claims.
Because prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 8, and 13. Therefore, claims 1, 8, and 13 are hereby deemed to be allowable over prior art. Originally numbered dependent claims incorporate the allowable features of originally numbered independent claims through dependency, respectively.
However, the claims are still rejected under 101 reciting Abstract Idea.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED M AHSAN whose telephone number is (571)272-5018. The examiner can normally be reached 8:30 AM - 6:00 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, William Korzuch can be reached at 571-272-7589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED M AHSAN/Primary Examiner, Art Unit 2491
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