DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA and is in response to communications filed on 12/22/2025 in which claims 1-20 are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/22/2025 has been entered.
Priority
Acknowledgment is made of provisional applications 63/667,946 and 63/644,798, filed on 7/05/2024 and 5/09/2024 respectively.
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.
Claims 11-19 have been fully considered for 101 non-statutory subject matter, especially with respect to the claimed, “processor system”. The claims are considered as containing statutory subject matter because reading this term in context with paragraphs [0149]-[0150], this term is clearly defined as containing processors which execute the functions within the computer system.
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.
Claims 1-2, 5-14, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler et al. US 20250016128 A1 (hereinafter referred to as “Wheeler”) in view of Crabtree et al. US 20240195841 A1 (hereinafter referred to as “Crabtree”) in view of Chen et al. US 20050171941 A1 (hereinafter referred to as “Chen”) and further in view of Ivanova et al. US 20130144833 A1 (hereinafter referred to as “Ivanova”).
As per claim 1, Wheeler teaches:
A method for standardizing asset metadata and for facilitating access to the resulting standardized data via one or more application programming interfaces (APIs), said method being implemented by a service and comprising:
receiving non-standardized data (Wheeler, [0051] – Receive real-time alerts and notifications from their applications, regardless of their origin. [0068] – The Ingester module undertakes a series of meticulously orchestrated operations upon receiving data from various applications. These operations encompass data validation, normalization, and transformation, ensuring the integrity, consistency, and compatibility of incoming data with the platform's data model)
comprising data that includes:
…
(ii) first sensor data obtained from one or more sensors associated with the first asset (Wheeler, [0072] – Receiving sensor data from IoT devices) and
converting the first format of the first asset metadata, which is included in the non- standardized data, into a standardized format, resulting in generation of first standardized data, wherein the first standardized data is included in a first hierarchically organized data structure comprising a plurality of defined categories into which various portions of the first standardized data are categorized (Wheeler, [0085] – The module employs traditional data normalization techniques to standardize product descriptions, pricing information, and unit measurements. It converts product prices to a standardized currency format, such as USD, and ensures that all product dimensions are expressed consistently, whether in inches or centimeters);
converting the second format of the second asset metadata, which is also included in the non-standardized data, into the same standardized format, resulting in generation of second standardized data, wherein (Wheeler, [0086] – Additionally, deep learning models learn and adapt to pricing fluctuations, ensuring that product prices are converted accurately to the standardized currency format with a confidence level of 90%. [0112] – Deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can learn hierarchical representations of data, enabling the module to derive new attributes or calculated fields based on existing data. [0124] – Transaction categories);
the second standardized data is included in a second hierarchically organized data structure that also includes the same plurality of defined categories into which various portions of the second standardized data are also categorized (Wheeler, [0086] – Additionally, deep learning models learn and adapt to pricing fluctuations, ensuring that product prices are converted accurately to the standardized currency format with a confidence level of 90%. [0112] – Deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can learn hierarchical representations of data, enabling the module to derive new attributes or calculated fields based on existing data. [0124] – Transaction categories),
generating a first performance trend for the first asset using the first sensor data (Wheeler, [0058] – Organizations can gain insights into application usage patterns, performance metrics, and user interactions, allowing them to optimize their workflows and resource allocation strategies);
generating a second performance trend for the second asset using the second sensor data (Wheeler, [0149] – It identifies trends, anomalies, and optimization opportunities within the task workflows, enabling organizations to refine action card designs, streamline task processes, and improve user engagement);
generating a data model that includes the first standardized data, the second standardized data, the first performance trend, and the second performance trend (Wheeler, [0109]-[0112] - Supervised learning algorithms, such as decision trees and support vector machines, can be trained on labeled datasets to classify and standardize data formats, units of measurement, and encoding schemes. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can learn hierarchical representations of data, enabling the module to derive new attributes or calculated fields based on existing data. For example, RNNs can be trained to predict future trends in time-series data, aiding in demand forecasting and inventory management); and
providing access to the data model via one or more APIs (Wheeler, [0140] – The Action Card Creator module aggregates relevant data from disparate sources, including application databases, external APIs, and event triggers, to compile a comprehensive set of information for inclusion within the action card. It utilizes data retrieval mechanisms and integration protocols to fetch real-time data updates and dynamic content elements, ensuring the action card remains current and accurate).
Wheeler teaches sensor data as well as metadata, however, Wheeler isn’t clear with respect to first and second asset data including metadata from different domains and formats, however, Crabtree teaches:
(i) first asset metadata describing a first asset and second asset metadata describing a second asset, said first asset metadata being obtained from a first domain and having a first format and said second asset metadata being obtained from a second domain and having a second, different format (Crabtree, [0147] – Domain specific NLP processor 2718 may feed legal and domain-specific technical data into workflows for both knowledge graph enrichment and dataset contextualization, together with a local and global graph generator), and
…
second sensor data obtained from one or more sensors associated with the second asset (Crabtree, [0142] – Network analyzers 2506 are a contiguous web of sensors that continuously monitor and report on all transactions regarding data requests to the ledger engine);
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Wheeler’s invention in view of Crabtree in order to include first and second asset data with metadata from different domains and formats; this is advantageous because it allows all data to be embedded into knowledge graph enrichment and dataset contextualization for scoring and rating which ultimately allows the system to determine specific types of data in a precise manner (Crabtree, paragraph [0119]).
Wheeler as modified doesn’t teach standardization of every aspect of data, however, Ivanova teaches:
the same standardized format includes a same predefined number of levels and a same predefined number of the plurality of defined categories in each level for asset metadata using the same standardized format (Chen, [0045] – The invention applies predefined uniform field structures, formats, and coding categories so that common standardized categories and ratings may be available for consumers, manufacturers, professional use, and the like);
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Wheeler’s invention as modified in view of Chen in order to standardize all aspects of data; this is advantageous because it provides a user the option of viewing data from a single database in its original format or simultaneously viewing and comparing data from a plurality of databases (Chen, paragraph [0045]).
Wheeler as modified doesn’t explicitly teach predetermined hierarchical levels, however, Ivanova teaches:
wherein a first category included in a particular hierarchical position in the second hierarchically organized data structure represents a same category as a first category included in a corresponding hierarchical position in the first hierarchically organized data structure (Ivanova, [0029] – Levels, groups and specific data within hierarchical levels. [0038] – Multidimensional online analytical processing (OLAP) model available);
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Wheeler’s invention as modified in view of Ivanova in order to include a predetermined hierarchy; this is advantageous because hierarchies that are static can be implemented into a logical structure of data in the data warehouse (Ivanova, paragraph [0062]).
As per claim 2, Wheeler as modified teaches:
The method of claim 1, wherein the method further includes the service communicating with an Internet of Things (IoT) device that controls a condition associated with the first asset, and wherein controlling the condition associated with the first asset results in a modification to a performance of the first asset (Wheeler, [0072] – When receiving sensor data from IoT devices, the module validates the range of temperature readings to ensure they are within acceptable limits).
As per claim 5, Wheeler as modified teaches:
The method of claim 1, wherein the service includes a large language model (LLM) agent (Wheeler, [0106] – Particularly regarding the utilization of Large Language Models (LLMs) and Machine Learning (ML) techniques).
As per claim 6, Wheeler as modified teaches:
The method of claim 1, wherein the service includes a generative pre-trained transformer (GPT) (Wheeler, [0107] – LLMs, such as GPT (Generative Pre-trained Transformer) models).
As per claim 7, Wheeler as modified teaches:
The method of claim 1, wherein the first asset is an industrial machine included in a factory environment (Wheeler, [0088] – Manufacturer is interpreted as a factory environment. [0316]).
As per claim 8, Wheeler as modified teaches:
The method of claim 1, wherein the one or more APIs includes an anomaly detection API (Crabtree, [0132]-[0133] – API system, [0126] – The trust map may then be analyzed to identify anomalies, for example using community detection algorithms that may discover when new references are being created, and this may be used to identify vulnerabilities that may arise as a byproduct of the referential nature of a DNS hierarchy. See also [0150]-[0151]).
As per claim 9, Wheeler as modified teaches:
The method of claim 1, wherein the one or more APIs includes a forecasting API (Wheeler, [0112] – RNNs can be trained to predict future trends in time-series data, aiding in demand forecasting and inventory management).
As per claim 10, Wheeler as modified teaches:
The method of claim 1, wherein the one or more APIs includes an optimization API (Wheeler, [0176] – These rules can be configured using a graphical user interface (GUI) or through application programming interfaces (APIs), enabling organizations to create custom routing logic tailored to their communication workflows and business requirements. [0177] – Organizations can define rules to prioritize certain notification platforms over others, ensure compliance with regulatory requirements, or optimize delivery based on user engagement metrics and historical interaction patterns).
Claim 11 is directed to a computer system performing steps recited in claim 1 with substantially the same limitations. Therefore, the rejection made to claim 1 is applied to claim 11.
As per claim 12, Wheeler as modified teaches:
The computer system of claim 11, wherein the data model is structured to include a selectable user interface (UI) element, wherein the selectable UI element is associated with a first portion of the first standardized data, wherein the selectable UI element, when selected, displays a second portion of the non-standardized data, and wherein said first portion of the first standardized data is related to the second portion of the non-standardized data (Wheeler, [0152] – Each application may possess its own distinct user interface, navigation schema, and lexicon, further confounding user experience. [0154] – AI-driven design recommendations to enhance visual appeal and user engagement. By analyzing user interaction data and design preferences, the module offers personalized template suggestions that align with the organization's branding guidelines and user interface standards. Additionally, AI algorithms dynamically adjust template layouts based on recipient feedback and performance metrics, ensuring continuous optimization for maximum effectiveness).
As per claim 13, Wheeler as modified teaches:
The computer system of claim 11, wherein the data model is supplemented with additional standardized data originating from other assets (Wheeler, [0161] – The module supports data integration from external sources, application databases, and third-party APIs, ensuring that the action card content remains current and relevant to recipients).
As per claim 14, Wheeler as modified teaches:
The computer system of claim 11, wherein the one or more APIs includes an anomaly API that is used by a large language model (LLM) agent in identifying a cause for a detected anomaly associated with the first asset, and wherein the LLM agent, via the anomaly API, identifies the detected anomaly based, at least in part, on the first performance trend (Crabtree, [0132]-[0133] – API system, [0126] – The trust map may then be analyzed to identify anomalies, for example using community detection algorithms that may discover when new references are being created, and this may be used to identify vulnerabilities that may arise as a byproduct of the referential nature of a DNS hierarchy. [0139] – Machine learning models 1901 may be used to identify patterns and trends in any aspect of the system, but in this case are being used to identify patterns and trends in the data which would help the data to rule mapper 1904 determine whether and to what extent certain data indicate a violation of certain rules. See also [0150]-[0151]).
As per claim 17, Wheeler as modified teaches:
The computer system of claim 11, wherein the one or more APIs includes an optimization API that is used by a large language model (LLM) agent in facilitating modification of a performance of the first asset, where the modification of the performance is based on a determination that said modification will result in a prolonging of a lifespan of the first asset (Wheeler, [0091] – Performs data transformation operations to enrich the transaction data with additional customer demographic information obtained from external data sources. It aggregates transaction data to derive summary statistics such as total sales revenue, average order value, and customer lifetime value. Additionally, it derives new customer segmentation attributes based on transaction frequency, recency, and monetary value to support targeted marketing and personalized customer experiences, wherein the lifetime of the customer is interpreted as the lifespan of the data asset and modifying the data is an attempt to prolong the customer lifespan. See also [0189] – By leveraging customer data and transaction history stored in the loyalty program database, the module facilitates targeted communication and engagement with loyal customers, driving retention and lifetime value).
Claim 20 is directed to hardware storage devices performing steps recited in one of claims 8-10 with substantially the same limitations. Therefore, the rejection made to one of claims 8-10 is applied to claim 20.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler in view of Crabtree in view of Chen in view of Ivanova and further in view of Theriappan et al. US 20230267273 A1 (hereinafter referred to as “Theriappan”).
As per claim 3, Wheeler as modified doesn’t explicitly teach OCR, however, Theriappan teaches:
The method of claim 1, wherein the first domain is one of a user manual domain, a procedure manual domain, or a parts inventory manual domain, and wherein the method further includes executing optical character recognition (OCR) on the first asset metadata (Theriappan, [0028] – Examples of the enterprise documents may include, but are not limited to, relatively large technical manuals, legal documents, product descriptions, invoices or the like. More specific examples might include engineering manuals that specify repair procedures (e.g., in the vehicle context, procedures for repairing, servicing, or refurbishing parts or assembled systems from a vehicle)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wheeler’s invention as modified in view of Theriappan in order to include OCR on asset metadata; this is advantageous because it allows the system to identify how to repair assets using the respective manuals of the assets (Theriappan, paragraph [0028]).
As per claim 4, Wheeler as modified with Theriappan teaches:
The method of claim 1, wherein converting the first format of the first asset metadata to the standardized format includes:
executing optical character recognition (OCR) on the first asset metadata, resulting in generation of a set of tokens for the first asset metadata (Theriappan, [0034] – The server system 106 is configured to identify one or more tokens and candidate entities in the enterprise document based at least on the trained machine learning (ML) model that uses the document features);
causing a large language model (LLM) agent to classify at least some of the set of tokens into a least some of the categories included in the plurality of defined categories, such that the LLM agent generates classified tokens (Crabtree, [0145] – Extraction processor 2701 performs a set of systematic natural language processing (NLP)-based data extraction single-purpose generic micro-functions including Tokenizer 2708, Acronym Normalizer 2709, Lemmatizer 2710, Name Entity Recognizer (NER) 2711, pattern recognizer 2713, and a rules processor 2713. Tokenizer 2708, given a character sequence and a defined document unit, tokenizes the character sequence up into pieces, called tokens, and optionally discards certain characters such as punctuation);
generating a plurality of different groups of classified tokens by grouping together specific classified tokens that are identified as belonging to a common category (Crabtree, [0162] – The search tasks 2010 that may be generated may be classified into several categories. While this category list is by no means exhaustive, several important categories of reconnaissance data are domain and internet protocol (IP) address searching tasks); and
inserting the plurality of different groups of classified tokens into the first hierarchically organized data structure, wherein said inserting includes organizing the plurality of different groups of classified tokens according to their respective categories (Crabtree, [0162] – The search tasks 2010 that may be generated may be classified into several categories. While this category list is by no means exhaustive, several important categories of reconnaissance data are domain and internet protocol (IP) address searching tasks).
Claims 15-16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wheeler in view of Crabtree in view of Chen in view of Ivanova and further in view of Chawda et al. US 20230326212 A1 (hereinafter referred to as “Chawda”).
As per claim 15, Wheeler as modified doesn’t explicitly teach parts for replacement, however, Chawda teaches:
The computer system of claim 11, wherein the one or more APIs includes a forecasting API that is used by a large language model (LLM) agent in forecasting when a part for the first asset is due for replacement (Chawda, [0111] – Recognize that a number of machinery (e.g., tanks) are due for maintenance within a window of time and schedule servicing based on parts/tools availability, technician schedules, space within the warehouse, etc.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wheeler’s invention as modified in view of Chawda in order to track parts of a machine; this is advantageous because it allows the system to suggest when a part should be repaired or replaced (Chawda, paragraph [0095]).
As per claim 16, Wheeler as modified teaches:
The computer system of claim 11, wherein the one or more APIs includes a forecasting API that is used by a large language model (LLM) agent in identifying an alternative replacement part for the first asset, where the alternative replacement part is an alternative for an original equipment manufacturer (OEM) part for the first asset (Chawda, [0081] – Those that include complicated componentry that may deviate from original equipment manufacturer (OEM) builds. [0095] – Recommend that a component be replaced or repaired and accordingly determine a cost effective and efficient option by considering various factors, such as inventory, status, cost/time of repair, cost/time of replacement, etc.).
As per claim 18, Wheeler as modified teaches:
The computer system of claim 17, wherein said modification is tested using a digital twin for the first asset (Chawda, [0097] – Asset Lookup Agent 1014 may identify physical assets through fine-tuning on training data, as shown by 1026, that serves as a digital twin of a warehouse’s inventory in the system’s asset and training databases, as shown by Asset Database 1020 and Training Database 1024).
As per claim 19, Wheeler as modified teaches:
The computer system of claim 17, wherein said modification is a deviation from a recommended operational state of the first asset (Chawda, [0099] – The reference materials, such as documents, may be related to the asset in question both directly (e.g. when the documents pertain directly to the asset) as well as indirectly (e.g. when an asset’s componentry deviates from OEM builds, due to the agent’s neural network being auto-regressive)).
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are generally moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Subramanian et al. US 12259888 B2 teaches searching data exchanges and assets with non-homogenous functionality and non-standardized data descriptions using credentials corresponding to users (Abstract).
Sehon et al. US 20240061866 A1 teaches a data asset generator based on a knowledge-graph-based recommendation engine to present data asset type options to a user generating a data asset (Abstract).
Sodhani et al. US 10902322 B2 teaches a standardized data model (“SDM”) includes standardized data types that indicate classifications of data elements (Abstract).
Demertzi, V.; Demertzis, S.; Demertzis, K. An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities. Appl. Sci. 2023, 13, 790. https://doi.org/10.3390/app13020790
Malak et al. US 10885056 B2 teaches a first technique, which is that standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. Then, a second technique, which is that a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data (Abstract).
Belyy et al. US 20070294677 A1 teaches a technique to parse a COBOL copybook based on the description of the COBOL copybook, and to create a standardized data record schema based on the COBOL copybook. The description of the COBOL copybook includes information about the format of the COBOL copybook (Abstract).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J. Ellis whose telephone number is (571)270-3443. The examiner can normally be reached on Monday-Friday 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached at (571)270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
January 26, 2026
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2152