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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/28/2025 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a collection system configured to”, such as recited within claims 1-4 and 5, and “processing systems is configured to” in claims 1-4 and 5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112, sixth paragraph limitation: Fig 2 and paragraphs 0028-0030.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, 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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over WHEELER (US 20250016128 A1), hereafter WHEELER in view of LINDE (US 20220147645 A1), hereafter LINDE.
WHEELER provides, regarding claim 1: A computer system, comprising: a collection system configured to collect business data (WHEELER [AB] “Key components of the system include an Ingester Module that standardizes data from various sources”, [0053] “Ingester Module: Responsible for ingesting data from various applications and standardizing it for further processing. This can be from user or business entity.”) including values of a plurality of items from a plurality of business systems (WHEELER [0064] “The Ingester module stands as a cornerstone within the software platform, orchestrating the reception of data emanating from an array of sources vital to organizational operations. These sources encompass a spectrum ranging from enterprise applications, databases, web services, to IoT devices, encompassing a broad swath of data types and formats.”); and a data processing system configured to process the business data (WHEELER [AB] “Key components of the system include an Ingester Module that standardizes data from various sources, an Action Card Creator that transforms this data into interactive notifications,”), the collection system being coupled to an Al processing system configured to execute, through use of encrypted business data(WHEELER [0125] “In this version, instead of relying solely on rule-based parsing algorithms, the Action Card Creator module could employ deep learning models trained on a diverse dataset of transaction records. These models can learn to recognize patterns in transaction data and extract essential details such as transaction amounts, types, and timestamps with greater accuracy and efficiency.”, [0537] “Security within the Input Queue is paramount, with mechanisms in place to ensure that action cards are encrypted while in transit to the Routing Logic.”), at least one of training processing of generating a machine learning model (WHEELER [0487] “From a technical vantage point, “Ingester Input” (64) may be fortified with AI/ML capabilities to enhance its functionality. AI algorithms can be deployed to perform preliminary assessments of the data,” [0488] “Additionally, Machine Learning models within “Ingester Input” (64) can be trained to detect anomalies in data patterns that might indicate a deviation from the norm”) or inference processing of executing inference through use of the machine learning model, the data processing system being configured to: obtain the business data from one of the plurality of business systems(WHEELER [0053-0054] “Ingester Module: Responsible for ingesting data from various applications and standardizing it for further processing. This can be from user or business entity. // Action Card Creator: Converts ingested data into actionable cards or messages that can be sent to notification platforms.”); encrypt a value of any one of the plurality of items of the business data through use of an encryption algorithm to generate the encrypted business data (WHEELER [0537] “Security within the Input Queue is paramount, with mechanisms in place to ensure that action cards are encrypted while in transit to the Routing Logic. The queue itself is secured against unauthorized access, with strict access controls and authentication requirements for any entity that interacts with it.”); and transmit the encrypted business data to the collection system (WHEELER [0487] “From a technical vantage point, “Ingester Input” (64) may be fortified with AI/ML capabilities to enhance its functionality. AI algorithms can be deployed to perform preliminary assessments of the data,” [0488] “Additionally, Machine Learning models within “Ingester Input” (64) can be trained to detect anomalies in data patterns that might indicate a deviation from the norm”), and wherein the collection system is configured to transmit the encrypted business data to the Al processing system (WHEELER [0125] “In this version, instead of relying solely on rule-based parsing algorithms, the Action Card Creator module could employ deep learning models trained on a diverse dataset of transaction records. These models can learn to recognize patterns in transaction data and extract essential details such as transaction amounts, types, and timestamps with greater accuracy and efficiency.”, [0537] “Security within the Input Queue is paramount, with mechanisms in place to ensure that action cards are encrypted while in transit to the Routing Logic. The queue itself is secured against unauthorized access, with strict access controls and authentication requirements for any entity that interacts with it.” In the example that the encrypted action card information is assessed by the AI/ML of the Ingester, WHEELER provides for transmitting the encrypted business data to the collection system, and wherein the collection system is configured to transmit the encrypted business data to the AI processing system.
Further regarding claim 1, WHEELER teaches the limitations previously demonstrated, however does not appear to explicitly teach the following limitations demonstrated by LINDE:
plurality of data processing systems (LINDE [0024] “As shown in FIG. 1, system 100 also comprises user equipment (UE) 101a-101n (collectively referred to as UE 101) that may include or be associated with applications 103a-103n (collectively referred to as applications 103)… In one embodiment, the UE 101 has connectivity to a private data discovery platform 109 via a communication network 107, e.g., a wireless communication network; this can be considered an enterprise network (e.g., within a single network domain or administration). In one embodiment, the private data discovery platform 109 performs one or more functions associated with generating an adjustable ruleset for fingerprinting data by applying artificial intelligence (AI) models.”, [0029] “In addition, it is noted that the private data discovery platform 109 may be… a part of the one or more services… or the UE 101.” See FIG. 1, demonstrating a plurality of data processing systems 105 a-n, and FIG. 2, a detailed view of private data discovery platform demonstrating data obtainment, data encryption (transformation function within transformation module [0031]), and data transmission to communication network 107.):
an irreversible encryption algorithm (LINDE [0031] “The transformation function can include a hash function, encryption, or other obfuscation.”)
Since WHEELER and LINDE are from the same field of endeavor as both are AI/ML training based on disparate sources, which is within the same field of endeavor as the claimed invention, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify and combine the teachings of WHEELER and LINDE by incorporating the teachings of LINDE into WHEELER. The motivation to combine is to improve the security of training data applied to AI/ML instances. (WHEELER [AB]; LINDE [AB]). This motivation is equally applicable for rejections hereafter.
WHEELER-LINDE provides, regarding claim 2: The computer system according to claim 1, wherein the each of the plurality of data processing systems is configured to encrypt the value of the any one of the plurality of items of the business data through use of a hash function, and wherein the hash functions used by the plurality of data processing systems are the same (LINDE [0031] “The transformation function can include a hash function, encryption, or other obfuscation.”, [0040-0041] “In one embodiment, the data processing module 203 automatically processes the data collected by the data collection module 201 to generate, via a hashing scheme, hash values corresponding to one or more attributes of the collected data. // In another embodiment, the data transformation module 205 may convert the processed data into tokens (e.g., hash values) based on the configured rules.”).
WHEELER-LINDE provides, regarding claim 3: The computer system according to claim 2, wherein the each of the plurality of data processing systems is configured to: execute formatting processing of executing processing of converting a name of at least one of the plurality of items to a name of an item common among the plurality of business systems (WHEELER [0086] “In another embodiment, AI/ML algorithms are utilized to enhance the data normalization process. Machine learning models analyze product descriptions to identify common patterns and semantic similarities, allowing for more accurate normalization across a wide range of product variations. Natural Language Processing (NLP) algorithms assist in standardizing product names and descriptions by identifying and correcting spelling or grammatical errors with a confidence level of 95%.”), and processing of converting a value of at least one of the plurality of items to a value in a unit common among the plurality of business systems(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%.”); and encrypt the value of the any one of the plurality of items of the business data on which the formatting processing has been executed (WHEELER [0537] “Security within the Input Queue is paramount, with mechanisms in place to ensure that action cards are encrypted while in transit to the Routing Logic. The queue itself is secured against unauthorized access,” The information within the input queue is encrypted to ensure that further objects, such as the action card, are preemptively encrypted prior to subsequent processing.).
WHEELER-LINDE provides, regarding claim 4: The computer system according to claim 3, wherein the each of the plurality of data processing systems is configured to obtain setting information (WHEELER [0069] “Upon receipt of data from an application, the Ingester module initiates the data validation process to ascertain the accuracy, completeness, and adherence to predefined data schemas.”, [0122] “The Action Card Creator module parses the standardized data streams to identify and isolate relevant data fields and elements. It in one embodiment utilizes parsing algorithms and techniques tailored to the data format and schema, ensuring accurate extraction of essential content elements.” The data schema definition is mapped to the setting information, as WHEELER demonstrates different definitions depending on the desired context. See [0070-0072] where WHEELER teaches definitions dependent on different implementations such as customer information within a CRM system ([0070]) and sensor readings within a system of IoT devices ([0072]) that are obtained by the Action Card Creator.) relating to the name of the at least one of the plurality of items being a target of the formatting processing (WHEELER [0070] “Field Validation: The Ingester module verifies the presence and correctness of mandatory fields within the incoming data, ensuring that essential data elements are not missing or erroneous. For instance, when receiving customer information from a CRM system, the module validates the presence of required fields such as name, email address, and contact number.”) and the value of the at least one of the plurality of items being the target of the formatting processing (WHEELER [0071] “Format Validation: Data format validation ensures that incoming data adheres to predefined format standards, such as date and time formats, numerical formats, and string formats. The module validates the format of each data field against specified format rules to ensure consistency and interoperability.”), and transmit the setting information to the collection system, wherein the collection system is configured to: determine, based on the setting information, a format rule for the name of the at least one of the plurality of items being the target of the formatting processing and a format rule for the value of the at least one of the plurality of items being the target of the formatting processing(WHEELER [0083] “Machine learning algorithms can be trained to recognize patterns in data formats, units of measurement, and encoding schemes. For instance, deep learning models can learn to identify date and time formats, currency formats, and common units of measurement across different datasets. Natural language processing (NLP) algorithms can analyze textual data to detect linguistic patterns and standardize text encoding.”, [0078] “Following data validation, the Ingester module undertakes the task of data normalization, which involves standardizing data formats, units of measurement, and encoding schemes to ensure uniformity and consistency across diverse datasets.”), to thereby generate format rule information(WHEELER [0079] “Format Standardization: The module standardizes data formats to adhere to predefined conventions, such as ISO date and time formats, currency formats, and units of measurement.”); and transmit the format rule information to the each of the plurality of data processing systems, and wherein the each of the plurality of data processing systems is configured to execute the formatting processing based on the format rule information (WHEELER [0122] “Data Parsing: The Action Card Creator module parses the standardized data streams to identify and isolate relevant data fields and elements. It in one embodiment utilizes parsing algorithms and techniques tailored to the data format and schema, ensuring accurate extraction of essential content elements.”).
Regarding claims 5-8, claims 5-8 recite substantially similar limitations as claims 1-4, but for recitation in the form of a method, such as taught by WHEELER-LINDE: (WHEELER [0002] “The field of the disclosed invention relates to software systems and methods for facilitating the interconnection of users with one or more applications.”). Thus, claims 5-8 are rejected for similar reasoning as claims 1-4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
US 20190392001 A1; Carothers; Christopher D. discloses a computer-implemented method is disclosed. The method may include providing, in a computer-readable storage device, a dataspace. The dataspace may include a plurality of logical segments, and dataspace data stored in the plurality of logical segments. The method may include receiving, from a data source external to the dataspace, a data artifact. The data artifact may include a plurality of data elements. The method may include processing, at a plurality of data filters, the plurality of data elements. A data filter of the plurality of data filters may process a data element of the plurality of data elements, based on a structure of the plurality of the data elements, or a configuration of the data filter. The method may include generating, based on the processed plurality of data elements, processed data. The method may include sending the processed data to the dataspace. [AB]
US 20200342290 A1; Carothers; Christopher D. discloses a computer-implemented method may include generating an optimistically executable echo state network (ESN), including a reservoir, input layer, and output layer, each with its own group of neurons and synapses; training the optimistically executable ESN, including updating a weight of each output synapse; and performing inference calculation via the optimistically executable ESN. During the inference calculation, at least one neuron may determine, based on a sequence of synapses messages received at the neuron, whether the neuron processed the sequence of synapse messages out of order. If the neuron processed the sequence of synapse messages out of order, the neuron may reverse-compute at least one computation performed by the neuron and perform the at least one computation based on receipt of the sequence of synapse messages in a correct order as determined by the timestamp of each synapse message in the sequence of synapse messages. [AB]
US 20250030704 A1; KIM; Ki Hong discloses a cyber threat information processing method including receiving a CTI analysis request for assembly code from a client; analyzing the assembly code to obtain analysis information of the CTI for the assembly code; generating a CTI query related to a file based on the analyzed CTI and delivering the CTI query to a natural language model; and providing natural language description information according to the CTI query obtained from the CTI for the assembly code and the natural language model as visualization information based on a web service. [AB]
US 20250181607 A1; Lisic; Jonathan J. discloses a method, system and computer-readable medium including receiving input data that is organized into a set of rows and a set of columns, maintaining a machine learning header model that is trained on tabular data with header rows, supplying the input data to generate header row identification data that identifies a set of header rows that is a subset of the set of rows, to generate column label data that applies a set of defined labels to the set of columns, predicting one of the set of defined labels for the column, determining a column confidence score, generating a notification seeking user feedback when the column confidence score is below a threshold for the column, applying user feedback to predict the one of the set of defined labels for the column, and generating output data that is organized into rows and columns. [AB]
US 20260148171 A1; Deming; Richard discloses a computing platform performs automated due diligence by acquiring project scope information and documents, standardizing all data across all document types into a uniform format optimized for analysis and subjecting the data to repeated querying by large language model agents to triangulate it against a body of human content expertise coded into the platform. The platform compares variables with a project proforma, verifies permits by parsing permit data and cross checking a permitting authority portal, evaluates design completeness by detecting standards of readiness status, and measures the bankability of project agreements compared to industry standards. The platform computes environmental, social, and governance metrics, assesses counterparty risk and community sentiment using searches of public sources, and validates a reported capital stack by analyzing documents and applying industry standards thereto. The platform calculates risk metrics, applies weights to produce two scores, and generates a report that includes remediation steps. [AB]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kamryn Gillespie whose telephone number is 703-756-5498. The examiner can normally be reached on Monday through Thursday from 9am to 6pm.
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/K.J.G./Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408
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