DETAILED ACTION
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 .
Status of Claims
This action in repose to the amendment filed 26 March 2026. Claims 1, 12, 15-17 and 20 have been amended. Claims 1-20 are currently pending and have been examined.
Claim Objections
Claims 12-14 objected to because of the following informalities: The preamble of claim 12 recites a system, however, the claim 12 is depending on claim 1, which a method claim . Appropriate correction is required.
Claims 15-17 objected to because of the following informalities: The preamble of claim 15 recites a machine-learning model, however, the claim 15 is depending on claim 1, which a method claim. Appropriate correction is required.
Claim 18 objected to because of the following informalities: The preamble of claim 18 recites a training data set , however, the claim 15 is depending on claim 15, which a machine learing model claim. Appropriate correction is required.
Claims 19 objected to because of the following informalities: The preamble of claim 19 recites a use of method, however, the claim 19 is depending on claim 1, which a method claim. Appropriate correction is required.
Claims 20 objected to because of the following informalities: The preamble of claim 1 recites a computer program, however, the claim 20 is depending on claim 1, which a method claim. Appropriate correction is required.
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.
Step 1: The claims 1-11 and 16-19 are a method, claim 12-15 are a system and claim 20 is a computer program. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the 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 1: claim 1 recites obtaining one or more data sets; processing the data within the one or more data sets to extract and normalize content into normalized attributes; generating vectorized embeddings of the normalized attributes; producing an output data set comprising one or more candidate outputs for interaction with the recipient; generating vectorized embeddings of the one or more candidate outputs; comparing the vectorized embeddings of the normalized attributes and the vectorized embeddings of the one or more candidate outputs; and selecting zero or more candidate outputs from the output data set based on a predefined selection criterium applied to the comparison results. These limitation as drafted is a process, under its broadest reasonable interpretation, covers gathering, processing and comparing information to make a selection in the human mind, which fails in metal process.
The generation of vectorized embeddings” and the comparing limitation as drafted is a process, under its broadest reasonable intepration covers performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within the mathematical concepts grouping and the mental process grouping. Simply put, these limitation merely describe analyze and compare embedding to select optimal output based on predefined criteria, which is clearly a mental concept. Claims 2-20, merely provide additional abstract concepts and narrow the abstract idea of claim 1. Further, claims 1-20, are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content and authorize compensation.
Step 2A- Prong 2: The only additional elements in independent claim 1 is obtaining data, the obtaining step is recited at high level of generality (i.e., as a general means of gathering data for ser comparing step) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Further, dependent claims recite additional limitation of AI model recited at a high-level of generality (i.e., as a generic computer components for performing generic computer function of processing data and a generic memory storing data) such that it amounts no more adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the AI model). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer
component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
TLI Communications provides an example of a claim invoking computers and other
machinery merely as a tool to perform an existing process. The court stated that the claims
describe steps of recording, administration and archiving of digital images, and found them to be
directed to the abstract idea of classifying and storing digital images in an organized manner. 823
F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of
performing these functions using a telephone unit and a server and noted that these elements
were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and
operate as a digital camera including compressing images and transmitting those images, and the
server simply receives data, extracts classification information from the received data, and stores
the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at
1747-48.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walters et al (US Patent No., 11,113,124 B2) in view of Bhojwani et al (US Pub., No., 2022/0050854 A1) and futher view of Fortkort et al (US Pub., 2024/0395388 A1)
With respect to claims 1 and 20 , Walters teaches a method for selecting data related to a recipient(Col. 2, lines 7-9, discloses provide unconventional method and system for searching dataset and classifying dataset (e.g., determining whether dataset are related)) , the method comprising:
obtaining one or more data sets(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106, remote database 108 or client device 110);
producing an output data set comprising one or more candidate outputs for interaction with the recipient(Col. 2 lines 35-37, and lines 58-63, discloses generating a similarity metric based on the reference data model output and the test data module output .., transmitting to the client device information comprising the classification of the test dataset [for interaction] discloses generating a test data model output using a data model.., processing the test data model output, and Col. 6, lines 20-32, dislcies generating data-model output based on the input dataset .., a machine learing model trained to generate synthetic data ..));
generating vectorized embedding of the one or more candidate outputs(Fig. 7, discloses received data-model output, perform an encoding method, perform a factorizing method and perform a vectorizing method, Col. 12, lines 26-49, discloses analyzer may be configured to perform a vectorizing method, consistent with disclosed embodiments. In some embodiments, a vectorizing method may include transforming two-dimensional data (e.g., rows and columns) into one-dimensional data ..., for example, vectorizing may include transforming row and column data into a vector by appending res .., searchable dat index based on data set (e.g., a b-tree) ….).
Walters teaches the above elements including processing the data within the one or more data sets and normalize content into normalized attributes(Col. 15, lines 13-19, and Col. 16, lines 28-30 discloses methods of normalizing or filtering data); generating vectorized embeddings of the normalized attributes(Col. 15, lines 15-18, discloses vectorizing method, Col. 18, lines 33-34, discloses an encoding method of step and include encoding [embedding] output of the vectorizing method of step), using at least one machine learing model comprising at least one of the following: a large language model, generative AI model, a machine learning model, transformer model, a diffusion model, or deep neural network (Col. 9, lines 1-10, discloses machine-learning models my include a neural network model, a generative adversarial model (GAN) recurrent neural model (RNN) model, a deep learing model, etc.,) and selecting a candidate data model candidate outputs from the output data set based on a predefined selection criterium applied to the comparison results(Col. 13, lines 4-7) and, data-modeling system may select one or more candidate data models.. (Col. 14, lines 12-20)). Walters failed to teach the normalized data is extracted, transforming normalized data into numerical representation for process by machine learing ; handling missing data by generating embedding for the missing data by reconstructing approximating or estimating it form existing data comparing the vectorized embedding of the normalized attributes and the vectorized embeddings of the one or more candidate outputs; and selecting zero or more candidate outputs from the output data set based on a predefined selection criterium applied to the comparison results.
However, Bhojwani teaches extracting and normalize content (Fig. 5, 530 discloses extracting a value form the unstructured description corresponding to the predefined attribute and normalizing value, and paragraph [0021], discloses the ML software where it can be extracted, cleaned, transformed validated and normalized.., paragraph [0023], discloses the ML software is designed and name applicable to wide range of attribute value extraction, attribute value-to-name linking, and attribute value and name normalization task is a heterogenous product catalog and paragraph [0051], dislcies extracting a value to generate a normalized attribute value ), compare vectorized embedding of the normalized attributes and the vectorized embeddings of the one or more candidate outputs (paragraphs [0017] and [0033], discloses identifying attribute values of product from a product description, paragraph [0032], discloses identify raw values from the unstructured product decision which have values that are not normalized and paragraph [0038], discloses identify high-quality attribute that are assocted with each commodity or product ) generating vectorized embeddings (paragraph [0022], discloses the ML software may utilize a world-level embedding representation the encode both syntactic and semitic testing information and paragraph [0055], dislcies the output 630 may output data to an embedded display ); and selecting zero or more output (paragraph [0051], discloses selecting a predefine attribute from amount the predefined attributes). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for vectorizing and normalizing method of Walters with a featuring of extracting a value from the unstructured description of Bhojwani in order to modifying the extracted value to generate a normalized attribute (see Bhojwani, abstract).
The combination of Walters and Bhojwani teaches the above elements, Walters futher teaches a non-negative matrix factorization method 40 (NMF) to transform a matrix into component vectors (Col. 12, lines 18-19 and Col.18, lines 39-40), and transforming two-dimensional data (e.g., rows and column) int one-dimensional data. For example, vectorizing may include transforming row and column into a vector by appending rows (Col 18, lines 55-59) and Bhojwani teaches the catalog data may be transferred to the ML software 114 where it can be extracted, cleaned, transformed, validated, and normalized (paragraph [0021]) , Walters and Bhojwani failed to teach transforming normalized data into numerical representation for process by machine learing ; and handling missing data by generating embedding for the missing data by reconstructing approximating or estimating it form existing data.
However, Fortkort teaches transforming normalized data into numerical representation for process by machine learing (paragraph [0102], dislcies normalizing number data or transforming categorical valuable into a suitable numeral form, paragraph [0108], dislcies data includes categorial variable, these may also need to be converted to a suitable numerical form, and paragraph [0167], dislcies data might be converted into a suitable vectored space representation) ; and
handling missing data by generating embedding for the missing data by reconstructing approximating or estimating it form existing data(paragraph [0114], discloses encoding categorical variables and handing missing data, paragraph [0142], discloses handling missing value, and encoding categorical variable, and paragraph [0154], discloses handling missing vales) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for , vectorizing may include transforming row and column into a vector by appending rows of Walters and transformed, validated, and normalized of Bhojwani with a features of transforming categorical variable into suitable numerical form and handing the missing data of Fortkort in order to reducing the amount of parameters and computation (see Fortkort, paragraph [0147]) and in order to deal with missing data, and are resistant to overfitting, making them a strong choice for systems and methodologies of the type
disclosed herein that involve complex, real-world data (see Fortkort, paragraph [0106]).
With respect to claim 2, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the method comprises repeating the steps iteratively to account for changes in the one or more data sets(Col. 16, lines 62-66, discloses steps 610 through 610 may be repeated any number of times).
With respect to claim 3, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the method further comprises enriching the data of the one or more data sets using external data sources or internal data sources(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106,[external] remote database 108 or client device 110).
With respect to claim 4, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 3, furthermore, Walters teaches the method wherein enriching the one or more data sets includes at least one of: search engine search, social media search, web scraping, or database querying(Col. 15, lines 32-35, discloses the dataset index may be a searchable data index).
With respect to claim 5, Walters in view of Bhojwani teaches elements of claim 1, furthermore, Walters teaches the method wherein the one or more data sets comprise customer data comprising at least one of:
gender, age, address, purchase history, communication history, credit rating, relationship status, or income level(Col. 3, lines 42-46, discloses data set may include transaction data, financial data, demographic data ).
With respect to claim 6, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the one or more data sets comprise customer interaction and performance metrics comprising at least one of: purchase data, click-through data, satisfaction metrics, lifetime value, or conversion data(Col. 3, lines 42-46, discloses data set may include transaction data, financial data, demographic data ).
With respect to claim 7, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method comprises assigning weights to data within the one or more data sets based on the time at which the data was generated or occurred(Col. 14, lines 15-16, discloses model parameter may include weights, coefficients, offsets or the like).
With respect to claim 8, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the output data set is generated using an AI model comprising at least one of the following: a large language model, a generative AI model, a machine learning model, a transformer model, a diffusion model, or a deep neural network(Col. 5, lines 5-23, discloses data models (e.g., machine-learning models or statical models), and Col. 6, lines 33-35, discloses model may include a plurality of neural networks nodes) .
With respect to claim 9, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 8, furthermore, Walters teaches the method wherein the AI model is configured to use at least one data set of the one or more data sets to generate the output data set(Fig. 6, 606, discloses generate test data-model output based on the test dataset).
With respect to claim 10, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the method further comprises one or more of the following:
using the desired output data for communication with the recipient in at least one of: email, text message, or web page(Col. 8, lines 15-20, discloses receive data including sensitive data from another system (e.g., a file a message in a publication and subscription framework..));
using in the normalization of unstructured data at least one AI model comprising at least one of the following: a large language model, a generative AT model, a machine learning model, a transformer model, a diffusion model, or a deep neural network; handling missing data by generating embeddings for the missing data using at least one AT model comprising at least one of the following: a large language model, a generative AT model, a machine learning model, a transformer model, a diffusion model, or a deep neural network(Col. 5, lines 5-23, discloses data models (e.g., machine-learning models or statical models), Col. 6, lines 33-35, discloses model may include a plurality of neural networks nodes and Col. 15, lines 15-19, discloses method so normalizing or filtering data); receiving one or more outcome data sets and conditioning an AI model comprising at least one of the following: a large language model, a generative AI model, a machine learning model, a transformer model, a diffusion model, or a deep neural network using at least one of the outcome data sets and at least one of the one or more data sets(Col. 5, lines 5-23, discloses data models (e.g., machine-learning models or statical models), Col. 6, lines 33-35, discloses model may include a plurality of neural networks nodes and Col. 15, lines 63-67, dislcies dat-modeling system 102 may receive a test dataset ..);
tracking information associated with the recipient and incorporating the tracked information into the one or more data sets; using at least one of the one or more data sets for conditioning an AI model comprising at least one of the following: a large language model, a generative AI model, a machine learning model, a transformer model, a diffusion model, or a deep neural network; anonymizing or pseudonymizing recipient-specific data in the one or more data sets; obtaining feedback from the recipient and incorporating the feedback into the one or more data sets; performing at least one of the normalization steps or the embedding step using one or more of batch processing, asynchronous processing, or parallel processing to efficiently handle large data sets; segmenting recipients based on the one or more data sets into groups of recipients with similar characteristics; updating the AI model by training it incrementally using newly obtained data without performing complete retraining; forming clusters of similar embeddings to optimize the comparison and selection of the desired output data(Col. 17, lines 5-12, discloses one or more dataset cluster) ; storing embeddings of frequently accessed data to reduce computational overhead during repeated comparisons; dynamically updating the selected desired output data during ongoing interactions with the recipient to provide real-time recommendations or responses(Col. 17, lines 38-47, discloses updating the dataset index may include storing test-data model output in the dataset index).
With respect to claim 11, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the method wherein the one or more data sets comprise at least two types of data selected from text, images, video, or audio (Col. 3, lines 43-44, discloses dataset may involve numeric data, text data, and/or image data).
With respect to claim 12, Walters teaches a system for selecting data related to a recipient(Col. 2, lines 7-9, discloses provide unconventional method and system for searching dataset and classifying dataset (e.g., determining whether dataset are related)) , the system comprising:
one or more processors configured to process the data within the one or more data set(Col. 2, lines 30-34, discloses one or more processors configured to execute instructions to perform operation):
a data acquisition module configured to for obtaining one or more data sets(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106, remote database 108 or client device 110);
a data normalization module configured to for processing the data within the one or more data sets and normalize content into normalized attributes(Col. 15, lines 13-19, discloses methods of normalizing or filtering data);
an embedding module configured for generating vectorized embeddings of the normalized attributes(Col. 15, lines 15-18, discloses vectorizing method, Col. 18, lines 33-34, discloses an encoding method of step and include encoding [embedding] output of the vectorizing method of step); and
a data generation module configured for producing an output data set comprising one or more candidate outputs for interaction with the recipient(Col. 2 lines 35-37, and lines 58-63, discloses generating a similarity metric based on the reference data model output and the test data module output .., transmitting to the client device information comprising the classification of the test dataset [for interaction] discloses generating a test data model output using a data model.., processing the test data model output, and Col. 6, lines 20-32, dislcies generating data-model output based on the input dataset .., a machine learing model trained to generate synthetic data ..)).
Walters teaches the above elements including generating vectorized of the one or more candidate outputs(Fig. 7, discloses received data-model output, perform an encoding method, perform a factorizing method and perform a vectorizing method, Col. 12, lines 26-49, discloses analyzer may be configured to perform a vectorizing method, consistent with disclosed embodiments. In some embodiments, a vectorizing method may include transforming two-dimensional data (e.g., rows and columns) into one-dimensional data ...) and selecting a candidate data model candidate outputs from the output data set based on a predefined selection criterium applied to the comparison results(Col. 13, lines 4-7) and, data-modeling system may select one or more candidate data models.. (Col. 14, lines 12-20)). Walters failed to teach the corrosinding normalized data is extracted, the corrosinding performed vectorizing method includes embedding, the corresponding vectorized embedding of the normalized attributes and the vectorized embeddings of the one or more candidate outputs is compared and the corresponding electing a candidate data model is selecting zero or more candidate outputs from the output data set based on a predefined selection criterium applied to the comparison results.
However, Bhojwani teaches extracting and normalize content (Fig. 5, 530 discloses extracting a value form the unstructured description corresponding to the predefined attribute and normalizing value, and paragraph [0021], discloses the ML software where it can be extracted, cleaned, transformed validated and normalized.., paragraph [0023], discloses the ML software is designed and name applicable to wide range of attribute value extraction, attribute value-to-name linking, and attribute value and name normalization task is a heterogenous product catalog and paragraph [0051], dislcies extracting a value to generate a normalized attribute value ), generating vectorized embeddings (paragraph [0022], discloses the ML software may utilize a world-level embedding representation the encode both syntactic and semitic testing information); and selecting zero or more output (paragraph [0051], discloses selecting a predefine attribute from amount the predefined attributes). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for vectorizing and normalizing method of Walters with a featuring of extracting a value from the unstructured description of Bhojwani in order to modifying the extracted value to generate a normalized attribute (see Bhojwani, abstract).
With respect to claim 13, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the system wherein the system further comprises one or more of the following: a feedback module configured to dynamically adjust embeddings based on real-time interactions with the recipient; a storage module configured to store embeddings of frequently accessed data to reduce computational overhead during repeated comparisons; or a dynamic update module configured to adjust the selected desired output data during ongoing interactions with the recipient to provide real-time recommendations or responses(Col. 17, lines 38-47, discloses updating the dataset index may include storing test-data model output in the dataset index).
With respect to claim 14, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1, furthermore, Walters teaches the system wherein the data normalization module and the embedding module are configured to operate in parallel to improve scalability and processing efficiency for large data sets(Col. 7, lines 11-15, discloses data-modeling system may be a scalable system configured to efficiently manage resource.., and lines 32-55, dislcies a single core or multiple core process that executed parallel process simultaneously.., configured to provide parallel processing functionalities to allow execution of 45 multiple processes simultaneously).
With respect to claim 15, Walters teaches a machine-learning model for selecting data related to a recipient(Col. 9, lines 39-40, discloses model optimizer may be configured to select model training parameters ..) , for use in the method of claim 1,
wherein the machine-learning model comprises structural components (Col. 9, lines 1-16, discloses machine-learning models may include a neural network model, generative adversarial model, a recurrent neural network, etc.) configured to generate the recipient related output data, and wherein the machine-learning model is trained to:
process the vectorized and the normalized attributes and vectorized embeddings of output data sets(Col. 15, lines 15-18, discloses vectorizing method, Col. 18, lines 33-34, discloses an encoding method of step and include encoding [embedding] output of the vectorizing method of step); and
identify desired output data related to the recipient based(Col. 2, lines 39-40, discloses retrieving a reference data model output form a dataset index).
Walters failed to teach the , the corresponding vectorized embedding of the normalized attributes and the vectorized embeddings of the one or more candidate outputs is compared.
However, Bhojwani teaches compare vectorized embedding of the normalized attributes and the vectorized embeddings of the one or more candidate outputs (paragraph [0017], discloses identifying attribute values of product from a product description ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for vectorizing and normalizing method of Walters with a featuring of extracting a value from the unstructured description of Bhojwani in order to modifying the extracted value to generate a normalized attribute (see Bhojwani, abstract).
With respect to claim 16, Walters teaches a computer-implemented method of training the machine-learning model of claim 15, the method comprising:
Obtaining the one or more data sets(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106, remote database 108 or client device 110);
processing the obtained one or more the data sets to form normalized data(Col. 15, lines 13-19, discloses methods of normalizing or filtering data);
forming embeddings of the normalized data(Col. 16, line 20-22, discloses embedding ) ;
forming a training data set by associating the embeddings with predefined outcome data sets(Col. 16, lines 10-15, discloses retrieving a model trained via process);
training the machine-learning model using the training data set to generate an output data related to a recipient(Col. 18, lines 19-35, discloses machine learning model trained stimulatingly .., data model output assocted with plurlity of dataset) ; and
updating the machine-learning model using feedback data derived from prior output data(Col. 9, lines 40-45 discloses select model training parameters , the selection may be based on model performance feedback received.., and Col. 17, lines 38-47, dislcies data-modeling system 102 may update the dataset index..).
With respect to claim 17, Walters teaches A computer-implemented method of generating a training data set for the machine-learning model of claim 15,the method comprising:
obtaining the one or more data sets; obtaining one or more outcome data sets(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106, remote database 108 or client device 110);
processing the obtained one or more data sets to form normalized data(Col. 15, lines 13-19, discloses methods of normalizing or filtering data);
forming embeddings of the normalized data(Col. 16, line 20-22, discloses embedding ) ;
creating a training data set comprising the embeddings and associated outcome data sets(Col. 18, lines 19-35, discloses machine learning model trained stimulatingly .., data model output assocted with plurlity of dataset). Walters failed to teach the determining association between the embeddings of the normalized data and one or more outcome data sets.
However, Bhojwani teaches determining association between the embeddings of the normalized data and one or more outcome data sets (paragraph [0017], discloses identifying attribute values of product from a product description ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for vectorizing and normalizing method of Walters with a featuring of extracting a value from the unstructured description of Bhojwani in order to modifying the extracted value to generate a normalized attribute (see Bhojwani, abstract).
With respect to claim 18, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 15, furthermore, Walters teaches the training the machine-learning model comprising:
normalized embeddings derived from a one or more data sets; outcome data sets associated with the embeddings(Fig. 5, 502, and Col. 14, lines 67 discloses receive a dataset.., dataset from another component of system 100 (e.g., dataset database 106, remote database 108 or client device 110);
With respect to claim 19, Walters in view of Bhojwani and futher view of Fortkort teaches elements of claim 1 , furthermore, Walters the method for at least one of: generating recipient-specific recommendations in customer marketing; personalizing marketing messages based on recipient specific preferences and behaviors; recommending best products and services based on customer
data; determining the next best action with respect to a customer; personalizing web site elements to a specific customer; personalizing emails to a specific customer; personalizing text messages to a specific customer(Col. 3, lines 42-46, discloses data set may include transaction data, financial data, demographic data ).
Prior arts:
Walters et al (US Patent No., 11,113,124 B2) discloses systems and methods for searching datasets and classifying datasets are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a test dataset from a client device and generating a test data model output using a data model, based on the test dataset
Bhojwani et al (US Pub., No., 2022/0050854 A1) discloses provided is a method and system for normalizing catalog item data to create higher quality search results. In one example, the method may include receiving a record comprising an unstructured description of an object, identifying a type of the object from among a plurality of object types and identifying a predefined attribute of the identified type of object, extracting a value from the unstructured description corresponding to the predefined attribute and modifying the extracted value to generate a normalized attribute value.
Fortkort et al. (Pub. No.: US 2024/0395388 Al) discloses a system is provided for promoting wellness. The system includes a software client, an instance of which is installed on a plurality of client photo biomodulation (PBM) devices, wherein each client PBM device is associated with one of a plurality of users; a
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-20 filed on 26 March 2026 have been fully considered but they are not persuasive. Applicants’ arguments of the claims are directed to an abstract idea—specifically "collecting, analyzing, and displaying data" or a "method of organizing human activity"—without adding an inventive concept that constitutes "significantly more”
Likely Basis of 101 Rejections (Alice/Mayo Test)
Step 2A, Prong One (Abstract Idea): Claim are directed to "mathematical concepts" (vectorization), "methods of organizing human activity" (selecting data based on criteria), or "mental processes" (comparing/evaluating).
Step 2A, Prong Two (Integration into Practical Application): The steps—obtaining data, normalizing, generating embeddings, and comparing—are conventional, routine, and generic steps that could be performed by any computer, and therefore do not improve the computer's functionality.
Missing Data/AI as Mere Tool: The handling of missing data using a large language model or generative AI, if not described as a specific, novel technical improvement to the AI model itself, may be seen as using a generic AI tool to perform a conventional analysis
Therefore, the 35 U.S.C 101 rejections with respect to claim 1-20 is maintained.
Applicant’s arguments of 35 U.S.C 103 rejections filed on 26 March 2026 with respect to claim(s)1-20 have been considered but are 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
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 SABA DAGNEW whose telephone number is (571)270-3271. The examiner can normally be reached 9-6:45.
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, Waseem Ashraf can be reached at (571) 270 -3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621
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