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 is a final office action in response to the amendment filed 26 February 2026. Claims 1 and 12 have been amended. Claims 4, 11, 13, 15, and 22-23 have been canceled. Claims 1-3, 5-10, 12, 14, and 16-21 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1 and 12 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the PTAB decision in Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 20025) and the Kim memorandum dated 05 December 2025 “significantly amends MPEP 2106.04(d) and changes the Office’s approach for fully assessing the ‘integration’ prong,” such that the amended claim is patent subject matter eligible.
Applicant asserts that the claims are directed to a specific, technologically rooted method for inferentially extracting and normalizing commodity attributes from transaction data using a particular machine learning architecture, and addresses a concrete technical problem: “how to process transactions that lack standardized commodity attribute information across disparate, multi-tenant transaction data sources” using a particular active learning technique that targets uncertain predictions to improve model performance iteratively, to transform unstructured transactional input into a structured, hierarchically organized output using a specific computational architecture.
Applicant further submits that the amended claim is not directed to a judicial exception, but instead to a specific computer-implemented sequence-tagging and normalization architecture operating in a distributed multi-tenant computing environment. Applicant asserts that the claim recites “an improvement to the functioning of the computer system itself, which is able to process and classify commodity data that it previously could not classify without manual intervention or a pre-existing reference database.” Applicant further asserts that the claim integrates this technical extraction process into a practical application that produces concrete, tangible results in the form of prescriptive and actionable output, providing an automated response to a statistically detected pricing anomaly rooted in the technically derived attribute data. Applicant additionally asserts that “taken as a whole, the claim recites an ordered combination of specific technical steps that, together, constitute an improvement in computer-implemented commodity attribute extraction and normalization,” in a manner that does not preempt all methods of analyzing transaction data or comparing prices.
Examiner respectfully disagrees with Applicant’s assertions. While the BIOE sequence tagging machine learning model is an additional element considered in Step 2A Prong Two, the claimed limitations are construed as falling within the fundamental economic principles and practices grouping of abstract concepts because per Applicant’s Specification at paragraph [0007] the claim limitations are directed to solving a business problem of determining “that a record from one particular transaction involves the same or similar commodity as many other transactions, and to use the resulting data for statistical comparisons to yield new data that is useful in automatic contract negotiation, other transactions, or changes in configuration of a SaaS-based spend management system.” The application of a specific machine learning model for performing the data analysis in a results based manner, without more, is insufficient to confer patent subject matter eligibility. The claim is analogous to ineligible claim 2 of Example 47 because the data analysis steps, including the machine learning elements are used to process collected data to generate a set of items for further data analysis and business decision making. Therefore, the claims are properly construed as reciting an abstract idea of collecting and analyzing transaction data and presenting the results for business decision making by a human, and falls within the fundamental economic principles and practices and mental processes grouping of abstract concepts.
The claim limitations “apply” the claimed OpenTag algorithm BIOE sequence tagging machine learning model to extract attributes for spending line data (See Specification [para. 0080]), the “BiLSTM model is used to execute the tagging” “and then a CRF layer is applied on top of it,” and “the Tag Flipping method is used for the Query strategy” “that selects the most informative samples from unlabeled data” to reduce “the amount of labelled training data required.” Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). While the machine learning process combines multiple known additional elements for data analysis, and the model is trained to extract new attribute values, the additional elements in combination improve the abstract idea of collecting, analyzing, and outputting data results for an improved business process determination. The underlying data analysis techniques applied as additional elements are not improved, therefore they do not amount to a practical application of the recited abstract idea. The output of the machine learning model is used for further data processing and outputting of the results, without significantly more than presentation for human action and decision making. As claimed herein, the tag flipping method, OpenTag algorithm, BIOE sequence tagging machine learning model at best are general links to a particular technological environment for data analysis and do not integrate the abstract idea into a practical application. Further, when considered as a whole do not amount to significantly more than the recited abstract idea. Here, the claimed use of the deep learning model is intended to improve the mental process itself as opposed to improving the computer functionality.
In Ex parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential), the claimed invention improves how the machine learning model itself operates. In Desjardins, the Board held eligible a recited method of training a machine learning model, where (1) the model was trained on a first machine learning task using first training data to determine first values of machine learning model parameters, where a respective measure of performance was determined for a parameter of the first task and assigned to each parameter, and (2) the machine learning model was trained on a second machine learning task with second training data to adjust the first parameter values to optimize the machine learning model’s performance on the second machine learning task while protecting the model’s performance on the first machine learning task. See Desjardins at 2–3. In arriving at its eligibility conclusion, the Board noted that the claimed invention’s adjustment of the first values of plural parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task constituted an improvement to how machine learning model itself operates. See id. at 9. That is not the case here. Automating an abstract process using a machine does not amount to a technological improvement if the machine is generic. Alice Corp. Pty. Ltd. v. CLS Bank Intern., 573 U.S. 208, 223 (2014). In addition, as acknowledged in Recentive, requirements that a machine learning model be iteratively trained or dynamically adjusted do not represent a technological improvement, because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) (holding that “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions” amounts to “no more than claiming the abstract idea itself”). Per Applicant’s Specification at paragraph [0080]: “For training data, Active Learning is used, since labelled data is needed as a training set, initially a small manually labelled set is used, and then the model iteratively requests other labels of samples from the pool of un- labelled data until a specified criterion is met. … This method reduces to an extent the amount of labelled training data required.” (See also Spec at [0083]: “Active Learning is used to reduce the amount of labeled data needed for training the model.”). Similarly, as is the case here, determining to retrain a machine learning model using a new training data or filtered training data is not itself a technological improvement. Reducing the amount of data processed by a computer is not an improvement to the functioning of a computer, but the pre-processing of data used to generate result of the data collection and analysis steps.
"Transformation" of an article means that the "article" has changed to a different state or thing, wherein an "article" includes a physical object or substance. MPEP 2106.05(c). Changing to a different state or thing usually means more than simply using an article or changing the location of an article. Id. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. Id. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. Id. As claimed herein, the corpus of information is not a physical object or substance, but rather, this information is simply data, and thus, the information is not an article. Analyzing a data set (transaction descriptions) to generate a new set of data (item attributes and classification hierarchies) for further analysis and comparison by a generic computer acting as a tool to perform the abstract idea of generating a trend/prediction in price for a particular commodity and statistical price comparisons, does not transform or reduce the analyzed data into a different state or thing. It is merely further manipulation of data by applying mathematical concepts (classification and comparisons algorithms) to manipulate existing information to generate cross-entity statistical price benchmarking and price comparison. Accordingly, contrary to Applicant’s assertion, the alleged transformation is not in fact a transformation of an article into a different state or thing.
The Specification at [0077-0078] states: “… embodiments are programmed to identify attributes of items without a priori digitally stored data that describes the attributes; instead, attributes are derived inferentially as transactions are processed. For example, the following input of TABLE 1 can be processed to produce the specified output. [0078] TABLE 1: Extracting item attribute values from product descriptions. Type, Size, Weight all are attributes of Paper, but the attributes are not known or stored beforehand; instead, the data preprocessor 108 is programmed to automatically develop such a classification.” Classifying data is a mental process. Classifying extracted data “without a priori digitally stored data that describes the attributes” is not a practical application or significantly more than the abstract idea, and does not transform the ineligible concepts into patent eligible subject matter. Data extraction and storage is insignificant extra-solution activity because it does not meaningfully limit the claim, but amounts to data gathering steps that provide input for the recited data processing steps. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
While the particular application of the additional elements may be novel, eligibility over the prior art is not the standard for 35 U.S.C. 101 subject matter eligibility. The memorandum does not change how 35 U.S.C. 101 is applied for examination purposes. The 35 U.S.C. 101 analysis detailed in MPEP 2106 has been properly applied in the examination of the claims herein. Examiner has made the requisite prima facie case showings for satisfying: Step 2a Prong One –the directed to findings, Prong Two –findings supporting additional elements that do not integrate the abstract idea into a practical application; and Step 2B –findings of not significantly more. See detailed 35 U.S.C. 101 rejection updated below, as necessitated by amendment.
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 1-3, 5-10, 12, 14, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a process, independent claim 12 recites a product for automatically discovering data trends using anonymized data. Independent claims 1 and 12 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites the following limitations:
establishing programmatic connections of a programmed computer system to a plurality of application instances comprising transaction data, the transaction data comprising transaction descriptions and commodities involved in the transactions, the commodities having item attributes;
wherein the data for transactions of one entity associated with a first application instance among the plurality of application instances is logically isolated from and inaccessible to a second, different entity that is associated with a second application instance among the plurality of application instances;
executing, using the programmed computer system: normalizing transaction descriptions and determining item attributes for a plurality of the commodities by accessing a plurality of different digitally stored buying data sources including two or more of digital catalogs, punchouts, and external sources, taxonomy data, community transactions data, and an item attribute library, wherein the item attributes comprise a plurality of variables describing the plurality of commodities, and wherein the normalizing of transaction descriptions includes transferring values of attributes from source data into a classification hierarchy that organizes the values from broad categories to classifications that support similarity grouping and a comparison of values hierarchically;
generating a set of training items determined using a tag flipping method, wherein the tag flipping method identifies training items for which predictions of a BIOE sequence tagging machine learning model exhibit a specified level of uncertainty, the tag flipping method being implemented by an OpenTag algorithm;
using the set of training items, iteratively training the BIOE sequence tagging machine learning model, the BIOE sequence tagging machine learning model comprising a word embedding layer coupled to a BiLSTM (Bidirectional Long Short Term Memory) layer, a Conditional Random Fields (CRF) layer following the BiLSTM layer, and an attention mechanism, wherein the attention mechanism is between the BiLSTM layer and the CRF layer, the BiLSTM layer comprising one or more LSTM models that capture a sequential nature of tokens apart from a sequential nature of tags, the CRF layer being implemented to enforce tagging consistency and extract cohesive chunks of attribute values;
extracting and storing the item attributes derived inferentially as transactions are processed in item sets, without access to a priori digitally stored data that describes the attributes, by executing BIOE sequence tagging via the BIOE sequence tagging machine learning model;
based on the item sets, grouping similar items and executing statistical price comparison calculations on the item sets;
outputting one or more prescriptions, at least one of the one or more prescriptions specifying a community trend in a price of a particular commodity among the plurality of the commodities across all the application instances;
displaying one or more graphical visualizations of the prescriptions in a graphical user interface of a computer display device by executing generated presentation instructions using the programmed computer system;
identifying a lower-cost supplier in response to an average price of a commodity in a set of past transactions for the commodity is greater than one standard deviation from a median price of that commodity as indicated in a normal distribution of community of transactions for that commodity;
and in response to the identifying, automatically generating an interface element comprising an alert line, the alert line comprising a plurality of hyperlinks, wherein a first hyperlink from the plurality of hyperlinks is configured to link to supplier information and a second hyperlink from the plurality of hyperlinks is configured to link to information about the commodity being represented by a normalized commodity identifier.
Under Step 1 of the eligibility analysis, independent claims 1 and 12 recite at least one step or act, including normalizing transaction descriptions and determining item attributes. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One of the eligibility analysis, the limitations for establishing programmatic connections; normalizing transaction descriptions and determining item attributes; generating a set of training items determined using a tag flipping method; iteratively training the BIOE sequence tagging machine learning model; extracting and storing the item attributes in item sets; executing BIOE sequence tagging; grouping similar items and executing statistical price comparison calculations on the item sets; outputting one or more prescriptions; displaying one or more graphical visualizations; identifying a lower-cost supplier; and generating an interface element comprising an alert line comprising a plurality of hyperlinks, as drafted, are directed to an abstract idea of gathering and collecting data, analyzing the data, and manipulating the data to generate recommendations for display and user decision making. The claims are directed to an item similarity framework for pricing that allows a buyer computer to assess thousands to millions of transactions and modify other programmatic interactions with suppliers (see Specification paragraph [0027]). Because item pricing, interactions with suppliers, and transaction management pertain to marketing or sales activities or behavior the claims properly fall within the fundamental economic principles or practices grouping of abstract ideas. See MPEP 2106.04(a)(2)(II).
The step for normalizing transaction description and determining item attributes is described in the specification at paragraph [0090] as “text normalization is executed by stemming, using a rule-based process of stripping suffixes such as "ing", "ly", "es", "s", from a word. Embodiments may implement special-purpose rules for attributes involving numbers, such as "8 1/2- 11 in." The claim language in view of the specification, under its broadest reasonable interpretation, covers performance of the “normalization” step in the mind, and is construed as an abstract concept that falls within the mental processes grouping. See MPEP 2106.04(a)(2)(III). Similarly, the steps for determining item attributes, generating a set of training items, grouping similar items, executing a statistical price comparison, outputting one or more prescriptions, and identifying a lower-cost supplier, are each steps that could be performed by a human using pen and paper or mentally and therefore fall within the mental processes grouping of abstract ideas.
Independent claims 1 and 12 (and dependent claims 6 through 10 and 17 through 21) include limitations for calculating statistical price comparisons, calculating total amounts spent and savings potential, and a commodity trend in a price. Each of these limitations properly fall within the mathematical concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2)(I). Analyzing a data set (transaction descriptions) to generate a new set of data (item attributes and classification hierarchies) for further analysis and comparison by a generic computer acting as a tool to perform the abstract idea of generating a trend/prediction in price for a particular commodity and statistical price comparisons, does not transform or reduce the analyzed data into a different state or thing. It is merely further manipulation of data by applying mathematical concepts (classification and comparisons algorithms) to manipulate existing information to generate cross-entity statistical price benchmarking and price comparison.
The limitations directed to extracting and storing item attributes, outputting one or more prescriptions, and displaying visualizations amount to data gathering and transmission steps that are construed as insignificant extra-solution activity. See MPEP 2106.05(g). The claim language for “wherein the data for transactions of one entity … is logically isolated from an inaccessible to a second, different entity” is broadly and generically claimed. This language is interpreted as relating to generic data storage. That certain data is isolated from a second entity is construed as limiting access, which is a mental process or method of managing behavior or relationships abstract concept that does not confer patent subject matter eligibility and does not meaningfully limit the claimed invention.
Under Step 2A Prong Two, the judicial exception of claim 1 is not integrated into a practical application. In particular the claims only recite a processor, display device, and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0051] states: “a computer system 100 comprises components that are implemented at least partially by hardware at one or more computing devices, such as one or more hardware processors executing stored program instructions stored in one or more memories for performing the functions that are described herein. In other words, all functions described herein are intended to indicate operations that are performed using programming in a special-purpose computer or general-purpose computer.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(d). Accordingly, the 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. Therefore, the claims are directed to an abstract idea.
The presence of a machine learning algorithm or computer implementations does not necessarily restrict the claim from reciting an abstract idea. While the claim includes additional elements in the form of “training a machine learning model using a set of training items generated by the normalizing of the transaction descriptions, the set determined using a tag flipping method, wherein the tag flipping method identifies training items for which predictions of the machine learning model exhibit a specified level of uncertainty,” the tag flipping method being implemented by “generating a set of training items determined using a tag flipping method, wherein the tag flipping method identifies training items for which predictions of a BIOE sequence tagging machine learning model exhibit a specified level of uncertainty, the tag flipping method being implemented by an OpenTag algorithm; using the set of training items, iteratively training the BIOE sequence tagging machine learning model, the BIOE sequence tagging machine learning model comprising a word embedding layer coupled to a BiLSTM (Bidirectional Long Short Term Memory) layer, a Conditional Random Fields (CRF) layer following the BiLSTM layer, and an attention mechanism, the BiLSTM layer comprising one or more LSTM models that capture a sequential nature of tokens apart from a sequential nature of tags, the CRF layer being implemented to enforce tagging consistency and extract cohesive chunks of attribute values,” the recited machine learning and modeling steps using the stated layers are merely used as tools to process data and generate an output based on known data. The application of a specific machine learning model for performing the data analysis in a results based manner, without more, is insufficient to confer patent subject matter eligibility.
The claim is analogous to ineligible claim 2 of Example 47 because the data analysis steps, including the machine learning elements are used to process collected data to generate a set of items for further data analysis and business decision making. Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). While the machine learning process combines multiple known additional elements for data analysis, and the modeled is trained to extract new attribute values, the additional elements in combination improve the abstract idea of collecting, analyzing, and outputting data results for an improved business process determination. The underlying data analysis techniques applied as additional elements are not improved, therefore they do not amount to a practical application of the recited abstract idea. The limitations when taken individually or as an ordered combination do not offer an inventive concept that may amount to significantly more than the recited abstract concepts.
The limitations for automatically generating an interface element comprising an alert line comprising a plurality of hyperlinks merely use the interface to access and present data output, without significantly more. The function of the interface is not modified or improved by the recited limitations, therefore the interface related claim limitations merely use the interface as a tool to implement the abstract idea of presenting information to a user, and does not provide a practical application of the recited abstract idea.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amounts to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2-3, 5-10, 14, and 16-21 include the abstract ideas of the independent claims. The dependent claims recite additional limitations directed to data processing steps used to implement the abstract idea of discovering data trends using anonymized data by gathering and collecting data, analyzing the data, and manipulating the data to generate more data for display and human decision making. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation, therefore the claims are directed to an abstract idea. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly independent claim 12 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1-3, 5-10, 12, 14, and 16-21 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Figueroa et al. (US 11,551,241) - the process of training a predictive model may employ unsupervised training or semi-supervised training. For example, the training process may comprise extracting unsupervised features from marketplace data. For example, the model network for pre-processing the input data (e.g., marketplace data) for feature extraction may comprise an autoencoder. During the feature extraction operation, the autoencoder may be used to learn a representation of the input data for dimensionality reduction or feature learning. The autoencoder can have any suitable architecture such as a classical neural network model (e.g., sparse autoencoder, denoising autoencoder, contractive autoencoder) or variational autoencoder (e.g., Generative Adversarial Networks). In some embodiments, a sparse autoencoder with an RNN (recurrent neural network) architecture, such as LSTM (long-short-term memory) network, may be trained to regenerate the inputs for dimensionality reduction. For example, an encoder-decoder LSTM model with encoder and decoder layers may be used to recreate a low-dimensional representation of the input data to the following model training despite a latent/hidden layer. The output of the predictive model may comprise recommendation information. The recommendation information may comprise information about improving the score. For example, the recommendation may comprise a recommended keyword/search terms of the product, description about the product, presentation of the product (e.g., image, video, etc), price, marketing strategy (e.g., paid presence), and the like. In some cases, the recommendations may be personalized so that one or more actions as recommended are executable to the user. user devices may run dedicated mobile applications or software applications for viewing product health assessment (e.g., score, marketplace statistics, competitor information, new entrants in a related category, etc) and recommendation provided by the digital shelf analytic system. The software applications for conducting an online transaction and viewing assessment of the product/brand may be different applications. The digital shelf analytic system may deliver information and content to the user devices 103 related to a digital shelf analytic result (e.g., a report, a score, recommendations and marketplace statistics) and various others, for example, by way of one or more web pages or pages/views of a mobile application.
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 LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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