DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 9/9/2026.
Claims 1, 11, and 20 have been amended, claims 2-3 and 12-13 have been cancelled, and claims 23-25 have been added new.
Claims 1, 4-11, and 14-25 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1, 4-11, and 14-25, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed towards an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that running predictions on a general purpose computer for managing maintenance and production volumes is an abstract idea (See rejection below). Applicant’s arguments are not persuasive.
Applicant argues the claims recite a technical improvement. The Examiner respectfully disagrees. The Examiner notes that the alleged improvement to the predictions are only interpreted by a human user and do not improve the system or functioning of the system itself. Rather the Applicant’s claims are merely using a general-purpose computer with generic machine learning to implement the abstract idea (See MPEP 2106.05). Please look at Applicant’s specification Figure 5 which shows how the nested graph is all customer analysis in a specific manner, which is abstract. The Examiner notes that all the results are only interpreted by a human and any decisions made based on the results are all performed by a human. Even the newly amended control signal is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05), because the control signal recites no specific on what action is being done or what triggers the action to be done. Applicant’s arguments are not persuasive.
The rejection does not direct the claims towards a Mental Process. These arguments are considered moot.
Applicant argues the claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. The Examiner asserts the claims recite “using an obsolescence optimizer”, which appears to merely be software running on a general purpose computer. The hardware and software (e.g. machine learning) is recited at such a high level of generality that it merely amounts to adding the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues that the forecast error rate is reduced, but the forecast calculation is part of the abstract idea and does not improve the functioning of the computer itself. Applicant’s arguments are not persuasive.
Applicant argues the claims are related to Enfish. The Examiner respectfully disagrees. Applicant’s claims recite general purpose computer equipment recited at a high level of generality for implementing the abstract idea, which merely adds the words apply it with the judicial exception (See MPEP 2106.05) and recite no details regarding databases in any way. The nested graph is being modified and adjusted based on human input, which in turn modifies the results so a human can tailor the results to there specific needs, which is the abstract idea. The Applicant does not properly identify the additional elements or say what is actually being improved. Applicant’s arguments are not persuasive.
Applicant argues the claims recite significantly more. The Examiner respectfully disagrees. Applicant does not properly identify the additional elements or state what is the non-conventional or non-generic arrangement. The Examiner asserts that Applicant’s claims only recite general purpose computer components for implementing the abstract idea (See rejection below). All Applicant’s arguments revolve around improved results, which is just an improved/narrow abstract idea. Applicant’s arguments are not persuasive.
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, 4-11, and 14-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1, 4-11, and 14-25 are directed toward a process, a product, and a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system, comprising: one or more processor; and one or more memory communicably coupled to the one or more processor, wherein the memory comprises processor-executable instructions which, when executed by the processor, cause the processor to: obtain, using a data extraction module, multi-dimensional data corresponding to a product from a plurality of data sources, wherein the multi-dimensional data comprises at least one of a product size, product ingredients, a geographic location of the product, a customer lifestyle data, climate conditions, and a current sales data for the product; identify, using a data cluster module, properties associated with the product based on the obtained multi dimensional data; determine, using a properties and relationship determination module, relationships between each of the identified properties of the product based on type of the properties and type of the product; create, using the properties and relationship determination module, a multi-dimensional nested graph for the product based on the identified properties and the determined relationships using a nested graph technique, wherein the multi-dimensional nested graph represents relationship of the determined properties with market demands of the product; generate, using a relationship representation module, a plurality of nested relationship models from the created multidimensional nested graph, wherein the plurality of nested relationship models represent the identified properties and the determined relationships for a group of products, and wherein the plurality of nested relationship models are generated based on nested relationships common to specific group of products; create, using the relationship representation module, at least one transactional data node indicating a relationship between the product and the current sales data based on the generated plurality of nested relationship models; create, using a node embedding module, a plurality of graph embedding values based on the created at least one transactional data node and the nested relationships, wherein the plurality of graph embedding values capture at least one of customer purchase patterns, a product trending history, product information, customer information, and demographics data; predict, using an obsolescence predictor, an obsolescence prediction of an obsolescence data for the product based on the created plurality of graph embedding values, the customer purchase patterns, a product inventory forecast data and a sales data; output the predicted obsolescence data for the product on a user interface of a user device; generate, using a model training module, an updated multi-dimensional data based on the created plurality of graph embedding values; select, using the model training module, an updated model architecture for the updated multi-dimensional data based on a model capacity; retrain, using the model training module, a plurality of machine learning models based on the updated multidimensional data and the selected model architecture using a plurality of hyperparameters; determine, using a model evaluation module, a forecast error for the predicted obsolescence data by validating performance of the retrained plurality of machine learning models based on weights assigned to each of the retrained plurality of machine learning models; determine, using the model evaluation module, an optimal machine learning model among the retrained plurality of machine learning models based on results of validation and the determined forecast error; fine-tune, using the model evaluation module, the predicted obsolescence data for the product based on the determined optimal machine learning model; simulate, using a model deployment module and a data flow management module, performance of the product in a virtual environment based on the predicted obsolescence data for the product; define, using the model deployment module and the data flow management module, at least one action to be performed on the product based on results of simulation, wherein the at least one action comprises modify a product maintenance cycle by adjusting a frequency or type of maintenance activities required for the product based on a predicted lifespan of the product, modify a product production cycle by adjusting production volumes or introducing design changes to mitigate obsolescence depending on an obsolescence forecast, and modify inventory numbers by triggering adjustments to inventory levels to avoid overstocking items nearing obsolescence; remotely execute, in real-time, the defined at least one action at an industrial plant of the product by transmitting, using the data flow management module, the defined at least one action as a control signal to the industrial plant where the product is used for further management of operations within the industrial plant; refine, using an obsolescence optimizer, the nested relationships within the multi- dimensional nested graph, wherein the obsolescence optimizer is configured to: scrutinize product features associated with nodes in the multi-dimensional nested graph; identify nodes associated with unsuccessful prediction outcomes; reevaluate embeddings and relationships associated with the identified nodes; and realign connections among the identified nodes based on the reevaluated embeddings and relationships to generate optimized embeddings; and apply the optimized embeddings to new products for an updated obsolescence prediction that improves overall accuracy of the obsolescence prediction (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing product data to determine relationships to customers and creating nested graphs showing the relationships between product and customer including demand and purchase patterns that can be used for analysis and forecasting potential errors to fine tune the predictions, which is a commercial interaction.
Dependent claims 4-10, 14-19, and 21-25 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system, comprising: one or more processor; and one or more memory communicably coupled to the one or more processor, wherein the memory comprises processor-executable instructions which, when executed by the processor, cause the processor to (claim 1)”; “by a processor; by the processor (claim 11)”; “a non-transitory computer readable medium comprising a processor- executable instructions that cause a processor to (claim 20)”; “using a data extraction module, using a data cluster module, using a properties and relationship determination module, using the properties and relationship determination module, using a relationship representation module, using the relationship representation module, using a node embedding module, using an obsolescence predictor, on a user interface of a user device; using a model training module, using the model training module; retrain, using the model training module, a plurality of machine learning models; using a model evaluation module, using the model evaluation module, using the model evaluation module; using a model deployment module and a data flow management module, using the model deployment module and the data flow management module, using the data flow management module, transmitting, using the data flow management module, the defined at least one action as a control signal to the industrial plant where the product is used for further management of operations within the industrial plant; using an obsolescence optimizer (claims 1, 11, and 20)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 4-10, 14-19, and 21-25 further narrow the abstract idea and dependent claims 4-5, 7, 10, 14, 16, 19, and 23-25 additionally recite “a trained machine-learning model (claims 4 and 14)”; “using a data clustering model; using a generative artificial intelligence (AI) model (claim 5)”; “using a large language model; using a trained graph network model (claims 7 and 16)”; “a trained forecast machine learning (ML) model (claims 10 and 19)”; “train a transformer model with deep learning techniques using a knowledge inference engine (claims 23-25)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1, 11, and 20 recite “a system, comprising: one or more processor; and one or more memory communicably coupled to the one or more processor, wherein the memory comprises processor-executable instructions which, when executed by the processor, cause the processor to (claim 1)”; “by a processor; by the processor (claim 11)”; “a non-transitory computer readable medium comprising a processor- executable instructions that cause a processor to (claim 20)”; “using a data extraction module, using a data cluster module, using a properties and relationship determination module, using the properties and relationship determination module, using a relationship representation module, using the relationship representation module, using a node embedding module, using an obsolescence predictor, on a user interface of a user device; using a model training module, using the model training module; retrain, using the model training module, a plurality of machine learning models; using a model evaluation module, using the model evaluation module, using the model evaluation module; using a model deployment module and a data flow management module, using the model deployment module and the data flow management module, using the data flow management module, transmitting, using the data flow management module, the defined at least one action as a control signal to the industrial plant where the product is used for further management of operations within the industrial plant; using an obsolescence optimizer (claims 1, 11, and 20)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0117-0120 and Figures 11. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 4-10, 14-19, and 21-25 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 4-5, 7, 10, 14, 16, 19, and 23-25 additionally recite “a trained machine-learning model (claims 4 and 14)”; “using a data clustering model; using a generative artificial intelligence (AI) model (claim 5)”; “using a large language model; using a trained graph network model (claims 7 and 16)”; “a trained forecast machine learning (ML) model (claims 10 and 19)”; “train a transformer model with deep learning techniques using a knowledge inference engine (claims 23-25)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1, 4-11, and 14-25 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1, 11, and 20 disclose a system, product, and method for analyzing product data to determine relationships to customers and creating nested graphs with embedded values showing the relationships between product and customer including demand and purchase patterns that can be used for analysis and forecasting potential errors to fine tune the sales predictions.
Regarding a possible 103 rejection: The closest prior art of record is:
Singh et al. (US 2022/0318263 A1) – which discloses data visibility for nested transactions in distributed systems (specific citations shown in previous OA’s).
Frazer et al. (US 2014/0222506 A1) – which discloses consumer financial behavior modeling based on historical spending data to predict future spending (specific citations shown in previous OA’s).
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1, 11, and 20, such as analyzing product data to determine relationships to customers and creating nested graphs with embedded values showing the relationships between product and customer including demand and purchase patterns that can be used for analysis and forecasting potential errors to fine tune the sales predictions. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a system, comprising: one or more processor; and one or more memory communicably coupled to the one or more processor, wherein the memory comprises processor-executable instructions which, when executed by the processor, cause the processor to: obtain, using a data extraction module, multi-dimensional data corresponding to a product from a plurality of data sources, wherein the multi-dimensional data comprises at least one of a product size, product ingredients, a geographic location of the product, a customer lifestyle data, climate conditions, and a current sales data for the product; identify, using a data cluster module, properties associated with the product based on the obtained multi dimensional data; determine, using a properties and relationship determination module, relationships between each of the identified properties of the product based on type of the properties and type of the product; create, using the properties and relationship determination module, a multi-dimensional nested graph for the product based on the identified properties and the determined relationships using a nested graph technique, wherein the multi-dimensional nested graph represents relationship of the determined properties with market demands of the product; generate, using a relationship representation module, a plurality of nested relationship models from the created multidimensional nested graph, wherein the plurality of nested relationship models represent the identified properties and the determined relationships for a group of products, and wherein the plurality of nested relationship models are generated based on nested relationships common to specific group of products; create, using the relationship representation module, at least one transactional data node indicating a relationship between the product and the current sales data based on the generated plurality of nested relationship models; create, using a node embedding module, a plurality of graph embedding values based on the created at least one transactional data node and the nested relationships, wherein the plurality of graph embedding values capture at least one of customer purchase patterns, a product trending history, product information, customer information, and demographics data; predict, using an obsolescence predictor, an obsolescence prediction of an obsolescence data for the product based on the created plurality of graph embedding values, the customer purchase patterns, a product inventory forecast data and a sales data; output the predicted obsolescence data for the product on a user interface of a user device; generate, using a model training module, an updated multi-dimensional data based on the created plurality of graph embedding values; select, using the model training module, an updated model architecture for the updated multi-dimensional data based on a model capacity; retrain, using the model training module, a plurality of machine learning models based on the updated multidimensional data and the selected model architecture using a plurality of hyperparameters; determine, using a model evaluation module, a forecast error for the predicted obsolescence data by validating performance of the retrained plurality of machine learning models based on weights assigned to each of the retrained plurality of machine learning models; determine, using the model evaluation module, an optimal machine learning model among the retrained plurality of machine learning models based on results of validation and the determined forecast error; fine-tune, using the model evaluation module, the predicted obsolescence data for the product based on the determined optimal machine learning model; simulate, using a model deployment module and a data flow management module, performance of the product in a virtual environment based on the predicted obsolescence data for the product; define, using the model deployment module and the data flow management module, at least one action to be performed on the product based on results of simulation, wherein the at least one action comprises modify a product maintenance cycle by adjusting a frequency or type of maintenance activities required for the product based on a predicted lifespan of the product, modify a product production cycle by adjusting production volumes or introducing design changes to mitigate obsolescence depending on an obsolescence forecast, and modify inventory numbers by triggering adjustments to inventory levels to avoid overstocking items nearing obsolescence; remotely execute, in real-time, the defined at least one action at an industrial plant of the product by transmitting, using the data flow management module, the defined at least one action as a control signal to the industrial plant where the product is used for further management of operations within the industrial plant; refine, using an obsolescence optimizer, the nested relationships within the multi- dimensional nested graph, wherein the obsolescence optimizer is configured to: scrutinize product features associated with nodes in the multi-dimensional nested graph; identify nodes associated with unsuccessful prediction outcomes; reevaluate embeddings and relationships associated with the identified nodes; and realign connections among the identified nodes based on the reevaluated embeddings and relationships to generate optimized embeddings; and apply the optimized embeddings to new products for an updated obsolescence prediction that improves overall accuracy of the obsolescence prediction (as required by independent claims 1, 11, and 20)”, thus rendering claims 1, 11, 20 and their dependent claims as allowable over the prior art.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
