DETAILED ACTION
This FINAL office action is in response to Applicant’s amendment filed June 5, 2026. Applicant’s June 5th amendment amended claims 1, 8 and 11 and canceled claims 2-4, 7, and 10. Currently claims 1, 5, 6, 8, 9 and 11 are pending. Claims 1, 8 and 11 are the independent claims.
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 Amendment
The 35 U.S.C. 101 rejection of claims 1, 5, 6, 8, 9 and 11 in the previous office action is maintained.
Response to Arguments
Applicant's arguments filed June 5, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea (e.g. computer implemented pipeline, cannot be performed in the human mind or via pen and paper - GMM, XGBOOST, etc., tied to specific data preparation step, specific computational modules, specific technical implementation; Remarks: Page 10; Paragraph 1, Page 11); the claims integrate the abstract idea into a practical application (e.g. improves AI-driven inventory forecasting/categorization at warehouse product batch level, Remarks: Pages 11, 12); similar to the Dejardinis et al. decision (e.g. multi-level framework to forecast demand using LTSM, ARIMA, XGBOOST, etc.; Specification: Paragraph 56-68; 66; Remarks: Page 12; e.g. improved automated inventory categorization for perishable goods, specific technical steps, automated computational pipeline; Last Two Paragraphs, Page 13); the claims are similar to McRO (Remarks: Paragraph 1, Page 13); similar to Subject Matter Eligibility Example 47, Claim 3 (Remarks: Paragraph 2, Page 14; Paragraph 2, Page 16); similar to Bascom (Last Two Paragraphs, Page 14; Page 15); and the claims recite an inventive concept/significantly more than an abstract idea (Remarks: Pages 15, 16)
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea/cannot be performed in the human mind, the examiner respectfully disagrees.
Independent claims 1, 8 and 11 are directed to the well-known business practice (fundamental economic practice) of demand forecasting or more specifically forecasting inventory demand (Title: Systems and Methos for Forecasting Inventory). Forecasting demand is fundamental economic practice that fall into the abstract idea subcategories of sales activities and/or commercial interactions.
Additionally, the claims are directed to a mental process practically being capable of being performed in the human mind via observation, evaluation, judgement and opinion. Claims 1, 8 and 11, as argued by Applicant, are directed to computer-based inventory management (inventory forecasting/categorization at warehouse product batch level).
The step of retrieve a set of parameters comprising at least ONE of sales or product or daily inventory or batch level inventory data can be performed by the human mind/via pen and paper via observation of data. Additionally, the retrieve step is directed to insignificant pre-solution activity (mere data input). The step of preprocess the set of one or more data parameters into one or more batches can be performed in the human mind by evaluation and judgement. The generic AI engine comprising one or more machine learning models and a feature engineering module is merely software per se, generic AI/ML models recited at a high level of generality and merely used as a tools to apply the abstract idea. The pre-process steps is merely results based as it fails to specifically recite how the generic AI engine, Feature Engineering Module or machine learning models function. The step of estimate a demand associated with a product by calculating a demand forecast using a forecasting algorithm may be performed in the human mind/via pen and paper using evaluation. That the forecasting algorithm includes at least ONE of an Autoregressive Integrated Moving Average Model (ARIMA), a Long short-term memory model (LSTM), an Extreme Gradient Boosting model (XGBOOST), and Holt-Winters merely recites the use of old, well-known, conventional and routine forecasting algorithm (mathematical formulas) readily performed by a human via pen and paper or using a generic computer performing generic computer functions. The estimate step is also direction to a mathematical concept/operation. The step of correct the calculated demand forecast using exogenous variables, including selecting a set of forecast algorithm by performing an ensemble forecast (as newly claimed) and generating and applying an event causality factor (number) is capable of being performed by the human mind or via pen and paper. The correct step is also directed to a mathematical operation/concept. The step adjust the forecasted demand of each product at a fulfillment center level to a batch level can be performed by a human mind/via pen and paper via observation and judgement. The step of classify the product into different batches based on at least ONE of forecasted sales or shelf life OR historical sales OR exogenous variables can be performed via human mind/via pen and paper using judgement and opinion. The step of categorize each batch inventory into different buckets at a product batch level using GMM and Bayesian based categorization of GMM clusters can be performed by the human mind/via pen and paper using evaluation and judgement. This step is also directed to a mathematical concept/operation and Gaussian Mixture Model clustering and Bayesian based categorization are well known mathematical approaches. The step of forecast a warehouse level inventory demand can be performed in the human mind via evaluation. This forecast step is also directed to a mathematical operation/concept. The step of send an alert to one or more users can be performed via observation and opinion in the human via pen and paper (Claims 1 and 8). Additionally, the send step is directed to insignificant post solution activity (mere data output). The step of transmit the categorized inventory/forecast demand data and generate an alert indicative of warehouse level inventory can be performed via observation and opinion in the human/via pen and paper (Claim 11). Additionally, the transmit/generate step is directed to insignificant post solution activity (mere data output).
The recited generic system, acquisition engine (software per se), Artificial Intelligence (AI) engine (software per se), user equipment (UE), feature engineering module (software per se), event impact module (software per se), processors and memories are recited at a high level of generality and as such represent nothing more than mere instructions to apply the abstract idea using a generic computer. The mere nominal recitation of a generic processor/computer performing generic computer functions does not take the claim limitation out of the mental processes grouping.
More specifically artificial intelligence engine comprising one or more machine learning models (software per se; Specification: Paragraphs 52, 53), is recited at a high level of generality and is used by a generic computer/processor, also recited at a high level of generality, to estimate/calculate product demand (mental step, mathematical concept), select an optimal forecast algorithm (mental step, mathematical operation), and categorize batch inventory into buckets amounts to no more than mere instructions to implement an abstract idea on a generic computer. The artificial intelligence engine is used to generally apply the abstract idea without limiting how the artificial intelligence engine functions. The artificial intelligence engine (software per se) is described at a high level such that it amounts to using a computer with a generic artificial intelligence engine to apply the abstract idea. The recitation of an artificial intelligence engine comprising one or more machine learning models (also recited at a high level of generality) in the claims does not negate the mental nature of these limitations because the artificial intelligence engine is merely used at a tool to perform an otherwise mental process. The generic computer and generic artificial intelligence engine are used for their old, well-known, routine and conventional purposes - in this case performing a series of method steps readily performed by a human/via pen and paper.
Accordingly, the claims are directed to both a fundamental economic practice and a mental process without significantly more and are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s arguments that the claims are patent eligible under 35 U.S.C. 101 because the claims integrate the abstract idea into a practical application, the examiner respectfully disagrees.
As discussed above the claims are directed to forecasting warehouse/fulfillment center level inventory demand. While the claims may represent an improvement to the business process of forecasting warehouse/fulfillment center level demand they in no way either claimed or disclosed represent a practical application.
Under MPEP § 2106.05, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)- (h)). A claim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
For example, limitations that are indicative of "integration into a practical application" include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include 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(±);
Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
In view of the guidance, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract elements recited in the independent claims are user (a human) and the generic computing elements: system, acquisition engine (software per se), Artificial Intelligence engine (software per se), user equipment (UE), feature engineering module (software per se), event impact module (software per se), processors and memories. These generic computer hardware merely performs generic computer functions of acquiring/retrieving, processing and transmitting data and represent a purely conventional implementation of applicant’s demand forecasting in the general field of inventory management and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
These recited additional elements are merely generic computer components. The claims do present any other issues as set forth guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The recited artificial intelligence engine comprising machine learning models (software per se) is recited at a high level of generality and is used by a generic computer/processor, also recited at a high level of generality, to estimate/calculate product demand (mental step, mathematical concept), select an optimal forecasting algorithm (mental step, mathematical concept), and amounts to no more than mere instructions to implement an abstract idea on a generic computer. The artificial intelligence engine is used to generally apply the abstract idea without limiting how the artificial intelligence engine functions. The artificial intelligence engine comprising machine learning models (software per se) is described at a high level such that it amounts to using a computer with a generic artificial intelligence engine to apply the abstract idea. The recitation of an artificial intelligence engine in the claims does not negate the mental nature of these limitations because the artificial intelligence engine is merely used at a tool to perform an otherwise mental process. The limitations only recite outcomes without any details about how the outcomes are accomplished.
Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), the claims do not integrate the judicial exception into a practical application.
For the reasons outlined above, that claims 1, 8 and 11 recite a method of organizing human activity, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (e.g., processor, memory, user equipment, AI engine, etc.) is no more than a generic computer component used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
Accordingly, the claims are directed to an abstract idea.
Step Two of the Mayo/Alice Framework (Step 2B)
Having determined under step one of the Mayo/Alice framework that the claims are directed to an abstract idea, we next consider under Step 2B of the Guidance, the second step of the Mayo/Alice framework, whether the claims include additional elements or a combination of elements that provides an “inventive concept,” i.e., whether an additional element or combination of elements adds specific limitations beyond the judicial exception that are not “well-understood, routine, conventional activity” in the field (which is indicative that an inventive concept is present) or simply appends well-understood, routine, conventional activities previously known to the industry to the judicial exception.
Under step two of the Mayo/Alice framework, the elements of each claim are considered both individually and “as an ordered combination” to determine whether the additional elements, i.e., the elements other than the abstract idea itself, “transform the nature of the claim” into a patent-eligible application. Alice Corp., 573 U.S. at 217 (citation omitted); see Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself’ (emphasis added) (citation omitted)).
Here the only additional element recited in claims 1, 8 and 11 beyond the abstract idea are the processors, memory, artificial intelligence engine (software per se), acquisition engine (software per se), feature engineering module (software per se), event impact module (software per se), and user equipment (generic computer),” i.e., generic computer component. See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
Regarding the recited AI engine comprising one or more machine learning models for calculating a demand forecast, select an optimal forecast algorithm and categorize each batch of inventory into difference buckets, the AI engine comprising one or more machine learning models is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using generic machine learning models network on a generic computer, also recited at a high level of generality. The AI engine comprising one or more machine learning models is used to generally apply the abstract idea without limiting how the AI engine. The AI engine comprising one or more machine learning models is described at a high level such that it amounts to using a generic computer with generic machine learning models to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further the recitation of an AI engine comprising one or more machine learning models in the claims does not negate the mental nature of these limitations because the AI engine is merely used at a tool to perform an otherwise mental process.
With regards to Applicant’s argument that the claimed invention provides a specific technical improvement to inventory management, enables more granular inventory categorization and the like these argued ‘practical’ applications and/or ‘improvements’ are at best improvements in the abstract idea itself (i.e. business solution to business problems). The argued wished-for benefits do not integrate the abstract idea into a practical application, do not provide a technical solution to a technical problem, do not improve the underlying technology (e.g. process, memory, AI, etc.) and does not improve another technical field (e.g. inventory management, demand forecasting, etc. are not technical fields, they are well-known, established, conventional and routine economic practices).
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
Accordingly, the claims do not integrate the abstract idea into a practical application and are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to the recent Appeals Review Panel review of Ex parte Desjardins et al., the examiner respectfully disagrees.
While the Desjardins decision cautions against overbroad application of 35 U.S.C. 101 to artificial intelligence inventions, such inventions not categorically excluded from patentability, the thrust of the decision made clear that improvements to an AI model itself can be sufficient for the purpose of patent eligibility, even when the claims recite, on their face, an ostensibly “abstract idea.” Specifically, the Appeals Review Panel found that the claims under review provided a technical improvement in the functioning of machine learning models by enabling continual learning, reducing storage requirements, and preserving performance across tasks. In particular, the decision emphasized that the claimed invention addresses a technical problem ("catastrophic forgetting") and improves the operation of AI systems, not just through generic computer implementation but by a specific training strategy. To support this determination, the Appeals Review Panel looked to the specification which, on its own, disclosed how the invention would improve functioning of an AI model--in particular, the specification explained how the proposed invention would use less “storage capacity” and lead to “reduced system complexity." These improvements, which the Appeals Review Panel found were incorporated into the claims as a whole, constituted an “improvement to how the machine learning model itself operates”.
None of Applicant’s arguments, disclosure or claims discusses at any level that the generically applied/utilization of AI engine comprising one or more machine learning models or the use of LTSM for forecasting/estimating demand represents or provides an improvement in machine learning itself.
As for the newly amended step wherein the AI engine automatically selects an optimal forecast algorithm from a set of forecast algorithms be performing ensemble forecast generation, this step is performed by a generic (black box) AI engine recited at a very high level of generatily and merely recites the wished for results (select optimal forecast) without any details as to how the AI engine functions. Ensemble forecasting is a well-known, conventional and routine mathematical approach to forecasting and does not represent an improvement in AI or ML itself. The AI engine/he machine learning modesl are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic machine learning on a generic computer, also recited at a high level of generality. The machine learning is used to generally apply the abstract idea without limiting how the machine learning functions. The machine learning is described at a high level such that it amounts to using a generic computer with generic machine learning to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further nowhere in Applicant’s disclosure is there any discussion at any level that the utilization of a generic AI engine comprising machine learning models to select an optimal forecasting algorithm is directed to improving the general field of machine learning or addresses a technical problem in the field of machine learning or provides an improvement to a specific machine learning model, algorithm, technique or the like.
Accordingly, the claims are nothing like those in the Desjardins decision and are therefore not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims provide an improvement in computer related technology/similar to McRo, the examiner respectfully disagrees.
In McRO, the Federal Circuit concluded that the claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules were not directed to an abstract idea. McRO, 837 F.3d at 1316, 120 USPQ2d at 1103. The basis for the McRO court's decision was that the claims were directed to an improvement in computer animation. The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. 837 F.3d at 1313, 120 USPQ2d at 1101. The McRO court found that the claims clearly improved the functioning of the claimed computer and that the claims directed to recite improvement (e.g. rules). Further the court found that the specification clearly disclosed that the claimed improvement improved the functioning of the computer.
In sharp contrast to the McRO decision the instant application merely claims to mere instructions to apply the abstract idea using conventional, routine, well-understood and widely used computers and generic machine learning models (AI engine). Further the claims merely recite a general linking to the use of the abstract idea to a particular technological environment (e.g. processor, memories, AI engine, etc.). The recite processor/computer merely performs generic computer functions of acquiring/retrieving, processing and outputting/transmitting data. The performance of the processor/computer is not improved in any way. Further Applicant’s disclosure lacks any discussion of improving the performance of the underlying technological environment. The processor/computer merely ‘executes’ the abstract idea and is used merely a tool. The claims are not directed to improving computer performance and do not recite any such benefit. Further Applicant’s specification does not disclose any teachings related to improving computer performance.
At best the claimed/disclosed may improve forecasting warehouse/fulfillment center level inventory demand, however forecasting inventory demand, including doing so through the use of AI engine/machine learning models, Bayesian optimization, automatic selection of forecasting algorithms or the like does not improve any of the underlying technological elements/components nor does it improve another technical field (i.e. neither inventory management nor demand forecasting represent a technical field). None of the argued benefits represent an improvement in an underlying technology (e.g. the claims processors, memories, artificial intelligence engine (software per se), acquisition engine, etc.) or represent an improvement in another technical field (e.g. inventory management is a business field/problem, demand forecasting is a business field/problem).
As made clear in Uniloc USA, Inc. v. LG Electronics USA, Appeal No. 19-1835 (Fed. Cir. Apr. 30, 2020), the Federal Circuit reaffirmed that software inventions are patentable in the U.S. with a bright-line statement: “Our precedent is clear that software can make patent-eligible improvements to computer technology, and related claims are eligible as long as they are directed to non-abstract improvements to the functionality of a computer or network platform itself.” The instant application fails to disclose, much alone claim, a technical solution to a technical problem; fails to disclose an improvement in computer or computer networks or computer related technology (e.g. none of the recite processor, memory, user equipment, or the like is improved in any way – each is recited at a high level of generality and the claims merely recite instructions to apply the abstract idea using the generic computers/generic AI engine comprising one or more machine learning models).
There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
The claims do not recite a technical problem or technical solution necessarily rooted in computers, computer networks or another technical field.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant's argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to Subject Matter Eligibility Example 47, claim 3, the examiner respectfully disagrees.
Subject Matter Eligibility Example 47, claim 3, is directed to a system and method that utilizes a trained artificial neural network to identify/detect and drop malicious network packets in real-time wherein the trained ANN detects anomalies in network traffic more accurately than traditional network anomaly detection methods and provides for faster training times. The claimed invention is directed to providing a technical solution to a technical problem. More specifically providing, similar to the findings in DDR, "the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." Further that the invention established an "inventive concept" for resolving an Internet-centric problem.
In sharp contrast the instant application and claimed invention are directed to forecasting one inventory forecasting/categorization at warehouse product batch level– a business problem, not a technical problem, not a solution necessarily rooted in computer technology, not a solution to overcome a problem arising from the realm of computer networks.
While the claims recite an AI engine comprising one or more machine learning for pre-process a set of data parameters, an AI engine to calculate a demand forecast using one or more well-known forecasting algorithms, correct calculated demand forecasting using exogenous variables and selecting/pick best ensemble of forecasting models, to adjust forecasted demand of each product at a FC level to a batch level models and categorize each batch of inventory into different buckets at the product batch level, the claims merely recite the conventional, well-understood, and routine use of an “AI engine” and/or one or more machine learning models wherein the claims generally apply the abstract idea with limiting how the “AI engine” and/or one or more machine learning models are trained or function. The “AI engine” and/or one or more machine learning models are described at such a high level that the claims amount to using a computer with a generic one or more machine learning models to apply the abstract idea. Further the dependent claims do not shift the focus of the invention away from forecasting inventory demand, the dependent claims do not provide a technical solution to a technical problem, nor do the dependent claims improve the underlying technology or a technical field.
Accordingly, the claims are not similar to those found patentable in Subject Matter Eligibility Example 47, claim 3 and are therefore not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to Bascom Global Internet vs. AT&T Mobility (2016), the examiner respectfully disagrees.
In Bascom the court found that the combination of additionally elements specifically the installation of a filtering tool at a specific location remote from the end-users with customizable filtering features specific to each user wherein the filtering tool at the ISP was able to identify individual accounts that communicate with the ISP server and to associate a request for internet content with a specific individual account were held to be meaningful limitations because the confined the idea of content filtering to a particular, practical application of the abstract idea.
In sharp contrast to the instant application which is directed to using well-known, conventional and routine inventory forecasting. The claims fail to recite customizable filtering, fail to recite a computer network/Internet, fail to recite a remote computers or the like. The instant application is in no way even remotely related to filtering Internet content by Internet Service Providers as is the case in Bascom.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims recite an inventive concept/significantly more than the abstract idea, the examiner respectfully disagrees.
As discussed above, the claims are directed to both a fundamental economic practice (demand forecasting) as well as directed to a series of mental process steps readily capable of being performing by a human mind or via pen and paper. At best the recited generic computing elements (e.g. processor, memories, AI engine, etc.) are mere instructions to apply the abstract idea and merely involve generic computers performing generic computer functions of retrieving, processing and outputting data. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Moreover, when viewed as a whole with such additional elements considered as an ordered combination, the claim modified by adding a generic computer would be nothing more than a purely conventional computerized implementation of applicant's demand forecasting in the general field of inventory forecasting/management and would not provide significantly more than the judicial exception itself. The recitation of a generic computer in these claims does not negate the mental nature of these limitations because the generic computing elements are merely used at a tool to perform an otherwise mental process.
Automating the selection of a forecasting algorithm nor utilizing a well-known mathematical approach (Bayesian) to categorize inventory into buckets is not enough for eligibility when it is recited at this level of generality without technical implementation details. See Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (generic speed and efficiency increases from applying a computer to a task do not improve computer functioning); Cel/spin Soft, Inc. y. Fitbit, Ine., 927 F.3d 1306, 1316 (Fed. Cir. 2019) (“But the need to perform tasks automatically is not a unique technical problem.”); Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“[A]utomation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”’); O/P Techs., 788 F.3d at 1363 (“But relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”’).
The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of demand forecasting (Claims 1, 8) or data processing (Claim 11). In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, memories, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in independent claims 1, 8 and 11 through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
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, 5, 6, 8, 9 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent Claims 1, 8 and 11, the claims are directed to the abstract idea of forecast (inventory) demand. This is a process (i.e. a series of steps) which (Statutory Category – Yes –process).
The claims recite a judicial exception, a method for organizing human activity, forecasting (inventory) demand (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to forecasting warehouse/fulfillment center level inventory demand (Claims 1, 8) or transmitting categorized inventory data and demand forecast data and generating an alert (Claim 11), wherein forecasting (inventory) demand are a fundamental economic practice that fall into the abstract idea subcategories of sales activities and/or commercial interactions. Further all of the steps of “retrieve”, “pre-process”, “estimate”, “correct”, adjust”, “classify”, “categorize”, “forecast”, and “send” (Claims 1, 8) and “retrieve”, “pre-process”, “estimate”, “correct”, “adjust”, “classify”, “categorize”, “transmit” and “generate” (Claim 11) recite functions of the demand forecasting are also directed to an abstract idea that falls into the abstract idea subcategories of sales activities and/or commercial interactions. The intended purpose of independent claims 1, 8 and 11 to generate a warehouse/fulfillment center level inventory demand forecast.
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of sales activities and/or commercial interactions. The exceptions are the user (who is a human, claims 1 and 8 only) and the additional generic computer elements: one or more processors, memory comprising process-executable instructions, user equipment, system, artificial intelligence engine comprising one or more machine learning models (software per se; Specification: Paragraphs 52, 53; Figure 2, Element 214; Figure 4, Element 400), acquisition engine (software per se) , feature engineering module (software per se), event impact module (software per se) and user equipment (Specification: Paragraph 47 – generic computer).
Accordingly, the claims recite an abstract idea under Step 2A, Prong One, we proceed to Step 2A, Prong Two. Considering whether the additional elements set forth in the claim integrate the abstract idea into a practical application, the previously identified non-abstract elements directed to generic computing components include: one or more processors, memory comprising process-executable instructions, artificial intelligence engine comprising one or more machine learning models (software per se), acquisition engine (software per se), feature engineering module (software per se), event impact module (software per se) and user equipment. These generic computing components are merely used to acquire/retrieve, process and output/transmit data as described extensively in Applicant’s specification (Figures 2, 10). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Moreover, when viewed as a whole with such additional elements considered as an ordered combination, the claim modified by adding a generic computer would be nothing more than a purely conventional computerized implementation of applicant's forecasting in the general field of business management and would not provide significantly more than the judicial exception itself. Note McRo, Inc. v. Bandai Namco Games America Inc. (837 F.3d 1299 (Fed. Cir. 2016)), guides: "[t]he abstract idea exception prevents patenting a result where 'it matters not by what process or machinery the result is accomplished."' 837 F.3d at 1312 (quoting O'Reilly v. Morse, 56 U.S. 62, 113 (1854)) (emphasis added). The claims are not directed to a particular machine nor do they recite a particular transformation (MPEP § 2106.05(b)).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues as set forth in the guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), claims 1, 5, 6, 8, 9 and 11 do not integrate the judicial exception into a practical application.
Regarding the use of the generic (known, conventional) recited one or more processors, memory comprising process-executable instructions, artificial intelligence engine (software per se), acquisition engine (software per se), feature engineering module (software per se), event impact module (software per se) and user equipment the Supreme Court has held "the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. 208, 223. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The claims as a whole do not recite more than what was well-known, routine and conventional in the field (see MPEP § 2106.05(d)). In light of the foregoing and under the guidance, that each of the claims, considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
Regarding the recited AI engine comprising one or more machine learning models for calculating a demand forecast, select an optimal forecast algorithm and categorize each batch of inventory into difference buckets, the AI engine comprising one or more machine learning models is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using generic machine learning models network on a generic computer, also recited at a high level of generality. The AI engine comprising one or more machine learning models is used to generally apply the abstract idea without limiting how the AI engine. The AI engine comprising one or more machine learning models is described at a high level such that it amounts to using a generic computer with generic machine learning models to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Additionally, the claims recite a judicial exception, a mental processes, which can be performed in the human mind or via pen and paper (Judicial Exception – Yes – mental process).
The claimed steps of pre-process the set of data parameters, estimate a demand associated with a product by calculating a demand forecast, correct the calculated demand forecast, automatically select an optimal forecast algorithm, adjust the forecasted demand, classify the product into different batches, categorize the inventory into different buckets and forecast a warehouse level inventory demand (Claims 1, 8); and pre-process the set of data parameters, estimate a demand associated with a product by calculating a demand forecast, correct the calculated demand forecast, automatically select an optimal forecast algorithm, classify the product into different batches, and categorize the inventory into different buckets (Claim 11) all describe the abstract idea. These limitations as drafted are directed to a process that under its reasonable interpretation covers performance of the steps in the mind but for the recitation of the generic computer components. Other than the recitation of a one or more processors, memory comprising process-executable instructions, artificial intelligence engine comprising one or more machine learning models (software per se), feature engineering module (software per se), event impact module (software per se) and user equipment nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea because the steps of receiving a set of data parameters from a data base recites insignificant pre-solution activity (i.e. data gathering). The step of send an alert to one or more users (Claims 1, 8) transmit the categorized inventory data and the demand forecast data (Claim 11) and generate an alert at the UI (Claim 11) recite insignificant post-solution activity (i.e. data output) – wherein it is noted that the intended use of the alert of claims 1 and 8 “to act on product batches that are bound to expire or cause losses” is directed to an non-functional intended use of the alert and has not been given patentable weight (the alert may or may not actually received by the one or more users who may or may not actually act) and the alert generated at the UE and the data transmitted to the UE of Claim 11 may or may not actually received or viewed or acted upon a user/human (mere data on a screen, non-functional descriptive material, extra-solution activity). The mere nominal recitation of a generic processor/computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. (Judicial Exception recited – Yes – mental process).
As for the recited artificial intelligence engine comprising one or more machine learning models (software per se), the AI engine is recited at a high level of generality and is used by a generic computer/processor, also recited at a high level of generality, to estimate/calculate product demand (mental step, mathematical concept), automatically select a forecasting algorithm and categorize each batch of inventory into different buckets and amounts to no more than mere instructions to implement an abstract idea on a generic computer. The artificial intelligence engine is used to generally apply the abstract idea without limiting how the artificial intelligence engine functions. The artificial intelligence engine comprising one or more machine learning models (software per se) is described at a high level such that it amounts to using a computer with a generic artificial intelligence engine/one or more machine learning models to apply the abstract idea. The recitation of an artificial intelligence engine in the claims does not negate the mental nature of these limitations because the artificial intelligence engine is merely used at a tool to perform an otherwise mental process. The limitations only recite outcomes without any details about how the outcomes are accomplished.
The claims do not integrate the abstract idea into a practical application. The generic one or more processors, memory comprising process-executable instructions, artificial intelligence engine comprising one or more machine learning models (software per se) and user equipment are recited at a high level of generality merely performs generic computer functions of acquire/retrieve, process and output/transmit data. The generic processor/computer merely applies the abstract idea using generic computer components. The 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 claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
As discussed above the additional elements in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the retrieve and transmit steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applications specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is ineligible (Provide Inventive Concept – No).
Regarding the recited AI engine comprising one or more machine learning models for calculating a demand forecast, select an optimal forecast algorithm and categorize each batch of inventory into difference buckets, the AI engine comprising one or more machine learning models is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using generic machine learning models network on a generic computer, also recited at a high level of generality. The AI engine comprising one or more machine learning models is used to generally apply the abstract idea without limiting how the AI engine. The AI engine comprising one or more machine learning models is described at a high level such that it amounts to using a generic computer with generic machine learning models to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further the recitation of an AI engine comprising one or more machine learning models in the claims does not negate the mental nature of these limitations because the AI engine is merely used at a tool to perform an otherwise mental process.
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 5, 6, and 9, the claims are directed to the abstract idea of forecasting and/or alerting and merely further limit the abstract idea claimed in independent claims 1, 8 and 11.
Claim 5 further limits the abstract idea by estimate a future category of inventory level based on the forecasted demand (a more detailed abstract idea remains an abstract idea). Claim 6 further limits the abstract idea by providing and updating key metrics (a more detailed abstract idea remains an abstract idea). Claim 9 further limits the abstract idea by predicting demand forecast data for one or more upcoming weeks (a more detailed abstract idea remains an abstract idea).
None of the limitations considered as an ordered combination provide eligibility because taken as a whole the claims simply instruct the practitioner to apply the abstract idea to a generic computer.
Further regarding claims 1, 5, 6, 8, 9 and 11, Applicant’s specification discloses that the claimed elements directed to a user (human) and the additional generic computing elements comprising processor, memory, AI engine (software per se) acquisition engine (software per se), feature engineering module (software per se), event impact module (software per se) and user equipment at best merely comprise generic computer hardware which is commercially available (Figures 2, 10). More specifically Applicant’s claimed features directed to a system do not represent custom or specific computer hardware circuits, instead the terms merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims merely recite manipulating data utilizing generic computer hardware (e.g. memory, processor, etc.). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Further the lack of detail of the claimed embodiment in Applicant’s disclosure is an indication that the claims are directed to an abstract idea and not a specific improvement to a machine.
Accordingly given the broadest reasonable interpretation and in light of the specification the claims are interpreted to include the process steps being performed by a human mind or via pen and paper. The claim limitations which recite a computer implemented method is at best recite generic, well-known hardware. However, the recited generic hardware simply performs generic computer function of storing, accessing, displaying or processing data. Generic computers performing generic, well known computer functions, alone, do not amount to significantly more than the abstract idea. Further the recited memories are part of every conventional general-purpose computer.
Applicant has not demonstrated that a special purpose machine/computer is required to carry out the claimed invention. A special purpose machine is now evaluated as part of the significantly more analysis established by the Alice decision and current 35 U.S.C. 101 guidelines. It involves/requires more than a machine only broadly applying the abstract idea and/or performing conventional functions.
Applicant’s specification discloses that the claimed elements directed to a one or more processors, memory comprising process-executable instructions, artificial intelligence engine (software per se) and user equipment merely comprise generic computer hardware which is commercially available (Specification: Figures 2, 10; Paragraphs 52, 53; Figure 4, Element 400). More specifically Applicant’s claimed features directed to a system and components do not represent custom or specific computer hardware circuits, instead the term system merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
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 SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
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