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 .
DETAILED ACTION
1. This office action is responsive to amendment filed on 05/05/2026. Claims 1-20 are amended. Claims 1-20 are pending examination.
Claim Rejections - 35 USC § 101
2. 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 1-20 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.
Claim(s) 1 is/are drawn to method (i.e., a process), claim(s) 2 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 3 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 1-3 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. Specifically, claim(s) 1-3 recite(s) (a) utilizing a combination to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a that forecasts a consumption profile of a global population from birth to death; (b) executing an exception handler that identifies individuals whose consumption patterns deviate from said by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a that trains without transmitting raw data; (ii) optimizing a global supply chain that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning and the non-retail architecture of (c)(iii) and (d),, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, grouped as following:
(a) utilizing a combination to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a that forecasts a consumption profile of a global population from birth to death, which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
(b) executing an exception handler that identifies individuals whose consumption patterns deviate from said by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
(c) provisioning for entire needs of said global population by: (i) aggregating demand via a that trains without transmitting raw data; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
(ii) optimizing a global supply chain that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms that automatically select lowest-cost, quality-verified offers; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
(d) fulfilling said needs through a non-retail architecture implemented as said distributed, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning and the non-retail architecture of (c)(iii) and (d); which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform perform(s) the steps or functions of (a) utilizing a combination to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a that forecasts a consumption profile of a global population from birth to death; (b) executing an exception handler that identifies individuals whose consumption patterns deviate from said by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a that trains without transmitting raw data; (ii) optimizing a global supply chain that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning and the non-retail architecture of (c)(iii) and (d),. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical 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 (Vanda Memo), the claims do not apply the abstract idea with, or by use of, 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 (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. As discussed above, taking the claim elements separately, the convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform perform(s) the steps or functions of (a) utilizing a combination to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a that forecasts a consumption profile of a global population from birth to death; (b) executing an exception handler that identifies individuals whose consumption patterns deviate from said by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a that trains without transmitting raw data; (ii) optimizing a global supply chain that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning and the non-retail architecture of (c)(iii) and (d),. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 4-20 further describe the abstract idea of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. Claim(s) 4-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform, quantum computing, quantum processor, variational quantum eigensolver (VQE) to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. As discussed above, taking the claim elements separately, the convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform, quantum computing, quantum processor, variational quantum eigensolver (VQE) perform(s) the steps or functions of wherein the predictive demand forecasting further employs a recurrent to update individualized life-path estimators covering birth-to-death consumption across housing, food, healthcare, education, and transportation; wherein on- demand delivery scheduling is based on geospatial user density and regional logistics optimization, and wherein the exception handler is governed to dynamically reassign resources; wherein for real-time consumption feedback and product lifecycle monitoring, and wherein supply generation is automated using a bipartite graph matching optimized by reinforcement learning; wherein optimize between price, quality, and delivery speed using consumer utility scorecards, and wherein product distribution is optimized by blockchain smart contracts implementing a proof-of-delivery consensus; wherein high-accuracy demand forecasts are deployed simulations executed, to approximate complex demographic interactions; wherein the platform includes built-in tools for governments to simulate budgetary allocations using a digital twin of a regional economy, and wherein the system maintains a poverty alleviation score indexed to provided cost reductions; wherein a delivery optimization uses a time-expanded graph algorithm to minimize delivery routes while maximizing batch fulfillment rates; (i) receiving, by a real-time consumer, consumer demand information associated with one or more goods or services; (ii) generating, by the real-time consumer, demand data for the one or more goods or services for one or more consumers based on the consumer demand information; (iii) providing, the demand data to a scheduling, one or more localized supply channels, and a modular production module; (iv) generating, by the scheduling module, fulfillment scheduling data for the one or more goods or services based on the demand data; (v) transmitting, the fulfillment scheduling data and the demand data to one or more producers for production and delivery of the one or more goods or services according to the demand data; and (vi) causing, the one or more goods or services to be provided from the one or more producers to the one or more consumers in said non-retail architecture that bypasses wholesale and retail intermediaries, thereby further reducing computational latency and bandwidth consumption. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Prior Art
3. As for the amendment submitted on 05/05/2026, the Office is unaware of any references that teach the combination of limitations found in the claims.
In reference to independent claims 1-3, the Office is unaware of any references that teach, individually or without an unreasonable combination of references, the combination of limitations steps found in the claims especially limitation that says: “(a) utilizing a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a predictive model that forecasts a consumption profile of a global population from birth to death; (b) executing an algorithmic exception handler that identifies individuals whose consumption patterns deviate from said predictive model by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals using edge computing nodes; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a federated learning framework that trains local models on user devices without transmitting raw data; (ii) optimizing a global supply chain using a graph neural network (GNN) that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms via blockchain smart contracts that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed edge computing network, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning using the reverse- bidding procurement mechanisms and the non-retail architecture of (c)(iii) and (d).”. No reference found that would teach the above limitation(s).
No reference found that would teach any of the limitations of claim 1. Therefore, it lacks the combination of claimed elements as claimed by the independent claims.
All these references listed above teaches some of the features in the limitations of the claim but when combining it becomes not obvious and the references would teach the claim as a whole.
Examiner note: no reference found that would teach the combination of limitations of claim 1-3, especially claim limitations: (a) utilizing a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a predictive model that forecasts a consumption profile of a global population from birth to death; (b) executing an algorithmic exception handler that identifies individuals whose consumption patterns deviate from said predictive model by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals using edge computing nodes; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a federated learning framework that trains local models on user devices without transmitting raw data; (ii) optimizing a global supply chain using a graph neural network (GNN) that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms via blockchain smart contracts that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed edge computing network, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning using the reverse- bidding procurement mechanisms and the non-retail architecture of (c)(iii) and (d), and which is an idea of acquiring capture images from different places and extracting user face area or facial feature information from images and then generating composite image based on satisfying image generation condition and face authentication from different places then it transmits information indicating points the user has visited already at different times and which points should be visited to satisfy the image generation condition to the display device.
When taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available prior art suggest or otherwise render obvious further modification of the evidence at hand. Such modifications would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
NPL Reference
7.A. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions” describes “Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.”.
7.B. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Cloud-based configurable data stream processing architecture in rural economic development” describes “This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth. The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data. Reinforcement learning further optimizes the model, enhancing the analysis and accuracy of multidimensional data inputs for more reliable price predictions.”.
Pertinent Art
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20240362653 teaches similar invention which describes The method and platform created to implement this method MMPALL+S+SS+M, or simply MPP (the method, platform, patent) C N M ∈ BB N M b n m + .Math. i = 1 ∞ ( AI i + I i + P i + .Math. ) To N>No M>M or NNo or N best quality and lowest price in the global market, they are naturally the products with the best cost-performance ratio among (global) similar products or goods; This means that the manufacturers on this platform produce, supply, and distribute to consumers or members globally the products or goods with the best cost-performance ratio, quality, and lowest price; The patent aims to completely eradicate, eliminate, and eliminate the random production and consumption that has existed in the production, circulation, and consumption of goods since ancient times throughout human civilization, In another word, eliminate random production and consumption; The method asserts that the complete eradication, elimination, and removal of blind or random production and consumption will eliminate the enormous waste resulting from the imbalance of supply and demand in various industries and products; Therefore, the method aims to eliminate waste in every industry caused by blind or random production and consumption; For examples as pointed out by Alibaba Research Institute (see the First Finance Journal, January 2021), the Chinese clothing industry generates annual waste of 800 billion to 1.2 trillion yuan, with 400 billion yuan in production and 400 billion to 800 billion yuan in distribution and consumption; The method eliminates waste in each & every industry in each and every product in productions, distributions & consumptions; The method proposes to eliminate inefficient and ineffective small, medium, large, and even huge enterprises in every industry and every product; Thus, the enterprises and merchants under this method and platform are the most efficient and effective in the world or in similar enterprises; Consumers and members under this method and platform can demand production, supply, and distribution according to their needs, and are supported by the method and the platform, i.e., eliminates inefficient and ineffective small, medium, large and even huge enterprises in each and every industry in each and every products, goods and/or services; Therefore, the companies and merchants on this platform using this method are the most efficient and effective among global or similar companies; Consumers and members on this platform can exchange volume for price, produce, supply, distribute, and support the according to demand; Furthermore, the companies and manufacturers under the method and platformer do not incur fixed costs, such as advertising costs, sales expenses, market expenses, and product storage costs in their product production, circulation, and consumption processes, and more importantly, no financial costs such as bank loan interest costs; This is because the companies and manufacturers on this platform (usually) have already received full payment from this platform for the products or goods required by consumers on this platform using this method or do not need to obtain bank loans at all, as is the case with some products or goods on this platform, such as a real estate project; For example, in the case of a commodity house, there is no need for bank loans, such as in the case of “crowdfunding to build a house”, all the funds raised or paid by buyers have already covered the entire price of the commodity house, thus eliminating the need for additional bank loans and the resulting interest costs, which in turn do not drive up house prices; Because there are no fixed costs for companies, such as advertising, sales, market, and bank loan interest, which are typically required for general companies outside of this platform, consumers on this platform can purchase commodity houses of the same quality at prices 25% to 40% lower than the market price, i.e., no cost of advertisement, sales, marketing and most of all no cost of finance or cost of banking loan, or interests are needed for most of enterprises under the method the platform and the patent; This method and platform have no advertising fees, no sales expenses, no market expenses, and no financial or bank loan interest costs; The reason for the absence of financial or bank loan interest costs is that consumers under this platform and method have fully paid the manufacturers or companies producing products or goods under this method and platform, and thus the manufacturers or companies do not need to borrow any money from banks, as clarified and stated previously; In addition to the aforementioned, there are no advertising fees, sales expenses, or market expenses for the manufacturers and companies under this platform and method because the businesses and manufacturers under this platform and method have been reverse-bid and ordered through this platform, including but not limited to reverse ordering and customization, pushing products to consumers in advance, and supplying and distributing products based on demand at the best quality and lowest price compared to similar products; Therefore, the businesses under this method and platform can transfer the fixed costs that are typically incurred by other businesses, such as advertising fees, sales expenses, and market expenses, to consumers in the form of products that are produced and supplied on demand; This gives the manufacturers and companies under this method and platform unparalleled competitive advantages (unfair competitive advantages) in terms of pricing, quality, and sales volume, and they do not need to worry about selling their products because their products are usually sold out to consumers through this platform before production even begins, and they typically receive payment for all the products or goods in advance, thus, the businesses and manufacturers under this platform and method have not only unfair competitive advantages compared to other businesses and manufacturers that do not use this method and platform, but they can also eliminate and completely replace those businesses and manufacturers that are not competitive with them, making it impossible for them to survive in the industry; In other words, non-users of this method and platform in the industry cannot be as efficient and effective as the businesses and manufacturers under this method and platform, and this method and platform can eliminate ineffective and inefficient small, medium, large, and even huge enterprises in each and every industry in each and every product, good, and service, i.e., eliminate ineffective and inefficient small, medium, large and even huge enterprises in each and every industry in each and every products, goods and services; This method, platform, and patent save people's time and money; Firstly, it saves money for consumers or members using this method and platform because manufacturers under this method and platform do not have the fixed costs that general companies must have, such as advertising, sales, marketing, and even interest and financial costs from bank loans; Moreover, this method and platform use guidance from artificial intelligence and other technologies to produce products specifically for consumers and offer full payment in advance for orders generated from the platform, allowing manufacturers to offer discounted or lowest prices, therefore, it is undoubtedly true that this method and platform save consumers a lot of money in most cases; In addition, according to the method and platform, when consumers or members purchase real estate from developers under this method and platform using a model similar to “crowdfunding for construction,” they can acquire the property at a price 25%-40% lower than the market price due to the absence of advertising, sales, and marketing costs, as well as the lack of interest from bank loans, hence, consumers or members using this method and platform not only save a lot of money but also obtain an investment return of about 25%-40% when purchasing properties; This method and platform also save people's time; This is because, the platform can re-commend and provide the best quality products or goods with the lowest price and the best value for money to consumers or members at all stages of life based on their various needs; Hence, consumers or members using this method and platform do not need to spend time researching, considering, and comparing what products or goods to buy; For instance, when a new mother who lacks experience in childcare does not have enough breast milk, the platform can use artificial intelligence to re-commend the most suitable and high-quality formula at the lowest price and the best value for money for her baby, such as Feihe and Junlebao in China, which are both better in quality and price than other well-known brands in China or globally; Therefore, the mother does not need to spend any time considering what formula to buy; Another example is for white-collar women who usually spend time considering what clothes to wear for work, leisure, and at home; However, if they are consumers or members of this method and platform, the platform can re-commend and produce clothes that meet their needs seamlessly using artificial intelligence and advanced manufacturing technology, such as the Alibaba Group's advanced manufacturing project; The method and platform MMPALL+S+SS+M enable the entire manufacturing industry, particularly in the fashion sector, to become a service industry. This is achieved through the implementation of AI technologies in the design and production processes, as well as the collection of consumer needs on the platform, i.e., The method and the platform MMPAL+S+SS+M are able to make manufacturing industry to become service industry, which means no more “so called” traditional First, Second, Third Industry anymore; This revolutionizes the concept and statistical criteria of the traditional First, Second, and Third Industry; The fusion of these industries into one under this method and platform's AI technology applies to all industries, including both light and heavy industries; This revolutionarily redefine, reshape, redefine modern commercial rules, economic theories, and human civilization progress; As a result, the traditional concepts of the first, second, and third industries no longer exist; The integration of the first, second, and third industries means that, under this method and platform, artificial intelligence has transformed the manufacturing industry into the service industry; There is no longer any concept or statistical criteria for the traditional first, second, and third industries; It is particularly emphasized that the transformation of the manufacturing industry into the service industry by the artificial intelligence of this method and platform is applicable to all industries, sectors, and types of businesses, not just the clothing industry mentioned in example in clothing industry (strictly speaking, the clothing industry belongs to the light industry, which is the traditional second industry); It is applicable to any other light industry (the second industry) and any other heavy industry (the third industry); This method and platform constitute why this method, platform, and patent are considered revolutionary and disruptive, reshaping and redefining the rules of modern commerce, and modern economic theory, whether it is microeconomics or macroeconomics, as well as the progress of human civilization, human being civilization and the reasons behind them; This method and platform have created a wave (more thorough, revolutionary, disruptive, and reshaping than the first, second, third, and fourth industrial revolutions, affecting the manufacturing and service industries, and even the GDP of a country or region), including artificial intelligence, Industry 4.0, or “Smart Industry 5.0”; Leveraging unprecedented method comprehensive and unprecedented commercial rules, and unprecedented modern economic and human civilization perspectives, this method and platform have deeply revolutionized, disrupted, and reshaped modern business rules, fundamentally or at least significantly changing the principles of modern economics, the foundation of human civilization, and effective responses to global climate change and low-carbon initiatives, thereby creating a wave including, but not limited to, “Smart Industry 5.0,” “Industrial Internet,” “Digital Industrialization,” “Industrial Digitalization,” and the supply-side structural reform, consumer upgrading and sinking, domestic and international dual circulation, new development stages, concepts, and patterns, allowing the market to play a decisive role in the allocation of resources and production factors; This patent, this method, and this platform will enable manufacturers and companies under the platform to closely collaborate with manufacturers, factories, and companies related to the platform; Even manufacturers and companies that collaborate with the platform will not incur fixed costs that are typically required by general manufacturers and must be passed on to consumers, such as advertising costs, sales expenses, market expenses, and especially financial costs such as bank loan interest; For the majority of manufacturers and companies under this patent, this method, and this platform, as claimed by “non-retail,” production and supply are based on demand, and consumers or members under the platform can order the lowest-priced and highest-quality products, goods, or services globally by paying the full price in advance; Of course, manufacturers and companies under this patent, including those closely collaborating or cooperating with the platform, will not incur advertising costs, sales expenses, market expenses, or bank loan costs; For example, “crowdfunding for housing” under this method and platform is a good and perfect example that confirms and proves there are no advertising, sales, market, or bank loan interest costs; The method also supports that this platform will completely eliminate small, medium, large, and even huge manufacturers and companies that produce various products inefficiently and ineffectively; This is because no manufacturer or company can sell products or services at the lowest price with the best quality and cost-effectiveness globally, and without incurring financial costs such as advertising, sales, market, and bank loan interest costs, at a price lower than 25%-40% below market price; Therefore, manufacturers and companies that are not under this patent, this method, or this platform, in any industry, product, goods, or service, cannot compete with manufacturers and companies under this patent, this method, or this platform, or those closely collaborating or cooperating with the platform; As a result, it won't take long for manufacturers and companies under this patent, this method, and this platform to gradually and eventually eliminate various products, goods, and service manufacturers and companies in all industries, especially inefficient and ineffective small, medium, large, and even huge manufacturers and companies, i.e., For Bnm belongs to MMPALL+S+SS+M and sells to Cnm for products, goods and services Pnm, there will be no cost of advertisement, sales, marketing etc. and most of all, especially no cost of finance, no cost of interests for banking loans.
Response to Arguments
6. Applicant's arguments filed 05/05/2026 have been fully considered but they are not persuasive.
A. Applicant argues that the 112(b) rejection should be withdrawn.
Examiner agrees. 112(b) is withdrawn based on the new set of claims added and amended on 05/05/2026.
B. Applicant argues that the 112(a) rejection should be withdrawn.
Examiner agrees. 112(a) is withdrawn based on the new set of claims added and amended on 05/05/2026.
C. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong One. Examiner respectfully disagrees. As for Step 2A Prong One, of the Abstract idea is directed towards the abstract idea of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)), (MPEP § 2106.04).
D. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong Two. Examiner respectfully disagrees. As for Step 2A Prong Two, the claim limitations do not include additional elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and the claim is not more than a drafting effort designed to monopolize the judicial exception and the claim limitation simply describe the abstract idea. The limitation directed to recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products does not add technical improvement to the abstract idea. The recitations to “convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform” perform(s) the steps or functions of (a) utilizing a combination to process demographic, behavioral, economic, geographic, and lifecycle data patterns, thereby generating a that forecasts a consumption profile of a global population from birth to death; (b) executing an exception handler that identifies individuals whose consumption patterns deviate from said by a threshold, including those in poor economic conditions or experiencing social class mobility, and upon identification, triggers a higher-frequency data sampling process for said individuals; (c) provisioning for entire needs of said global population by: (i) aggregating demand via a that trains without transmitting raw data; (ii) optimizing a global supply chain that performs multi-objective optimization across producers, logistics, and consumers; and (iii) executing reverse-bidding procurement mechanisms that automatically select lowest-cost, quality-verified offers; (d) fulfilling said needs through a non-retail architecture implemented as said distributed, wherein fulfillment operations bypass wholesale and retail intermediaries, thereby reducing computational latency and bandwidth consumption associated with centralized retail data processing; and (e) delivering products to consumers based on said provisioning and the non-retail architecture of (c)(iii) and (d),. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical 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 (Vanda Memo), the claims do not apply the abstract idea with, or by use of, 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 (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
E. Applicant argues that the claims are not directed to a judicial exception under Step 2B.
Examiner respectfully disagrees. As for Step 2B, The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the limitation directed to recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products does not add significantly more to the abstract idea. Furthermore, using well-known computer functions to execute an abstract idea does not constitute significantly more. The recitations to “convolutional neural networks (CNNs), long short-term memory (LSTM) network, predictive model, predictive model, edge computing nodes, federated learning framework, local models, user devices, graph neural network (GNN), blockchain smart contracts, edge computing network, reverse-bidding procurement mechanisms, system, processors, memory, non transitory computer readable storage medium, AI enabled platform” are generically recited computer structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of recognizing individuals with poor economic conditions by a prediction model and fulfill peoples lifetime needs by delivering the worlds lowest prices and best quality products. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
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 extension fee 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.
Conclusion
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
