DETAILED ACTION
This communication is a Non-Final Rejection Office Action in response to the 11/1/2024 filling of Application 18/934,358. Claims 1-18 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case, Claims 1-6 are directed toward a method for managing a supply chain. Claims 7-12 are directed toward an system for managing a supply chain. Claims 13-18 are directed toward an system for managing a supply chain. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 recites the abstract idea of analyzing
the plurality of dynamics of the food supply chain network and analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and evaluating sustainability of the food supply chain network based on the efficacy of each of the set of modelling abstractions which falls into the abstract idea categories of certain methods of organizing human activity and mental processes. The elements of Claim 1 that represent the Abstract idea include:
A method, comprising:
providing a virtual representation of a food supply chain network using a stock-and-flow based model, wherein the stock-and-flow based model represents a plurality of dynamics of the food supply chain network;
determining a status of a plurality of attributes associated with each of the plurality of food items using the plurality of data, wherein the plurality of attributes are indicative of a perishability aspect of each food item from the plurality of food items;
analyzing the plurality of dynamics of the food supply chain network based on the status of the plurality of attributes associated with each of the plurality of food items using a set of modelling abstractions;
determining in real time efficacy of each of the set of modelling abstractions for analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and
evaluating sustainability of the food supply chain network based on the efficacy of each of the set of modelling abstractions.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the steps of: determining a status of a plurality of attributes associated with each of the plurality of food items using the plurality of data, wherein the plurality of attributes are indicative of a perishability aspect of each food item from the plurality of food items; analyzing the plurality of dynamics of the food supply chain network based on the status of the plurality of attributes associated with each of the plurality of food items using a set of modelling abstractions; determining in real time efficacy of each of the set of modelling abstractions for analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and evaluating sustainability of the food supply chain network based on the efficacy of each of the set of modelling abstractions are directed fundamental economic principles or practices and business relations.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
The instant claims recite mental processes including observation, evaluation, judgment, opinion. For example, the steps directed to: providing a virtual representation of a food supply chain network using a stock-and-flow based model, wherein the stock-and-flow based model represents a plurality of dynamics of the food supply chain network; determining a status of a plurality of attributes associated with each of the plurality of food items using the plurality of data, wherein the plurality of attributes are indicative of a perishability aspect of each food item from the plurality of food items; analyzing the plurality of dynamics of the food supply chain network based on the status of the plurality of attributes associated with each of the plurality of food items using a set of modelling abstractions; determining in real time efficacy of each of the set of modelling abstractions for analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and evaluating sustainability of the food supply chain network based on the efficacy of each of the set of modelling abstractions.
are directed to mental processes. A human with the aid of a pen and paper can providing a virtual representation of a food supply chain network using a stock-and-flow based model. Further, the determining, analyzing and evaluating steps are directed to observation, evaluation, judgment, opinion.
There is nothing is nothing the claims that preclude these steps from being performed mentally. As such, the claims recite abstract ideas.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in 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, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
A processor implemented method, comprising:
obtaining, via the one or more hardware processors, a plurality of data associated with each of a plurality of food items at one or more instances in the virtual representation of the food supply chain network using a food digital twin, wherein the plurality of data is inputted to the stock-and-flow based model,
However, the computer elements are recited at a high-level of generality (i.e., as a generic processor performing the abstract idea) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Further MPEP 2105.05(g) explains that data gathering and data output can be considered pre-solution activity and post-solution activity. See MPEP 2106.05(g) that states:
An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.
In the instant case, the claims do not provide any particular way that the data relating to a company is received. As such, the broadly recited obtaining a plurality of data associated with each of a plurality of food items amounts to insignificant pre-solution activity.
Viewing the generic data gathering in combination with the generic computer does not add more than when viewing the elements individually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In step 2B, the examiner must be determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d). As discussed with respect to Step 2A Prong Two, the processing circuitry in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, nothing in the specification indicates that the retrieving of data is anything other than conventional. Further, MPEP 2106.05(d) states “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."” Further, MPEP 2106.05(d) also states that creating output data has been identified as conventional (see Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017)).
Viewing the generic data gathering in combination with the generic computer does not add more than when viewing the elements individually. Accordingly, the additional elements do provide and inventive concept.
Further Claims 2-6 further limit the mental processes and business practices recited in the parent claim, but fail to remedy the deficiencies of the parent claim as they do not impose any additional elements that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 1-6 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. The presentment of claim 1 otherwise styled as a computer program product, or system, for example, would be subject to the same analysis. As such, claims 7-18 are also rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6, 7-9, 12, 13-15, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramachandran US 2018/0285810 A1 in view of Khuti US 2020/0067789 A1.
As per Claim 1 Ramachandran teaches a processor implemented method, comprising:
providing, via one or more hardware processors, a virtual representation of a food supply chain network using a stock-and-flow based model, wherein the stock-and-flow based model represents a plurality of dynamics of the food supply chain network; (Ramachandran para. 39 teaches this unification allows the blockchain to follow food products in a unique way from seed to table by recording information about a physical product as it evolves over time. For example, in some embodiments, the original data posted to the blockchain (e.g., Grower ABX seeded tomato filed 12Z on March 14) serves as a block record. As food moves along the supply chain, various types of data can be posted to the blockchain as entries in the ledger (e.g., tomatoes were harvested and packed on June 7). Another entry might record that the temperature on a truck transporting the food was 55 degrees over 274 miles traveled. These individual entries can then be associated, enriching the data associated with the shipment and essentially creating a virtual copy of the physical item. This virtual copy is the sum of the entries associated with the unique item, ultimately becoming the history of the food product through its lifecycle through the food supply chain. With this information, businesses can improve traceability, analyze environmental conditions through harvest and transportation, and gather auditable documentation on the history of a product. Additionally, retailers can track a shipment's current location and condition; food processors can better monitor storage conditions; etc. If consumers are allowed access to the data, the consumers can have visibility into data such as the grower and the grower's farming practices, food miles traveled, ripeness indicators or previews of taste.)
obtaining, via the one or more hardware processors, a plurality of data associated with each of a plurality of food items at one or more instances in the virtual representation of the food supply chain network using a food digital twin, wherein the plurality of data is inputted to the stock-and-flow based model,
Ramachandran para. 39 teaches this unification allows the blockchain to follow food products in a unique way from seed to table by recording information about a physical product as it evolves over time. For example, in some embodiments, the original data posted to the blockchain (e.g., Grower ABX seeded tomato filed 12Z on March 14) serves as a block record. As food moves along the supply chain, various types of data can be posted to the blockchain as entries in the ledger (e.g., tomatoes were harvested and packed on June 7). Another entry might record that the temperature on a truck transporting the food was 55 degrees over 274 miles traveled. These individual entries can then be associated, enriching the data associated with the shipment and essentially creating a virtual copy of the physical item. This virtual copy is the sum of the entries associated with the unique item, ultimately becoming the history of the food product through its lifecycle through the food supply chain. With this information, businesses can improve traceability, analyze environmental conditions through harvest and transportation, and gather auditable documentation on the history of a product. Additionally, retailers can track a shipment's current location and condition; food processors can better monitor storage conditions; etc. If consumers are allowed access to the data, the consumers can have visibility into data such as the grower and the grower's farming practices, food miles traveled, ripeness indicators or previews of taste.
determining, via the one or more hardware processors, a status of a plurality of attributes associated with each of the plurality of food items using the plurality of data, wherein the plurality of attributes are indicative of a perishability aspect of each food item from the plurality of food items; Ramachandran para. 44 teaches whole chain traceability in a food supply chain can also radically improve food safety. Current one-step forward and backward traceability can be replaced by the ability to track through the entire chain, with associated records of labels and documentation. Being able to access the entire product journey from a single platform would eliminate the difficulty of going through the supply chain step by step, vastly improving response times in the event of a food safety incident. Additionally when a large number of objects are tracked through the blockchain, the chain could identify cross-contamination events or exposure, as well as any other incidents such as a spoiled or damaged shipment, enabling preventative action to be taken on a select group of shipments instead of a whole lot.
analyzing, via the one or more hardware processors, the plurality of dynamics of the food supply chain network based on the status of the plurality of attributes associated with each of the plurality of food items using a set of modelling abstractions; Ramachandran para. 39 teaches this unification allows the blockchain to follow food products in a unique way from seed to table by recording information about a physical product as it evolves over time. For example, in some embodiments, the original data posted to the blockchain (e.g., Grower ABX seeded tomato filed 12Z on March 14) serves as a block record. As food moves along the supply chain, various types of data can be posted to the blockchain as entries in the ledger (e.g., tomatoes were harvested and packed on June 7). Another entry might record that the temperature on a truck transporting the food was 55 degrees over 274 miles traveled. These individual entries can then be associated, enriching the data associated with the shipment and essentially creating a virtual copy of the physical item. This virtual copy is the sum of the entries associated with the unique item, ultimately becoming the history of the food product through its lifecycle through the food supply chain. With this information, businesses can improve traceability, analyze environmental conditions through harvest and transportation, and gather auditable documentation on the history of a product. Additionally, retailers can track a shipment's current location and condition; food processors can better monitor storage conditions; etc. If consumers are allowed access to the data, the consumers can have visibility into data such as the grower and the grower's farming practices, food miles traveled, ripeness indicators or previews of taste. Further, para.107 teaches the system described above has numerous applications, as one skilled in the art would understand. One exemplary implementation is to develop a value scorecard for a given product. For example, a scorecard can be developed for a tomato or batch of tomatoes grown by a farmer. In one example application, a combination of blockchain and sensors/Internet of Things technology aims to compute a fair price for tomatoes at each stage of their development and eventual consumption. The same concepts can be applied to other types of produce as well. Typically, the amount of information available on a given tomato is limited. Usually, little is known about a given tomato, beyond its physical appearance and some basic labeling information that accompanies it. As a result, tomatoes (and other types of produce) are priced inefficiently all along the food supply chain. There are no tools available needed to reward quality or penalize mediocrity, and by default, markets price tomatoes as commodities. One goal of the disclosed system is to create transparency on the intrinsic value of a tomato, which will allow more efficient pricing of the tomato. Another goal of the system is to allow each stakeholder the food supply chain to develop new processes and practices that increase the value of their tomatoes. Both goals require building a model of what constitutes value for a tomato at its various stages of development. For the purposes of this description, this model referred to as a “tomato scorecard”.
evaluating, via the one or more hardware processors, sustainability of the food supply chain network
Ramachandran para. 109 teaches FIG. 7 also shows various product and process variables 702 associated with a given value outcome 700. In the example shown, 28 product and process variables are used. For example, to determine value outcome “safe”, the product and process variables include bacteria, mycotoxins, pesticides, and metals. Each of these variables relate to the safety of food. Similarly, to determine the value outcome “local”, the product and process variables include distance traveled and tracking information throughout the supply chain. To determine the value outcome “affordable”, the product and process variables include cost and yield. To determine the value outcome “sustainable”, the product process variables include amount of wasted produce, energy use, water use, fertilizer use, content of organic matter in the soil, and fair labor content. To determine the value outcome “healthy”, the product and process variables include vitamins, minerals, antioxidants, protein, fiber, and calories. To determine the value outcome “delicious”, the product and process variables include ripeness, appearance, internal integrity, sugar, acid, salt, water, and furaneol.
evaluating, via the one or more hardware processors, sustainability of the food supply chain network Ramachandran para. 109 teaches FIG. 7 also shows various product and process variables 702 associated with a given value outcome 700. In the example shown, 28 product and process variables are used. For example, to determine value outcome “safe”, the product and process variables include bacteria, mycotoxins, pesticides, and metals. Each of these variables relate to the safety of food. Similarly, to determine the value outcome “local”, the product and process variables include distance traveled and tracking information throughout the supply chain. To determine the value outcome “affordable”, the product and process variables include cost and yield. To determine the value outcome “sustainable”, the product process variables include amount of wasted produce, energy use, water use, fertilizer use, content of organic matter in the soil, and fair labor content. To determine the value outcome “healthy”, the product and process variables include vitamins, minerals, antioxidants, protein, fiber, and calories. To determine the value outcome “delicious”, the product and process variables include ripeness, appearance, internal integrity, sugar, acid, salt, water, and furaneol.
Ramachandran does not teach determining in real time, via the one or more hardware processors, efficacy of each of the set of modelling abstractions for analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and the evaluating sustainability of the food supply chain network is based on the efficacy of each of the set of modelling abstractions However. Khuti para. 325 teaches Some embodiments of the invention provide a Sustainability Index feature, building on the PARCS™ model discussed above, to collect data across the supply chain, e.g., from the farmer to the retailer, and create a sustainability index that can then be shown on each consumer product to drive smarter buying habits. The analogy is the Energy Index shown on electrical products such as washing machines, to illustrate the cost of energy consumption per annum. FIG. 22 below illustrates how such a sustainability index is used
Further, para. 330 teaches Analytical/Machine Learning Framework (PARCS): for industrial and software engineers to write code in Java, R, Scala, Python etc. creating analytics that monitor & predict the behaviour of an asset, group of assets or system over time periods, and generate confidence indices and diagnostic networks to validate the accuracy of the analytical models. Both Ramachandran and Khuti are directed to evaluating sustainability. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Ramachandran to include determining in real time, via the one or more hardware processors, efficacy of each of the set of modelling abstractions for analyzing the plurality of dynamics of the food supply chain network by simulating the stock-and-flow based model using a simulator; and the evaluating sustainability of the food supply chain network is based on the efficacy of each of the set of modelling abstractions as taught by Khuti to generate a more accurate model and provide a more accurate analysis of sustainability (see para. 330).
As per Claim 2 Ramachandran teaches the processor implemented method of claim 1, wherein the plurality of dynamics of the food supply chain network comprises dynamics of storage, dynamics of transportation, and dynamics of retailer. Ramachandran para. 57 teaches in the system, “evidence” is a datum posted (signed) by one or multiple user accounts. Evidence can be posted manually or automatically by or on behalf of a user. An evidence transaction may have various data attached to it such as: a version number, a version number, input counter, inputs, outputs counter, outputs, EvidenceData, and signatures. evidence can be automatically captured by Internet of things sensors distributed in fields, trucks, storage facilities, etc. Evidence can also come from third-party databases to capture specific information at specific points in time. For example, evidence may include weather conditions, government accreditation, etc. Evidence data may be captured in JSON format. This data may be provided in clear text, encrypted, or as a reference to an external data source (e.g., a URL, torrent id, IPFS id, etc.). Evidence directly related to prior evidence can be stringed together through inputs and outputs (e.g., hourly temperature readings within a distribution warehouse, etc.). Further, para. 78-84 teaches the Verigo solution (an exemplary third party entity) captures data from sensors on the food pallets as John the Truck delivers the shipment to the Retailer. [0079] The Verigo sensors post Evidence of Temperature. Humidity, Geolocation, and Shock to the Blockchain of Food. [0080] John the Trucker's profile automatically self-Evaluates his Assertions by linking them to available Evidence. [0081] The Retailer's receiving associate validates that John the Trucker's Assertions and self-Evaluation are within contractually accepted ranges. [0082] The Retailer accepts the shipment and moves selected pallets to the retail floor. [0083] A floor associate updates the Price, Description, and Blockchain of Food QR Code for the associated product. [0084] A consumer using the retailer's proprietary smart-phone application is able to scan the Blockchain of Food QR Code and get proof of the product's origins.
As per Claim 3 Ramachandran teaches the processor implemented method of claim 1, wherein the plurality of attributes comprises a ripening state and remaining shelf-life of each food item from the plurality of food items. Ramachandran para. 39 teaches this virtual copy is the sum of the entries associated with the unique item, ultimately becoming the history of the food product through its lifecycle through the food supply chain. With this information, businesses can improve traceability, analyze environmental conditions through harvest and transportation, and gather auditable documentation on the history of a product. Additionally, retailers can track a shipment's current location and condition; food processors can better monitor storage conditions; etc. If consumers are allowed access to the data, the consumers can have visibility into data such as the grower and the grower's farming practices, food miles traveled, ripeness indicators or previews of taste. Para/ 43 teaches additionally, the creation of holistic data sets allows longitudinal correlations between events and variables both upstream and downstream. For example, new shelf life predictions can be conducted given a baseline of prior environmental conditions, allowing for more accurate estimation of lifespan and the creation of new best practices.
As per Claim 6 Ramachandran teaches the processor implemented method of claim 1, wherein the dynamics of retailer is analyzed based a grade distribution, grade flow and demand distribution of the plurality of food items at one or more retailer points. Ramachandran para. 89-90 teaches FIGS. 3A and 3B show two examples of sequence diagram for the system. FIG. 3A shows various components of a food supply chain, including sensors, a farmer, a distributor, a food processor, a certifier, a system provider, and the blockchain. FIG. 3A also shows various items and how the items are used by the various components of the food supply chain. For example, the sensors capture evidence which is provided to the blockchain. The farmer creates a chain bundle and provides it to the blockchain. The farmer also creates and assigns claims to the bundle, which is provided to the blockchain. The blockchain provides review evidence to a certifier, so that the certifier can provide certified claims to the blockchain. The system provider provides a scorecard to the blockchain for a given bundle. The distributor provides a public key to the farmer. The farmer transfers a bundle to the distributor and provides related information to the blockchain. The distributor creates and assigns claims to the bundle and provides related information to the blockchain. The food processor provides a public key to the distributor. The distributor transfers the bundle to the food processor and provides related information to the blockchain. The food processor consumes the bundle and provides related information to the blockchain. FIG. 3B shows another example of a sequence diagram similar to FIG. 3A. FIG. 3B shows various components of a food supply chain from a seed to the blockchain, including a farm, transportation, distribution, processing and retail, consumer, and certification. Like FIG. 3A, FIG. 3B also shows various items and how the items are used by the various components of the food supply chain. For example, assertions are created for a given seed and provided to the blockchain. At the farm, assertions, evidences, and bundles are created and provided to the blockchain. At the farm, update assertions and evidences with bundle IDs are provided to the blockchain. Similarly, update assertions and bundles with certification IDs are provided to the blockchain. Similarly, update bundles are also provided to the blockchain with a transporter key. The blockchain provides review evidence and assertions to a certifier, which creates bundle certifications and provides them to the blockchain. The transportation system creates assertions, evidences, and update bundles and provides the information to the blockchain. The blockchain provides review evidence and assertions to the certifier, which creates update bundles certification to the blockchain. The processing and retail system receives review assertions and certifications from the blockchain and creates assertions which are provided back to the blockchain. In response, the blockchain again provides review evidence and assertions to the certifier, which creates update bundles certification to the blockchain. The consumer receives review assertions and certifications from a blockchain and provides a consume bundle to the blockchain.
Claim(s) 7-9, 12 is/are rejected for similar reason as claim(s) 1-3, 6 and is/are rejected for similar reasons. Further, Ramachandran teaches a system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to perform the recited steps (Ramachandran para. 26-27).
Claim(s) 13-15, 18 is/are rejected for similar reason as claim(s) 1-3, 6 and is/are rejected for similar reasons. Further, Ramachandran teaches One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause (Ramachandran para. 26-27).
Claim(s) 4, 10, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramachandran US 2018/0285810 A1 in view of Khuti US 2020/0067789 A1 as applied to claim 1 and in further view of Adato US 2020/0074402 A1 in view of Rodriguez US 20240428176 A1.
As per Claim 4 Ramachandran teaches the processor implemented method of claim 1, wherein the dynamics of storage is analyzed based on an aggregated remaining shelf-life, Ramachandran para. 43 teaches additionally, the creation of holistic data sets allows longitudinal correlations between events and variables both upstream and downstream. For example, new shelf life predictions can be conducted given a baseline of prior environmental conditions, allowing for more accurate estimation of lifespan and the creation of new best practices.
one or more variables representing storage-to-storage movements, Ramachandran para. 161 teaches the distribution information includes a timeline showing stops at a distribution center and warehouse, including the total miles between each stop and the relevant dates and times.
one or more cost associated factors, and Ramachandran para. 109 teaches FIG. 7 also shows various product and process variables 702 associated with a given value outcome 700. In the example shown, 28 product and process variables are used. For example, to determine value outcome “safe”, the product and process variables include bacteria, mycotoxins, pesticides, and metals. Each of these variables relate to the safety of food. Similarly, to determine the value outcome “local”, the product and process variables include distance traveled and tracking information throughout the supply chain. To determine the value outcome “affordable”, the product and process variables include cost and yield. To determine the value outcome “sustainable”, the product process variables include amount of wasted produce, energy use, water use, fertilizer use, content of organic matter in the soil, and fair labor content. To determine the value outcome “healthy”, the product and process variables include vitamins, minerals, antioxidants, protein, fiber, and calories. To determine the value outcome “delicious”, the product and process variables include ripeness, appearance, internal integrity, sugar, acid, salt, water, and furaneol.
Ramachandran does not teach the dynamics of storage is analyzed based on a computed quantity of storage stock, a spoilage rate, a purchase rate, However, Adato para. 321 teaches in some embodiments, the at least one processing device may use suggestion determination module 1810 to determine EP promotion suggestion 1925 and SP promotion suggestion 1932. In this disclosure, the term “promotion” may include a change in price or an advertisement. EP promotion suggestion 1925 may include an indication of a temporary price for at least some of the plurality of perishable products to be applied prior to arrival of the additional perishable products. Similarly, SP promotion suggestion 1932 may include an indication of the price for the additional perishable products after they have been delivered to retail store 105. In particular, the at least one processing device may determine the recommended promotion for at least one type of perishable products based on the information about the displayed inventory (i.e., at least one EP indicator) and the information about the additional perishable products (i.e., at least one SP indicator). For example, when the determined EP demand indicator 1904 indicates that the demand for avocados is low and SP ETA indicator 1920 indicates that another shipment of avocados is scheduled to be delivered later today to retail store 105, the at least one processing device may recommend lowering the price and of avocados and display a “sale” sign. Consistent with the present disclosure, the at least one processing device may use the following list of variables to determine promotion suggestions for a type of perishable products: quantity of existing products, quantity of scheduled products, quality of existing products, quality of scheduled products, predicted shelf-life for existing products, predicted shelf-life for scheduled products, average quantity sales per day, predicted demand, retail price, costs, costs of discarding the perishable products, costs of returning the perishable products to the supplier, and more. The price may be dynamically determined for each type of perishable product based on the financial impact. In some cases, the at least one processing may autonomously control a price display (e.g., an electronic display) to present the determined price for the perishable products. Both Ramachandran and Adato are directed to supply chain analysis. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Ramachandran to include the dynamics of storage is analyzed based on a computed quantity of storage stock, a spoilage rate, a purchase rate as taught by Adato to identifying trends, optimizing product sales, and/or implementing marketing strategies across a large number of retail stores (see para. 281).
Ramachandran does not teach a donation parameter at one or more storage points. However, Rodriguez para. 66 teaches in a third example of food waste reduction program 122, a large grocery store chain is looking for ways to reduce food waste and improve efficiency. The large grocery store chain decides to implement food waste reduction program 122 in the large grocery store chain's stores, connecting all of the refrigerators and freezers to a virtual refrigerator network. Food waste reduction program 122 helps the stores keep track of the expiration dates of the perishable items and to suggest recipes to customers based on the ingredients available. Food waste reduction program 122 also sends alert notifications to employees when items are close to expiration, allowing the employees to remove the items from the shelves and donate or compost the items before the items spoil. As a result, the grocery store chain significantly reduces food waste and improves the efficiency of the grocery store chain's operations. The grocery store chain saves money on food costs and reduces the grocery store chain's environmental impact. Both Ramachandran and Rodriguez are directed to supply chain analysis. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Ramachandran to include a donation parameter at one or more storage points as taught by Rodriguez to reduces food waste and reduces environmental impact (see para. 66).
Claim(s) 10 is/are rejected for similar reason as claim(s) 4 and is/are rejected for similar reasons. Further, Ramachandran teaches a system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to perform the recited steps (Ramachandran para. 26-27).
Claim(s) 16 is/are rejected for similar reason as claim(s) 4 and is/are rejected for similar reasons. Further, Ramachandran teaches One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause (Ramachandran para. 26-27).
Claim(s) 5, 11, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramachandran US 2018/0285810 A1 in view of Khuti US 2020/0067789 A1 as applied to claim 1 and in further view of Agrawal US 12,405,124 B2.
As per Claim 5 Ramachandran teaches The processor implemented method of claim 1, wherein the dynamics of transportation is analyzed based on type of vehicles, Ramachandran para. 133
teaches processors such as foodservice companies, food companies, or commercial kitchens can add value through a five-step value-adding process (for example) bracketed by a buy and a sell transaction. For example, in a buy transaction, the processor will want to determine what tomatoes to buy, at what price, and from whom. Following are five exemplary process steps that a processor may use: [0134] Inbound transportation (What mode of transportation? What carrier?) [0135] Receive and store (How to manage inventory?) [0136] Preparation (e.g., wash, cut, peel, bunch, mix) (Should I do any value-added prep? Which type?) [0137] Design menu, cook, assemble, serve (what recipe? what menu?) [0138] Dispose of waste (what to compost? frequency of pick-ups? how to reap value for waste? etc.)
Ramachandran does not teach frequency of the vehicles, temperature control capability and the cost associated with the vehicles used for transportation. However, Agrawal columns 3-4 teach In some embodiments, the processor may simulate the transportation of the object utilizing each of the one or more potential vehicles using digital twin simulation. In some embodiments, the processor may retrieve a digital twin simulation for a type of vehicle stored in a repository of simulations. In some embodiments, the processor may generate a new digital twin simulation of the vehicle. In some embodiments, the digital twin may simulate the overall condition of the vehicle to provide a simulation of the outcome and conditions associated with transportation of an object from its origin to its destination (e.g., fuel economy of the vehicle during successful travel over the route, halted travel along the route resulting from a breakdown in the vehicle, damage to the object resulting from route conditions, the capabilities of/features of (e.g., type of suspension) the vehicle used for transportation, etc.). In some embodiments, the digital twin may simulate transportation from origin to destination based on location, object details, and background context associated with the transportation (e.g., poor/moderate condition vehicle may not travel long distance without incidence). In some embodiments, the processor may receive information about the outcome of the simulated transportation, including the timeline for delivery, fuel usage, damage or wear to vehicle, status of the transportation task (e.g., successful completion vs. breakdown along the route).
In some embodiments, the processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation. In some embodiments, the optimization factor may relate to the timeline of transportation (e.g., fastest), costs associated with the transportation (e.g., fuel, toll, wear and tear on the vehicle), environmental factors (e.g., least fuel consumption), other delivery factors (e.g., use of the same vehicle for multiple deliveries along the same or similar transportation route). In some embodiments, the optimization factor may be selectable from (e.g., by a user or by the processor): transportation cost, transportation time, damage (e.g., from wear and tear) to the vehicle, distance to be traveled, number of objects to be transported along the same route or portion of a route (e.g., lump packages to same locality in one delivery truck to minimize number of miles driven), etc.
In some embodiments, the processor may generate the optimization factor. In some embodiments, generating the optimization factor may include analyzing the digital twin simulation for one or more transportation impacts. In some embodiments, the processor may select at least one of the one or more transportation impacts on which to base the optimization factor. In some embodiments, one or more transportation impacts may include the timeline of transportation of the first object, costs associated with the transportation of the first object, environmental factors associated with the transportation of the first object, damage to vehicles, distance to be traveled, number of objects to be transported along the same route or portion of a route, other delivery factors (e.g., use of fewest vehicles), etc.
In some embodiments, the processor may send a command to a processor associated with the selected vehicle. In some embodiments, the processor may, based on the command, schedule transportation of the object. In some embodiments, the command may be sent to a processor associated with an autonomous vehicle that controls the timing and route of travel of the autonomous vehicle. In some embodiments, the command may be sent to a processor of a device that runs scheduling software that stores information regarding upcoming tasks (e.g., transportation routes and objects), the time of the upcoming tasks, the amount of time required for the completion of the task, reminders regarding the upcoming scheduled task, etc.
In some embodiments, the processor may select a second vehicle of the one or more potential vehicles based on an optimization of the optimization factor associated with a digital twin simulation of the second vehicle transporting the first object. In some embodiments, the processor may determine a first transportation route for the first vehicle and a second transportation route for the second vehicle for the transportation of the object based, at least in part, on the optimization of the optimization factor. In some embodiments, more than one vehicle may be used to transport the object along one or more portions of the transportation route between the origin and destination. In some embodiments, the processor may select the additional vehicles (e.g., second or more) based on optimization of the optimization factor used to select the first vehicle. In some embodiments, the processor may also determine the route that the first vehicle is to transport the object and the route that the second vehicle is to transport the vehicle. In some embodiments, the routes or portions of the route (for transportation of the object by the first and second vehicles) may also selected based on an optimization of the optimization factor. In some embodiments, a command may be sent to a processor associated with the first vehicle and the second vehicle to schedule transportation of the object.
In some embodiments, the processor may receive second object data. In some embodiments, the processor may simulate transportation of the second object. In some embodiments, the simulation may be based on the combination of the constraints associated with the first object data and the second object data. In some embodiments, the processor may select the first vehicle based on a combined optimization factor, where the combined optimization factor combines a set of constraints associated with the first object and a set of constraints associated with the second object. In some embodiments, the constraints associated with the first and/or second object may include: factors associated with the first object related to the transportation of the object, factors associated with the second object related to the transportation of the object, delivery time, delivery location for the first and/or second object, conditions needed for delivery based on both objects (e.g., refrigeration, high care for fragile items), the size of the first object and/or the second object, the weight of the first object and/or the second object, the shape of the first object and/or the second object, or the dimensions of the first object and/or the second object, the materials from which the first object and/or the second object are made, characteristics of the packaging or container for the first object and/or the second object that may affect the outcome or process of transportation of the first object and/or the second object, packaging/container dimensions, the route the first object is to be transported, the route the second object is to be transported, etc. Both Ramachandran and Agrawal are directed to supply chain analysis. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Ramachandran to include teach frequency of the vehicles, temperature control capability and the cost associated with the vehicles used for transportation as taught by Agrawal to optimize assignment of vehicles in a supply chain (see Abstract).
Claim(s) 11 is/are rejected for similar reason as claim(s) 5 and is/are rejected for similar reasons. Further, Ramachandran teaches a system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to perform the recited steps (Ramachandran para. 26-27).
Claim(s) 17 is/are rejected for similar reason as claim(s) 5 and is/are rejected for similar reasons. Further, Ramachandran teaches One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause (Ramachandran para. 26-27).
Relevant Art Not Relied Upon in a Rejection
Taper US 20120271669 A1-[0094] The model parameters may be reassessed if any factual sustainability metrics have been derived as a result of an implementation of a previously modelled scenario. In this case, any factual data relating to the implementation of the model scenario is firstly compared with the theoretical predictions of the model. The comparison is then used to recalibrate or otherwise adjust any modelling parameters or routines such that their prediction is in closer conformity with the factual data. In this way, the predictive accuracy of the employed modelling routines and parameters is continuously reviewed and improved.
Rakshit US 20220083976 A1 - [0041] As illustrated in FIG. 2, in some embodiments, operations for supply chain routing determinations begin at operation S252, where a computing system (e.g., server computer 200 of FIG. 1 or the like) obtains a digital replica (e.g., digital twin) model (e.g., digital replica of physical machines, objects, processes, systems, services, etc.) for each machine and/or system of each entity in a supply chain network, for example, each machine associated with each supplier, manufacturer, and/or the like of components (e.g., parts, assemblies, work product, etc.) within a supply chain such as a supplier, manufacture. As an example, digital twin modeling module 320 of FIG. 3 and/or the like can access a digital replica (e.g., digital twin) library (e.g., library 310 of FIG. 1, etc.), identify associated parts for each machine of each supplier in the supply chain network along with associated data (e.g., model components, bill of materials, capabilities, features, etc.), and obtain a digital replica (e.g., digital twin) model for each supplier machine in the supply chain as appropriate to provide for real-time simulation of each machine in the supply chain network. Each machine of each supplier can be identified based on the types of components, work products, etc. that is generated.
Conclusion
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625