DETAILED ACTION
Claims 1-20 are presented for examination.
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 .
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 2-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 2-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “planning and managing crops” (Spec: ¶ 4) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Apparatus (claims 2-8, 10-20)
Independent claim:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claim recites:
[Claim 18] greenhouse and warehouse management:
creates and manages one or more crop groups based on information received from the user;
creates one or more harvest groups based on the user's designation of one or more crops from one or more existing crop groups;
further comprising the tracking of the one or more crop groups as defined by their harvest groupings.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “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)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations cited above, such as creating and managing crop groups, creating harvest groups, and tracking crop groups. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 18 recites a device for greenhouse and warehouse management:
wherein the device comprises an edge server and a farm management platform, wherein the edge server hosts a local mesh network of IoT devices and interfaces to one or more mobile devices and IoT devices at a given farm site;
wherein the farm management platform creates and manages one or more crop groups based on information received from the user via web browser or mobile application;
wherein the farm management platform creates one or more harvest groups.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 1-2; ¶ 46 – “The Edge servers are ready to use, "out-of-the box," as most of the system configuration is completed at production before shipment. Any IoT devices would be delivered similarly, pre-programmed to communicated over a secure network.”).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] creates and manages one or more crop groups based on information received from the user;
creates one or more harvest groups based on the user's designation of one or more crops from one or more existing crop groups.
[Claim 3] computes a tracking of crops in one or more groups as defined by a seed lot seeding date, one or more transplant dates, and a harvest maturity date.
[Claim 4] data recall and food safety information storage.
[Claim 5] tracking of the one or more crop groups as defined by their harvest groupings.
[Claim 6] manages one or more assets of CEA farms or warehouses with one or more crop stages that one or more crops may occupy during a given stage.
[Claim 7] tracks asset usage over time, indicating capacity, and allowing form projected usage and availability for future crop groups.
[Claim 8] asset labeling with static QR codes for quick reference of assets' current status and histories.
[Claims 10-17] Claims 10-17 collectively incorporate the operations recited in claims 2-8 (as cited above).
[Claims 18-20] Claims 18-20 collectively incorporate the operations recited in claims 2-8 (as cited above).
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “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)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations cited above, such as creating and managing crop groups, creating harvest groups, computing a tracking of crops, data recall and food safety operations, tracking crop groups, managing assets, tracking asset usage over time, labeling assets with QR codes, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claims 2-8 incorporate the device for greenhouse and warehouse management: wherein the device comprises an edge server and a farm management platform (referencing language explicitly recited in independent claim 1).
Claims 2-8 further recite wherein the edge server hosts a local mesh network of IoT devices and interfaces to one or more mobile devices and IoT devices at a given farm site and recite that various operations are generally performed by the farm management platform and that information is received from the user via web browser or mobile application (referencing language explicitly recited in claim 2).
Claims 3-8 further recite that the device generally performs the recited operation (referencing language explicitly recited in claim 3).
Claims 4-8 further present one or more RFID tags accompanying the one or more crop groups (referencing language explicitly recited in claim 4).
Claims 6-8 further recite that the device generally performs the recited operation and that one or more crop stages are defined by a hardware configuration (referencing language explicitly recited in claim 6).
Claims 7-8 further recite that the device generally performs the recited operations (referencing language explicitly recited in claim 7).
Claim 8 further recites that the device includes asset labeling with static QR codes.
Claims 10-17 collectively incorporate the additional elements recited in claims 1-8.
Claims 18-20 collectively incorporate the additional elements recited in claims 1-8.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 1-2; ¶ 46 – “The Edge servers are ready to use, "out-of-the box," as most of the system configuration is completed at production before shipment. Any IoT devices would be delivered similarly, pre-programmed to communicated over a secure network.”).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
NOTE: Claims 1 and 9 present eligible subject matter under 35 U.S.C. § 101 since they fall into at least one statutory class of subject matter (in this case, apparatus) and they are not directed to any abstract ideas.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramachandran et al. (US 2018/0285810) in view of Cook et al. (US 2024/0275608) in view of Pandit et al. (US 2022/0164900).
[Claim 1] Ramachandran discloses a device for greenhouse (¶ 116 – greenhouse) and warehouse (¶¶ 57, 124, 161 – Warehouse, storage) management:
wherein the device comprises collection of edge devices (¶ 155 – Internet of things sensors are examples of data sources.) and a farm management platform (¶ 40 – farming; ¶¶ 47-50 -- food marketplace platform).
While Ramachandran references network arrangements that likely utilize a server (Ramachandran: ¶ 41 – “the blockchain technology described herein can be hosted and supported by the member organizations involved in a particular supply-chain.”; ¶ 87 – “FIG. 1 shows an exemplary system architecture diagram. In FIG. 1, a blockchain section 100 shows a public ledger, a permissioned ledger, smart contracts, and a sensor vendor. An application section 102 shows a supply-chain business rules orchestrator, an integration engine operatively coupled to various sensor vendors and cloud API's. The application section 102 also shows data normalization, scorecard engine, and analytics engine blocks, as well as a database (DBMS). An interfaces section 104 shows software as a service (SaaS) UX stack that can interface with web, tablet and phone devices and a SaaS API stack and corresponding API and blockchain explorer blocks.”), Ramachandran does not explicitly disclose use of an edge server.
However, both Pandit and Cook provide evidence that the use of edge servers (including Internet of Things (IoT)) in a farming (including a smart agriculture) environment was known in the art and trending in popularity prior to Applicant’s invention (e.g., see Pandit: ¶¶ 64, 65, 73 – edge servers, IoT sensors, farming; Cook: ¶¶ 374, 463, 487 -- sensors as a service, mesh network (including of sensors), local servers, edge devices, IoT devices, smart agriculture). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Ramachandran to explicitly use an edge server with its edge and IoT devices since “certain mesh network configurations facilitate greater redundancy/fault tolerance (i.e., network traffic could be rerouted through alternative paths), self-healing capabilities (i.e., a sensor can be removed from or added to a network), scalability (i.e., new sensors can be added), extended coverage (i.e., additional sensors can span a wider area), increased throughput (i.e., multiple paths can be simultaneously pursued), flexibility (i.e., wireless local networks, sensor network, Internet-of-things application), etc.” (Cook: ¶ 374) and since “the Internet fog middleware platforms may leverage local servers, edge devices, and cloud resources to process and store data near the source, reducing latency and improving overall system efficiency. Additionally, communication resources such as picocells, femtocells, and hotspots may extend network coverage and connectivity to localized areas. Moreover, data from picocells, femtocells, and/or hotspots may be more granular than data collected from other sources. Sensors and actuators embedded in IoT devices may further enhance the Internet fog middleware platform infrastructure by facilitating real-time data collection and control at the network's edge. This amalgamation of computing, storage, communication, and IoT resources forms a dynamic and responsive Internet fog middleware platform layer, optimizing the performance of applications and services, such as by providing low latency and high responsiveness.” (Cook: ¶ 463)
[Claim 2] Ramachandran discloses wherein the collection of edge devices comprises IoT devices and interfaces to one or more mobile devices (¶ 154 – “The food browser 1306 is a customizable front end application that can integrate with smart menus, digital displays, mobile devices, etc.”; ¶ 57 – “evidence can be automatically captured by Internet of things sensors distributed in fields, trucks, storage facilities, etc.”) and IoT devices at a given farm site (¶ 57 – “evidence can be automatically captured by Internet of things sensors distributed in fields, trucks, storage facilities, etc.”);
wherein the farm management platform creates and manages one or more crop groups (¶¶ 52-53 – FOOD BUNDLES) based on information received from the user via web browser or mobile application (figs. 5a, 5b, ¶ 92 – “FIGS. 5A-5B show state diagrams of an exemplary system. The state diagram of FIG. 5A begins with a farmer creating a food bundle 500. Next, the farmer creates claims 502 relating to the created food bundle. The claims are provided to a certifier who certifies the claims 504. Along with the claims, the certifier uses evidence captured by sensors 506. The certified claims are provided back to the farmer, as well as to the system, which creates scorecards 508. When the farmer creates the food bundle, it is also provided to a distributor that purchases the food bundle 510. The distributor creates claims 512 and provides the claims to the certifier. The certifier also receives evidence captured by sensors 516 and certifies these claims 514. The certifier provides certified claims back to the distributor, as well as to the system. The distributor also provides information to the processor which purchases the bundle 518. The processor also retails the bundle 520 which is ultimately provided to consumers 522. With the information available to consumers, a consumer gains greater visibility into food provenance, quality, and safety.”; ¶ 45 – “As mentioned above, smart contracts can also simplify and allow the automation of multiple processes between participants on the blockchain. For example, a local restaurant owner could post a smart contract on the blockchain offering to buy a set quantity of produce over a set amount of time, as long as the produce meets a certain set of specified conditions. A farmer wanting to plan his crop could bid on that contract and promise to deliver the produce at a certain price. As the produce grows and ripens, each party can be notified if growing or handling conditions exceed boundaries set in the purchase smart contract. In the case when some value is deemed to be sufficiently out-of-bound as to violate the terms of the smart contract, the produce could be redirected to other purposes, while another farmer could fill the smart contract with surplus satisfactory produce.”; ¶ 87 – “An application section 102 shows a supply-chain business rules orchestrator, an integration engine operatively coupled to various sensor vendors and cloud API's. The application section 102 also shows data normalization, scorecard engine, and analytics engine blocks, as well as a database (DBMS). An interfaces section 104 shows software as a service (SaaS) UX stack that can interface with web, tablet and phone devices and a SaaS API stack and corresponding API and blockchain explorer blocks.”; ¶ 154 – “As mentioned above, FIGS. 13-14 relate to a common platform where information about food can be publicly and privately exchanged among the supply chain participants. The present blockchain solution provides a decentralized repository for various types of objects, captured in signed blockchain transactions. The present disclosure provides that each object have a basic set of operations associated with it. These operations are to be implemented as smart contracts within the present invention's API (Application Programming Interface) and CLI (Command Line Interface). The blockchain platform represented in FIG. 13 generally comprises FOOD BUNDLES 1302 (described in detail above), a web-of-trust reputation network 1304 (described in detail above), and a food browser 1306. The food browser 1306 is a customizable front end application that can integrate with smart menus, digital displays, mobile devices, etc. The food browser aggregates all the published information known to the blockchain of food about a particular food item.”; ¶ 157 – Internet; ¶ 52 – “The system uses unique identifiers, each representing a unit of food at a particular time and place along the supply chain. For the purposes of this disclosure, the unique identifiers will be referred to as “FOOD BUNDLES™”, which is a trademark of Ripe Technology, INC. FOOD BUNDLES can take many forms; bag of seeds, area of a field, specific plant, crates of fruits, pallets, etc. A bundle of food can be merged into another to form a 3rd bundle; alternately a bundle can also be separated into two or more bundles, each inheriting a selected set of characteristics from the parent bundle.”);
wherein the farm management platform creates one or more harvest groups based on the user's designation of one or more crops from one or more existing crop groups (¶¶ 102-104 – “[0102] Farmer 3 will buy bundles from Farmer 2 with manual confirmation [0103] Farmer 3 will buy bundles from Farmer 1 automatically if procurement contract conditions are met [0104] Farmer 1 would automatically sell bundles to Distributor 1 if procurement contract conditions are met, based on Introducer trust level with Farmer 3”; ¶ 45 – “As mentioned above, smart contracts can also simplify and allow the automation of multiple processes between participants on the blockchain. For example, a local restaurant owner could post a smart contract on the blockchain offering to buy a set quantity of produce over a set amount of time, as long as the produce meets a certain set of specified conditions. A farmer wanting to plan his crop could bid on that contract and promise to deliver the produce at a certain price. As the produce grows and ripens, each party can be notified if growing or handling conditions exceed boundaries set in the purchase smart contract. In the case when some value is deemed to be sufficiently out-of-bound as to violate the terms of the smart contract, the produce could be redirected to other purposes, while another farmer could fill the smart contract with surplus satisfactory produce.”; ¶ 52 – “The system uses unique identifiers, each representing a unit of food at a particular time and place along the supply chain. For the purposes of this disclosure, the unique identifiers will be referred to as “FOOD BUNDLES™”, which is a trademark of Ripe Technology, INC. FOOD BUNDLES can take many forms; bag of seeds, area of a field, specific plant, crates of fruits, pallets, etc. A bundle of food can be merged into another to form a 3rd bundle; alternately a bundle can also be separated into two or more bundles, each inheriting a selected set of characteristics from the parent bundle.”).
While Ramachandran references network arrangements that likely utilize a server (Ramachandran: ¶ 41 – “the blockchain technology described herein can be hosted and supported by the member organizations involved in a particular supply-chain.”; ¶ 87 – “FIG. 1 shows an exemplary system architecture diagram. In FIG. 1, a blockchain section 100 shows a public ledger, a permissioned ledger, smart contracts, and a sensor vendor. An application section 102 shows a supply-chain business rules orchestrator, an integration engine operatively coupled to various sensor vendors and cloud API's. The application section 102 also shows data normalization, scorecard engine, and analytics engine blocks, as well as a database (DBMS). An interfaces section 104 shows software as a service (SaaS) UX stack that can interface with web, tablet and phone devices and a SaaS API stack and corresponding API and blockchain explorer blocks.”), Ramachandran does not explicitly disclose use of an edge server wherein the edge server hosts a local mesh network of IoT devices.
However, both Pandit and Cook provide evidence that the use of edge servers (including Internet of Things (IoT)) and a mesh network configuration in a farming (including a smart agriculture) environment were known in the art and trending in popularity prior to Applicant’s invention (e.g., see Pandit: ¶¶ 64, 65, 73 – edge servers, IoT sensors, farming; Cook: ¶¶ 374, 463, 487 -- sensors as a service, mesh network (including of sensors), local servers, edge devices, IoT devices, smart agriculture). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Ramachandran to explicitly use an edge server with its edge and IoT devices, wherein the edge server hosts a local mesh network of IoT devices, since “certain mesh network configurations facilitate greater redundancy/fault tolerance (i.e., network traffic could be rerouted through alternative paths), self-healing capabilities (i.e., a sensor can be removed from or added to a network), scalability (i.e., new sensors can be added), extended coverage (i.e., additional sensors can span a wider area), increased throughput (i.e., multiple paths can be simultaneously pursued), flexibility (i.e., wireless local networks, sensor network, Internet-of-things application), etc.” (Cook: ¶ 374) and since “the Internet fog middleware platforms may leverage local servers, edge devices, and cloud resources to process and store data near the source, reducing latency and improving overall system efficiency. Additionally, communication resources such as picocells, femtocells, and hotspots may extend network coverage and connectivity to localized areas. Moreover, data from picocells, femtocells, and/or hotspots may be more granular than data collected from other sources. Sensors and actuators embedded in IoT devices may further enhance the Internet fog middleware platform infrastructure by facilitating real-time data collection and control at the network's edge. This amalgamation of computing, storage, communication, and IoT resources forms a dynamic and responsive Internet fog middleware platform layer, optimizing the performance of applications and services, such as by providing low latency and high responsiveness.” (Cook: ¶ 463)
[Claim 3] Ramachandran discloses wherein the device computes a tracking of crops in one or more groups as defined by a seed lot seeding date (¶ 39 – “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.”), one or more transplant dates (¶ 39 – “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).”; ¶ 161 – “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.” Movements/locations of the food along the supply chain are examples of transplant events.), and a harvest maturity date (¶ 39 – “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).”).
[Claim 4] Ramachandran is open to the use of radio frequency signals (Ramachandran: ¶ 28 – “Communications between computers implementing embodiments can be accomplished using any electronic, optical, radio frequency signals, or other suitable methods and tools of communication in compliance with known network protocols.”) and has the ability to scan information associated with a food product for additional product data (Ramachandran: ¶¶ 83-84: “[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.”). Ramachandran invention’s is helpful in maintaining food safety throughout the food supply chain (Ramachandran: ¶ 44 – “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. In the case of a recall, if the root cause was identified, specific shipments downstream of the incidents could also be retrieved in a pinpointed recall, saving a company millions compared to a blanket recall. If the data sets were to be shared with consumers, post-recall brand trust and loyalty could be repaired quickly as consumers could validate and verify the claims themselves.”).
However, Ramachandran does not explicitly disclose one or more RFID tags accompanying the one or more crop groups for ease of data recall and food safety information storage.
Pandit describes the following use of radio frequency identifier sensors for the inputs and outputs of machines processing crops/grains and machines that move the crops/grains throughout the supply chain:
[0064] FIG. 5 is a flow chart illustrating one method 500 of training the event analysis module 410 embodiment, consistent with some embodiments. A user may begin by loading training vectors from a plurality of different entities in the agricultural ecosystem at operation 510, and the underlying data may be collected throughout the agriculture supply chain, e.g., from static data about a particular farm, to pre-season activities at that farm (e.g., fleet/machinery selection, tillage, plantation records, field maps, etc.), to in-season activities at that farm (e.g., planting, field imagery for crop health, field level weather and soil monitoring, fertilizer application, fertilizer records and field maps, crop scouting, etc.), to post-season activities (harvest information, yield map, local storage, crop planning for next cycle, etc.), to storage activities (where, when, how long, type, etc.), to wholesale distribution activities, to final distribution activities (e.g., date delivered to a particular restaurant or supermarket, etc.). For example, for significance identification with respect to a farm, the training vectors may include static information such as: ownership information for a field, location information about a sub-field, physical information about a sub-field (e.g., elevation, slope), etc. The labeled training vectors for a farm may also include a time series of events that occurred at that farm, e.g., the particular seed type planted, daily weather, fertilization days and types, irrigation days and amounts, insecticide application dates and types, herbicide application dates and types, pruning events, and harvesting events (e.g., yield metrics, quality metrics, etc.). The training vectors may further include movement of particular machines (e.g., a planter, a truck) through the particular entity (e.g., a sub-field, a delivery route) together with time-stamped or geo-stamped outputs from various sensors on that equipment. The training vectors may further include where, how, and how long the product is stored (e.g., grain contract management). The training vectors may further include details about food processing (what facility received the product and on what date, what final product was produced, etc.), intermediate distribution (e.g., wholesale facilities), and final distribution(e.g., what supermarket or restaurant received the final product and on what dates, etc.)
[0065] In some embodiments, data for the training vectors may be automatically collected using Internet of Things sensors attached to various pieces of machinery and radio frequency identifier sensors attached to various inputs and outputs to those machines. The training vectors may also include remotely sensed information, such as images from a satellite or drone. For an agriculture processor, the training vectors may include information about where its various inputs were sourced (e.g., the field or sub-field), the specific trucks used to move the inputs and their load histories (e.g., what did they haul before the current input), the specific machines used to process the input and their processing histories, time-stamped operational settings for those processing machines, when any serviceable parts on those machines were last replaced (e.g., cutting blades), etc.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Ramachandran to include one or more RFID tags accompanying the one or more crop groups for ease of data recall and food safety information storage in order to diversify the manners in which Ramachandran may collect and convey its data recall and food safety information, thereby providing greater support to more customers who utilize different types of sensors and means of communication. Additionally, Ramachandran already tracks and conveys data recall and food safety-related information using sensors (like edge devices, such as IoTs) and QR codes. Substitution the use of RFID tags to track and convey such information with Ramachandran’s devices would have been well within the technical capability of those skilled in the art prior to Applicant’s invention and the substitution would have yielded predictable and expected results, especially given the long-known use of RFIDs in the area of communications to gather and convey information.
[Claim 5] Ramachandran discloses the tracking of the one or more crop groups as defined by their harvest groupings (¶¶ 52-53 – FOOD BUNDLES; ¶ 52 – “The system uses unique identifiers, each representing a unit of food at a particular time and place along the supply chain. For the purposes of this disclosure, the unique identifiers will be referred to as “FOOD BUNDLES™”, which is a trademark of Ripe Technology, INC. FOOD BUNDLES can take many forms; bag of seeds, area of a field, specific plant, crates of fruits, pallets, etc. A bundle of food can be merged into another to form a 3rd bundle; alternately a bundle can also be separated into two or more bundles, each inheriting a selected set of characteristics from the parent bundle.”).
[Claim 6] Ramachandran discloses wherein the device manages one or more assets of CEA [controlled environment agriculture – per Applicant’s Spec: ¶ 1] farms (¶ 116 – greenhouse; ¶ 40 – farming; ¶¶ 47-50 -- food marketplace platform) or warehouses (¶¶ 57, 124, 161 – Warehouse, storage) with one or more crop stages defined by a hardware configuration that one or more crops may occupy during a given stage (¶ 39 – “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.”; ¶ 67 – “Farmer Brown's profile is configured to automatically generate Assertion transactions for any FOOD BUNDLE associated with a particular crop. In this case, an Assertion transaction stating: “This FOOD BUNDLE was created using IMP practices” is signed using one of Farmer Brown's private keys and posted to the Blockchain of Food.”).
[Claim 7] Ramachandran discloses wherein the device tracks asset usage over time (¶ 39 – “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.”), indicating capacity (¶ 125 – Space in storage/warehouse for produce), and allowing form projected usage (¶ 125 – Space in storage/warehouse for produce; ¶ 126 – “Should I do any value-added preparation?”) and availability for future crop groups (¶ 43 – “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.”; ¶ 122 – “How deep should the seeds be planted for maximum yield?”; ¶ 111 – “Conceptually, a tomato is continuously adding or subtracting value at any moment of its development over the food supply chain. For example, as a tomato grows from seed to plant ripe fruit, it gradually improves its nutritional value, taste, yield, etc. At any given time, a tomato may also lose value because of drought or flood, becoming less safe due to pollutants, attacked by pests that reduce yield or require pesticides, etc. While the major stakeholders will be able to continuously monitor the progress of a given tomato on the scorecard, the scorecard may have its greatest utility when the tomato is bought or sold.”; ¶¶ 52-53 – FOOD BUNDLES).
[Claim 8] Ramachandran discloses wherein the device includes asset labeling with static QR codes for quick reference of assets' current status and histories (¶¶ 83-84 – “[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.” The product’s origins convey both current status and histories. The price and description also exemplify current status.; ¶ 52 – “The system uses unique identifiers, each representing a unit of food at a particular time and place along the supply chain. For the purposes of this disclosure, the unique identifiers will be referred to as “FOOD BUNDLES™”, which is a trademark of Ripe Technology, INC. FOOD BUNDLES can take many forms; bag of seeds, area of a field, specific plant, crates of fruits, pallets, etc. A bundle of food can be merged into another to form a 3rd bundle; alternately a bundle can also be separated into two or more bundles, each inheriting a selected set of characteristics from the parent bundle.” Multiple FOOD BUNDLES, i.e., assets, may be tracked.).
[Claims 9-17] Claims 9-17 recite limitations already addressed by the rejections of claims 1-8 above; therefore, the same rejections apply.
[Claims 18-20] Claims 18-20 recite limitations already addressed by the rejections of claims 1-8 above; therefore, the same rejections apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Farooq, Muhammad Shoaib et al. "A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming." IEEE Access, Vol. 7, November 6, 2019 – Discusses the use of IoTs for smart farming.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A