DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on September 9, 2025, for the application with serial number 18/430,272.
Claims 1 and 11 are amended.
Claims 1-20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims are subject matter eligible because the claims recite sensors and computer hardware. See Remarks p. 7. In response, the Examiner points to the rejection, below, which concludes that the sensors and computer components amount to generic computer hardware that does not provide a practical application or significantly more than an abstract idea. No specific computer or hardware structure is recited in the claims. The use of blockchain technology, artificial intelligence, and the internet of things amounts to a technological environment that does not provide a practical application. The recitations of the claims amount to the generation of reports through data gathering. Artificial intelligence and blockchain technology are used in a conventional manner. No apparent improvement to technology or a technical field is recited in the claims. Contrary to the Applicant’s assertions, no improvement to image processing technology is recited. Instead, the merely recite the application of image processing technology. See exemplary independent claim 1: “wherein the photographic data is pre-processed using image recognition algorithms tailored to identify signs of product spoilage, damage, or quality degradation in food and agriproducts.” The claims are not comparable to the claims from Example 37 because the claims from Example 37 recite a process for relocating icons on a graphical user interface that is rooted in computer technology. In contrast, creating food inspection reports is not rooted in computer technology. Contrary to the Applicant’s assertions on p. 9, the claims do not positively recite ethylene or CO2 treatment. The claims are directed to a method of organizing human activity because the claims collect data to generate reports. The steps of the claims could be implemented mentally or on paper by a human being, but a general purpose computer employing artificial intelligence and blockchain technology is recited for implementation. The claims are not comparable to Example 3, which recites subject matter related to a blue noise mask. It is unclear to the Examiner why the Applicant believes the present claims have subject matter comparable to a blue noise mask.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. The previously cited Cella reference, now referred to as Cella ‘070, has been replaced with Cella ‘494. The Applicants arguments are moot in light of the newly cited reference. The Examiner notes that certain recitations, such as cross border trade, are not functional design elements that are afforded patentable weight. Providing such a context or environment does not provide a patentable, functional distinction over the prior art.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to generating inspection reports (as evidenced by exemplary independent claim 1; “analyze the integrated data using artificial intelligence (AI) models to assess Quality, Quantity, and Weight (QQW) risks . . . and generate inspection reports based on the AI analysis”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “receive photographic data;” “integrate the photographic data with IoT data;” “analyze the integrated data using artificial intelligence (AI) models to assess [ ] risks;” and “generate inspection reports based on the AI analysis.” The steps are all steps for managing personal behavior related to the abstract idea of generating inspection reports that, when considered alone and in combination, are part of the abstract idea of generating inspection reports. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating inspection reports. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes using images and shipment data to analyze shipping risks associated with cross-border trade.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (“inspection devices” that capture photographic data and a computer system with a processor and memory in independent claim 1; and a computer with inspection device that capture photographic data in independent claim 11). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of artificial intelligence, internet of things, and blockchain technologies, but the abstract idea of generating inspection reports is generally linked to an artificial intelligence, internet of things, and blockchain environment for implementation. Therefore, the artificial intelligence, internet of things, and blockchain merely amount to a technological environment for implementing the abstract idea that does not provide a practical application or significantly more than the abstract idea. See MPEP §2106.05(h). The claims require no more than a generic computer (“inspection devices” and a computer system with a processor and memory in independent claim 1; and a computer in independent claim 11) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1-4, 7-11, 13, 14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200327137 A1 to Farver et al. (hereinafter ‘FARVER’) in view of US 20220366494 A1 to Cella et al. (hereinafter ‘CELLA ‘494’).
Claim 1 (Currently Amended)
FARVER discloses a system for automated generation of AI-enabled inspection reports for cross-border trade of food and agriproducts (see ¶[0021]; digital data objects that characterize transactions for trade and customs).
FARVER does not explicitly disclose, but CELLA ‘494 discloses, the system comprising: one or more inspection devices associated with inspectors (see ¶[0267], [0272]. [0285]; the term “party’ includes an inspector. Initiate an inspection process. Use an automated inspection system. See also ¶[0032]; sensor data received from sensors), wherein the inspection devices are adapted to capture photographic data of the food and agriproducts (see ¶[0272] and [0275]; distributed sensors or cameras) under varying environmental conditions (see again ¶[0275]; examples of sensors may be sensors for sensing movement of entities, for sensing temperatures, pressures or other attributes about entities or their environments, cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like) that lead to damage or spoilage during shipment (see ¶[0980]; exposure to biological factors including pathogens. See also ¶[2057], [2073], [2101]-[2102], and 2155; data streams collected form physical entities (e.g., machinery, a building, a shipping container, or the like. Shipping facilities. Shipping information. A shipping environment).
FARVER further discloses an Internet of Things (IoT) module to gather shipment related data as IoT data (see ¶[0020]-[0022]; IoT devices that transfer digital data objects amongst companies. Modify the attribute of a transaction using an IoT device)
FARVER does not explicitly disclose, but CELLA ‘494 discloses, that determine product handling and potential damage during transit (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors. A party whose actions or lack of actions can be directly measured as causing damage (e.g., the item was dropped and dented while in the party's possession), may be automatically allocated responsibility for the damage. See also ¶[0612] and [1824]; a food production facility, which may include an inspection facility. A hot food delivery marketplace).
FARVER further discloses a blockchain framework comprising an immutable ledger that secures all data (see abstract; processes to digital data objects utilizing blockchain and artificial intelligence)
FARVER does not explicitly disclose, but CELLA ‘494 discloses, including photographic inspection data (see ¶[0020]; a secure blockchain. See again ¶[0275]; cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like).
FARVER further discloses, IoT data (see ¶[0022]; receive data from an IoT device).
FARVER does not explicitly disclose, but CELLA ‘494 discloses, and risk assessments (see ¶[0019]-[0021]; manage risk. See also ¶[1336]; risk management).
FARVER further discloses ensuring tamper- proof traceability (see abstract and ¶[0027]; apply lifecycle processes to digital data objects utilizing blockchain and artificial intelligence. With the blockchain 20, the digital data object's origin, chain of possession, and modifications can be tracked, traced, and presented chronologically in the cryptographically-verified ledger 21 to each participant of the blockchain, e.g., nodes); and a computer system connected with the one or more inspection devices, the IoT module and the blockchain framework (see ¶[0004] and [0039]; communications networks with a computer systems interface connection. A computer-implemented system), the computer system including:
a processor (see ¶[0022]; the AI module may include a processor); and a memory unit (see ¶[0027]; memory storage) configured to store machine readable instructions (see ¶[0036]; program instructions).
FARVER does not explicitly disclose, but CELLA ‘494 discloses, that, when executed by the processor, cause the computer system to: receive photographic data related to the shipment from the one or more inspection devices (see ¶[0275] and [2044]; examples of sensors include cameras. Process sensor data from sensor kits. See also ¶[0612]; a food inspection facility) wherein the photographic data is pre-processed using image recognition algorithms (see ¶[0597], [1307] and [1336]; artificial intelligence applied to an image recognition task) tailored to identify signs of product spoilage, damage, or quality degradation in food and agriproducts (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors);
integrate the photographic data with IoT data (see ¶[0237]; IoT sensors) correlating environmental factors with potential risks such as spoilage, damage, or improper handling during transport (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors);
analyze the integrated data using artificial intelligence (AI) models to assess Quality (see ¶[0257]; detect or measure a physical quality), Quantity (see ¶[1831]; quantity of assets), and Weight (QQW) risks (see ¶[0612] and [0689]; weight sensors. Examiner Note: weight is a quantity. See also ¶[0100], [1895], and [2073]; a regulatory compliance system. A contract may include agricultural commodities) including, early signs of product damage or spoilage (see again ¶[0980]; (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, )deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors), and the efficacy of the delivery process (see ¶[0032]; physically inspecting and physically delivering an asset); and
generate inspection reports based on the AI analysis (see ¶[0019] and [0032]; reporting capabilities. An artificial intelligence system), the inspection reports detailing at least assessed QQW risks, indications of damage or spoilage with supporting photographic and IoT evidence, and findings regarding the efficacy of the delivery process, and record the inspection reports or corresponding cryptographic records in the blockchain framework (see again ¶[0019]-[0020] and [0032]; reporting capabilities. An artificial intelligence system. A secure blockchain).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). It would have been obvious to include the sensors gathering weight and quality data to assess likely compliance as taught by CELLA ‘494 in the system executing the method of FARVER with the motivation to report compliance to regulatory bodies.
Claim 2 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER does not specifically disclose, but CELLA ‘494 discloses, wherein the one or more inspection devices are selected from a group comprising digital cameras and video recorders (see ¶[0275] and [2044]; examples of sensors include cameras. Process sensor data from sensor kits. Video images).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). It would have been obvious to include the sensors gathering weight and quality data to assess likely compliance as taught by CELLA ‘494 in the system executing the method of FARVER with the motivation to report compliance to regulatory bodies.
Claim 3 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER does not specifically disclose, but CELLA ‘494 discloses, wherein the IoT data includes at least temperature (see ¶[0275]; sense temperature), location (see ¶[0275]; GPS sensors), and humidity data (see ¶[0513]; humidity).
FARVER further discloses collected during shipment (see ¶[0030]; the lifecycle of the digital data objects migrate across the organization's supply chain).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). It would have been obvious to include the sensors gathering weight and quality data to assess likely compliance as taught by CELLA ‘494 in the system executing the method of FARVER with the motivation to report compliance to regulatory bodies.
Claim 4 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER further discloses wherein the blockchain framework comprises an immutable ledger configured to securely log inspection data (see ¶[0001]-[0002]; use distributed ledger technologies, i.e., blockchain).
Claim 7 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER further discloses wherein the computer system further comprises a communication module configured to transmit the integrated data and inspection reports wirelessly (see ¶[0039]; a wireless communication network).
Claim 8 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER further discloses wherein the computer system is further configured to update the inspection reports in real-time as new data is received (see abstract and ¶[0022]; embodiments of the systems and methods provide for interacting with a digital data object from a variety of different source systems as well as dynamically modifying particular attributes associated with the digital data objects based on real-time information and/or user-specified requirements. Receive real-time updates).
Claim 9 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
FARVER further discloses wherein the inspection reports are configured to be accessed via user interfaces on associated stakeholder devices (see ¶[0041]; the personal computing devices may be equipped with an integral or connectable liquid crystal display (LCD), electroluminescent display, a light emitting diode (LED), organic light emitting diode (OLED) or another display screen, panel or device for viewing and manipulating files, data and other resources, for instance using a graphical user interface (GUI) or a command line interface (CLI)..
Claim 10 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 9,
FARVER further discloses wherein the stakeholder devices are selected from a group comprising laptops, mobile phones, wearable watches or bands, desktop computers, and portable handheld devices with computing capabilities (see ¶[0041]; the personal computing devices may include desktop computers, laptop computers, tablet computers, smart phones, and other mobile computing devices, for example).
Claim 11 (Currently Amended)
FARVER discloses a computer-implemented method (see ¶[0004]-[0005]; computer-implemented systems and methods) for automated generation of AI-enabled inspection reports for cross-border trade of food and agriproducts (see ¶[0021]; digital data objects that characterize transactions for trade and customs).
FARVER does not explicitly disclose, but CELLA ‘494 discloses, the computer-implemented method comprising: receiving photographic data related to the shipment from one or more inspection devices associated with inspectors (see ¶[0267], [0272]. [0285]; the term “party’ includes an inspector. Initiate an inspection process. Use an automated inspection system. See also ¶[0032]; sensor data received from sensors), wherein the inspection devices are adapted to capture photographic data of the food and agriproducts (see ¶[0272] and [0275]; distributed sensors or cameras) under varying environmental conditions (see again ¶[0275]; examples of sensors may be sensors for sensing movement of entities, for sensing temperatures, pressures or other attributes about entities or their environments, cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like) that lead to damage or spoilage during shipment (see ¶[0980]; exposure to biological factors including pathogens. See also ¶[2057], [2073], [2101]-[2102], and 2155; data streams collected form physical entities (e.g., machinery, a building, a shipping container, or the like. Shipping facilities. Shipping information. A shipping environment).
FARVER further discloses integrating the photographic data with IoT data (see ¶[0020]-[0022]; IoT devices that transfer digital data objects amongst companies. Modify the attribute of a transaction using an IoT device)
FARVER does not explicitly disclose, but CELLA ‘494 discloses, that determine product handling and potential damage during transit (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors. A party whose actions or lack of actions can be directly measured as causing damage (e.g., the item was dropped and dented while in the party's possession), may be automatically allocated responsibility for the damage. See also ¶[0612] and [1824]; a food production facility, which may include an inspection facility. A hot food delivery marketplace).
FARVER further discloses from an IoT module (see ¶[0020]-[0022]; IoT devices that transfer digital data objects amongst companies. Modify the attribute of a transaction using an IoT device)
FARVER does not explicitly disclose, but CELLA ‘494 discloses, correlating environmental factors with potential risks such as spoilage, damage, or improper handling during transport (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors. A party whose actions or lack of actions can be directly measured as causing damage (e.g., the item was dropped and dented while in the party's possession), may be automatically allocated responsibility for the damage. See also ¶[0612] and [1824]; a food production facility, which may include an inspection facility. A hot food delivery marketplace);
analyzing the integrated data using artificial intelligence (AI) models to assess Quality (see ¶[0257]; detect or measure a physical quality), Quantity (see ¶[1831]; quantity of assets), and Weight (QQW) risks (see ¶[0612] and [0689]; weight sensors. Examiner Note: weight is a quantity. See also ¶[0100], [1895], and [2073]; a regulatory compliance system. A contract may include agricultural commodities) including, early signs of product damage or spoilage (see again ¶[0980]; (see ¶[0980]; the sensor data may indicate the state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of the environment in which it is located, environmental humidity, movements of the item (such as resulting from impacts, vibration, transport, or the like), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, and the like), bearing of loads, bearing of weight, exposure to stress, exposure to strain, exposure to impacts, damage (such as dents, deformations, )deflections, disconnects, breaks, cracks, shatters, tears and many others), exposure to biological factors (including pathogens), extent of progressive damage (such as rust), and other factors), and the efficacy of the delivery process (see ¶[0032]; physically inspecting and physically delivering an asset); and
generating inspection reports based on the AI analysis (see ¶[0019] and [0032]; reporting capabilities. An artificial intelligence system) the inspection reports detailing at least assessed QQW risks, indications of damage or spoilage with supporting photographic and IoT evidence, and findings regarding the efficacy of the delivery process, and record the inspection reports or corresponding cryptographic records in the blockchain framework (see again ¶[0019]-[0020] and [0032]; reporting capabilities. An artificial intelligence system. A secure blockchain).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). It would have been obvious to include the sensors gathering weight and quality data to assess likely compliance as taught by CELLA ‘494 in the system executing the method of FARVER with the motivation to report compliance to regulatory bodies.
Claim 13 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
FARVER does not specifically disclose, but CELLA ‘494 discloses, wherein the IoT data includes at least temperature (see ¶[0275]; sense temperature), location (see ¶[0275]; GPS sensors), and humidity data (see ¶[0513]; humidity).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). It would have been obvious to include the sensors gathering weight and quality data to assess likely compliance as taught by CELLA ‘494 in the system executing the method of FARVER with the motivation to report compliance to regulatory bodies.
Claim 14 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
FARVER additionally discloses further comprising securing the integrated data using a blockchain framework (see abstract; apply lifecycle processes to digital data objects utilizing blockchain and artificial intelligence).
Claim 17 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
FARVER further discloses further comprising wirelessly transmitting the integrated data and inspection reports to associated stakeholder devices (see ¶[0039]; a wireless communication network).
Claim 18 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
FARVER further discloses further comprising updating the inspection reports in real-time as new data is received (see abstract and ¶[0022]; embodiments of the systems and methods provide for interacting with a digital data object from a variety of different source systems as well as dynamically modifying particular attributes associated with the digital data objects based on real-time information and/or user-specified requirements. Receive real-time updates).
Claim 19 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
FARVER further discloses further comprising accessing the inspection reports via user interfaces on associated stakeholder devices (see ¶[0041]; the personal computing devices may be equipped with an integral or connectable liquid crystal display (LCD), electroluminescent display, a light emitting diode (LED), organic light emitting diode (OLED) or another display screen, panel or device for viewing and manipulating files, data and other resources, for instance using a graphical user interface (GUI) or a command line interface (CLI)..
Claim 20 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 19,
FARVER further discloses wherein the associated stakeholder devices are selected from a group comprising laptops, mobile phones, wearable watches or bands, desktop computers, and portable handheld devices with computing capabilities (see ¶[0041]; the personal computing devices may include desktop computers, laptop computers, tablet computers, smart phones, and other mobile computing devices, for example).
Claim(s) 5, 12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200327137 A1 to FARVER et al. in view of US 20210360070 A1 to CELLA ‘494 et al. as applied to claim 1 above, and further in view of US 20200394578 A1 to Taggart et al. (hereinafter ‘TAGGART’).
Claim 5 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
The combination of FARVER and CELLA ‘494 does not specifically disclose, but TAGGART discloses, wherein the AI models include image recognition algorithms configured to analyze photographic data (see ¶[0023]-[0024]; confirm ground truth for a smart contract using image recognition. Authenticate transaction processing for a delivery).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). TAGGART discloses controlling components of an operation using a processing system that includes image recognition to authenticate transactions on delivery. It would have been obvious to include the image recognition as taught by TAGGART in the system executing the method of FARVER and CELLA ‘494 with the motivation to authenticate delivery and compliance of goods in smart contracts transactions.
Claim 12 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
The combination of FARVER and CELLA ‘494 does not specifically disclose, but TAGGART discloses, wherein receiving photographic data includes capturing images and videos at shipment or delivery locations (see ¶[0023]-[0024]; confirm ground truth for a smart contract using image recognition. Authenticate transaction processing for a delivery).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). TAGGART discloses controlling components of an operation using a processing system that includes image recognition to authenticate transactions on delivery. It would have been obvious to include the image recognition as taught by TAGGART in the system executing the method of FARVER and CELLA ‘494 with the motivation to authenticate delivery and compliance of goods in smart contracts transactions.
Claim 15 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
The combination of FARVER and CELLA ‘494 does not specifically disclose, but TAGGART discloses, wherein analyzing the integrated data includes using machine learning techniques for image and pattern recognition (see ¶[0023]-[0024]; confirm ground truth for a smart contract using image recognition. Authenticate transaction processing for a delivery).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). TAGGART discloses controlling components of an operation using a processing system that includes image recognition to authenticate transactions on delivery. It would have been obvious to include the image recognition as taught by TAGGART in the system executing the method of FARVER and CELLA ‘494 with the motivation to authenticate delivery and compliance of goods in smart contracts transactions.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200327137 A1 to FARVER et al. in view of US 20210360070 A1 to CELLA ‘494 et al. as applied to claim 1 above, and further in view of US 11263586 B2 to Siegel et al. (hereinafter ‘SIEGEL’).
Claim 6 (Original)
The combination of FARVER and CELLA ‘494 discloses the system as set forth in claim 1,
The combination of FARVER and CELLA ‘494 does not specifically disclose, but SIEGEL discloses, wherein the generated inspection reports are configured to detail the QQW risks for stakeholders in the cross-border trade (see abstract and claim 1; A mobile Quality Management/Control system for performing mobile product inspections is provided. A mobile device, such as a tablet, is configured to communicate with one or more databases and allow for real time entry (and subsequent access) of the details of product inspections for quality control and management purposes. The details of such inspections are maintained and available for all subsequent inspections. The mobile device is further configured to provide inspectors with inspection procedures and/or tutorials associated with the inspections being performed. Social compliance audits relate to an inspection of an entity to verify whether the entity's operation complies with social and ethical responsibilities, health and safety regulations, and labor laws, and security audits include compliance with the Customs-Trade Partnership Against Terrorism (C-TPAT).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). SIEGEL discloses mobile quality management inspections including details of compliance reports with respect to the Customs-Trade Partnership Against Terrorism (C-TPAT). It would have been obvious to include the detailed customed compliance reports as taught by SIEGLEL in the system executing the method of FARVER and CELLA ‘494 with the motivation to ensure compliance with contracts and applicable social and ethical responsibilities.
Claim 16 (Original)
The combination of FARVER and CELLA ‘494 discloses the computer-implemented method as set forth in claim 11,
The combination of FARVER and CELLA ‘494 does not specifically disclose, but SIEGEL discloses, wherein generating inspection reports includes detailing the assessed QQW risks (see abstract and claim 1; A mobile Quality Management/Control system for performing mobile product inspections is provided. A mobile device, such as a tablet, is configured to communicate with one or more databases and allow for real time entry (and subsequent access) of the details of product inspections for quality control and management purposes. The details of such inspections are maintained and available for all subsequent inspections. The mobile device is further configured to provide inspectors with inspection procedures and/or tutorials associated with the inspections being performed. Social compliance audits relate to an inspection of an entity to verify whether the entity's operation complies with social and ethical responsibilities, health and safety regulations, and labor laws, and security audits include compliance with the Customs-Trade Partnership Against Terrorism (C-TPAT).
FARVER discloses applying lifecycle processes to digital data objects using blockchain technology and artificial intelligence that includes trade and customs compliance and smart contracts transactions (see ¶[0024]-[0025]). CELLA ‘494 discloses sensor kits for generating feature vectors for monitoring and managing industrial settings, including a food inspection process (see ¶[0612]), that uses artificial intelligence and sensor data to report data regarding likely compliance to a regulatory body, including smart contracts scenarios (see ¶[0019]-[0020]). SIEGEL discloses mobile quality management inspections including details of compliance reports with respect to the Customs-Trade Partnership Against Terrorism (C-TPAT). It would have been obvious to include the detailed customed compliance reports as taught by SIEGLEL in the system executing the method of FARVER and CELLA ‘494 with the motivation to ensure compliance with contracts and applicable social and ethical responsibilities.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624