DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on January 15, 2026, for the application with serial number 18/105,072.
Claims 1, 3-5, 11, 12, 16, and 19 are amended.
Claim 22 and 23 are canceled.
Claims 24 and 25 are added.
Claims 1-9 ,11-13, 16-21, 24, and 25 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
In light of the Applicant’s amendments, the rejection for lack of subject matter eligibility is withdrawn.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Gunawardena reference; cited in the rejections, below. Independent claims 1, 5, and 16 now stand rejected as being obvious over Kring in view of Goldman, Vasilenko, Gunawardena, Piccicuto, and Schroeder.
The Applicant traverses the rejection of the independent claims, contending that Goldman does not teach the limitation: “determining, by the computing system, respective amounts of energy consumption that will be used to treat each of the different products.” See Remarks p. 22. In response, the Examiner points to cited ¶[0307] and [0314] of Goldman, which teaches using knowledge trees to map energy profile relationships in a food production process. Therefore, Goldman discloses this limitation. Vasilenko is cited for teaching other features, such as a level of pathogen. The Examiner notes that the present claims generally recite steps for pasteurization of food, which generally uses heat to sterilize a product. Once a critical temperature is reached, the microorganism contaminants have bee killed. See Vasilenko col 1, ln 37-47. Therefore, the critical consideration is the temperature, not the level of contamination. This interpretation is consistent with the Examiner’s reading of the Specification. See Specification ¶[0011] and [0020]. Contrary to the Applicant’s assertions, the cited portions of Vasilenko at least suggest determining an amount of energy required to reduce a pathogen level. In cited col 1, ln 37-47, Vasilenko discloses that pasteurization requires substantial energy to kill microorganisms. This at least suggests that the amount of energy used is tracked and quantified.
The Examiner reiterates the response provided above, in regards to independent claims 5 and 16. Independent claims 5 and 16 are substantially similar to independent claim 1.
The rejection of the dependent claims stands or falls with the rejection of independent claims 1, 5, and 16.
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, 2, 4-6, 12, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to Kring et al. (hereinafter ‘KRING’) in view of US 20020052858 A1 to Goldman et al. (hereinafter ‘GOLDMAN’), US 7682641 B1 to Vasilenko (hereinafter ‘VASILENKO’), US 11234444 B1 to Gunawardena et al. (hereinafter ‘GUNAWARDENA,’) US 20200380448 A1 to Piccicuto et al. (hereinafter ‘PICCICUTO’) and US 20170178047 A1 to Schroeder et al. (hereinafter ‘SCHROEDER’).
Claim 1 (Currently Amended)
KRING discloses a method comprising: receiving, by a computing system, over a network (see ¶[0005]-[0006]; a server and a computer), contaminant data associated with a raw food product delivered to a food processing facility (see ¶[0016]-[0023], [0031] and [0076]; a batch parameter of a collected food batch. Example batch parameters include contaminants and microbial data),
wherein the raw food product is usable for producing different food products (see ¶[0050]-[0051]; meat, corn, vegetables, dairy and/or juice), and wherein the contaminant data is usable for triaging the raw food product (see again ¶[0016]-[0023], [0031], and [0076]; a batch parameter that is compared to reference data. The batch parameter is used to create a logistic plan. See also ¶[0076]; example batch parameters include temperature, pH, volume, weight, concentration, color, composition, contaminants, and microbial data); and
determining, by the computing system, based at least in part on the contaminant data, a level of a pathogen in the raw food product (see ¶[0076], [0097] and [0163]-[0167]]; example batch parameters include concentration and contaminants. The derived batch parameter preferably is a calculation of microbial data and/or contamination data and/or quantity data at a present or future point of time based on known, expected and/or estimated data, such as temperature data, quantity data, pH data and/or combinations thereof, optionally the derived batch parameter is a derived batch parameter for the batch or for a mixture of batches comprising the batch, such as a mixture comprising collected batches.).
KRING does not specifically disclose, but GOLDMAN discloses, determining, by the computing system, respective amounts of energy consumption that will be used to treat each of the different food products (see ¶[0307] and [0314]; a knowledge tree maps energy profile relationships);
The combination of KRING and GOLDMAN does not specifically disclose, but VASILENKO discloses, with heat treatment to reduce the level of the pathogen in each of the different food products when the different food products are produced using the raw food product (see col 1, ln 37-47; heat pasteurization of produce requires substantial energy in order to raise the temperature of the produce to a level suitable to kill microorganisms that can be responsible for the degradation of quality in foods).
KRING does not specifically disclose, but GOLDMAN discloses, designating, by the computing system, the raw food product for use in producing a food product of the different food products that will result in a lowest amount of energy consumed based at least in part on the respective amounts of energy consumption (see ¶[0314]-[0315]; before a recipe can be determined, the knowledge tree creates a logical map. Cut costs and discard powder that is out of spec that results in poor quality yield, loss of material, and excessive energy consumption when optimal settings are not used. At each stage, determine an individualized recipe and the material state); and
processing the raw food product within the food processing facility by carrying out a food production process to produce the food product (see again ¶[0314]; the food powder production process).
KRING does not specifically disclose, but GUNAWARDENA discloses, wherein the food production process comprises treating the food product produced using the raw food product with the heat treatment by using a programmable logic controller (PLC) within the food processing facility to cause an element to heat a tank containing the food product until a temperature of the food product within the tank is increased to an elevated temperature at, or above, a threshold temperature and maintaining the food product at the elevated temperature for a threshold amount of time (see abstract and col 9, ln 39-col 10, ln 2; the control system also includes a suitable controller, such as a programmable logic controller 124 linked to the processor 31 and having an appropriate interface 126 for connecting the various measuring devices and instruments. See also col 2, ln 25-30; In the pasteurization step, the raw food product is exposed to pasteurization zone for a maximum time period selected from the maximum time periods of about 30 seconds, about 25 seconds, about 20 seconds, and about 15 seconds. Further, saturated steam is supplied to the pasteurization zone at a pressure range of about 5 to 20 psig.).
KRING further discloses receiving, by the computing system, over the network (see abstract; a server system coupled to a database system), data associated with different steps of the food production process (see ¶[0004] and [0072]; an assessment of the quality of the food is usually performed by withdrawing samples at the collection point for performing an initial assessment, and additional samples are withdrawn at the receiving food facility. One or more food receiver stations is a food processing station, a packing station, a market and/or a disposal station);
determining, by the computing system (see abstract and ¶[0005]; a server and computers), based at least in part on the data, a releasability metric associated with the food product (see ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but PICCICUTO discloses, the releasability metric indicative of a probability of the food product being ready to be released from the food processing facility (see abstract and ¶[0028] & [0035]-[0037]; determining a square error between said cumulative relative frequencies and cumulative theoretical probabilities P.sub.x of having ≤x occurrences over N samples for a ZIB distribution. Maintain a high confidence interval of an ascertained quality level. Provide alert levels according to predetermined thresholds of confidence values. Provide for a facilitated quality control where released batches may be associated with confidence values of alert levels according to statistical tests).
KRING further discloses causing, by the computing system, a user interface (see ¶[0033]-[0036]; the first mentioned valid logistic plan is a previously calculated logistic plan or a user-provided logistic plan, e.g. fed to the server system via a user interface) to be displayed on a user device (see ¶[0094]; a handheld device, such as a cell phone), wherein the user interface includes:
information that indicates whether the food product is ready to be released from the food processing facility (see ¶[0025] and claim 1; if said batch parameter exceeds a threshold data of reference data; calculate a logistic plan and transmit the logistic plan to the data receiver. See also ¶[0096] and claims 32 and 33; the data receiver is configured for displaying collecting address and delivery address) ,based at least in part on the releasability metric (see again ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but SCHROEDER discloses multiple interactive elements comprising: a first interactive element that is selectable for authorizing a release of the food product from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
a second interactive element that is selectable for requesting that the food product be held from release from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
receiving, by the computing system, over the network, from the user device, an indication of a selection of one of the multiple interactive elements (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged, where the food may be milk. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe. It would have been obvious to determine the optimal recipe as taught by GOLDMAN in the system executing the method of KRING with the motivation to reduce costs (see GOLDMAN ¶[0314] and KRING ¶[0031]).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe, including energy profile relationships. VASILENKO discloses that pasteurization to kill microorganisms requires energy. It would have been obvious to include the energy consumption as taught by VASILENKO in the system executing the method of KRING and GOLDMAN with the motivation to compare methods and reduce costs (see VASILENKO col 8, ln 1-col 9, ln 19).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GUNAWARDENA discloses steam pasteurization using a programmable logic controller for connecting devices and instruments to implement the pasteurization process. It would have been obvious for one of ordinary skill in the art at the time of invention to include the programmable logic controller as taught by GUNAWARDENA in the system executing the method of KRING with the motivation to reduce microbial and contaminant levels in food.
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. PICCICUTO discloses determining a microbiological risk level in a food batch that includes determining probabilities of contaminant levels in food for issuing an alert. It would have been obvious to include the probabilities of contaminant levels as taught by PICCICUTO in the system executing the method of KRING with the motivation to maintain a high confidence interval of an ascertained quality level (see PICCICUTO ¶[0028]).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. SCHROEDER discloses quality assurance, where a manufacturing administrator toggles a pass/fail button before releasing batches of approved products. It would have been obvious to include the toggling of pass/fail as taught by SCHROEDER in the system executing the method of KRING with the motivation to discharge batches of food with microbial levels below a threshold.
Claim 2 (Original)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the method as set forth in claim 1.
KRING further discloses wherein the information further indicates one or more of: whether the food product meets a safety standard; whether the food product meets a quality standard (see ¶[0003]-[0005] and [0031]; ensure an acceptable food quality); whether the food product meets a packaging standard; or whether the food product meets a labeling standard.
Claim 4 (Currently Amended)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the method as set forth in claim 1.
KRING further discloses wherein the data originated from devices distributed about the food processing facility (see ¶[0005]; milk is stored at storage facilities in vats having an array of sensors determining parameters relevant for milk quality).
Claim 5 (Currently Amended)
KRING discloses a system comprising: one or more processors; and memory storing computer-executable instructions (see abstract and ¶[0005], [0017], and [0094]; a server and a computer. A handheld device, such as a cell phone. The server is programmed) that, when executed by the one or more processors, cause performance of operations comprising: receiving, over a network (see again abstract and ¶[0005], [0017], and [0094]; a server and a computer), contaminant data associated with a raw food product delivered to a food processing facility (see ¶[0016]-[0023], [0031] and [0076]; a batch parameter of a collected food batch. Example batch parameters include contaminants and microbial data), wherein the raw food product is usable for producing different food products (see ¶[0050]-[0051]; meat, corn, vegetables, dairy and/or juice), and wherein the contaminant data is usable for triaging the raw food product (see ¶[0016]-[0023] and [0031]; a batch parameter that is compared to reference data. The batch parameter is used to create a logistic plan. See also ¶[0076]; example batch parameters include temperature, pH, volume, weight, concentration, color, composition, contaminants, and microbial data); and
determining, by the computing system, based at least in part on the contaminant data, a level of a pathogen in the raw food product (see ¶[0076], [0097] and [0163]-[0167]]; example batch parameters include concentration and contaminants. The derived batch parameter preferably is a calculation of microbial data and/or contamination data and/or quantity data at a present or future point of time based on known, expected and/or estimated data, such as temperature data, quantity data, pH data and/or combinations thereof, optionally the derived batch parameter is a derived batch parameter for the batch or for a mixture of batches comprising the batch, such as a mixture comprising collected batches.).
KRING does not specifically disclose, but GOLDMAN discloses, determining respective amounts of energy consumption that will be used to treat each of the different food products (see ¶[0307] and [0314]; a knowledge tree maps energy profile relationships);
The combination of KRING and GOLDMAN does not specifically disclose, but VASILENKO discloses, with heat treatment to reduce the level of the pathogen in each of the different food products when the different food products are produced using the raw food product (see col 1, ln 37-47; heat pasteurization of produce requires substantial energy in order to raise the temperature of the produce to a level suitable to kill microorganisms that can be responsible for the degradation of quality in foods).
KRING does not specifically disclose, but GOLDMAN discloses, designating the raw food product for use in producing a food product of the different food products that will result in a lowest amount of energy consumed based at least in part on the respective amounts of energy consumption (see ¶[0314]-[0315]; before a recipe can be determined, the knowledge tree creates a logical map. Cut costs and discard powder that is out of spec that results in poor quality yield, loss of material, and excessive energy consumption when optimal settings are not used. At each stage, determine an individualized recipe and the material state); and
processing the raw food product within the food processing facility by carrying out a food production process to produce the food product (see again ¶[0314]; the food powder production process).
KRING does not specifically disclose, but GUNAWARDENA discloses, wherein the food production process comprises treating the food product produced using the raw food product with the heat treatment by using a programmable logic controller (PLC) within the food processing facility to cause an element to heat a tank containing the food product until a temperature of the food product within the tank is increased to an elevated temperature at, or above, a threshold temperature and maintaining the food product at the elevated temperature for a threshold amount of time (see abstract and col 9, ln 39-col 10, ln 2; the control system also includes a suitable controller, such as a programmable logic controller 124 linked to the processor 31 and having an appropriate interface 126 for connecting the various measuring devices and instruments. See also col 2, ln 25-30; In the pasteurization step, the raw food product is exposed to pasteurization zone for a maximum time period selected from the maximum time periods of about 30 seconds, about 25 seconds, about 20 seconds, and about 15 seconds. Further, saturated steam is supplied to the pasteurization zone at a pressure range of about 5 to 20 psig.).
KRING further discloses, receiving, over the network (see abstract; a server system coupled to a database system), data associated with different steps of the food production process (see ¶[0004] and [0072]; an assessment of the quality of the food is usually performed by withdrawing samples at the collection point for performing an initial assessment, and additional samples are withdrawn at the receiving food facility. One or more food receiver stations is a food processing station, a packing station, a market and/or a disposal station);
determining, based at least in part on the data, a metric associated with the food product (see ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but PICCICUTO discloses, the metric indicative of a probability of the food product being ready to be released from the food processing facility (see abstract and ¶[0028] & [0035]-[0037]; determining a square error between said cumulative relative frequencies and cumulative theoretical probabilities P.sub.x of having ≤x occurrences over N samples for a ZIB distribution. Maintain a high confidence interval of an ascertained quality level. Provide alert levels according to predetermined thresholds of confidence values. Provide for a facilitated quality control where released batches may be associated with confidence values of alert levels according to statistical tests).
KRING further discloses causing a user interface (see ¶[0033]-[0036]; the first mentioned valid logistic plan is a previously calculated logistic plan or a user-provided logistic plan, e.g. fed to the server system via a user interface) to be displayed on a user device (see ¶[0094]; a handheld device, such as a cell phone) wherein the user interface includes:
information that indicates whether the food product is ready to be released from the food processing facility (see ¶[0025] and claim 1; if said batch parameter exceeds a threshold data of reference data; calculate a logistic plan and transmit the logistic plan to the data receiver. See also ¶[0096] and claims 32 and 33; the data receiver is configured for displaying collecting address and delivery address) based at least in part on the releasability metric (see again ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but SCHROEDER discloses, multiple interactive elements comprising: a first interactive element that is selectable for authorizing a release of the food product from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
a second interactive element that is selectable for requesting that the food product be held from release from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
receiving, by the computing system, over the network, from the user device, an indication of a selection of one of the multiple interactive elements (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged, where the food may be milk. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe. It would have been obvious to determine the optimal recipe as taught by GOLDMAN in the system executing the method of KRING with the motivation to reduce costs (see GOLDMAN ¶[0314] and KRING ¶[0031]).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe, including energy profile relationships. VASILENKO discloses that pasteurization to kill microorganisms requires energy. It would have been obvious to include the energy consumption as taught by VASILENKO in the system executing the method of KRING and GOLDMAN with the motivation to compare methods and reduce costs (see VASILENKO col 8, ln 1-col 9, ln 19).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GUNAWARDENA discloses steam pasteurization using a programmable logic controller for connecting devices and instruments to implement the pasteurization process. It would have been obvious for one of ordinary skill in the art at the time of invention to include the programmable logic controller as taught by GUNAWARDENA in the system executing the method of KRING with the motivation to reduce microbial and contaminant levels in food.
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. PICCICUTO discloses determining a microbiological risk level in a food batch that includes determining probabilities of contaminant levels in food for issuing an alert. It would have been obvious to include the probabilities of contaminant levels as taught by PICCICUTO in the system executing the method of KRING with the motivation to maintain a high confidence interval of an ascertained quality level (see PICCICUTO ¶[0028]).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. SCHROEDER discloses quality assurance, where a manufacturing administrator toggles a pass/fail button before releasing batches of approved products. It would have been obvious to include the toggling of pass/fail as taught by SCHROEDER in the system executing the method of KRING with the motivation to discharge batches of food with microbial levels below a threshold.
Claim 6 (Original)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
KRING further discloses wherein the information further indicates one or more of: whether the food product meets a safety standard; whether the food product meets a quality standard (see ¶[0003]-[0005] and [0031]; ensure an acceptable food quality); whether the food product meets a packaging standard; or whether the food product meets a labeling standard.
Claim 12 (Currently Amended)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
KRING further discloses wherein the second data originated from equipment distributed about the food processing facility (see ¶[0005]; milk is stored at storage facilities in vats having an array of sensors determining parameters relevant for milk quality).
Claim 16 (Currently Amended)
KRING discloses one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors (see ¶[0005]-[0006]; a server and a computer), cause performance of operations comprising:
receiving, over a network (see again ¶[0005]-[0006]; a server and a computer), contaminant data associated with a raw food product delivered to a food processing facility (see ¶[0016]-[0023], [0031] and [0076]; a batch parameter of a collected food batch. Example batch parameters include contaminants and microbial data),
wherein the raw food product is usable for producing different food products (see ¶[0050]-[0051]; meat, corn, vegetables, dairy and/or juice), and wherein the contaminant data is usable for triaging the raw food product (see ¶[0016]-[0023] and [0031]; a batch parameter that is compared to reference data. The batch parameter is used to create a logistic plan. See also ¶[0076]; example batch parameters include temperature, pH, volume, weight, concentration, color, composition, contaminants, and microbial data); and
determining, by the computing system, based at least in part on the contaminant data, a level of a pathogen in the raw food product (see ¶[0076], [0097] and [0163]-[0167]]; example batch parameters include concentration and contaminants. The derived batch parameter preferably is a calculation of microbial data and/or contamination data and/or quantity data at a present or future point of time based on known, expected and/or estimated data, such as temperature data, quantity data, pH data and/or combinations thereof, optionally the derived batch parameter is a derived batch parameter for the batch or for a mixture of batches comprising the batch, such as a mixture comprising collected batches.).
KRING does not specifically disclose, but GOLDMAN discloses, determining respective amounts of energy consumption that will be used to treat each of the different food products (see ¶[0307] and [0314]; a knowledge tree maps energy profile relationships);
The combination of KRING and GOLDMAN does not specifically disclose, but VASILENKO discloses, with heat treatment to reduce the level of the pathogen in each of the different food products when the different food products are produced using the raw food product (see col 1, ln 37-47; heat pasteurization of produce requires substantial energy in order to raise the temperature of the produce to a level suitable to kill microorganisms that can be responsible for the degradation of quality in foods).
KRING does not specifically disclose, but GOLDMAN discloses, designating the raw food product for use in producing a food product of the different food products that will result in a lowest amount of energy consumed based at least in part on the respective amounts of energy consumption (see ¶[0314]-[0315]; before a recipe can be determined, the knowledge tree creates a logical map. Cut costs and discard powder that is out of spec that results in poor quality yield, loss of material, and excessive energy consumption when optimal settings are not used. At each stage, determine an individualized recipe and the material state); and
processing the raw food product within the food processing facility by carrying out a food production process to produce the food product (see again ¶[0314]; the food powder production process).
KRING does not specifically disclose, but GUNAWARDENA discloses, wherein the food production process comprises treating the food product produced using the raw food product with the heat treatment by using a programmable logic controller (PLC) within the food processing facility to cause an element to heat a tank containing the food product until a temperature of the food product within the tank is increased to an elevated temperature at, or above, a threshold temperature and maintaining the food product at the elevated temperature for a threshold amount of time (see abstract and col 9, ln 39-col 10, ln 2; the control system also includes a suitable controller, such as a programmable logic controller 124 linked to the processor 31 and having an appropriate interface 126 for connecting the various measuring devices and instruments. See also col 2, ln 25-30; In the pasteurization step, the raw food product is exposed to pasteurization zone for a maximum time period selected from the maximum time periods of about 30 seconds, about 25 seconds, about 20 seconds, and about 15 seconds. Further, saturated steam is supplied to the pasteurization zone at a pressure range of about 5 to 20 psig.).
KRING further discloses receiving, over the network (see abstract; a server system coupled to a database system), data associated with different steps of the food production process (see ¶[0004] and [0072]; an assessment of the quality of the food is usually performed by withdrawing samples at the collection point for performing an initial assessment, and additional samples are withdrawn at the receiving food facility. One or more food receiver stations is a food processing station, a packing station, a market and/or a disposal station);
determining, based at least in part on the data, a metric associated with the food product (see ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but PICCICUTO discloses, the metric indicative of a probability of the food product being ready to be released from the food processing facility (see abstract and ¶[0028] & [0035]-[0037]; determining a square error between said cumulative relative frequencies and cumulative theoretical probabilities P.sub.x of having ≤x occurrences over N samples for a ZIB distribution. Maintain a high confidence interval of an ascertained quality level. Provide alert levels according to predetermined thresholds of confidence values. Provide for a facilitated quality control where released batches may be associated with confidence values of alert levels according to statistical tests).
KRING further discloses causing a user interface (see ¶[0033]-[0036]; the first mentioned valid logistic plan is a previously calculated logistic plan or a user-provided logistic plan, e.g. fed to the server system via a user interface) to be displayed on a user device (see ¶[0094]; a handheld device, such as a cell phone), wherein the user interface includes:
Information that indicates whether the food product is ready to be released from the food processing facility (see ¶[0025] and claim 1; if said batch parameter exceeds a threshold data of reference data; calculate a logistic plan and transmit the logistic plan to the data receiver. See also ¶[0096] and claims 32 and 33; the data receiver is configured for displaying collecting address and delivery address) based at least in part on the metric (see again ¶[0054] & [0162]-0167] and Fig. 6; threshold data for discharging a food batch. Determine a batch parameter before filling the food batch in a bulk container).
KRING does not specifically disclose, but SCHROEDER discloses, multiple interactive elements comprising: a first interactive element that is selectable for authorizing a release of the food product from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
a second interactive element that is selectable for requesting that the food product be held from release from the food processing facility (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options); and
receiving, over the network, from the user device, an indication of a selection of one of the multiple interactive elements (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged, where the food may be milk. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe. It would have been obvious to determine the optimal recipe as taught by GOLDMAN in the system executing the method of KRING with the motivation to reduce costs (see GOLDMAN ¶[0314] and KRING ¶[0031]).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GOLDMAN discloses data mining for decision making that includes a knowledge tree for making decisions in milk food powder production to create an optimal recipe, including energy profile relationships. VASILENKO discloses that pasteurization to kill microorganisms requires energy. It would have been obvious to include the energy consumption as taught by VASILENKO in the system executing the method of KRING and GOLDMAN with the motivation to compare methods and reduce costs (see VASILENKO col 8, ln 1-col 9, ln 19).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. GUNAWARDENA discloses steam pasteurization using a programmable logic controller for connecting devices and instruments to implement the pasteurization process. It would have been obvious for one of ordinary skill in the art at the time of invention to include the programmable logic controller as taught by GUNAWARDENA in the system executing the method of KRING with the motivation to reduce microbial and contaminant levels in food.
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. PICCICUTO discloses determining a microbiological risk level in a food batch that includes determining probabilities of contaminant levels in food for issuing an alert. It would have been obvious to include the probabilities of contaminant levels as taught by PICCICUTO in the system executing the method of KRING with the motivation to maintain a high confidence interval of an ascertained quality level (see PICCICUTO ¶[0028]).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. SCHROEDER discloses quality assurance, where a manufacturing administrator toggles a pass/fail button before releasing batches of approved products. It would have been obvious to include the toggling of pass/fail as taught by SCHROEDER in the system executing the method of KRING with the motivation to discharge batches of food with microbial levels below a threshold.
Claim 17 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the one or more non-transitory computer-readable media as set forth in claim 16.
KRING further discloses wherein the information further indicates one or more of: whether the food product meets a safety standard; whether the food product meets a quality standard (see ¶[0003]-[0005] and [0031]; ensure an acceptable food quality); whether the food product meets a packaging standard; or whether the food product meets a labeling standard.
Claim(s) 7, 9, 18, 21, 24, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to KRING et al., US 20020052858 A1 to GOLDMAN et al., US 7682641 B1 to VASILENKO, US 11234444 B1 to GUNAWARDENA et al., US 20200380448 A1 to PICCICUTO et al., and US 20170178047 A1 to SCHROEDER et al. as applied to claim 5 above, and further in view of US 20210398065 A1 to Johnsen et al. (hereinafter ‘JOHNSEN’).
Claim 7 (Original)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the metric is determined using a trained machine learning model that was trained using a training dataset of historical data associated with the different steps of the food production process (see ¶[0048]; sensor data is processed by a machine learning component to generate observation metrics associated with a good).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning. It would have been obvious to use the machine learning as taught by JOHNSEN in the system executing the method of KRING with the motivation to generate observations metrics from parameters for determining acceptable quality levels before the food is discharged.
Claim 9 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
KRING further discloses the operations further comprising: determining that the metric fails to satisfy a threshold indicating that the food product is unlikely to be ready for release from the food processing facility (see ¶[0162]-[0168]; Threshold 3: “if content <0.5* and >0.3*X->if content in the totality of collected milk in the tank has an average content <0.3 then calculate logistic plan such that the total of the milk in the tank and the milk batches to be collected based on estimated content will result in a total content <0.3”).
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the multiple interactive elements further comprise a third interactive element that is selectable for redirecting the food product to a second end usage different than a first end usage for which the food production process was carried out to produce the food product. (see ¶[0058]; If the good is not deemed acceptable, the good is rejected at operation 218. Rejection may be for any number of reasons, such as a score below a defined threshold, a score determined to be a “failing” score, or an insufficient score relative to other criteria such as location of the distribution center. Notifications and alerts may be sent whether a good is deemed acceptable or not. The notifications may identify a good or good type for which inspection has been performed. In addition to notifications, scores, alerts and the like, recommendations related to a good or good type. For example, a good that meets USDA specifications but not a context-specific rule may be deemed appropriate for sale at another location or through another channel. Multiple recommendations may be generated for a particular good or good type. Recommendations are not limited to rejected goods).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning, where recommendations regarding sale channels may be made based on a comparison to a threshold. It would have been obvious to use the recommendations based on a threshold as taught by JOHNSEN in the system executing the method of KRING with the motivation to only sell acceptable goods in appropriate sales channels.
Claim 18 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the one or more non-transitory computer-readable media as set forth in claim 16.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the metric is determined using a trained machine learning model that was trained using a training dataset of historical data associated with the different steps of the food production process (see ¶[0048]; sensor data is processed by a machine learning component to generate observation metrics associated with a good).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning. It would have been obvious to use the machine learning as taught by JOHNSEN in the system executing the method of KRING with the motivation to generate observations metrics from parameters for determining acceptable quality levels before the food is discharged.
Claim 21 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the one or more non-transitory computer-readable media as set forth in claim 16.
KRING further discloses the operations further comprising: determining that the metric fails to satisfy a threshold indicating that the food product is unlikely to be ready for release from the food processing facility (see ¶[0162]-[0168]; Threshold 3: “if content <0.5* and >0.3*X->if content in the totality of collected milk in the tank has an average content <0.3 then calculate logistic plan such that the total of the milk in the tank and the milk batches to be collected based on estimated content will result in a total content <0.3”).
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the multiple interactive elements further comprise a third interactive element that is selectable for redirecting the food product to a second end usage different than a first end usage for which the food production process was carried out to produce the food product (see ¶[0058]; If the good is not deemed acceptable, the good is rejected at operation 218. Rejection may be for any number of reasons, such as a score below a defined threshold, a score determined to be a “failing” score, or an insufficient score relative to other criteria such as location of the distribution center. Notifications and alerts may be sent whether a good is deemed acceptable or not. The notifications may identify a good or good type for which inspection has been performed. In addition to notifications, scores, alerts and the like, recommendations related to a good or good type. For example, a good that meets USDA specifications but not a context-specific rule may be deemed appropriate for sale at another location or through another channel. Multiple recommendations may be generated for a particular good or good type. Recommendations are not limited to rejected goods).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning, where recommendations regarding sale channels may be made based on a comparison to a threshold. It would have been obvious to use the recommendations based on a threshold as taught by JOHNSEN in the system executing the method of KRING with the motivation to only sell acceptable goods in appropriate sales channels.
Claim 24 (New)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the method as set forth in claim 1.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the data comprises results determined by an image processing device within the food processing facility based on processing infrared (IR) images of the raw food product captured by an IR camera within the food processing facility, and wherein the IR images are held at the food processing facility and not sent over the network (see ¶[0021]; the IR camera(s) enables infrared thermography to generate accurate, automated non-contact temperature measurements of a good).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning, where measurements are taken using an infrared camera. It would have been obvious to use the infrared camera as taught by JOHNSEN in the system executing the method of KRING with the motivation to collect measurements on batches of food.
Claim 25 (New)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JOHNSEN discloses, wherein the data comprises results determined by an image processing device within the food processing facility based on processing infrared (IR) images of the raw food product captured by an IR camera within the food processing facility, and wherein the IR images are held at the food processing facility and not sent over the network (see ¶[0021]; the IR camera(s) enables infrared thermography to generate accurate, automated non-contact temperature measurements of a good).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning, where measurements are taken using an infrared camera. It would have been obvious to use the infrared camera as taught by JOHNSEN in the system executing the method of KRING with the motivation to collect measurements on batches of food.
Claim(s) 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to KRING et al., US 20020052858 A1 to GOLDMAN et al., US 7682641 B1 to VASILENKO, US 11234444 B1 to GUNAWARDENA et al., US 20200380448 A1 to PICCICUTO et al., and US 20170178047 A1 to SCHROEDER et al. as applied to claim 5 above, and further in view of US 6671698 B2 to Pickett et al. (hereinafter ‘PICKETT’)..
Claim 11 (Currently Amended)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but PICKETT discloses, wherein the data comprises one or more of: labeling data indicating a characteristic of labeling for the food product, wherein the characteristic of the labeling is usable to determine whether the food product meets a labeling standard (see col 21, ln 13-28; processing information may concern labeling requirements); or
maintenance data indicating a maintenance action performed on food processing equipment in the food processing facility.
Claim 19 (Currently Amended)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the one or more non-transitory computer-readable media as set forth in claim 16.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but PICKETT discloses, wherein the data comprises one or more of: labeling data indicating a characteristic of labeling for the food product, wherein the characteristic of the labeling is usable to determine whether the food product meets a labeling standard (see col 21, ln 13-28; processing information may concern labeling requirements).
T The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JUNG discloses, maintenance data indicating a maintenance action performed on food processing equipment in the food processing facility (see ¶[0583]; the residential food management information (e.g. oven operation, etc.) including (e.g. tangle, etc.) one or more food related residential conditions (e.g. maintenance procedure on residential equipment, etc.) affecting (e.g. tangle, etc.) residential service of (e.g. mobile kitchen residential food item purchasing, etc.) one or more residential food items (e.g. weight loss, etc.) at least in part including information regarding residential occupant associated handling of one or more food related residential conditions (e.g. compliance with maintenance and cleaning procedures for the residential food item preparation equipment, etc.).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. SCHROEDER discloses quality assurance, where count checks and packaging checks are conducted. It would have been obvious to include count checks and packaging checks as taught by SCHROEDER in the system executing the method of KRING with the motivation to discharge batches of food products that do not comply with quality requirements.
KRING discloses collecting batches for food, where governments have restrictive rules regarding acceptable food quality. JUNG discloses a residential food service management system that includes providing information regarding maintenance actions provided in the food handling process. It would have been obvious for one of ordinary skill in the art to include the maintenance information as taught by JUNG in the system executing the method of KRING with the motivation to record and report food handing conditions.
Claim(s) 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to KRING et al., US 20020052858 A1 to GOLDMAN et al., US 7682641 B1 to VASILENKO, US 11234444 B1 to GUNAWARDENA et al., US 20200380448 A1 to PICCICUTO et al., and US 20170178047 A1 to SCHROEDER et al. as applied to claims 1 and 5 above, and further in view of US 20170369924 A1 to Pilarski et al. (hereinafter ‘PILARSKI’).
Claim 13 (Original)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the system as set forth in claim 5.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not explicitly disclose, but PILARSKI discloses, wherein the metric is determined after a final step of the food production process is completed (see ¶[0003]-[0005]; the meat industry currently uses multiple intervention strategies, rigorous hazard analysis critical control point (HACCP) plans and end-product testing to assure the microbiological safety of its products).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. PILARSKI discloses testing food products with end-product testing to assure safety. It would have been obvious for one of ordinary skill in the art at the time of invention to test the end product as taught by PILARSKI in the system executing the method of KRING with the motivation to ensure safety of a product based on microbial testing.
Claim 20 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the one or more non-transitory computer-readable media as set forth in claim 16.
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not explicitly disclose, but PILARSKI discloses, wherein the metric is determined after a final step of the food production process is completed (see ¶[0003]-[0005]; the meat industry currently uses multiple intervention strategies, rigorous hazard analysis critical control point (HACCP) plans and end-product testing to assure the microbiological safety of its products).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. PILARSKI discloses testing food products with end-product testing to assure safety. It would have been obvious for one of ordinary skill in the art at the time of invention to test the end product as taught by PILARSKI in the system executing the method of KRING with the motivation to ensure safety of a product based on microbial testing.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to KRING et al., US 20020052858 A1 to GOLDMAN et al., US 7682641 B1 to VASILENKO, US 11234444 B1 to GUNAWARDENA et al., US 20200380448 A1 to PICCICUTO et al., and US 20170178047 A1 to SCHROEDER et al. as applied to claim 1 above, and further in view of US 6671698 B2 to PICKETT et al. and US 20140122168 A1 to US 20140122168 A1 to Jung et al. (hereinafter ‘JUNG’).
Claim 3 (Currently Amended)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER discloses the method as set forth in claim 1.
KRING further discloses wherein the data comprises: additional contaminant data indicating levels of contaminants detected in the food product, wherein the levels of contaminants are used to determine whether the food product meets a safety standard (see ¶[0003] and [0067]; quality comprises presence of contaminants and microbial data. Keep high control with the collection and handling of food due to food safety); and
food data indicating characteristics of the food product, wherein the characteristics of the food product are used to determine whether the food product meets a quality standard (see abstract and ¶[0005] & [0076]; determine parameters relevant for milk quality. Examples of batch parameters include temperature, pH value, volume, weight, concentration, color, food composition, contaminants, microbial data, etc.).
KRING does not specifically disclose, but SCHROEDER discloses, packaging data indicating characteristics of packaging for the food product, wherein the characteristics of the packaging are used to determine whether the food product meets a packaging standard (see ¶[0040] and [0049]; the invention relates to processes for getting information regarding goods that are being packaged. For example, a count check might be connected with a weight or average weight check to ascertain the correct number of products of a defined range of weights are packaged together. (see FIG. 13-14. See also ¶[0070]; packaging film is blurry);
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but PICKETT discloses, labeling data indicating characteristics of labeling for the food product, wherein the characteristics of the labeling are used to determine whether the food product meets a labeling standard (see col 21, ln 13-28; processing information may concern labeling requirements).
KRING further discloses environmental data indicating values of environmental parameters associated with the food processing facility that were detected as the different steps of the food production process were carried out to produce the food product (see ¶[0005] and [0076]; parameters including temperature);
storage data indicating storage conditions of the food product (see claims 1 and 16; collect food batches and deliver to a receiver station, wherein a receiver station is a packing station).
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, and SCHROEDER does not specifically disclose, but JUNG discloses, maintenance data indicating maintenance actions performed on food processing equipment in the food processing facility (see ¶[0583]; the residential food management information (e.g. oven operation, etc.) including (e.g. tangle, etc.) one or more food related residential conditions (e.g. maintenance procedure on residential equipment, etc.) affecting (e.g. tangle, etc.) residential service of (e.g. mobile kitchen residential food item purchasing, etc.) one or more residential food items (e.g. weight loss, etc.) at least in part including information regarding residential occupant associated handling of one or more food related residential conditions (e.g. compliance with maintenance and cleaning procedures for the residential food item preparation equipment, etc.).
KRING discloses collecting batches for food, where a threshold microbial parameter is kept below a threshold before the food is discharged. SCHROEDER discloses quality assurance, where count checks and packaging checks are conducted. It would have been obvious to include count checks and packaging checks as taught by SCHROEDER in the system executing the method of KRING with the motivation to discharge batches of food products that do not comply with quality requirements.
KRING discloses collecting batches for food, where governments have restrictive rules regarding acceptable food quality. JUNG discloses a residential food service management system that includes providing information regarding maintenance actions provided in the food handling process. It would have been obvious for one of ordinary skill in the art to include the maintenance information as taught by JUNG in the system executing the method of KRING with the motivation to record and report food handing conditions.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190026662 A1 to KRING et al., US 20020052858 A1 to GOLDMAN et al., US 7682641 B1 to VASILENKO, US 11234444 B1 to GUNAWARDENA et al., US 20200380448 A1 to PICCICUTO et al., US 20170178047 A1 to SCHROEDER et al., and US 20210398065 A1 to JOHNSEN et al. as applied to claims 5 and 7 above, and further in view of US 20050021357 A1 to Schuetze et al. (hereinafter ‘SCHUETZE’).
Claim 8 (Previously Presented)
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, SCHROEDER, and JOHNSEN discloses the system as set forth in claim 7.
KRING does not specifically disclose, but SCHROEDER discloses, wherein: the information included in the user interface indicates that the food product is ready to be released from the food processing facility based at least in part on the metric (see ¶[0010] [0041]. and [0062] & Figs. 18-19; the checks may be broken down into one or more of the following options: texts, date/time, dropdown, average, toggle (pass/fail), weight audit, and/or count audit or other options).
The combination of KRING, GOLDMAN, VASILENKO, GUNAWARDENA, PICCICUTO, SCHROEDER, and JOHNSEN does not specifically disclose, but SCHUETZE discloses, the indication indicates the selection of the second interactive element, the selection of the second interactive element indicating a discordance between output from the trained machine learning model and a decision made by a user of the user device (see ¶[0011] and [0046] & Fig. 5; The expert can choose to view all of the training set (all previously labeled objects plus the initial training set); to view all objects that have the classification property and the current model predicts they don't have it (false negatives); to view all objects that have the classification property and the current model predicts that they have it (true positives); to view all objects that do not have the classification property and the model predicts they do not have it (true negatives); and to view all objects that do not have the classification property and the current model predicts that they have it (false positives); and the operations further comprise
using the indication indicating the selection of the second interactive element to retrain the trained machine learning model (see again ¶[0011] and [0046] & Fig. 5; a training set) at least in part by adding food release data associated with a production run of the food product where the user decided not to release the food product to the training dataset as added training data, and labeling the added training data with the indication indicating the selection of the second interactive element (see ¶[0011] and [0046] & Fig. 5; The expert can choose to view all of the training set (all previously labeled objects plus the initial training set); to view all objects that have the classification property and the current model predicts they don't have it (false negatives); to view all objects that have the classification property and the current model predicts that they have it (true positives); to view all objects that do not have the classification property and the model predicts they do not have it (true negatives); and to view all objects that do not have the classification property and the current model predicts that they have it (false positives).
KRING discloses collecting batches for food, where a threshold microbial parameter attained from sensors (see ¶[0005]) is kept below a threshold before the food is discharged. JOHNSEN discloses an automated inspection system that uses sensor data to generate observation metrics associated with a good via machine learning. SCHUETZE discloses training classifiers using false positive and false negatives. It would have been obvious to include the training data as taught by SCHUETZE in the system executing the method of KRING and JOHNSEN with the motivation to make recommendations regarding the discharge of products for sale to customers (see KRING ¶[0002] and [0005]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at 571-270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
Prosecution Insights