DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-30 are pending and under consideration in this action.
Priority
The instant application claims domestic benefit to U.S. Provisional Application No. 63/161,507, filed 3/16/2021, as reflected in the filing receipt mailed 3/22/2022. The claim for domestic benefit for claims 1-30 is acknowledged. As such, the effective filing date of claims 1-30 is 3/16/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/19/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) and 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference number “182” in Fig. 1C is not mentioned in the specification.
Reference numbers "854" in Fig. 8 and "954" in Line 2 of Para. [0173] of the specification have both been used to designate a display.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because it should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. (see Line 2, “The disclosure can provide…”). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 28 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 28 recites the limitation “by the one or more supply chain actors” in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim, since there is no prior mention of this phrase in claim 23, to which this claim depends. This rejection can be overcome by amendment of claim 28 to recite “by one or more supply chain actors”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-22 are directed towards a method and claims 23-30 are directed towards a system, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites mathematical concepts in “filtering, by the computing system, the spectral data”, “determining, by the computing system and based on applying a trained model to the filtered spectral data, a ripeness level of the food item”, and “wherein the trained model was trained using (i) one or more destructive measurements of other food items and (ii) spectral data for the other food items, wherein the other food items are of a same food type as the food item”; and a mental process (i.e., an evaluation of how the ripeness level was determined) in “wherein the ripeness level of the food item is determined without taking destructive measurements of the food item”.
Claim 2 recites a mental process (i.e., an evaluation of the model and the training data for the model) in “wherein the trained model includes one or more layers, wherein each of the layers includes (i) training images of the other food items and (ii) labels that indicate food item classifications for each of the other food items depicted by the training images”.
Claim 3 recites mathematical concepts in “wherein filtering the spectral data includes: trimming the spectral data; scaling the spectral data; and applying a Savitzky-Golay 2nd derivative filter to the spectral data to reduce noise”.
Claim 4 recites mental processes (i.e., an evaluation of whether a value is greater than or less than a threshold) in “determining that the food item is suitable for consumption based on the ripeness level of the food item exceeding a threshold value” and “determining that the food item is unsuitable for consumption based on the ripeness level of the food item being less than the threshold value”.
Claim 6 recites a mental process (i.e., an evaluation of the input data) in “wherein the ripeness level of the food item is further based on input data that includes at least one of (i) a place of origin of the food item, (ii) a storage temperature of the food item, and (iii) historic ripening information associated with the food item”.
Claim 7 recites mathematical concepts in “mapping, by the computing system, the value and durometer data from one or more durometers for the other food items to a firmness curve using orthogonal regression and projection”, “generating, by the computing system, an engineered firmness metric based on the mapping”, and “training, by the computing system, the model to predict the engineered firmness metric using the spectral data for the other food items”.
Claim 8 recites a mental process (i.e., an evaluation of the data) in “the penetrometer data includes depth data and force data”, and a mathematical concept in “the penetrometer data curve represents a relationship between the depth data and the force data”.
Claim 9 recites a mathematical concept in “wherein the value derived from the penetrometer data curve is a slope of the curve, the slope being a difference between two points of the force data over a predetermined range of the depth data”.
Claim 10 recites a mental process (i.e., an evaluation of the predetermined range) in “wherein the predetermined range of the depth data is 1.5mm to 2mm”.
Claim 11 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is a max force”.
Claim 12 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is an area under the penetrometer data curve”.
Claim 13 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is an area under the penetrometer data curve after a max force”.
Claim 14 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is a slope of the curve and a max force of the curve”, and a mathematical concept in “the method further comprising mapping, by the computing system, the slope, the max force, and the durometer data to the firmness curve using orthogonal regression and projection”.
Claim 15 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is a slope of the curve and an area under the curve”, and a mathematical concept in “the method further comprising mapping, by the computing system, the slope, the area under the curve, and the durometer data to the firmness curve using orthogonal regression and projection”.
Claim 16 recites a mental process (i.e., an evaluation of the value) in “the value derived from the penetrometer data curve is a max force of the curve and an area under the curve”, and a mathematical concept in “the method further comprising mapping, by the computing system, the max force, the area under the curve, and the durometer data to the firmness curve using orthogonal regression and projection”.
Claim 17 recites a mental process (i.e., an evaluation of the value) in “the value derived from the penetrometer data curve is a slope of the curve, a max force of the curve, and an area under the curve”, and a mathematical concept in “the method further comprising mapping, by the computing system, the slope, the max force, the area under the curve, and the durometer data to the firmness curve using orthogonal regression and projection”.
Claim 18 recites a mental process (i.e., a judgement of portions to include) in “selecting, by the computing system, portions of the penetrometer data and the durometer data”; and mathematical concepts in “determining, by the computing system, a ripeness metric for food items of the same food type based on the selected portions of the penetrometer data and the durometer data” and “generating, by the computing system, a machine learning trained model based on the ripeness metric, wherein the machine learning trained model correlates destructive measurements provided by the selected portions of the penetrometer data and the selected portions of the durometer data with non-destructive measurements provided by spectral data to model the ripeness metric for the food items of the same food type”.
Claim 19 recites a mental process (i.e., an evaluation of the plot) in “identifying an inflection point in the plotted penetrometer data and the plotted durometer data”, a mental process (i.e., a judgement of the portions to include) in “selecting portions of the penetrometer data and the durometer data based on the inflection point”, and a mental process (i.e., a judgement of portions to discard) in “discarding unselected portions of the penetrometer data and the durometer data”.
Claim 20 recites mental processes (i.e., a judgement of portions to include) in “selecting portions of the penetrometer data before the inflection point” and “selecting portions of the durometer data after the inflection point”; and mental processes (i.e., a judgement of portions to discard) in “discarding portions of the durometer data before the inflection point” and “discarding portions of the penetrometer data after the inflection point”.
Claim 21 recites mathematical concepts in “wherein generating the machine learning trained model comprises (i) correlating the selected portions of the penetrometer data before the inflection point with one or more wavelengths of spectral data that correspond to the plurality of test food items that are hard and (ii) correlating the selected portions of the durometer data after the inflection point with one or more wavelengths of spectral data that correspond to the test food items that are soft”.
Claim 22 recites mathematical concepts in “wherein the machine learning trained model further correlates destructive measurements provided by the selected portions of the penetrometer data and the selected portions of the durometer data with at least one of (i) a place of origin, (ii) a storage temperature, and (iii) historic ripening information associated with the food items of the same food type”.
Claim 23 recites a mental process (i.e., a judgement of the portions to include) in “select portions of the penetrometer data and the durometer data”; mathematical concepts in “determine a ripeness metric for the food items of the same food type based on the selected portions of the penetrometer data and the durometer data”, “generate a machine learning trained model based on the ripeness metric, wherein the machine learning trained model correlates destructive measurements provided by the selected portions of the penetrometer data and the selected portions of the durometer data with non-destructive measurements provided by spectral data to model the ripeness metric for the food items of the same food type”, “filter the spectral data of the food item of the same food type”, and “determine, based on applying the machine learning trained model to the filtered spectral data of the food item of the same food type, a ripeness level of the food item, wherein the ripeness level of the food item is determined without taking destructive measurements of the food item”; a mental process (i.e., an evaluation of the ripeness level) in “determine whether to modify the supply chain information for the food item based on the ripeness level of the food item”; and a mental process (i.e., an evaluation of the ripeness level to generate supply chain information) in “in response to a determination to modify the supply chain information, generate modified supply chain information based on the ripeness level of the food item, wherein the modified supply chain information includes one or more of a modified supply chain schedule and modified destination for the food item”.
Claim 24 recites a mental process (i.e., an evaluation of the imaging devices) in “wherein the one or more spectral imaging devices include a point spectrometer”.
Claim 25 recites a mental process (i.e., an evaluation of the model and the training data for the model) in “wherein the machine learning trained model includes one or more layers, wherein each of the layers includes (i) training images of the plurality of test food items of the same food type and (ii) labels that indicate food item classifications for each of the plurality of test food items depicted by the training images”.
Claim 26 recites mathematical concepts in “wherein the at least one computing system is further configured to generate the machine learning trained model based on (i) correlating the selected portions of the penetrometer data with one or more wavelengths of spectral data that correspond to plurality of test food items that are hard and (ii) correlating the selected portions of the durometer data with one or more wavelengths of spectral data that correspond to the plurality of test food items that are soft”.
Claim 27 recites a mental process (i.e., an evaluation of the supply chain instructions) in “wherein the modified supply chain information includes instructions that, when executed by one or more supply chain actors at the user computing device, cause the food item to be moved for outbound shipment to end-consumers that are geographically closest to a location of the food item”.
Claim 28 recites a mental process (i.e., an evaluation of the supply chain instructions) in “wherein the modified supply chain information includes instructions that, when executed by the one or more supply chain actors at the user computing device, cause the food item to be moved for outbound shipment to a food processing plant”.
Claim 29 recites mathematical concepts in “mapping, by the at least one computing system, the value and the durometer data to a firmness curve using orthogonal regression and projection”, “generating, by the at least one computing system, the ripeness metric based on the mapping”, and “training, by the at least one computing system, the model to predict the ripeness metric using the spectral data of the food item of the same food type”.
Claim 30 recites a mental process (i.e., an evaluation of the value) in “wherein the value derived from the penetrometer data curve is a slope of the curve”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following claims recite limitations that equate to additional elements:
Claim 1 recites “receiving, by a computing system and from a spectral imaging device, spectral data of a food item”, and “transmitting, by the computing system to a user computing device, the ripeness level of the food item for display at the user computing device”.
Claim 5 further recites “wherein the spectral imaging device captures the spectral data using light having a wavelength within a range of 530 nm to 950 nm”.
Claim 7 further recites “receiving, by the computing system, a value derived from a penetrometer data curve, wherein the penetrometer data curve is generated using penetrometer data from one or more penetrometers for the other food items”.
Claim 18 recites “receiving, by a computing system, (i) penetrometer data from one or more penetrometers and (ii) durometer data from one or more durometers for a plurality of test food items of a same food type”.
Claim 19 further recites “plotting the penetrometer data and the durometer data”.
Claim 23 recites “one or more penetrometers configured to measure penetrometer data for a plurality of test food items of a same food type”, “one or more durometers configured to measure durometer data for the plurality of test food items of the same food type”, “one or more spectral imaging devices configured to measure spectral data for food items of the same food type”, “at least one computing system”, “receive the penetrometer data and the durometer data”, “receive, from the one or more spectral imaging devices, spectral data of a food item of the same food type”, “identify supply chain information for the food item that includes a preexisting supply chain schedule and destination for the food item”, and “transmit, to a user computing device, (i) the ripeness level of the food item and (ii) the modified supply chain information for display at the user computing device”.
Claim 29 further recites “receiving, by the at least one computing system, a value derived from a penetrometer data curve, wherein the penetrometer data curve is generated using the penetrometer data for the food items of the same food type”.
Regarding the above cited limitations in claims 1, 5, 7, 18, 19, 23, and 29 of (I) receiving, by a computing system and from a spectral imaging device, spectral data of a food item (claims 1 and 23); (II) transmitting, by the computing system to a user computing device, the ripeness level of the food item for display at the user computing device (claim 1); (III) wherein the spectral imaging device captures the spectral data using light having a wavelength within a range of 530 nm to 950 nm (claim 5); (IV) receiving, by the computing system, a value derived from a penetrometer data curve, wherein the penetrometer data curve is generated using penetrometer data from one or more penetrometers for the other food items (claims 7 and 29); (V) receiving, by a computing system, (i) penetrometer data from one or more penetrometers and (ii) durometer data from one or more durometers for a plurality of test food items of a same food type (claims 18 and 23); (VI) plotting the penetrometer data and the durometer data (claim 19); (VII) one or more penetrometers configured to measure penetrometer data for a plurality of test food items of a same food type (claim 23); (VIII) one or more durometers configured to measure durometer data for the plurality of test food items of the same food type (claim 23); (IX) one or more spectral imaging devices configured to measure spectral data for food items of the same food type (claim 23); (X) identify supply chain information for the food item that includes a preexisting supply chain schedule and destination for the food item (claim 23); and (XI) transmit, to a user computing device, (i) the ripeness level of the food item and (ii) the modified supply chain information for display at the user computing device (claim 23). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of determining a ripeness level of the food item (claim 1), generating a machine learning trained model based on the ripeness metric (claim 18), or determining whether to modify supply chain information based on ripeness level (claim 23) (see MPEP § 2106.04(d)).
Regarding the above cited limitations in claim 23 of (XII) at least one computing system. These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-30 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 1, 7, 18, 23, and 29 of (I) receiving, by a computing system and from a spectral imaging device, spectral data of a food item (claims 1 and 23); (II) transmitting, by the computing system to a user computing device, the ripeness level of the food item for display at the user computing device (claim 1); (IV) receiving, by the computing system, a value derived from a penetrometer data curve, wherein the penetrometer data curve is generated using penetrometer data from one or more penetrometers for the other food items (claims 7 and 29); (V) receiving, by a computing system, (i) penetrometer data from one or more penetrometers and (ii) durometer data from one or more durometers for a plurality of test food items of a same food type (claims 18 and 23); and (XI) transmit, to a user computing device, (i) the ripeness level of the food item and (ii) the modified supply chain information for display at the user computing device (claim 23). These limitations equate to receiving/transmitting data over a network, which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in claims 5, 19, and 23 of (III) wherein the spectral imaging device captures the spectral data using light having a wavelength within a range of 530 nm to 950 nm (claim 5); (VI) plotting the penetrometer data and the durometer data (claim 19); (VII) one or more penetrometers configured to measure penetrometer data for a plurality of test food items of a same food type (claim 23); (VIII) one or more durometers configured to measure durometer data for the plurality of test food items of the same food type (claim 23); (IX) one or more spectral imaging devices configured to measure spectral data for food items of the same food type (claim 23); and (X) identify supply chain information for the food item that includes a preexisting supply chain schedule and destination for the food item (claim 23). These limitations are considered to be insignificant extra-solution activity of mere data gathering. Limitations (III) and (VI)-(IX) are incidental to the primary process of using a machine learning algorithm to determine a ripeness level, wherein data measured from the spectral imaging device, penetrometer, or durometer are merely inputs for the machine learning model. Additionally, limitation (X) is also incidental to the primary process of using a machine learning algorithm to determine a ripeness level, as the updated supply chain information will be determined based on the output ripeness level from the machine learning algorithm (see MPEP § 2106.05(g)).
Regarding the above cited limitations in claim 23 of (XII) at least one computing system. These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-30 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nipe et al. (U.S. Patent Application Publication US 2018/0365820 A1; published 12/20/2018).
Regarding claim 1, Nipe et al. teaches a non-destructive system for using hyperspectral imaging to determine ripeness of an object such as an avocado or another perishable item (i.e., a method for determining ripeness levels for food items using non-contact assessments of the food items) (Para. [0020] and [0026]). Nipe et al. further teaches that the at least one computing device is configured to receive data from and/or transmit data to other computing devices through the communication network. As an example, the imaging device may transmit each hyperspectral image to the computing device for analysis (i.e., receiving, by a computing system and from a spectral imaging device, spectral data of a food item) (Para. [0036]). Nipe et al. further teaches that the pre-processing module may perform pre-processing on the image using one or more sub-modules. The sub-modules may include a normalization module that may perform normalization on the image, object recognition using an object recognition module, feature extraction using a feature extraction module, and reduction of data using a reduction of data module (i.e., filtering, by the computing system, the spectral data) (Para. [0043]). Nipe et al. further teaches that the application may determine characteristics of the object by comparing the region of interest in the hyperspectral image with the plurality of images in the training set and may determine the ripeness of an object such as an avocado (i.e., determining, by the computing system and based on applying a trained model to the filtered spectral data, a ripeness level of the food item) (Para. [0017] and [0020]). Nipe et al. further teaches that the synthetic dataset generation module may obtain pixels from an image set that may be correlated with different quality parameters of an object such as a fruit (Para. [0057]). Nipe et al. further teaches that the quality parameters include firmness and that firmness may be measured using the Anderson Firmometer measure, and firmness may indicate a ripeness level (i.e., wherein the trained model was trained using (i) one or more destructive measurements of other food items) (Para. [0072]-[0073]). Nipe et al. further teaches that the output module may perform machine learning classification of an object and predict and assign quality parameters to objects. The output module may compare a region of interest in the hyperspectral image with the training set of data that may comprise the plurality of images of the object that may include the synthetic dataset (i.e., wherein the trained model was trained using (ii) spectral data for the other food items, wherein the other food items are of a same food type as the food item, wherein the ripeness level of the food item is determined without taking destructive measurements of the food item) (Para. [0063]). Nipe et al. further teaches that the at least one computing device is configured to receive data and/or transmit data through the communication network (Para. [0036]). Nipe et al. further teaches that the at least one computing device may display on a display a graphical user interface (or GUI) to generate a graphical user interface on the display (i.e., transmitting, by the computing system to a user computing device, the ripeness level of the food item for display at the user computing device) (Para. [0039]).
Regarding claim 2, Nipe et al. teaches that the machine learning classification may have four layers including one input layer, two hidden layers, and one output layer (i.e., wherein the trained model includes one or more layers) (Para. [0068]). Nipe et al. further teaches that a user of the system may initially train the machine learning classification module by assigning a label (e.g., avocado) and quality parameter information (e.g., firmness, dry matter content, amount of sugar) to one or more sets of images (i.e., wherein each of the layers includes (i) training images of the other food items and (ii) labels that indicate food item classifications for each of the other food items depicted by the training images) (Para. [0069]).
Regarding claim 5, Nipe et al. teaches that the imaging device is configured to collect images in the 400-1000 nm wavelength region, which corresponds to visible and near-infrared light (i.e., wherein the spectral imaging device captures the spectral data using light having a wavelength within a range of 530 nm to 950 nm) (Para. [0029]).
Therefore, Nipe et al. teaches all the limitations in claims 1-2, and 5.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Nipe et al., as applied to claims 1-2 and 5 above, and further in view of Schwartzer et al. (U.S. Patent Application Publication US 2019/0340749 A1; published 11/7/2019).
Regarding claim 3, Nipe et al. teaches that the image processing application may include a pre-processing module that may receive an image from the imaging device and perform pre-processing on the image. First, the pre-processing module may perform pre-processing on the image using one or more sub-modules. The sub-modules may include a normalization module that may perform normalization on the image, object recognition using an object recognition module, feature extraction using a feature extraction module, and reduction of data using a reduction of data module (i.e., trimming the spectral data and scaling the spectral data) (Para. [0043]). Nipe et al. further teaches that when using a linear push broom sensor array, the normalization may be performed per-sampling elements to account for spatially variant, static noise (Para. [0044]).
Nipe et al., as applied to claims 1-2 and 5 above, does not teach applying a Savitzky-Golay 2nd derivative filter to the spectral data to reduce noise; determining that the food item is suitable for consumption based on the ripeness level of the food item exceeding a threshold value; determining that the food item is unsuitable for consumption based on the ripeness level of the food item being less than the threshold value; and wherein the ripeness level of the food item is further based on input data that includes at least one of (i) a place of origin of the food item, (ii) a storage temperature of the food item, and (iii) historic ripening information associated with the food item.
Regarding claim 3, Schwartzer et al. teaches that hyperspectral imaging devices produce a substantial amount of 'raw' or unprocessed data. In order to make this data relevant in a horticultural or other commercial context, the raw data must be processed to generate an analysis frame, which can then be further analyzed by computer vision algorithms to generate quantifiable data (Para. [0079]). Though not explicitly disclosed by Nipe et al. or Schwartzer et al., it would have been obvious to one of ordinary skill in the art to process the raw data and reduce noise by applying a Savitzky-Golay 2nd derivative filter to the spectral data (i.e., applying a Savitzky-Golay 2nd derivative filter to the spectral data to reduce noise).
Regarding claim 4, Schwartzer et al. teaches that a profile is associated with a score and a scoring, classification or description of a fruit or vegetable may be based on the profile and associated score. For example, if characteristics of a fruit is matched with a profile then the score that is associated with profile may be assigned, given or linked to, or associated with, the fruit (Para. [0165]). Schwartzer et al. further teaches that the invention predicts the time, or remaining time, a stored fruit or vegetable will remain usable or otherwise fit for consumption (shelf life as known in the art). For example, in some embodiments, a database in storage system includes profiles and/or ripeness stages, indicators or other measures for a banana ranging, where the ripeness stages are from any ripeness stage to a stage of maximal, or full ripeness, e.g., maximal, or full ripeness may be a ripeness stage or level above which the banana is considered too ripe, past its best, stale or unsuitable for consumption (i.e., determining that the food item is suitable for consumption based on the ripeness level of the food item exceeding a threshold value and determining that the food item is unsuitable for consumption based on the ripeness level of the food item being less than the threshold value) (Para. [0170]).
Regarding claim 6, Schwartzer et al. teaches that the invention calculates and/or predicts the progress, in time, of a condition of a fruit or vegetable. For example, a database in the storage system includes profiles and/or ripeness stages, indicators or other measures for a banana ranging where the ripeness stages are from any ripeness stage to a stage of maximal, or full ripeness, e.g., maximal, or full ripeness may be a ripeness stage or level above which the banana is considered too ripe, past its best, stale or unsuitable for consumption. Additionally, ripeness levels in a database may be according to, or as a function of, various variables or conditions, e.g., the ripeness levels along a time line, included in a database, may be according to, or as a function of, temperature, humidity, light, etc. Additionally, characteristics or profiles as described may be calculated based on at least one of: a geographic region, a temperature and a date (i.e., wherein the ripeness level of the food item is further based on input data that includes at least one of (i) a place of origin of the food item, (ii) a storage temperature of the food item, and (iii) historic ripening information associated with the food item) (Para. [0159] and [0170]-[0171]).
Therefore, regarding claims 3-4 and 6, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of using hyperspectral imaging to characterize ripeness of food using machine learning of Nipe et al. with the teachings of Schwartzer et al. because the method of Schwartzer et al. not only analyzes the ripeness of foods, but also incorporates historic geographic information into the trained algorithm and evaluates quality and the presence of any defects (Schwartzer et al., Para. [0014]-[0015] and [0159]). One of ordinary skill in the art would be able to combine the teachings of Nipe et al. with Schwartzer et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for non-destructively characterizing the ripeness of foods using machine learning. Therefore, regarding claims 3-4 and 6, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
Claims 7-30 are free from the prior art because the prior art does not fairly suggest or teach the mapping of the value derived from the penetrometer data curve and the durometer data to a firmness curve, and subsequent generation of a firmness metric based on the mapping to train a model. The closest prior art is Schwartzer et al. (U.S. Patent Application Publication US 2019/0340749 A1). Schwartzer et al. discloses a method for non-destructively determining characteristics of food involving analyzing spectral, penetrometer, or durometer data, and using a machine learning algorithm to predict parameters (e.g., ripeness) of the foods. However, Schwartzer et al. does not disclose the combined mapping of the penetrometer data and durometer data to generate a firmness metric to train the model, as disclosed in instant claim 7. Additionally, Schwartzer et al. does not disclose that the machine learning model correlates destructive measurements of the penetrometer data and durometer data with non-destructive measurements provided by spectral data to predict ripeness, as disclosed in instant claims 18 and 23.
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/D.P.S./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687