DETAILED ACTION
Applicant’s response filed 4/1/2026 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied.
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 18-31 are pending and under consideration in this action. Claims 1-17 were canceled in the amendment filed 4/1/2026. Claim 31 is newly added.
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 18-31 is acknowledged. As such, the effective filing date of claims 18-31 is 3/16/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/1/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Drawings
The objections to the drawings are withdrawn in view of Applicant’s amendments to the drawings and Specification filed 4/1/2026 (Applicant’s Remarks, Pg. 9).
Specification
The objection to the abstract is withdrawn in view of Applicant’s amendments to the abstract filed 4/1/2026 (Applicant’s Remarks, Pg. 9).
Claim Rejections - 35 USC § 112(b)
The rejection of claim 28 under 35 U.S.C. 112(b) as being indefinite is withdrawn in view of Applicant’s amendments to the claims filed 4/1/2026 (Applicant’s Remarks, Pg. 9-10).
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 18-31 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)).
Any newly recited portion is necessitated by claim amendment.
Step 1:
In the instant application, claims 18-22 are directed towards a method and claims 23-31 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 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"; 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"; and a mental process (i.e., an evaluation of the ripeness level to modify supply information) in “generate modification to supply information for the food item based on the ripeness level to be used to instruct a modification of at least one shipment schedule for the food item at one or more supply chain actors”.
Claim 19 recites a mental process (i.e., an evaluation of the data for plotting) in “plotting the penetrometer data and the durometer data”; 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 the 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” and “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”; a mental process (i.e., an evaluation of the data) in “filter the spectral data of the food item of the same food type”; a mental process (i.e., an evaluation of the supply chain information) in “identify supply chain information for the food item that includes a preexisting supply chain schedule and destination for 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 instructions for modifying of at least one shipment schedule for the food item”.
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 instructions of the modified supply chain information, when executed, are configured to 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 instructions of the modified supply chain information, when executed, are configured to 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 machine learning trained 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”.
Claim 31 recites a mental process (i.e., an evaluation of the supply chain information) in “wherein the modified supply chain information includes one or more of a modified shipment schedule or modified destination for the food item”.
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.
Specifically, claim 18 involves nothing more than selecting data, determining a ripeness metric, generating a machine learning trained model based on the ripeness metric, and modifying supply chain information. Claim 23 involves nothing more than selecting data, determining a ripeness metric, generating a machine learning trained model based on the ripeness metric, filtering data, identifying supply chain information, analyzing the ripeness level to modify supply chain information, and modifying supply chain information. The steps reciting determining a ripeness metric and generating a machine learning trained model based on the ripeness metric are, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0019]) discloses that the model was trained using a process that included steps of mapping penetrometer data and durometer data to a firmness curve using orthogonal regression and projection and subsequently training the model to predict the ripeness metric using the spectral data. The instant Specification (see Para. [0022]) further discloses that generating the machine learning trained model can include (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. Additionally, since there are no specifics in the methodology, the steps of selecting data, filtering data, identifying supply chain information, analyzing the ripeness level to modify supply chain information, and modifying supply chain information, are something that under the BRI, one could perform mentally. As such, said steps are directed to judicial exceptions. 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 independent claims recite limitations that equate to additional elements:
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” and “providing the machine learning trained model for execution based on input including new spectra data of an evaluated food item of the same food type to determine ripeness level of the food item”.
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”, “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”, and “transmit (i) the ripeness level of the food item and (ii) the modified supply chain information so as to invoke execution of the instructions included in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item”.
Regarding the above cited limitations in claims 18 and 23 of (I) 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); (II) one or more penetrometers configured to measure penetrometer data for a plurality of test food items of a same food type (claim 23); (III) one or more durometers configured to measure durometer data for the plurality of test food items of the same food type (claim 23); (IV) one or more spectral imaging devices configured to measure spectral data for food items of the same food type (claim 23); (V) receive, from the one or more spectral imaging devices, spectral data of a food item of the same food type (claim 23); and (VI) transmit (i) the ripeness level of the food item and (ii) the modified supply chain information so as to invoke execution of the instructions included in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item (claim 23). Limitations (I)-(V) 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 metric and generating the trained model (see MPEP § 2106.04(d)). Limitation (VI) equates to a post-solution activity that is not integrated into the claim as a whole, similar to a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent (see MPEP § 2106.05(g)).
Regarding the above cited limitation in claim 23 of (VII) at least one computing system. This limitation requires 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.
Regarding the above cited limitations in claims 18 and 23 of (VIII) providing the machine learning trained model for execution based on input including new spectra data of an evaluated food item of the same food type to determine ripeness level of the food item (claim 18); and (IX) 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 (claim 23). These limitations equate to an extra-solution “apply it” steps, because the limitation are used to run the trained model on a generic computer without providing any details of how the ripeness level is determined for any food item (see MPEP § 2106.05(f)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claim 24 further limits the spectral imaging device and claim 29 recites a step of receiving data analogous to claims 18 and 23 above. As such, claims 18-31 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. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 18 and 23 of (I) 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); (V) receive, from the one or more spectral imaging devices, spectral data of a food item of the same food type (claim 23); and (VI) transmit (i) the ripeness level of the food item and (ii) the modified supply chain information so as to invoke execution of the instructions included in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item (claim 23). Limitations (I) and (V) do not include any specific steps for acquiring the penetrometer, durometer, or the spectral data. Under the BRI, these limitations are merely receiving data for the subsequent step of determining a ripeness metric and generating the trained model. Limitation (VI) does not include any specific steps for transmitting the ripeness level and modified supply chain information, because the limitation recites “so as to invoke execution of the instructions…”, which is a contingent limitation not required by the claim (see MPEP § 2111.04). Therefore, 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 claim 23 of (II) one or more penetrometers configured to measure penetrometer data for a plurality of test food items of a same food type; (III) one or more durometers configured to measure durometer data for the plurality of test food items of the same food type; and (IV) one or more spectral imaging devices configured to measure spectral data for food items of the same food type. These limitations are considered to be insignificant extra-solution activity of mere data gathering. These limitations are incidental to the primary process of determining a ripeness metric and generating the trained model, wherein data measured from the spectral imaging device, penetrometer, or durometer are merely inputs for the model. This limitation is similar to the data gathering recited in In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) of performing clinical tests on individuals to obtain input for an equation (see MPEP § 2106.05(g)).
Regarding the above cited limitation in claim 23 of (VII) at least one computing system. This limitation equates 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)).
Regarding the above cited limitation in claims 18 and 23 of (VIII) providing the machine learning trained model for execution based on input including new spectra data of an evaluated food item of the same food type to determine ripeness level of the food item (claim 18); and (IX) 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 (claim 23). These limitations when viewed individually and in combination are WURC limitations as taught by Nipe et al. (U.S. Patent Application Publication US 2018/0365820 A1; cited in the IDS dated 7/19/2022). Nipe et al. discloses a non-destructive system for using spectral imaging to determine ripeness of an object such as an avocado or another perishable item (Para. [0020] and [0026]). Nipe et al. further discloses that the system uses a machine learning algorithm to assign quality parameters and determine ripeness (limitations (VIII) and (IX)) (Para. [0069] and [0073]-[0075]).
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 18-31 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 4/1/2026 have been fully considered but they are not persuasive.
1. Applicant argues that claim 18 does not fall into the category of abstract ideas. The subject matter of claim 18 cannot be practically performed in the human mind, even with the aid of pen and paper, such as at least for example, “providing the machine learning trained model for execution based on input including new spectra data of an evaluated food item of the same food type to determine ripeness level of the food item and generate modification to supply information for the food item based on the ripeness level to be used to instruct a modification of at least one shipment schedule for the food item at one or more supply chain actors" cannot be performed in the human mind. In particular, a human mind is incapable of providing a machine learning model for execution, as recited in the claims (Applicant’s Remarks, Pg. 10-11).
It is respectfully submitted that this is not persuasive for the following reasons:
The limitation of “providing the machine learning trained model for execution based on input including new spectral data of an evaluated food item of the same food type to determine ripeness level of the food item” was identified as an additional element in Step 2A, Prong Two above. Only limitations that recited judicial exceptions, not additional elements, need to be practically performed in the human mind (see MPEP § 2106.04(a)).
The limitation of “generate modification to supply information for the food item based on the ripeness level to be used to instruct a modification of at least one shipment schedule for the food item at one or more supply chain actors” was identified as reciting a mental process in Step 2A, Prong One above. Under the BRI, this limitation equates to an evaluation of the ripeness level to determine a modification to a shipment schedule. Since there are no specifics in the methodology in the claim for steps to update the shipping schedule, this limitation is something that under the BRI, one could perform mentally. Therefore, amended claim 18 recites abstract ideas and this argument is not persuasive.
2. Applicant also argues that even if claim 18 were to be considered to recite an abstract idea, the claim features impose meaningful limits and integrate the abstract idea into a practical application. The present claim is directed at improving the quality of evaluation of food items to determine their ripeness level by using a machine learning model that model a ripeness metric for the food items of the same food type. Such improvement is supported by generating the machine learning trained model based on the ripeness metric to correlate destructive measurements provided by selected portions of penetrometer data and selected portions of durometer data with non-destructive measurements provided by spectral data. As described in the application, such non-destructive measure can be used in real-time, where "spectrometer data about a food item or a batch of food items can be collected and used as input for the one or more models to predict the ripeness of the food item or batch of food items." Specification at paragraph [0009] (Applicant’s Remarks, Pg. 11).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
The limitation indicated by Applicant of “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” has been identified as a judicial exception (mathematical concept) in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, the recited limitation is not considered as an improvement in evaluation of the quality of food items by determining their ripeness level using a machine learning model. This argument is thus not persuasive.
3. Applicant also argues that the subject matter of amended claim 23 cannot be practically performed in the human mind, even with the aid of pen and paper, such as at least for example, the provided features cannot be performed in the human mind. In particular, a human mind is incapable of applying a machine learning trained model to filtered spectral data to determine a ripeness level, to generate modified supply chain information based on the ripeness level that includes instructions for modifying at least one shipment schedule, and transmit the ripeness level and the modified supply chain information to invoke execution of the instructions in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item, as recited in the claims (Applicant’s Remarks, Pg. 12-13).
It is respectfully submitted that this is not persuasive for the following reasons:
The limitations of “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” and “transmit, to a user computing device, (i) the ripeness level of the food item and (ii) the modified supply chain information so as to invoke execution of the instructions included in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item” have been identified as additional elements in Step 2A, Prong Two above. Only limitations that recited judicial exceptions, not additional elements, need to be practically performed in the human mind (see MPEP § 2106.04(a)).
The limitation of “generate modified supply chain information based on the ripeness level of the food item, wherein the modified supply chain information includes instructions for modifying of at least one shipment schedule for the food item” has been identified as reciting a mental process in Step 2A, Prong One above. Similar to the arguments above for claim 18, the BRI of this limitation equates to an evaluation of the ripeness level to determine a modification to a shipment schedule. Since there are no specifics in the methodology in the claim for steps to update the shipping schedule, this limitation is something that under the BRI, one could perform mentally. Therefore, amended claim 23 recites abstract ideas and this argument is not persuasive.
4. Applicant also argues that even if claim 23 were to be considered to recite an abstract idea, the claim features impose meaningful limits on any such abstract idea and integrate the abstract idea into a practical application. The present claim is directed at improving the quality of evaluation of food items by using a machine learning trained model, while also improving accuracy and efficiency and managing actions performed with evaluated food items so that modifying supply chain instructions can be provided in real time and supply chain actors can modify the shipment schedules. Such improvements are related to efficiency in data processing for the food items, as well as instructing a shipment process to reduce costs and optimize resource expenditures (Applicant’s Remarks, Pg. 13).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100.
The alleged improvements indicated by Applicant are not commensurate in scope with the claimed invention. Applicant appears to assert that the claimed features may be used to instruct a shipment process to reduce costs and optimize resource expenditures in real time (Applicant’s Remarks, Pg. 13). However, the claim does not provide any indication that modifying the shipment process reduces costs or optimize resources. The limitation of “transmit (i) the ripeness level of the food item and (ii) the modified supply chain information so as to invoke execution of the instructions included in the modified supply chain information at one or more supply chain actors at the user computing device and modify the at least one shipment schedule for the food item” merely recites updating the shipment schedule. Furthermore, as described in Step 2B above, recitation of “…so as to invoke execution of the instructions…” is a contingent limitation and not actually required by the claim (see MPEP § 2111.04). Therefore, it appears the alleged improvements are not commensurate in scope with the claimed invention. This argument is thus not persuasive.
Conclusion
No claims allowed.
Claims 18-31 appear to be free from the prior art because the prior art does not fairly suggest or teach a machine learning model trained by correlating destructive measurements of the penetrometer data and durometer data with non-destructive measurements of the spectral data to determine a ripeness metric. The closest prior art is Schwartzer et al. (U.S. Patent Application Publication US 2019/0340749 A1; cited in the IDS dated 7/19/2022). 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. 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. Claims 19-22 and 24-31 appear to be free from the prior art due to their dependency on claims 18 and 23.
THIS ACTION IS MADE FINAL. 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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIANA P SANFORD whose telephone number is (571)272-6504. The examiner can normally be reached Mon-Fri 8am-5pm EST.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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