DETAILED ACTION
Notice of 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 .
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 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 01/15/2026, has been fully considered.
In view of the amendment and remarks from 01/15/2026, the rejection of the following claims are withdrawn:
claims 16-20 under 35 USC § 112(a);
claim 17 under 35 U.S.C. § 112(b).
The following rejections and/or objections are either maintained or newly applied for claims 1-20. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 10/14/2025.
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
This application is a 371 of PCT/US2020/041896 (07/14/2020) which claims benefit of 62/874,264 (07/15/2019) as reflected in the filing receipt mailed on 01/25/2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-20 is 07/15/2019.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/15/2026 was considered.
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 1-20 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a system (claims 1-12); a method (claims 13-15) and CRM (claims 16-20); each of which falls within one of the categories of statutory subject matter. [Step 1: Yes].
[Step 1: claims 1-20: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "calculate/calculating, using a time-series forecasting model, a predicted future flavor score for the item at a future time relative to the number of days based on the received time series data and based on a predicted flavor change of the item by the future time" (independent claims 1,13 and 16); and
• "calculate the predicted flavor score based on the identified at least one attribute value and at least a second attribute value of the item" (claim 5).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "calculating/determine the flavor profile score", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the score quantification. Further support for the mathematical quantification of the flavor profile score [0079], which describes algorithms for profile quantification purposes. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "identify/identifying at least one attribute value for at least one attribute of the item based on the spectral profile" (independent claims 1 and 13);
• "determine a flavor score for the item based on the at least one attribute value" (independent claim 1);
• "generate a flavor profile for the item based at least on the predicted future flavor score for the item" (independent claims 1 and 16);
• "determine at least one of the following based on the flavor profile: priority of placement of the item in a store from an inventory of an item type associated with the item, selection of a distribution center for the item, pricing of the item, inspection criteria for the item; safety stock number of the item at the store, shelf life of the item, quality metrics associated with different suppliers of the item type, and impact of weather on the quality of the item type" (claims 12 and 19);
• "determine at least one attribute and a corresponding score of the at least one attribute of the item based on the spectral profile" (independent claim 16); and
• "determine a flavor score of the item based on the calculated predicted attribute score" (independent claim 16).
Under the Broadest Reasonable Interpretation, the recited limitations are mental processes because a human mind is also sufficiently capable of identifying attributes of the item and determine priority of placement of the item in a store from an inventory of an item type associated with the item, selection of a distribution center for the item, pricing of the item, inspection criteria for the item; safety stock number of the item at the store, shelf life of the item, quality metrics associated with different suppliers of the item type, and impact of weather on the quality of the item type; upon data evaluation.
[Step 2A Prong One: claims 1-20: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 8, 13 and 16 recite additional elements that are not abstract ideas:
• "extract/extracting image data from the spectral images of the item" (independent claims 1,13 and 16);
• "convert/converting the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item" (independent claims 1,13 and 16);
• "obtain/obtaining time series data associated with the item corresponding to a number of days" (independent claims 1,13 and 16);
• "a hyperspectral camera, wherein the hyperspectral camera is configured to capture spectral images of an item" (independent claim 13);
• "a diffuse lighting system, wherein the diffuse lighting system is configured to provide indirect illumination for the item" (independent claim 13);
• "a computing system, wherein the computing system comprises: a memory device storing computer-executable instructions" (independent claim 13);
• "a processor configured to execute the computer-executable instructions to: obtain the spectral images of the item captured by the hyperspectral camera" (independent claim 13);
• "providing, by a diffuse lighting system, indirect illumination for an item" (independent claim 13);
• "obtaining, by a hyperspectral camera, spectral images of the item" (independent claim 13);
• "obtaining, from the hyperspectral camera, the spectral images of the item" (independent claim 13);
• "generating, by processing the spectral data based on the spectral images of the item, a spectral profile of the item, the spectral profile for the item representing a combination of physical attributes associated with the item" (independent claim 13);
• "receive transportation data associated with transportation of the item post-packing via at least one input device, and to update the flavor profile based at least in part on the received transportation data" (claim 8)
• "provide indirect illumination for an item" (independent claim 13); and
• "capture, by a hyperspectral camera, spectral images of the item" (independent claim 13).
Dependent claims 2 and 14 recite further details about the physical attributes associated with the item; dependent claims 3, 6, 9-11, 15, 17-18 and 20 recite further details about the flavor profile; and dependent claims 4 and 7 recite further details about the sensor data obtained.
Considerations under Step 2A, Prong Two
The recited limitations in claims 1, 8, 13 and 16 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
Limitations of claims 1, 5, 12-13, 16 and 19 are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
The recited "hyperspectral camera" to "capture/obtain images", "diffuse lighting system" to "provide illumination", "extracting data" and converting data" read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means.
Claims directed to "obtain/obtaining time", "receive transportation data" and "generate a spectral profile" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-20: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1, 8, 13 and 16 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
It is known in the art that the use of a hyperspectral imaging based computer techniques for the quality evaluation of fruits and vegetables is well-understood, routine and conventional - Lu et. al. “Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review” Appl. Sci. 7, 189 (2017) (pg. 1 para. 1) – which involves a diffuse lighting system (pg. 21 para. 4 Lu) with generation a spectral profile (pg. 28 Fig. 23 Lu) obtained along a time series data (pg. 28 para. 1 Lu) and information being received by a computer (pg. 30 para. 4 Lu).
With respect to the instant claims, the prior art review to Lu (“Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review” Appl. Sci. 7, 189 (2017); newly cited) discloses that the use of a hyperspectral imaging based computer techniques for the quality evaluation of fruits and vegetables is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, disclosing a diffuse lighting system (pg. 21 para. 4 Lu) with generation a spectral profile (pg. 28 Fig. 23 Lu) obtained along a time series data (pg. 28 para. 1 Lu) and information being received by a computer (pg. 30 para. 4 Lu).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-20: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 01/15/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 9 para. 5
The claim expressly recites a hyperspectral camera configured .. and a diffuse lighting system .., which are physical, non-generic components that directly interact with the item to generate data that does not exist absent such instrumentation. The processor recited in Applicant's claim 1 is not merely used to perform an abstract mental process, but is configured to execute computer-executable instructions that obtain the captured spectral images, …. .For example, a human cannot perceive hyperspectral wavelength bands, extract pixel-level spectral signatures, or convert those signatures into spectral data representing material attributes without the recited imaging system and processor, and thus the claim does not recite a mental process. Moreover, claim I is not directed to a mathematical concept or a mere result, but to a specific technological process for generating new machine-produced data from physical inputs. The spectral profile generated by the claimed system is not a mere abstract classification or analysis of existing information, but a newly created data structure derived from physical measurements captured by the hyperspectral camera under controlled illumination conditions. … The claim therefore does not fall within the categories of abstract ideas identified in MPEP §2106. 04( a), including mental processes or mathematical relationships divorced from a technological context. In view of the foregoing, Applicant submits that the computing system recited in claim I represents a uniquely configured and improved computing system with a uniquely programmed processor that, in combination do not represent a generic computer and perform functions that could not be performed in the human mind, and are not a mathematical concept, and do not represent a method of organizing human activities or a commercial or legal interaction or a fundamental economic practice, and do not represent an abstract idea for at least this reason
It is respectfully submitted that this is not persuasive because:
Regarding the argued improvement, at Step 2A Prong 2, the recited "hyperspectral camera" to "capture/obtain images", "diffuse lighting system" to "provide illumination", "extracting data" and converting data" read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means. The physical components such as a configured hyperspectral camera and a diffuse lighting system are mere instruments used to gathered the data used in subsequent calculations for the flavor profile (i.e. judicial exception), which reads on data gathering activities; thus not amounting in a practical application.
Regarding the argued improvement, at Step 2B, it is known in the art that the use of a hyperspectral imaging based computer techniques for the quality evaluation of fruits and vegetables is well-understood, routine and conventional - Lu et. al. “Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review” Appl. Sci. 7, 189 (2017) (pg. 1 para. 1) – which involves a diffuse lighting system (pg. 21 para. 4 Lu) with generation a spectral profile (pg. 28 Fig. 23 Lu) obtained along a time series data (pg. 28 para. 1 Lu) and information being received by a computer (pg. 30 para. 4 Lu).
Indeed, a human cannot perceive hyperspectral wavelength bands, extract pixel-level spectral signatures, or convert those signatures into spectral data. However, the previous office action did not identify the "capturing of hyperspectral wavelength bands, extract pixel-level spectral signatures, or conversion of those signatures into spectral data" as a mental process. Instead, the previous office action stated that "a human mind is sufficiently capable of identify an attribute based on a profile and generate a flavor profile based on the score data available." Regarding, the identification of hyperspectral wavelength bands, extract pixel-level spectral signatures, or conversion of those signatures into spectral data remains identified as data gathering activities since the information identified and obtained via the data conversion step is used for the subsequent calculation of the flavor profile (i.e. judicial exception). MPEP 2106.05(a)
Regarding pg. 10, para. 2 arguments directed to the combination of the lighting system, hyperspectral camera, and the conversion of spectral data, there is no disclosed improvement described in the instant specification which does not support the argued remarks. The claims merely generically describe extracting image data and there is no indication as to how such step is tied to the physical of light in the claims. Thus, the arguments appears to not be commensurate in scope with the claimed invention.
The argued "specific technological process for generating new machine-produced data from physical inputs" is related to using the data gathered from "capturing of hyperspectral wavelength bands, extract pixel-level spectral signatures, and conversion of those signatures into spectral data" as input to the recited flavor profile calculations (i.e. judicial exception). Regarding improvement to technology, the improvement cannot be in the judicial exception itself. Rather, the improvement is provided by the additional elements either on their own or in combination with the judicial exception. If the improvement is not realized in the additional elements then the improvement is in the judicial exception itself, which is not considered an improvement to technology. In this case, there is no improvement to technology realized by any additional element; as all the identified additional elements read on either data gathering activities, receiving data or outputting data; which are not sufficient to provide a practical application. MPEP 2106.05(a)
Applicant asserts in pg. 11 para. 2
As amended, claim 1 recites a specific system architecture that applies any alleged abstract idea using particular, non-generic hardware in a manner that produces a real-world technical result. In particular, the claim recites a hyperspectral camera configured to capture spectral images of an item and a diffuse lighting system configured to provide indirect illumination of the item. … Thus the claim applies any alleged abstract concept in conjunction with a particular machine, thereby satisfying MPEP § 2106.05(b). Further, the amended claim effects a transformation of a particular article to a different state or thing within the meaning of MPEP § 2106.05(c). Specifically, electromagnetic radiation reflected from a physical item is captured as hyperspectral image data under controlled diffuse illumination and is computationally transformed into spectral data and further into a spectral profile that represents physical attributes of the item that are not observable by the human eye. This is not a mere mental characterization of existing information, but a technical transformation of raw optical signals into a new machine-generated data structure that encodes material properties of the item. Such a transformation cannot be practically performed in the human mind and does not exist absent the claimed optical hardware and processor configuration. In addition, claim 1 improves the functioning of computing and imaging technology itself The claimed combination of diffuse lighting, hyperspectral imaging, and processor-implemented spectral conversion improves the accuracy, reliability, and usefulness of computer vision systems by enabling extraction of physical attribute information that conventional RGB imaging systems and generic data processing do not achieve. The improvement lies not in a business rule or organizational concept, but in the way the computing system acquires, conditions, and processes optical data to generate a technically meaningful output. Thus, the claim satisfies MPEP § 2106.05( a) by improving a technical field, i.e., hyperspectral computer vision and optical sensing systems. Further, the claim applies any alleged abstract idea in a meaningful way beyond generally linking it to a technological environment. The hyperspectral camera, diffuse lighting system, and processor are not recited at a high level of generality, nor do they merely serve as conduits for collecting and displaying data. Instead, they form an "ordered combination" in which each component cooperates to achieve a specific technical result that could not be achieved using generic computing components alone. MPEP 2106. The claim therefore does not risk preemption of an abstract idea, but instead confines its scope to a particular, machine-implemented technological solution.
It is respectfully submitted that this is not persuasive because:
the argued "non-generic hardware in a manner that produces a real-world technical result" refers to the use of a configured hyperspectral camera and a diffuse lighting system to obtain spectral data has been identified as well understood routine and conventional (Lu (“Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review” Appl. Sci. 7, 189 (2017)); thus, generic hardware usage and does not result in an improvement of a technical field.
Regarding the particular machine argument, MPEP 2106.05(b) states that there are 3 factors to consider when determining if the particular machine consideration applies. The analysis of the instant claims followed said factors, i.e. for example: I. Yes, the machine is particular; II. The judicial exception does not affect the operation of the camera or the lighting system, rather it seems the machine is merely an object on which the method operates; and III. the machines involvement is insignificant extra-solution activity (the camera and lighting system are part of the data gathering). MPEP 2106.05(b) states that when a the machine is merely an object on which the method operates it does not does not integrate the exception into a practical application or provide significantly more; which is the case of this claimed invention. In this case, the amended claim does not effects a transformation of a particular article to a different state or thing within the meaning MPEP § 2106.05(c). The use of a configured hyperspectral camera and a diffuse lighting system to gather data to be used in subsequent calculations for the flavor profile (i.e. judicial exception), is not a particular transformation which can provide a practical application because, for data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation (see CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)); MPEP 2106.05(c)).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1-16 and 18-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lu (“Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review” Appl. Sci. 7, 189 (2017)) in view of Li (“Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems” J Sci Food Agric 99: 1787–1794 (2018)) in view of Bosona (“Food traceability as an integral part of logistics management in food and agricultural supply chain” Food Control 33:32-48 (2013)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 1 recites:
a hyperspectral camera, wherein the hyperspectral camera is configured to capture spectral images of an item;
a diffuse lighting system, wherein the diffuse lighting system is configured to provide indirect illumination for the item; and
a computing system, wherein the computing system comprises: a memory device storing computer-executable instructions; and a processor configured to execute the computer-executable instructions to: obtain the spectral images of the item captured by the hyperspectral camera;
extract image data from the spectral images of the item,
convert the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item
identify at least one attribute value for at least one attribute of the item based on the spectral profile;
determine a flavor score for the item based on the at least one attribute value;
obtain time series data associated with the item corresponding to a number of days;
calculate, using a time-series forecasting model, a predicted future flavor score for the item at a future time relative to the number of days based on the received time series data and based on a predicted flavor change of the item by the future time; and
generate a flavor profile for the item based at least on the predicted future flavor score for the item;
• Lu teaches hyperspectral imaging systems (pg. 1 para. 1) composed by charge-coupled device camera sensors and a digital camera (i.e. a hyperspectral camera) (pg. 5 para. 1) with scanning approaches for rapid acquisition of hyperspectral images (pg. 3 para. 3) to evaluate food products for their appearance attributes (i.e. convert the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item), such as color, size or shape and absence of surface defects, and internal properties and characteristics like defects and eating quality that is defined by texture and flavor attributes (i.e. flavor profile by identified physical attributes associated with the item) (pg. 28 para. 1); wherein spectral data was obtained consistently over time of 23 days (i.e. obtain time series data associated with the item corresponding to a number of days) (pg. 28 para. 1); wherein an optical property analyzer with software (i.e. computing system/memory device storing computer-executable instructions; and a processor) for system control, image acquisition, real-time data processing and on-screen output display (i.e. generating the profile using a time-series forecasting model) (pg. 26 para. 1) composed by indirect illumination (pg. 26 Fig. 20); wherein optical absorption and scattering properties can be determined by quantifying the diffuse light remitted (i.e. a diffuse lighting system to provide illumination) (pg. 21 para. 4); wherein the multispectral profile was reduced to a 1D spatial scattering profile (pg. 10 para. 1).
• Lu does not teach “predicted flavor score”. However, Li teaches a hyperspectral imaging system (i.e. pg. 1788 col. 2 para. 2); wherein captured spectral images focused on the molecular differences of flavor components and external quality (color, size and shape) (pg. 1788 col. 1 para. 3) composed by a charge-coupled device camera sensor and a set of 150W halogen lamps for sample illumination (i.e. pg. 1788 col. 2 para. 2), wherein after optimization, the best SVM model for predicting the overall scores of tea samples was achieved (i.e. predicted flavor score) (pg. 1793 Table 2) after evaluation of tea quality which involved color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2).
• Lu does not teach “using a time-series forecasting model, a predicted flavor score for the item at a future time relative to the number of days.” However, Bosona teaches food traceability systems wherein data includes how food was transported including distribution route (pg. 40 col. 2 para. 2) following product quality and consumers (pg. 34 col. 2 para. 3) to ensure quality and nutritional values of food (pg. 37 Table 3) and estimate the remaining shelf life via freshness indicators (i.e. reading on forecasting model and a predicted flavor score for the item at a future time) (pg. 42 col. 1 para. 4).
Claims 2 and 14 recite:
wherein the at least one attribute comprises at least one of acidity, crunchiness, ripeness, firmness, and sugar levels
• Lu the prediction results of firmness for apples (pg. 29 Fig. 25).
Claims 3, 15 and 20 recite:
generate the flavor profile for each day of the number of days
• Lu teaches the observation of changes in spectra data for normal and bruised apples for a period of 23 days (pg. 28 para. 1).
Claim 4 recites:
obtain sensor data via at least one input device
• Lu teaches an optical property analyzer with software (i.e. memory device storing computer-executable instructions; and a processor) for system control, image acquisition via a camera (i.e. input device), real-time data processing and on-screen output display (pg. 26 para. 1).
Claim 5 recites:
calculate the predicted flavor score based on the identified at least one attribute value and at least a second attribute value of the item
• Lu teaches the recitation above. However, Li teaches a hyperspectral imaging system (i.e. pg. 1788 col. 2 para. 2); wherein captured spectral images focused on the molecular differences of flavor components and external quality (color, size and shape) (pg. 1788 col. 1 para. 3) composed by a charge-coupled device camera sensor and a set of 150W halogen lamps for sample illumination (i.e. pg. 1788 col. 2 para. 2), wherein after optimization, the best SVM model for predicting the overall scores of tea samples was achieved (i.e. predicted flavor score) (pg. 1793 Table 2) after evaluation of tea quality which involved color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2).
Claim 6 recites:
update the flavor profile at predetermined intervals
• Lu teaches that during acquisition of the flavor attributes, spectral parameters were updated by the algorithm to improve accuracy (pg. 24 para. 2).
Claim 7 recites:
comprising: one or more sensors, wherein the one or more sensors are configured to capture sensor data for the item
• Lu teaches the use of a charge-coupled device camera sensor use as detectors in the hyperspectral system (pg. 5 para. 1).
Claim 8 recites:
receive transportation data associated with transportation of the item post-packing via at least one input device, and to update the flavor profile based at least in part on the received transportation data
Claims 9 and 18 recites:
wherein the flavor profile further comprises information regarding origination of the item
Claim 10 recites:
wherein the flavor profile further comprises information regarding transportation of the item
Claim 11 recites:
wherein the flavor profile further comprises information regarding quantity of the item available
• Lu does not teach the recited limitation above. However, Bosona teaches food traceability systems wherein data includes how food was transported including distribution route (i.e. transportation of the item post-packing as in claim 8) (pg. 40 col. 2 para. 2) following product quality and consumers (i.e. update the flavor profile based at least in part on the received transportation data as in claim 8) (pg. 34 col. 2 para. 3); wherein mandatory data include lot number, product ID, product description, supplier ID (i.e. information regarding origination of the item as in claims 9 and 18), quantity (i.e. information regarding quantity of the item available as in claim 11), unit of measure, buyer ID; and optional data include supplier’s name, contact information, receipt date, country of origin, date of pack, trade unit, transportation vehicle ID, logistics service provider ID (i.e. information regarding transportation of the item available as in claim 10), buyer’s name, and dispatching date (pg. 36 col. 2 para. 2).
Claims 12 and 19 recite:
determine at least one of the following based on the flavor profile: priority of placement of the item in a store from an inventory of an item type associated with the item, selection of a distribution center for the item, pricing of the item, inspection criteria for the item; safety stock number of the item at the store, shelf life of the item, quality metrics associated with different suppliers of the item type, and impact of weather on the quality of the item type
• Lu teaches an integrated sensing mode that provides unique capabilities for simultaneous inspection of external quality characters (i.e., size, color and blemishes) and internal defects, and hence can be advantageous in food quality inspection (pg. 30 para. 3).
Claims 13 recites:
providing, by a diffuse lighting system, indirect illumination for an item; obtaining, by a hyperspectral camera, spectral images of the item; obtaining, from the hyperspectral camera, the spectral images of the item; extracting image data from the spectral images of the item; converting the extracted image data into spectral data; generating, by processing the spectral data based on the spectral images of the item, a spectral profile of the item, the spectral profile for the item representing a combination of physical attributes associated with the item; identifying at least one attribute value of at least one attribute of the item based on the spectral profile; obtaining time series data associated with the item corresponding to a number of days; calculating, using a time-series forecasting model, a predicted flavor score for the item at a future time relative to the number of days based on the obtained time series data and based on a predicted flavor change of the item by the future time; and generating a flavor profile for the item based on the flavor score and the item data
• Lu teaches hyperspectral imaging systems (pg. 1 para. 1) composed by charge-coupled device camera sensors and a digital camera (i.e. a hyperspectral camera) (pg. 5 para. 1) with scanning approaches for rapid acquisition of hyperspectral images (pg. 3 para. 3) to evaluate food products for their appearance attributes (i.e. convert the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item), such as color, size or shape and absence of surface defects, and internal properties and characteristics like defects and eating quality that is defined by texture and flavor attributes (i.e. flavor profile by identified physical attributes associated with the item) (pg. 28 para. 1); wherein spectral data was obtained consistently over time of 23 days (i.e. obtain time series data associated with the item corresponding to a number of days) (pg. 28 para. 1); wherein an optical property analyzer with software (i.e. computing system/memory device storing computer-executable instructions; and a processor) for system control, image acquisition, real-time data processing and on-screen output display (i.e. generating the profile using a time-series forecasting model) (pg. 26 para. 1) composed by indirect illumination (pg. 26 Fig. 20); wherein optical absorption and scattering properties can be determined by quantifying the diffuse light remitted (i.e. a diffuse lighting system to provide illumination) (pg. 21 para. 4); wherein the multispectral profile was reduced to a 1D spatial scattering profile (pg. 10 para. 1).
• Lu does not teach “predicted flavor score”. However, Li teaches a hyperspectral imaging system (i.e. pg. 1788 col. 2 para. 2); wherein captured spectral images focused on the molecular differences of flavor components and external quality (color, size and shape) (pg. 1788 col. 1 para. 3) composed by a charge-coupled device camera sensor and a set of 150W halogen lamps for sample illumination (i.e. pg. 1788 col. 2 para. 2), wherein after optimization, the best SVM model for predicting the overall scores of tea samples was achieved (i.e. predicted flavor score) (pg. 1793 Table 2) after evaluation of tea quality which involved color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2).
• Lu does not teach “using a time-series forecasting model, a predicted flavor score for the item at a future time relative to the number of days.” However, Bosona teaches food traceability systems wherein data includes how food was transported including distribution route (pg. 40 col. 2 para. 2) following product quality and consumers (pg. 34 col. 2 para. 3) to ensure quality and nutritional values of food (pg. 37 Table 3) and estimate the remaining shelf life via freshness indicators (i.e. reading on forecasting model and a predicted flavor score for the item at a future time) (pg. 42 col. 1 para. 4).
Claim 16 recites:
provide indirect illumination for an item; capture, by a hyperspectral camera, spectral images of the item; extract image data from the spectral images of the item, convert the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item; determine at least one attribute and a corresponding score of the at least one attribute of the item based on the spectral profile; obtain time series data associated with the item corresponding to a number of days; calculate, using a time-series forecasting model, a predicted future flavor score for the item at a future time relative to the number of days based on the received time series data and based on a predicted flavor change of the item by the future time; determine a flavor score of the item based on the calculated predicted attribute score; and generate a flavor profile for the item based at least on the flavor score for the item
• Lu teaches hyperspectral imaging systems (pg. 1 para. 1) composed by charge-coupled device camera sensors and a digital camera (i.e. a hyperspectral camera) (pg. 5 para. 1) with scanning approaches for rapid acquisition of hyperspectral images (pg. 3 para. 3) to evaluate food products for their appearance attributes (i.e. convert the extracted image data into spectral data, and process the spectral data to generate a spectral profile for the item, the spectral profile for the item representing a combination of physical attributes associated with the item), such as color, size or shape and absence of surface defects, and internal properties and characteristics like defects and eating quality that is defined by texture and flavor attributes (i.e. flavor profile by identified physical attributes associated with the item) (pg. 28 para. 1); wherein spectral data was obtained consistently over time of 23 days (i.e. obtain time series data associated with the item corresponding to a number of days) (pg. 28 para. 1); wherein an optical property analyzer with software (i.e. computing system/memory device storing computer-executable instructions; and a processor) for system control, image acquisition, real-time data processing and on-screen output display (i.e. generating the profile using a time-series forecasting model) (pg. 26 para. 1) composed by indirect illumination (pg. 26 Fig. 20); wherein optical absorption and scattering properties can be determined by quantifying the diffuse light remitted (i.e. a diffuse lighting system to provide illumination) (pg. 21 para. 4); wherein the multispectral profile was reduced to a 1D spatial scattering profile (pg. 10 para. 1).
• Lu does not teach “predicted flavor score”. However, Li teaches a hyperspectral imaging system (i.e. pg. 1788 col. 2 para. 2); wherein captured spectral images focused on the molecular differences of flavor components and external quality (color, size and shape) (pg. 1788 col. 1 para. 3) composed by a charge-coupled device camera sensor and a set of 150W halogen lamps for sample illumination (i.e. pg. 1788 col. 2 para. 2), wherein after optimization, the best SVM model for predicting the overall scores of tea samples was achieved (i.e. predicted flavor score) (pg. 1793 Table 2) after evaluation of tea quality which involved color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2).
• Lu does not teach “using a time-series forecasting model, a predicted flavor score for the item at a future time relative to the number of days.” However, Bosona teaches food traceability systems wherein data includes how food was transported including distribution route (pg. 40 col. 2 para. 2) following product quality and consumers (pg. 34 col. 2 para. 3) to ensure quality and nutritional values of food (pg. 37 Table 3) and estimate the remaining shelf life via freshness indicators (i.e. reading on forecasting model and a predicted flavor score for the item at a future time) (pg. 42 col. 1 para. 4).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-16 and 18-20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Lu in view of Li and Bosona because all references disclose methods for evaluation of food quality. The motivation would have been to:
• apply the technique for scoring food quality related to color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2 Li) and
• incorporate effective communication of traceability information to consumers (pg. 32 para. 1 Bosona).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the evaluation of food quality method of Lu to the methods by Li and Bosona because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for evaluation of food quality.
B. Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Lu, Li and Bosona as applied to claim 16 above further in view of Ahmed (“An overview of smart packaging technologies for monitoring safety and quality of meat and meat products” Packag. Technol. Sci. 31:449–471 (2018)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 17 recites:
access the flavor profile via data obtained from a bar code or QR code on a packing material associated with the item
• Lu and Li does not teach the recited limitation above. However, Ahmed teaches barcodes and radio-frequency identification tags employed in meat packaging for real time information about the authenticity (i.e. food attributes) , and traceability of the products in the supply chain (pg. 449 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Lu and Li in view of Ahmed because all references disclose methods for evaluation of food quality. The motivation would have been to monitor physical, microbial and chemical changes of the package contents from producer to the point of sale and even beyond (pg. 449 para. 1 Ahmed).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the evaluation of food quality method of Lu and Li to the methods by Ahmed because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for evaluation of food quality.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 01/15/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 13 para. 4
At most, Lu teaches retrospective or contemporaneous assessment of quality attributes derived from spectral measurements. However, Lu does not disclose or suggest forward-looking predictions of flavor evolution using a forecasting model that explicitly accounts for predicted flavor change. Moreover… time-series forecasting, future flavor score, and flavor profile generation, are simply not disclosed or suggested by Lu. … Li does not disclose predicting future flavor changes, applying a time-series forecasting model, or generating a future-looking flavor profile for an individual item based on a predicted flavor evolution of this item. Likewise, Li provides no disclosure or suggestion of determining a flavor score that is forecast forward in time based on item-specific time-series data. .., the Office Action does not set forth an articulated rationale grounded in Lu and Li themselves that would have motivated a person of ordinary skill in the art to modify Lu's hyperspectral quality evaluation framework with Li's multi-sensor classification techniques to arrive at Applicant's claimed system for predicting future flavor scores using time-series forecasting … Lu and Li fail to disclose or suggest the specific combination of elements recited in Applicant's independent claims as now amended, particularly the use of a time-series forecasting model to predict future flavor scores and generate a flavor profile based on those predictions. In other words, Applicant submits that absent impermissible hindsight, there is no rational reason for one of ordinary skill in
It is respectfully submitted that this is not persuasive because the amended "forecasting" step to predict a flavor profile at a future time is addressed in this instant examination. Bosona teaches food traceability systems wherein data includes how food was transported including distribution route (pg. 40 col. 2 para. 2) following product quality and consumers (pg. 34 col. 2 para. 3) to ensure quality and nutritional values of food (pg. 37 Table 3) and estimate the remaining shelf life via freshness indicators (i.e. reading on forecasting model and a predicted flavor score for the item at a future time) (pg. 42 col. 1 para. 4). The motivation to combine the teachings by Lu Li and Bosona would have been to apply the technique for scoring food quality related to color, size, shape, taste and aroma (pg. 1788 col. 1 para. 2 Li) and to incorporate effective communication of traceability information to consumers (pg. 32 para. 1 Bosona). Obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007)."It is well-established that a determination of obviousness based on teachings from multiple references does not require an actual, physical substitution of elements." In re Mouttet, 686 F.3d 1322, 1332, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012. Thus, the motivation to combine the art to Lu, Li and Bosona has been described in detail; which constitutes a sufficiently articulated grounded rationale for motivation to combine the teachings.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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