+DETAILED ACTION
Status of the Claims
1. This action is in response to the Request for Reconsideration dated April 1, 2026.
2. Claims 84-103 are currently pending and have been examined.
3. Claims 1-83 have been canceled.
4. The claims were preliminarily amended on April 28, 2025, canceling claims 1-83 and adding new claims 84-103.
5. Claims 84-88, 91, 96, 100-101, and 103 have been amended.
Notice of Pre-AIA or AIA Status
6. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
7. Claims 86-88, 91, 96-98, and 103 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The amendment filed stacks is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
As in Claim 96, the claim recites in part:
the system configured to allow users to assign values to individual entries on the user interface, after which the system:
a) an engine calculates values for descriptors,
In Claim 96, the use of “an engine” is broader than the specification discloses. In every instance, the specification refers to a “scoring engine” which is narrower than an engine. The specification does not support the broader terminology as currently recited. Dependent Claims 97-98 are further rejected as based upon a rejected base claim.
As in Claim 86, the claim recites in part:
wherein the tinting or shading is propagated back through any prior descriptor strings to a root category button corresponding to the bitterness attribute category, such that the root category button displays the net positive or negative contribution of the bitterness category to the overall coffee score;
While the specification does note “[t]his tinting and shading stacks back down through any prior descriptor strings to the root category button, which displays the net contribution – positive or negative to provide an overall coffee score”, it does not disclose “propagated back” as claimed. The context of the descriptors is described in terms of traditional machine learning in the specification, not neural networks, thus the recitation of propagated back is not supported by the disclosed stacks back language.
As in Claim 87, the claim recites in part:
wherein acidity is measured as an attribute category separately and independently from the sweetness attribute category, and wherein the method further comprises:
a) computing, by the scoring engine, a separate numerical acidity category score and a separate numerical sweetness category score;
b) displaying the separately computed acidity category score and the sweetness category score to the user on the digital user interface without permitting the user to directly input or modify either numerical score, such that the numerical scores are generated exclusively by the scoring engine from the user’s descriptor and intensity selections.
While the specification does note that in typical embodiments acidity is measured separately from sweetness, it does not disclose “separately and independently” as claimed. (See Applicant Spec page 3, lines 20-22; page 12, lines 3-5)
Further, the specification does not recite separate numerical acidity score and a separate numerical sweetness category score as claimed. The specification notes the following:
“A scoring engine is typically essential to the system and method. The scoring engine assigns value to individual entries, calculates values for descriptor strings, sorts and tallies category and then sample-level scores, processes the descriptor strings into natural language forms, and selects from a coffee’s generated descriptor pool for top-level descriptor output.” (See Applicant Spec page 4, lines 4-8)
While the scoring engine is generally disclosed to sort and tally category and then sample-level scores, there is no disclosure of a separate numerical acidity category score or a separate numerical sweetness category score as currently claimed. Dependent Claim 88 is further rejected as based upon a rejected base claim.
As in Claim 91, the claim recites in part:
f) transmits, from a first user device, a first set of descriptor selections and intensity selections for a coffee sample to a server over a network;
g) receive a second set of descriptor selections and intensity selections for the same coffee sample from at least a second user device associated with a different user;
h) computes a composite panel score for the coffee sample by aggregating the first set and the second set of descriptor selections and intensity selections through the scoring engine; and
i) transmits the composite panel score and a composite panel descriptor output to the first user device and the second user device for display.
The limitations are not fully supported by the specification. The specification discloses “a web app so can be used on a laptop, desktop, or tablet (no mobile support for the moment)”, however does not disclose a first user device, a second user device, a server or a network, nor the manner of generating or transmitting a composite panel score through a scoring engine as claimed. (See Applicant Spec page 14, lines 24-25) The process of creating a flight appears to be geared towards a table in a physical location where a cupping takes place and entering a coffee. The specification further notes that DemoX currently supports single extractions of each coffee and that they will not be displayed or calculated collectively in the Flight summary. (See Applicant Spec pages 15-16) The specification notes a composite result from data provided by the panel (everyone invited to the flight) however provides no details as to how that comes to be. Further, the flight results are disclosed to be shared by sending a link, but there is no disclosure of outputting to a first user device and a second user device.
As in Claim 103, the claim recites in part:
b) receiving a user selection traversing at least two tiers of the tiered structure within a single attribute category, thereby forming a multi-tier descriptor string;
c) receiving a user selection of one of exactly four discrete intensity levels for the multi-tier descriptor string;
d) wherein the selection of the intensity level simultaneously performs an endorsement of the multi-tier descriptor string without requiring a separate endorsement step
e) computing, by the scoring engine, a numerical contribution of the multi-tier descriptor string to the composite coffee score, wherein the numerical contribution is a function of the (i) the tier depth of the most specific descriptor in the multi-tier descriptor string, (ii) the content value assigned to that descriptor, and (iii) the selected intensity level;
f) automatically updating the visual display of the electronic flavor wheel by tinting the selected attribute category button to a lighter color if the multi-tier descriptor string has a net positive connotation, or shading the selected attribute category button to a darker color if the multi-tier descriptor string has a net negative connotation;
(g) automatically generating a natural language description string incorporating a linguistically appropriate intensity qualifier and the selected descriptors from the multi-tier descriptor string.
The specification discloses a rich content component that refers to two primary attributes, a tiered structure with the ability to endorse either simple individual descriptors or to build and endorse more complex descriptor strings that extend beyond these individual descriptors and an endorsement protocol that uses indication for intensity for each individual descriptor. (See Applicant Spec page 2) There is no disclosure of a multi-tier descriptor string. This is an over expansion of the disclosure. As such, all of the limitations referring to this multi-tier descriptor string do not have proper support.
The specification continues, disclosing in an embodiment that the coffee rose system is composed of seven attribute categories, four increasingly descriptive tiers and an intensity indicator. (Id). There is further disclosure of four intensity descriptors and in an embodiment including at least three intensity descriptors, however no disclosure of exactly four discrete intensity levels for the multi-tier descriptor string layered in the manner claimed. (See Applicant Spec page 5)
There is no disclosure of the selection of the intensity level simultaneously performs an endorsement of the multi-tier descriptor string without requiring a separate endorsement step. There is no disclosure of the negative limitation in the specification. There is no recitation of “simultaneously” performing an endorsement as claimed.
The specification does not disclose computing a numerical contribution of the multi-tier descriptor string to the composite coffee score, wherein the numerical contribution is a function of the tier depth of the most specific descriptor in the multi-tier descriptor string or disclose most specific descriptor or a content value assigned to that descriptor. There is no “automatically updating” of the visual display of the electronic flavor wheel disclosed in the specification, rather the inputs are selected and then based on the selection of inputs descriptors with a net positive connotation are tinted lighter and those with a net negative connotation are shaded darker. (See Applicant Specification page 2) There again is no disclosure of a multi-tier descriptor string.
The specification does not disclose automatically generating a natural language description string incorporating a linguistically appropriate intensity qualifier and the selected descriptors from the multi-tier descriptor string. The specification only discloses processing the descriptor strings into natural language forms in each reference to natural language in the entirety of the specification. (See Applicant Spec, at least page 4) The specification does not disclose linguistically appropriate intensity qualifiers or selected descriptors from the multi-tier descriptor string. The specification only notes that the descriptor strings are processed into natural language forms and selects from a coffee’s generated descriptor pool for top-level descriptor output.
Overall, Applicant is attempting to extend the bounds of the specification past its limits and encouraged to claim the invention within the bounds of the specification.
Applicant is required to cancel the new matter in the reply to this Office Action.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 84-95 and 100-103 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 84 recites the limitation "the digital user interface" in the third limitation. There is insufficient antecedent basis for this limitation in the claim as the first reference is to a user interface in the first limitation. The further limitations in the independent and in some subsequent dependent claims also uses “the digital user interface” language (see dependent Claim 85) and Dependent claims 85-95 are further rejected as dependent on a rejected base claim.
Claim 84 is rejected under 35 U.S.C. 112, second paragraph, as such claim is directed to neither a process nor a machine, but rather embraces and/or overlaps two different statutory classes of invention which has deemed ambiguous under 35 U.S.C. 112. This section requires a claim to particularly point out and distinctly claim the subject matter which the appellant regards as his invention. However, the “invention” referred to in the second paragraph of 35 USC 112 is also subject to the requirements of 35 USC 101. This section of the statute requires that in order to be patentable the invention must be a “new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof.” A claim intended to embrace or overlap two different statutory classes of invention set forth in 35 USC 101 is precluded by the express language of 35 USC 101 which is drafted so as to set forth the statutory classes of invention in the alternative only. A single claim which purposes to be both a product or machine and a process is ambiguous and is properly rejected under 35 USC 112, second paragraph, for failing to particularly point out and distinctly claim the invention. see Ex parte Lyell, 17 USPQ2d 1548 (BPAI 1990).
Here, the claim is broadcast to be a system claim in the preamble, however the first limitation, as amended, indicates “…the method further comprises;” followed by method type recitations. Additionally, the claim features a step b), c), and d), however does not have a step a) disclosed.
The mixing of statutory classes continues in the dependent claim, with Claims 86-88 being drawn to a system in the preamble and then referencing a method in the body of the claims.
The claims also feature a number of antecedent basis and/or confusing limitations. It is unclear if Applicant is intending to claim one scoring engine or a number of scoring engines. Applicant’s specification indicates various embodiments with different scoring engine functions and it is not clear what embodiment or embodiments Applicant is attempting to capture or if this is a lack of clean antecedent basis due to the extensive amendments made. Examiner has attempted to note every issue below, however appropriate clarification and correction is required
Claim 85 is dependent on Claim 84 and further appears to recite further method steps.
Further, the last limitation of Claim 85 recites “wherein the coffee evaluation system includes a scoring engine”, however, as recited in amended Claim 84, a scoring engine has already been recited. Is this the same scoring engine or is Applicant attempting to recite two separate scoring engines?
Further still, Claim 87, which relies on Claim 84 also recites “a scoring engine” and then “the scoring engine”, which again does not clearly indicate if this is a reference to the previous scoring engine or another scoring engine. Dependent Claims 89-91 have a substantially similar issue which will also need to be resolved.
As amended, Claim 100 recites a scoring engine and Claim 101, which is now dependent on Claim 100 refers to the scoring engine, however Claims 102 and 103, which are ultimately dependent on Claim 101 also refers to “a scoring engine”.
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.
9. Claims 84-103 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
ANALYSIS:
STEP 1:
Does the claimed invention fall within one of the four statutory categories of invention (process, machine, manufacture or composition matter?
Claims 84 and 96 recite system claims. Claim 99 recites a method claim. Currently, the systems and method claims are subject to a separate rejection as being non-statutory (as shown below), however Examiner assumes that Applicant will rectify the claims to properly claim the invention as within statutory categories.
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Claim 84 recites the abstract idea of coffee evaluation. The idea is described by the following limitations:
providing a tiered structure of attribute descriptors wherein the attribute descriptors are composed of seven attribute categories displayed as a flavor wheel, the seven attribute categories consisting of bitterness, saltiness, acidity, sweetness, sugar browning, herbal, and fruit, and wherein the method further comprises;
processes descriptor strings into natural language forms and
receiving a user selection of at least one attribute descriptor from at least one of the seven attribute categories;
b) expanding the selected attribute category to occupy a greater portion of the flavor wheel display in response to the user selection, thereby revealing additional tiers of sub-attribute descriptors for that category;
c) receiving a user selection of an intensity indicator for the selected attribute descriptor, the intensity indicator selected from at least three discrete intensity levels; and
d) generating a composite coffee score based at least in part on the selected attribute descriptor, the tier level of specificity of the selected attribute descriptor within the seven attribute categories, and the selected intensity indicator; and
providing an indication of intensity for at least some of the attribute descriptors.
Claim 96 recites the abstract idea of coffee evaluation. The idea is described by the following limitations:
providing a tiered structure of attribute descriptors; and
providing an intensity for at least some of the attribute descriptors;
allow users to assign values to individual entries and
calculates values for descriptors,
sorts and tallies category and then sample-level scores,
processes the descriptor strings into natural language forms, and
selects from a coffee’s generated descriptor pool for descriptor output.
Claim 99 recites the abstract idea of coffee evaluation. The idea is described by the following limitations:
providing a tiered structure of attribute descriptors; and
providing an indication of intensity for at least some of the attribute descriptors.
Under a broadest reasonable interpretation, the claims reflect no more than an approach to evaluate coffee based on a set of descriptors and in Claim 96, additionally tallying values to entries in order to provide an evaluation of coffee. The evaluation is driven by a user’s evaluation, assessment and judgment being entered into a user interface into a pre-established set of attribute descriptors.
As a result, the abstract ideas describe mental processes and certain methods of organizing human activity.
As the mental processes, the steps describe concepts performed in the human mind including an observation, evaluation, judgment and/or opinion (as seen above). These steps are performing a mental process in a computer environment that recites limitations receiving, observing and evaluating information and with the exception of generic computer-implemented steps [presumed, not claimed], there is nothing in the claims themselves that foreclose them from being practically performed by a human mentally.
As to certain methods of organizing human activity, the steps involve managing personal behavior or relationships or interactions between people (as seen above).
In the case of the instant claims, the claims recite no more than receiving information that a user manually recorded, using generic computer technology to process, analyze and output the received information as an evaluation of coffee. (Step 2A, Prong 1: Yes, the claims are abstract)
Prong Two: Does the Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application of the Exception? (If Yes, the claim is not directed to a judicial exception and qualifies as subject matter patent eligible material. If No, Proceed to Step 2B)
The claims do not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field, improvements to the functioning of the computer itself, are not applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, are not applying the judicial exception with, or by use of a particular machine, are not effecting a transformation or reduction of a particular article to a different state or thing, and are not applying the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Further, the method outlined in Claim 99 does not sufficiently tie the method steps to a particular machine within the body of the claim. As such, the recitations are further failing to integrate the judicial exception into a practical application on this basis.
Claim 84 recites a user interface, an electronic flavor wheel, a computer-implemented digital user interface, and a scoring engine.
Claim 96 recites a user interface, and an engine.
Claim 99 recites a user interface.
In particular, the claims only recite a user interface, an electronic flavor wheel, a computer-implemented user interface, an engine and a scoring engine which is recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 84, 96 and 99 are directed to an abstract idea without a practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application)
STEP 2B: If there is an exception, determine if the claim as a whole recites significantly more than the judicial exception itself.
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); ii) performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); iii) electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; v) electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and vi) a web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). (MPEP §2106.05(d)(II))
This listing is not meant to imply that all computer functions are well‐understood, routine, conventional activities, or that a claim reciting a generic computer component performing a generic computer function is necessarily ineligible. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. (MPEP §2106.05(d)(II) – emphasis added)
Below are examples of other types of activity that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016); shuffling and dealing a standard deck of cards, In re Smith, 815 F.3d 816, 819, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016); restricting public access to media by requiring a consumer to view an advertisement, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014); identifying undeliverable mail items, decoding data on those mail items, and creating output data, Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017); presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; and arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (MPEP 2106.05(d))
Here, the steps are receiving or transmitting data over a network; performing repetitive calculations; storing and retrieving information in memory and electronically scanning or extracting data– all of which have been recognized by the courts as well-understood, routine and conventional functions.
The claims are directed to an abstract idea with additional generic computer elements that do not add meaningful limitations to the abstract idea because they require no more than a generic computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry.
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well-understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
Applicant’s specification discloses the following:
“A scoring engine is typically essential to the system and method. The scoring engine assigns values to individual entries, calculates values for descriptor strings, sorts and tallies category and then sample-level scores, process the descriptor strings into natural language forms, and selects from a coffee’s generated descriptor pool for top-level descriptor output.” (See Applicant Spec page 4, lines 4-8)
“FIG. 7A is view of a digital user interface 700 in accordance with various embodiments herein, showing a series of selections for herbal properties 710, from herbaceous 720 to grassy 730 to green tea 740. The intensity is selected as reference 750, so this is a somewhat mild strength. FIG. 7B is a view of output from the digital user interface 700 of FIG. 7A, showing a score of 82.28 points, with “mellow green tea flavors”. Note that acidity and sweetness were also selected (but not shown expanded), and thus “tons of acidity and mild sweetness”. Is shown. [sic]” (See Applicant Spec page 14, lines 16-22)
“FIG. 8 shows a basic flowchart of a lightweight cupping tool utilizing the Coffee Rose allowing you to quickly build a flight, invite others, cup, and share the results with anyone. It’s a web app so can be used on a laptop, desktop or tablet (no mobile support for the moment).” (See Applicant Spec page 14, lines 23-26)
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Upon reconsideration of the indicia noted under Step 2A in concert with the Step 2B considerations, the additional claim element(s) amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim does not provide an inventive concept significantly more than the abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The independent claims 84, 96 and 99 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims 85-95, 97-98 and 100-103 further define the abstract ideas that are presented in the respective independent Claims 84, 96 and 99 and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. No additional hardware components other than those found in the respective independent claims is recited, thus it is presumed that the claim is further utilizing the same generic systemization as presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application of the exception or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 84-103 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 84-98 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
The preamble of Claims 84 and 96 indicate that the claims pertain to a system. However, the claims recite system elements comprising a series of steps and a user interface which, in the broadest reasonable interpretation, denotes software or a computer program. Software and computer programs are not physical "things." They are neither computer components nor statutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program's functionality to be realized.
However, it is unclear how the system of claims 84 and 96 functions when the "system" is only recited in its broadest sense. Specifically, there is no specific structure set forth delineating exactly what accomplishes the steps of the claim. It is unclear if applicant is seeking to claim every possible system that could perform the steps of claims 84 and 96, or if applicant is seeking to claim a specific system.
Further, Claim 84 is broadcast as a system claim, yet also recite a method in the body of the claim and continues to make references to “the method further comprises” in dependent Claims 86-88. This further leads to confusion as to the statutory category attempting to be claimed.
Appropriate correction is required. Claims 85-95 and 97-98 are further rejected as dependent on a rejected base claim. Thus, Claims 84-98 are deemed to be non-statutory.
Regarding Claims 99-103, Examiner notes that the method of Claims 99-102 would also have been rejected under the earlier §101 standards based upon In re Bilski, which have been superseded by the current §101 standards based upon the Alice-Mayo test. Specifically, Claim 99 contains an insufficient recitation of a machine or transformation as the involvement of the machine. As recited, the machine is merely nominally, insignificantly or tangentially related to the performance of the steps. Examiner notes that the only explicit reference to a machine is in the first line after the preamble of Independent Claim 99 as providing “a user interface”. There is no direct tie between a machine and the limitations of the independent claim, nor to most of the subsequent dependent claims. Examiner is only noting this as §101 under the Alice-Mayo test is considered a substantially higher bar than under In re Bilski. Examiner suggests Applicant incorporate language into the body of the claim reciting the machine elements performing the recited process.
Dependent Claim 103 does add an interactive electronic flavor wheel on digital user interface executed on a processor and a number of other limitations that are not properly supported by the specification and thus, if systemization elements that are properly supported by the specification were integrated into the independent claim from which this dependent claim depends, this issue may move towards resolution.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
10. Claim(s) 84, 89-90, and 92-102 are rejected under 35 U.S.C. 103 as being unpatentable over Borack (US PG Pub. 2016/0019559) in view of Jeon et al. (KR 20220020086A) [Foreign Patent Document provided in the IDS by Applicant citation F-3, translation only provided as attached]
Regarding Claim 96, Borack discloses the following:
A coffee evaluation system, the coffee evaluation system comprising:
providing a user interface having a tiered structure of attribute descriptors and (See Borack paras 38-40, 45-50, 52-53 and Figs. 6-7, 17-20 – application may be a web-based interface that operates within a web browser [user interface]; different layers representing tiered structure of attribute descriptors, in this case related to aroma and taste of the beverage)
providing an intensity for at least some of the attribute descriptors; (See Borack paras 45-50, 52-53, Figs. 6-7, 17-20 – taste screen with a first radii for complexity, second radii for bitterness, third radii for sweetness, fourth radii for aftertaste, fifth radii for body; acidity screen includes an intensity related to acidity)
the system configured to allow users to assign values to individual entries on the user interface, after which the system: (See Borack paras 5, 7, 57-59, 61, Cl. 1
a) an engine calculates values for descriptors, (See Borack paras 67-68, 85-87 and Figs. 21-22)
b) sorts and tallies category and then sample-level scores,
c) processes the descriptor strings into natural language forms, and
d) selects from a coffee’s generated descriptor pool for descriptor output.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries and discloses that the application may use statistical methods to determine what influences each user’s perception of value and calculates values as well as a computer apparatus including a processor (engine) used by the application for performing the method and other various applications, Borack does not fully further disclose the system calculating values for descriptors, sorts and tallies category and then sample-level scores, processes the descriptor strings into natural language forms and selects from a coffee’s generated descriptor pool for descriptor output.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – calculates values for descriptors)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level scores)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings into natural language forms])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph – [selects from a coffee’s generated descriptor pool for descriptor output]) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1 – [selects from a coffee’s generated descriptor pool for descriptor output]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 84, Borack discloses the following:
A coffee evaluation system, the coffee evaluation system comprising:
providing a user interface having a tiered structure of attribute descriptors wherein the attribute descriptors are composed of seven attribute categories displayed as an electronic flavor wheel on a computer-implemented digital user interface, the seven attribute categories consisting of bitterness, saltiness, acidity, sweetness, sugar browning, herbal, and fruit, and wherein the method further comprises; and (See Borack paras 38-40, 45-50, 52-53, 67 and Figs. 5-7, 17-20 – application may be a web-based interface that operates within a web browser [user interface]; different layers representing tiered structure of attribute descriptors, in this case related to aroma and taste of the beverage; at least Figs. 2-3, 5, 9 presents an electronic flavor wheel on a digital user interface; the application may include a taste screen for prompting a user to enter information pertaining to the taste of the beverage – the radar chart may include complexity, bitterness, sweetness, aftertaste and notes that other radar charts beyond this embodiment are contemplated; a separate acidity screen is shown; Figs. 17-20 disclose fruits, herbal; sensory perceptions may also include astringency, aroma, balance, finish, sourness, and taints)
wherein the coffee evaluation system includes a scoring engine that processes descriptor strings into natural language forms and
receiving, by a scoring engine, a user selection of at least one attribute descriptor from at least one of the seven attribute categories via the digital user interface; (See Borack paras 38-40, 45-50, 52-53, 67 and Figs. 2-3, 5-6, 9-10, 17-20)
b) dynamically expanding, on the digital user interface, the selected attribute category to occupy a greater portion of the electronic flavor wheel display in response to the user selection, thereby revealing additional tiers of sub-attribute descriptors for that category; (See Borack paras 51-53, 54-55, Figs. 5-6, 9-10, 17-20 – user navigates through the graphical user interface or color wheel, a button may open a graphical display of the entire color wheel displaying every possible selection at the same time)
c) receiving, by the scoring engine, a user selection of an intensity indicator for the selected attribute descriptor, the intensity indicator selected from at least three discrete intensity levels; and (See Borack Fig. 7 and para 53)
d) automatically generating, by the scoring engine, a composite coffee score based at least in part on the selected attribute descriptor, the tier level of specificity of the selected attribute descriptor within the seven attribute categories, and the selected intensity indicator; and (See Borack paras 38-40, 45-50, 51-53, 61-62, Figs. 6-7, 17-20)
providing an indication of intensity for at least some of the attribute descriptors. (See Borack paras 45-50, 52-53, Figs. 6-7, 17-20 – taste screen with a first radii for complexity, second radii for bitterness, third radii for sweetness, fourth radii for aftertaste, fifth radii for body; acidity screen includes an intensity related to acidity)
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a user interface having a tiered structure of attribute descriptors and discloses an electronic flavor wheel on a digital user interface with attribute categories that include complexity, bitterness, sweetness, astringency [related to saltiness], fruits, herbal, roast [related to sugar browning] and contemplates radar charts beyond the enumerated ones, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries and discloses that the application may use statistical methods to determine what influences each user’s perception of value and calculates values as well as a computer apparatus including a processor used by the application for performing the method and other various applications, it does not fully disclose all of the seven attribute categories defined in the claim; a scoring engine that processes descriptor strings into natural language forms; generating a composite coffee score based at least in part on the selected attribute descriptor, the tier level of the specificity of the selected attribute descriptor within the seven attribute categories and the selected intensity indicator.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring; calculates values for descriptors)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores; scoring engine). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level [composite scores])
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – sour taste, astringent [related to saltiness]; chocolate, nutty [related to sugar browning], [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings into natural language forms])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph – [selects from a coffee’s generated descriptor pool for descriptor output]) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1 – [selects from a coffee’s generated descriptor pool for descriptor output]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 99, Borack discloses the following:
A coffee evaluation method, the coffee evaluation method comprising:
providing a user interface having a tiered structure of attribute descriptors; and (See Borack paras 38-40, 45-50, 52-53 and Figs. 6-7, 17-20 – application may be a web-based interface that operates within a web browser [user interface]; different layers representing tiered structure of attribute descriptors, in this case related to aroma and taste of the beverage)
providing an indication of intensity for at least some of the attribute descriptors. (See Borack paras 45-50, 52-53, Figs. 6-7, 17-20 – taste screen with a first radii for complexity, second radii for bitterness, third radii for sweetness, fourth radii for aftertaste, fifth radii for body; acidity screen includes an intensity related to acidity)
Regarding Claims 89 and 98, these substantially similar claims recite the limitations of Claims 84 and 96 and as to those limitations are rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein the coffee evaluation system includes a scoring engine that sorts and tallies categories and sample-level scores.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system and method include a scoring engine that sorts and tallies categories and sample-level scores.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level scores, scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings into natural language forms])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 90, this claim recites the limitations of Claim 84 and as to those limitations is rejected for the same basis and reasons as recited above. Further Borack in view of Jeon discloses the following:
wherein the coffee evaluation system includes a scoring engine that selects from a coffee’s generated descriptor pool for top-level descriptor output.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system includes a scoring engine where each descriptor string has a value.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category [each descriptor string has a value], scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine that calculates values for each descriptor string, each of the three sections is a top level descriptor output)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph – [selects from a coffee’s generated description pool]) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 92, this claim recites the limitations of Claim 84 and as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
further comprising evaluation on basis of intensity wherein higher intensity descriptors have a greater impact on coffee description and score than lower intensity descriptors.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system and method include a scoring engine that sorts and tallies categories where higher intensity descriptors have a greater impact on coffee description and score than lower intensity descriptors.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level scores, scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes])
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine for each section, the higher intensity receives a higher score and will impact the coffee description/score more because intensity only has two sub attributes)
The intensity is divided into sour and bitter, and evaluated on a 10-point scale. (See Jeon page 7, paragraph 2) If the intensity of each item is absent or weak, 1 point can be given, and when the intensity is felt strong, 10 points can be given. (See Jeon page 7, paragraph 2)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3)
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 93, this claim recites the limitations of Claim 84 and as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein the system includes providing attribute scores sensitive both to how attributes are described qualitatively as well as to a quantitative intensity at which those descriptions are observed.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system and method include a scoring engine that sorts and tallies categories where higher intensity descriptors have a greater impact on coffee description and score than lower intensity descriptors.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level scores, scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes])
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine for each section, the higher intensity receives a higher score and will impact the coffee description/score more because intensity only has two sub attributes; thus the attribute scores are sensitive to quantitative and qualitative metrics for the descriptions observed)
The intensity is divided into sour and bitter, and evaluated on a 10-point scale. (See Jeon page 7, paragraph 2) If the intensity of each item is absent or weak, 1 point can be given, and when the intensity is felt strong, 10 points can be given. (See Jeon page 7, paragraph 2)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3)
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 94, this claim recites the limitations of Claim 84 and as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein the interface is sensitive to the degree of specificity noted within individual categories, the specific content of notations, and the quantitative intensity of each impression.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system and method include a scoring engine that sorts and tallies categories where higher intensity descriptors have a greater impact on coffee description and score than lower intensity descriptors.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category and then sample-level scores, scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes])
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine for each section, the higher intensity receives a higher score and will impact the coffee description/score more because intensity only has two sub attributes; the sections are separated, thus sensitive to a degree of specificity for individual categories and notated with specific content – this results in quantitative intensity of each impression)
The intensity is divided into sour and bitter, and evaluated on a 10-point scale. (See Jeon page 7, paragraph 2) If the intensity of each item is absent or weak, 1 point can be given, and when the intensity is felt strong, 10 points can be given. (See Jeon page 7, paragraph 2)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3)
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 95, this claim recites the limitations of Claim 84 and as to those limitations is rejected for the same basis and reasons as recited above and/or is otherwise taught by the rejections above.
Regarding Claim 97, this claim recites the limitations of Claim 96 as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein a first evaluative criteria comprise categories that are similar to attributes on a cupping form and provide context for a description but not descriptive detail.
Further, in addition to the rejections above as if recited herein in full, Jeon further discloses that the cupping form used by the Specialty Coffee Association (SCA) has an evaluation table used by Q-graders and is a conventional coffee evaluation method, however cupping uses technical terms and there is no conceptual explanation for it. (See Jeon page 3, last three paragraphs)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claims 100, this claim recites the limitations of Claim 99 and as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein the coffee evaluation system includes a scoring engine.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system and method include a scoring engine.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – sorts and tallies category, scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings into natural language forms])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 101, this claim recites the limitations of Claim 100 and as to those limitations is rejected for the same basis and reasons as recited above. Further, Borack in view of Jeon discloses the following:
wherein the scoring engine assigns values to individual entries of the attribute descriptors.
Borack discloses his invention as to an online experience for helping determine a user’s taste and teaching the user about coffee and the methods and online experience may also be applicable to other beverages besides coffee. (See Borack para 4)
While Borack discloses a tiered structure of attribute descriptors, providing an intensity for at least some of the attribute descriptors and presenting graphical representations of enjoyment of a particular beverage, indicating a perceived value, which allows a user to assign values to individual entries, Borack does not further disclose that the evaluation system includes a scoring engine that assigns values to individual entries of the attribute descriptors.
Jeon discloses his invention as to a quality prediction system for finished coffee products according to the blending ratio of raw green coffee beans and the characteristics of each roasting degree. (See Jeon, page 3, paras 1-2) The system for predicting the quality of finished coffee products includes a green coffee database for storing evaluation results of raw coffee beans, a single origin database for storing results of characteristics for each roasting degree of raw coffee beans based on an evaluation table for a plurality of characteristics of the raw coffee beans; and a product characteristic prediction module for deriving a quality prediction value of a finished coffee product according to a blending ratio between a plurality of green raw coffee beans and roasting degree of each of the raw coffee beans, based on a single origin database. (See Jeon page 4, para 1)
The evaluation table includes a preference item, an intensity item, a fragrance item and a sour taste type item. (See Jeon page 4, para 2) As an example, the predicted quality value is determined as the sum of values obtained by multiplying the blending ratio by the score for each item according to the roasting degree setting for each raw green coffee bean to be blended. (See Jeon page 4, para 3 – scoring)
In one example, the product characteristic prediction module derives the quality prediction value based on the type, roasting degree, and blending ratio of the raw green coffee beans to be blended determined by the user, and the blended raw coffee beans include first raw green coffee and a second raw coffee bean wherein the quality predicted value is a first sub-prediction value obtained by multiplying the score for each item according to the roasting degree setting of the first raw green coffee bean by a blending ratio, and a score for each item according to the roasting degree setting of the second raw green coffee bean it is determined as a value obtained by adding the second sub-prediction value multiplied by the blending ratio. (See Jeon page 4, paras 3-4 – sorts and tallies category scores). According to an example, the quality prediction value is expressed as a score for each item of the evaluation table. (See Jeon page 4, paras 4-5 – score for each item of the evaluation table [values to individual entries of the attribute descriptors], scoring engine)
The evaluation table 50 may be divided into large items of a preference item, an intensity item, a fragrance item, and a sour taste item [also reflects primary attributes]. (See Jeon page 6, last paragraph) Specifically, the evaluation table 50 includes 8 preference items (fragrance, sour taste, bitter taste, softness, body feeling, aftertaste, balance, overall taste), 2 intensity items (sour taste, bitter taste), fragrance type (flower flavor, fruit flavor), sweet, chocolate, nutty, grassy, etc.) and sour taste (sour, fresh, fresh and astringent, bitter, soft and sweet, sour, week acidity). (See Jeon pages 6-7, last paragraph of page 6 to first paragraph of page 7 – [also reflects sub-attributes]) All these sensory results can be displayed on a 10-point scale and the scored data can be displayed to the user. (See Jeon page 7, first paragraph)
All these sensory results can be displayed on a 10-point scale, and the scored data can be displayed to the user. (See Jeon page 7, paragraph 1) A point of 1 means a score given when a negative preference is felt in the sub-item, and a score of 10 means a score given when a positive preference is felt in the sub-item. (See Jeon page 7, paragraph 1) The score for each item of the evaluation table 50 can be divided into three sections and displayed in different colors, thereby facilitating the identification of scores for each score. (See Jeon page 7, paragraph 1 – scoring engine that calculates values for descriptor strings)
Fragrance can be divided into a total of 7 sub-items: floral, fruity, sweet, chocolate, nutty, grassy and others. (See Jeon page 7, paragraph 3) For example, the floral scent may include rose scent, jasmine scent, and acacia scent. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]) The fruit flavor may include a berry flavor, a citrus flavor, a raisin flavor, a peach flavor, a grape flavor, and an apple flavor. (See Jeon page 7, paragraph 3 – [also reflects sub-sub attributes and processing descriptor strings into natural language forms]). Chocolate flavor may include milk chocolate flavor, dark chocolate flavor, and cacao flavor and the nutty flavor may include a peanut flavor, a walnut flavor, an almond flavor, and a hazelnut flavor. (See Jeon page 7, paragraph 3 [also reflects sub-sub attributes and processing descriptor strings])
The product evaluation module may check the characteristics of the final product produced through the process of evaluating the produced product, and the characteristics of fragrance and acidity. (See Jeon page 8, last paragraph) For example, the product evaluation module 400 may collect data obtained through sensory evaluation of evaluators or measurement devices. (See Jeon page 8, last paragraph) The product evaluation module 400 may store the acquired data according to the evaluation table for each item, and then store the characteristics, fragrance, and acidity characteristics of the final product to which the score is given in the product database 500. (See Jeon page 9, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the online experience for helping determine a user’s taste for coffee of Borack with the scoring features of various descriptors as taught by Jeon in order to provide a more detailed prediction of the quality of a finished coffee product.
Regarding Claim 102, this claim recites substantially similar limitations as seen above and are rejected for the same basis and reasons and/or otherwise disclosed by the prior art.
Regarding Claims 85-88, 91 and 103, there is no prior art rejection being applied at this time. While there are elements that are taught by the prior art, there are also elements that are not disclosed. Examiner cautions that there is significant overlap with items that are also at issue under 112 and will need to revisit the prior art based on corrections and further amendments made.
Relevant Prior Art of Record Not Currently Applied
Fretheim, Ian. A New Tool for the Cupping Kit. Roast Magazine. March/April 2023. Pages 47-63.
Response to Arguments
Applicant's arguments filed April 1, 2026 have been fully considered as further disclosed below.
As to the Claim Objections:
Applicant is thanked for the corrections made to address the claim objections. (See Applicant Arguments dated 04/01/2026, page 8) The objections have been withdrawn.
As to the 101 Rejection:
Applicant traverses the rejection. (Id. at page 9) Examiner notes that the 101 rejection has been updated to account for the extensive amendments made.
Applicant disagrees that the claims are reciting an abstract idea. (Id.) Applicant continues, arguing that the Coffee Rose system and method are not directed to the general concept of evaluating coffee but rather the claims are directed to a specific computer-implemented platform that generates quantitative, algorithmically processed evaluation outputs from structured descriptor-and-intensity inputs. (Id.) Applicant argues that the scoring engine does not merely record what a human thinker has already decided, it computes descriptor string values, weights those values according to the intensity indication, tallies category and sample-level scores, converts descriptor strings to natural language forms and selects top-level descriptors from a dynamically generated pool. (Id.)
Applicant continues asserting these computation operations particularly the dynamic generation and combination of descriptor strings and the intensity-weighting scoring algorithm cannot be practically performed in the human mind. Examiner is of another opinion. Examiner notes that the specification does not disclose an algorithm at all nor a process for converting descriptor strings to natural language forms. The specification notes that a scoring engine processes descriptor strings into natural language forms, however presents no disclosure as to how that occurs. The operations do not necessarily reflect computational actions that cannot be performed in the human mind. The scoring appears to be reflective of the user’s inputs being tallied. Applicant appears to argue Enfish is applicable, however the claims and the invention appear to be drawn to processing data. The process being described is still the evaluation of coffee. This is an abstract idea.
Applicant then continues, arguing that the claims integrate any alleged abstract idea into a practical application and argues that the claimed system represents a specific improvement in computer-implemented data capture, structuring, and analysis for sensory evaluation. (Id. at page 10) Applicant argues that there has been creation of a structured data model that enables automated, algorithmic computation of weighted flavor scores and machine-readable natural-language descriptors and argues that it is purpose-built for computer processing and AI/machine learning ingestion. (Id.)
Applicant then refers to the sole line of the disclosure that even mentions AI applications. The disclosure notes that the Coffee Rose system and method digitalizes data for AI applications like machine learning ingestion and language model processing. (See Applicant Spec page 3, lines 16-18) The Coffee Rose system and method is what is being claimed. The intent for what the digital data may or may not be used for in the future or outside of the invention claimed, without further disclosure, does not provide for an architecture that discloses an improvement to computer functionality – rather the invention discloses collection of data. The specification does not even assert that an architecture is created. This is not analogous to Enfish and is not a persuasive argument.
Applicant then argues that the computer-implemented process produces a particular transformation of data and alleges the claims are analogous to those seen in McRO. (Id. at page 11) McRO dealt with activities that could not be done at all previously – that is simply not the case here. Compiling a user’s input and producing scoring that reflects the user’s input does not create a practical application of the abstract idea. This argument is not persuasive.
Applicant then argues that as a whole the claims recite significantly more. (Id.) Applicant argues that the tiered descriptor structure with intensity weighting is not a well-understood, routine or conventional way to capture sensory evaluations. (Id.) Examiner is of another opinion. The processes disclosed are using a user’s tastes and judgments to evaluate coffee. The underlying idea is still an abstract idea. The processing of the data by scoring and combining values attached to various descriptions are utilizing the information generated by the user to create composite scores. Applicant attempts to argue that this is a specific algorithmic innovation. (Id. at pages 11-12) Aside from the lack of support for a number of the newly recited limitations, there is also improper support to conclude that there is an algorithmic innovation invented by Applicant. The invention does not disclose significantly more than the abstract idea. The 101 rejection is maintained.
Regarding the 103 Rejections:
Examiner has had to apply additional disclosure from the prior art of reference to the claims as amended, as presented in the rejection in chief. While there are some dependent claims that currently do not have prior art applied to them, Examiner cautions against concluding that the prior art has been overcome completely as to all of those dependent claims. There are a number of limitations that are not supported by the specification properly that have been claimed currently that will need to be resolved and reevaluated upon response.
Applicant traverses the rejection. (Id. at page 12) Applicant attempts to argue a piecemeal analysis while also arguing the claims as amended. (Id. at pages 12-13)
In response to applicant's piecemeal analysis of the references, "one cannot show non-obviousness by attacking references individually where, as here, the rejections are based on combinations of references." see In re Keller, Terry, and Davies, 208 USPQ 871, 882 (CCPA 1981). In the instant case, applicant refutes each prior art reference individually, rather than viewing them in combination, in light of the totality of their combined teachings.
Further, Applicant asserts that there is no motivation to combine Borack and Jeon. (Id. at page 13) Examiner disagrees. Both disclosures are related to coffee evaluation and despite Applicant’s argument that a user’s input evaluation versus a trained evaluator’s input are incompatible for purposes of motivation to combine, Examiner is of another opinion. In both cases it is a user’s judgment and evaluation that are utilized to evaluate coffee.
The courts have stated that “[a] suggestion, teaching, or motivation to combine the relevant prior art teachings does not have to be found explicitly in the prior art, as the teaching, motivation, or suggestion may be implicit from the prior art as a whole, rather than expressly stated in the references...The test for an implicit showing is what the combined teachings, knowledge of one of ordinary skill in the art, and the nature of the problem to be solved as a whole would have suggested to those of ordinary skill in the art… there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.” see In re Kahn, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006). Examiner asserts that she has provided such “articulated reasoning” to support the legal conclusion of obviousness.
The 103 rejection, as currently asserted, is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AMBREEN A. ALLADIN/Primary Examiner, Art Unit 3691 June 11, 2026
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