DETAILED ACTION
This non-final rejection is responsive to communication filed June 17, 2025. Claims 1-9 are pending in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 17, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 2, 4, 5, and 7-9 are objected to because of the following informalities: The language “and/or” used in the claim is unclear in some instances. For example, in lines 2-3 of claim 1, it is unclear as to whether the term “unknown” refers to “feedstuff raw material” and “feedstuff”. Similarly, in lines 6-7 of claim 1, it is unclear as to whether the term “known” refers to “feedstuff raw materials” and “feedstuffs.” The examiner suggests using alternative language (i.e. at least one of) for all instances of the language “and/or” to make the claims clearer. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites the following limitations directed to an abstract idea because the broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
For example:
b) transforming absorption intensities of wavelengths or wavenumbers in the spectrum of step a) to produce a query vector (mental process – a user can transform absorption intensities of wavelengths or wavenumbers into a query vector mentally or manually by pen and paper);
wherein an outlier is removed from the set of database vectors (mental process – user can think about removing a pair of vectors from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c1) removing a pair of database vectors being the most dissimilar to each other in a set of database vectors from the set of database vectors (mental process – user can think about removing a pair of vectors from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c1a) calculating a similarity measure and/or a distance measure of each database vector in a set of database vectors to the other database vectors in the set of database vectors to give similarity values of pairs of database vectors (mental process – user can mentally or manually calculate a similarity measure and/or distance measure of each database vector);
c1b) ranking the similarity values obtained in step c1a) in descending order, when a similarity measure is calculated in step c1a), or in ascending order, when a distance measure is calculated in step c1a), wherein in any case the bottom-ranked similarity value relates to the two database vectors being the most dissimilar to each other (mental process – user can mentally rank similarity values using evaluation or thinking about ranking similarity values);
c1c) pairwise removing at least two database vectors with the lowest ranking in step c1b) from the set of database vectors (mental process – user can think about removing pairs of vectors from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c2) removing a database vector being the most dissimilar on average to the other database vectors in a set of database vectors from the set of database vectors (mental process – user can think about removing a vector from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c2a) calculating a similarity measure and/or a distance measure of each database vector in a set of database vectors to the other database vectors in the set of database vectors to give similarity values of each of a database vector to the other database vectors (mental process – user can mentally or manually calculate a similarity measure and/or distance measure of each database vector);
c2b) forming the sum of the similarity values obtained for each database vector in step c2a), and calculating the average similarity value for each database vector (mental process – user can mentally or manually sum similarity values and calculate average similarity value);
c2c) ranking the average similarity values obtained in step c2b) in descending order when a similarity measure is calculated in step c2b), or in ascending order when a distance measure is calculated in step c2b), wherein in any case the bottom-ranked average similarity value relates to the database vector being the most dissimilar on average to all other database vectors (mental process – user can mentally rank similarity values using evaluation or thinking about ranking similarity values);
c2d) removing the database vector with the lowest ranking in step c2c) from the set of database vectors (mental process – user can think about removing a vector from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c3) removing a database vector being the most dissimilar to the centroid of a set of database vectors from the set of database vectors (mental process – user can think about removing a vector from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
c3a) determining the centroid of all database vectors in a set of database vectors (mental process – user can think about which vector is centroid of all vectors);
c3b) calculating a similarity measure and/or a distance measure of each database vector to the centroid of step c3a) to give a similarity value for each database vector to the centroid (mental process – user can mentally or manually calculate a similarity measure and/or distance measure of each database vector);
c3c) ranking the similarity values obtained in step c3b) in descending order when a similarity measure is calculated in step c3b), or in ascending order when a distance measure is calculated in step c3b), wherein in any case the bottom-ranked similarity value relates to the database vector being the most dissimilar to the centroid (mental process – user can mentally rank similarity values using evaluation or thinking about ranking similarity values);
c3d) removing at least the database vector with the lowest ranking in step c3c) from the set of database vectors (mental process – user can think about removing a vector from consideration such as by ignoring or removing from his/her memory; if ranking vectors by similarity on pen and paper, a user can remove vectors by crossing off);
d) calculating a similarity measure and/or a distance measure between the query vector of step b) and each database vector of step c) to give a similarity value for each database vector with the query vector (mental process – user can mentally or manually calculate a similarity measure and/or distance measure of each database vector);
e) ranking the similarity values obtained in step d) in descending order when a similarity measure is calculated in step d), or in ascending order when a distance measure is calculated in step d), wherein in any case the top-ranked database vector has the highest similarity with the query vector (mental process – user can mentally rank similarity values using evaluation or thinking about ranking similarity values);
f) assigning the feedstuff raw material and/or feedstuff of the database vector with the highest similarity in step e) to the sample of step a) (mental process – user can think about assigning by mapping or associating feedstuff raw material and/or feedstuff of the database vector to the sample).
This judicial exception is not integrated into a practical application. The following additional elements, when considered individually and in combination, do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea:
a) providing a near infrared spectrum of a sample of an unknown feedstuff raw material and/or feedstuff (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
c) providing a set of database vectors of a population of spectra of known feedstuff raw materials and/or feedstuffs (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); and
(g) mixing at least two feedstuffs and/or feedstuff raw materials to yield a diet with a specific composition for a specific species based upon the assignment in step f) of the feedstuff raw material and/or feedstuff of the database vector to the sample of step a) (Adding the words “apply it” (or an equivalent) with the judicial exception; merely invoking computers or other machinery as a tool to perform an existing process - MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of providing a near infrared spectrum of a sample of an unknown feedstuff raw material and/or feedstuff and providing a set of database vectors of a population of spectra of known feedstuff raw materials and/or feedstuffs are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Further, the limitation “mixing at least two feedstuffs and/or feedstuff raw materials to yield a diet with a specific composition for a specific species based upon the assignment in step f) of the feedstuff raw material and/or feedstuff of the database vector to the sample of step a)” represents adding the words “apply it” (or an equivalent) with the judicial exception; merely invoking computers or other machinery as a tool to perform an existing process - MPEP 2106.05(f)). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Dependent claim 2 recites “wherein a database vector with a similarity value of 0 is removed from the set of database vectors in step c1b), c2c), and/or c3c)”. This limitation further describes the mental process of removing a database vector, and thus is also directed to a mental process. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 3 recites “wherein the vector in steps b) and c) is a multi-dimensional vector, with each dimension corresponding to an absorption intensity of a specific wavelength or wavenumber”. This limitation further describes the data being manipulated by the judicial exception of claim 1. As such, claim 3 is also drawn to the judicial exception. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 4 recites “wherein a corresponding outlier spectrum is removed from the infrared spectra of known feedstuff raw materials and/or feedstuffs which are to be transformed into the set of database vectors, and the steps cl), c2), and/or c3) are carried out with the infrared spectra of a population of known feedstuff raw materials and/or feedstuffs”. These limitations further describe the mental process of removing a database vector and the data set, and thus are also directed to a mental process. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 5 recites “wherein in step b) and/or c) the absorption intensities of equidistant wavelengths or wavenumbers in a spectrum are transformed to give a vector of a spectrum in step b) and/or c)”. This limitation further describes the mental process of transforming data, and thus is also directed to a mental process. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 6 recites “wherein the distances of the absorption intensities being transformed to vectors in step b) are identical with the distances of the absorption intensities transformed to vectors in step c)”. This limitation further describes the mental process of transforming data, and thus is also directed to a mental process. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 7 recites “wherein the population of spectra of known feedstuff raw materials and/or feedstuffs of step c) comprises at least 50 spectra of samples of each feedstuff raw material and/or feedstuff from each of its global growing areas”. This limitation further describes the data provided. The judicial exception is not integrated into a practical application because providing data represents adding insignificant extra-solution activity to the judicial exception. Further, the additional element is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(d)(II) indicates that merely “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Even when considered in combination, this additional element represents insignificant extra-solution activity, which do not provide an inventive concept.
Dependent claim 8 recites “wherein step e) comprises: e1) counting the number of occurrence of each of the feedstuff raw materials and/or feedstuff among the top-ranked database vectors in the ranking of step e), wherein said number of occurrences is indicated by the variable N; e2) weighting the first N similarity values of each of the feedstuff raw materials and/or feedstuffs according to their position in the ranking of step e1) to give weighted rank positions of each of the feedstuff raw materials and/or feedstuffs; and e3) forming the sum of the weighted rank positions of step e2) for the feedstuff raw materials and/or feedstuffs to give scores of each of the feedstuff raw materials and/or feedstuffs, wherein the highest score indicates the highest similarity”. These limitations are all directed to mental processes as a user can mentally or manually count occurrences, weight values and calculate a sum. There are no additional elements and therefore the judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 9 recites “wherein step a) comprises recording a near infrared spectrum of a sample of an unknown feedstuff raw material and/or feedstuff”. This additional limitation is not integrated into a practical application because it represents insignificant extra solution activity. Further, the additional element is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(d)(II) indicates that merely “storing and retrieving information in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Even when considered in combination, this additional element represents insignificant extra-solution activity, which do not provide an inventive concept.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 May 30, 2026
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