DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant application on 07/30/2021.
Response to Amendment
Applicant's amendment and argument filed 06/30/2025, in response to the final rejection, are acknowledged and have been fully considered. Any previous rejection or objection not mentioned herein is withdrawn.
Claims 12-13, 15-18 and 20-28 are pending of which claims 20-21 remain withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 09/05/2023.
This office action is in response to the amendment filed on February 23, 2026. Claims 12-13, 15-18 and 22-28 are being examined on the merits.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 12-13, 16, 22-25 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Franklin (WO2000035467A1), Kunle et. al. (Standardization of herbal medicines – A review, Int. J. Biodvers. Conserv., Vol. 4(3), pp. 101-112, March 2012), Takeuchi (US20020111769A1) all from the previous rejection and Nishida Yoshiharu (JP2017041063A). This is a new rejection based on the amendments filed on 02/23/2026.
Franklin’s general disclosure is to standardized botanical products and methods of preparing those products (see abstract).
Franklin teaches “A method for producing a standardized botanical comprising: determining an amount of marker substance to be contained in a botanical product, wherein at least a portion of said marker substance is provided by a botanical material; determining the amount of botanical material to be contained in the botanical product; analyzing the content of said marker substance in a sample of said botanical material; adding a sufficient amount of a dosage modifying material to said botanical material to provide a standardized botanical having the predetermined amount of maker substance” (see claim 1).
Franklin also teaches “for purposes of the present invention, the term "marker substance" is any substance naturally found in a botanical which can be measured directly or indirectly by appropriate analytic techniques, e.g., bioassays, gas or liquid chromatography, ultraviolet spectrophotometry, etc.” (see page 7 at line 11). Franklin also teaches that the botanical preparations have one or more marker substances (see filed on invention, 1st para.).
In this context and the broadest reasonable interpretation, Franklin teaches determining signal intensities (determining an amount of marker substance via chromatography) for phytonutrients (material found in a botanical) in two or more batches by means of a detector. Franklin teaches identifying at least one phytonutrient (material found in botanical) which makes a contribution to the variance and identifying the marker.
Franklin teaches “a sufficient amount of the dosage modifying material is added to adjust the content of the marker substance in the standardized botanical such that a unit dose of standardized botanical containing the appropriate predetermined amount of botanical to be included in a single dosage form also contains the predetermined amount of marker substance, wherein the botanical material contributes at least a portion of the marker substance content” (see bottom of page 8 and top of page 9).
Franklin teaches using liquid chromatography and ultraviolet spectrophotometry (see detailed description, 3rd para.).
Franklin also teaches wherein said dosage modifying material is a plant extract containing said marker substance (see claim 2).
Franklin does not specifically teach setting one or more limit values which are smaller than the natural span (variance marker) and mixing at least two batches, wherein at least one limit value is taken into consideration by means of a mixture calculator.
Kunle’s general disclosure is a review of standardized herbal medicines (see report).
Kunle teaches methods of analyzing active constituents in herbal medicines via LC-MS. Specifically Kunle teaches “TLC, HPLC, GC, quantitative TLC (QTLC), and highperformance TLC (HPTLC) can determine the homogeneity of a plant extract. Over-pressured layer chromatography (OPLC), infrared and UV-Visible spectrometry, MS, GC, liquid chromatography (LC) used alone, or in combinations such as GC-MS and LC-MS, and nuclear magnetic resonance (NMR), electrophoretic techniques, especially by hyphenated chromatographic techniques, are powerful tools, often used for standardization and to control the quality of both the raw material and the finished product. The results from these sophisticated techniques provide a chemical fingerprint as to the nature of chemicals or impurities present in the plant or extract (WHO, 2002c). Based on the concept of photo equivalence, the chromatographic fingerprints of herbal medicines can be used to address the issue of quality control” (see 2nd para. of page 110).
Kunle also teaches for standardization of herbal medicines this involves adjusting the herbal drug preparation to a defined content of a constituent or a group of substances with known therapeutic activity by adding excipients or by mixing herbal drugs or herbal drug preparations” (see page 107, standardization of herbal medicines).
Kunle teaches “when the active principles are unknown, marker substances should be established for analytical purposes and standardization. Marker substances are chemically defined constituents of an herbal drug that are important for the quality of the finished product. Ideally, the chemical markers chosen would also be the compounds that are responsible for the pharmacological effects in the body. There are two types of standardization. In the first category, “true” standardization, a definite phytochemical or group of constituents is known to have activity. Ginkgo with its 26% ginkgo flavones and 6% terpenes is a classic example. These products are highly concentrated and no longer represent the whole herb, and are now considered as phytopharmaceuticals. In many cases they are vastly more effective than the whole herb. However, the process may result in the loss of efficacy and the potential for adverse effects and herb–drug interactions may increase. The other type of standardization is based on the guarantee of the manufacturers for the presence of a certain percentage of marker compounds which are not indicators of therapeutic activity or quality of the herb” (see page 107, standardization of herbal medicines).
Takeuchi’s general disclosure is to a device, method and medium for predicting a probability of an occurrence of data using density calculators (see abstract).
Takeuchi teaches using density or mixture calculators can reduce logarithmic loss (see abstract) and teaches that this Bayes calculation can improve upon the Jeffery’s procedure which can accomplish a minimax property even when logarithmic regret is used a performance measure instead of redundancy (see 0011).
Takeuchi teaches using a mixture calculator for calculating approximation values of a Bayes mixture (see claim 1).
Yoshiharu’s general disclosure is to data analysis methods.
Yoshiharu teaches of a data analysis method for calculating principal components by processing a large amount of data obtained by sampling with a small amount of calculation (see abstract). Yoshiharu teaches that “principal component analysis (PCA) is a mathematical method for transforming original observations that are correlated between variables into values called uncorrelated principals using orthogonal rotation” (see page, para. 2).
Yoshiharu teaches prior methods for these calculations would be time consuming and the amount of data can be cumbersome (see page 5, para. 7) and also teaches wherein calculating a principal component vector based on the plurality of data obtained from sampling along with a high-pass filter and recurrence calculation using the data after the filtering process solves the issue of the PCA being time consuming (see page 10 and process of pages 6-8).
Therefore, it would have been obvious to persons skilled in the art before the effective filing date to carry out the steps of the instant invention in order to obtain a plant material having reduced variance in phytonutrient content, because Franklin and Kunle teach the steps of producing standardized botanicals using a detector (GS-MC). Utilizing a GS-MC would be the process for determining a plurality of signal intensities for the phytonutrients and identifying those botanical materials that contain less than the predetermined amount (or as instantly worded, makes a contribution, preferably the greatest contribution to the variance). Kunle teaches, in terms of standardizing herbal medicines, adjusting the herbal drug preparation to a defined content of constituents or a group of substances with known therapeutic activity by adding excipients or by mixing herbal drugs or preparations. A person skilled in the art or anyone wanting to control the variance of the specific component (phytonutrient) would want to mix the same extracts, herbal components, or starting material (batches) in with that of the component which is causing the variance in order to bring the outlier closer to the predetermined and acceptable levels. Thus, setting one or more limit values which is/are smaller than the “natural span” or variance marker especially one which makes the greatest contribution to the variance would have been obvious because one is trying to reduce the statistical noise. This process of standardizing batches with different variances by mixing different batches together is known in the art. Also, determining 5 or 10 batches of signal intensities would have been obvious to persons skilled in the art in order to obtain more information at once and would allow for faster data analysis.
It would have also been obvious to use a mixture calculator for determining how to mix the batches because that is what those particular calculators are utilized for. Additionally, Takeuchi teaches using mixture calculators for reducing logarithmic loss.
It would have been obvious to divide the signals into subranges and summing the signal intensities within each subrange in order to group phytonutrients together for therapeutic control and batch processing. This would cut down on the time it would take in having to determine each individual signals intensity and would help streamline batch processing.
It would have been obvious to use LC-MS to display signal intensities in a chromatogram in dependence of retention times because these are commonly used in the art and as Kunle teaches can be used to address issues of quality control through interpretation of chromatographic fingerprints (signal intensities).
It would have also been obvious to use PCA as a statistical means for displaying variance in batches because this process is known in the art for transforming original observations that are correlated between variables into values called uncorrelated principals using orthogonal rotation and Yoshiharu teaches of ways in which to analyze through means of PCA with advanced time saving calculations as can be appreciated from Yoshiharu’s entire patent document.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Franklin (WO2000035467A1, from IDS), Kunle et. al. (Standardization of herbal medicines – A review, Int. J. Biodvers. Conserv., Vol. 4(3), pp. 101-112, March 2012), Takeuchi (US20020111769A1) and Nishida Yoshiharu (JP2017041063A), as applied to claims 12-13, 16 and 22-25 above, and further in view of Likic (Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS), BioData Min, V.2: 6; Oct 12, 2009). This is a new rejection based on the amendments filed on 02/23/2026.
Franklin and Kunle each teach the method for producing plant material having reduced variance in phytonutrient content, however are silent on the signal intensities being at least 100, 200, or 300 or more for batch determination. Yoshiharu’s data analysis was discussed above.
Likic teaches “The ever-increasing scope of GC-MS applications is opening new challenges in data processing and analysis [3,6,28]. GC-MS experiments on complex biological and environmental samples may result in hundreds of signals and the detection of many compounds in parallel. For example, Fiehn and co-authors have quantified 326 metabolites in Arabidopsis thaliana leaf tissue extracts” (see background).
Therefore, it would have been obvious to any person having skill in the art before the effective filing date to collect at least 100, 200, or 300 signal intensities or more during GC-MS because as Likic teaches biological plant samples may result in hundreds of signals. Plant materials are known to contain many compounds which can be detected by GC-MS.
Claim 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Franklin (WO2000035467A1, from IDS), Kunle et. al. (Standardization of herbal medicines – A review, Int. J. Biodvers. Conserv., Vol. 4(3), pp. 101-112, March 2012), Takeuchi (US20020111769A1) and Nishida Yoshiharu (JP2017041063A), as applied to claims 12-13, 16 and 22-25 above, and further in view of Christie (The analysis of evening primrose oil, Industrial Crops and Products, Volume 10, Issue 2, September 1999, pages 73-83). This is a new rejection based on the amendments filed on 02/23/2026.
Franklin and Kunle each teach the method for producing plant material having reduced variance in phytonutrient content, however are silent on the plant material being Primula. Yoshiharu’s data analysis was discussed above.
Christie’s general disclosure is to the analysis of evening primrose oil using gas chromatography (see abstract).
Christie teaches “Evening primrose oil (Oenothera biennis) is being used in increasing amounts in nutritional and pharmaceutical preparations, and there are claims that it may alleviate various chronic disease states” (see introduction).
Christie also teaches extracting oils from the seeds of primrose and thus this would constitute a plant extract (see page 74, 2 extraction of seeds- oil content and composition).
Christie teaches the use of gas chromatography in separation of components of primrose (see Figure 1).
Therefore, it would have been obvious to any person having skill in the art before the effective filing date to select primrose, a plant of the Primula genus, because as Christie teaches, primrose is being used increasingly in nutritional and pharmaceutical preparations used in treating various diseases and gas chromatography would be useful in separating out specific components which are beneficial for such preparations.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Franklin (WO2000035467A1, from IDS), Kunle et. al. (Standardization of herbal medicines – A review, Int. J. Biodvers. Conserv., Vol. 4(3), pp. 101-112, March 2012), Takeuchi (US20020111769A1) and Nishida Yoshiharu (JP2017041063A), as applied to claims 12-13, 16 and 22-25 above, and further in view of Brems (A One-Stop Shop for Principal Component Analysis, (https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c)). This is a new rejection based on the amendments filed on 02/23/2026.
Franklin and Kunle each teach the method for producing plant material having reduced variance in phytonutrient content, however are silent on using principal component analysis (PCA). Yoshiharu’s data analysis was discussed above.
Brems’ general disclosure is a web-based article on using principal component analysis (see article).
Brem teaches “principal component analysis is a technique for feature extraction— so it combines our input variables in a specific way, then we can drop the “least important” variables while still retaining the most valuable parts of all of the variables!
As an added benefit, each of the “new” variables after PCA are all independent of one another.
This is a benefit because the assumptions of a linear model require our independent variables to be independent of one another. If we decide to fit a linear regression model with these “new” variables (see “principal component regression” below), this assumption will necessarily be satisfied” (see page 2 at bottom).
Brem teaches “finally, we make an assumption that more variability in a particular direction correlates with explaining the behavior of the dependent variable. Lots of variability usually indicates signal, whereas little variability usually indicates noise. Thus, the more variability there is in a particular direction is, theoretically, indicative of something important we want to detect” (see page 7, at bottom).
Therefore, it would have been obvious to any person having skill in the art before the effective filing date to use principal component analysis for the process in analyzing and visualizing the variables of phytonutrients within plants because this method can help point out the outliers or markers with the greatest contribution to the variance. It is a method known and commonly used for such purposes.
Claims 26 and 28 are under 35 U.S.C. 103 as being unpatentable over Franklin (WO2000035467A1, from IDS), Kunle et. al. (Standardization of herbal medicines – A review, Int. J. Biodvers. Conserv., Vol. 4(3), pp. 101-112, March 2012), Takeuchi (US20020111769A1) and Nishida Yoshiharu (JP2017041063A) as applied to claims 12-13, 16, 22-25 and 27 above, and further in view of Richard Simpson et. al. (High-performance liquid chromatography-time-of-flight mass spectrometry and its application to peptide analyses, Journal of Chromatography, 536 (1991) 143-153). This is a new rejection based on the amendments filed on 02/23/2026.
Franklin, Kunle and Takeuchi’s combined art teach the instant invention however are silent on using a HPLC/ToF-MS. Yoshiharu’s data analysis was discussed above.
Simpson’s general disclosure is a report on HPLC/ToF-MS (see abstract).
Simpson teaches that high-performance liquid chromatography has been successfully interfaced on-line with liquid secondary-ion time-of-flight mass spectrometry, utilizing a continuous-flow rate interface. Time-of-flight mass spectrometry is a low-resolution, high-mass-range technique, compatible with extremely rapid data acquisition rates. Thus, a TOF-MS system is extremely well suited for coupling with HPLC. This technique can resolve unresolved analytes on the chromatographic column (see abstract).
Therefore, it would have been obvious to persons having skill in the art before the effect filing date to utilize HPLC/ToF-MS as discussed by Simpson, because these can resolve unresolved analytes on the chromatographic column and because it has extremely rapid data acquisition rates.
Response to Arguments
Applicant's arguments filed 02/23/2026 have been fully considered but they are not persuasive. The applicant argues that the art does not teach “identifying, from a plurality of signals, at least one signal as a variance marker, which makes the greatest contribution to the variance of the plurality of signal intensities of the two or more batches, and identifying natural span of the identified at least one signal”. This is commonly done in the art for batch production. As discussed above, a person skilled in the art or anyone wanting to control the variance of the specific component (phytonutrient) would want to mix the same extracts, herbal components, or starting material (batches) in with that of the component which is causing the variance in order to bring the outlier closer to the predetermined and acceptable levels. Thus, setting one or more limit values which is/are smaller than the “natural span” or variance marker especially one which makes the greatest contribution to the variance would have been obvious because one is trying to reduce the statistical noise. Determining the signals on a GS-MC would be the process for determining a plurality of signal intensities for the phytonutrients and identifying those botanical materials that contain less than the predetermined amount (or as instantly worded, makes a contribution, preferably the greatest contribution to the variance). A GS-MC readout is in “signals” and determining which signals are outliers is well within the purview of a skilled artisan’s abilities during batch production. Any outlier can be the applicant’s broadly claimed marker.
The applicant argues that the prior art alone does not teach “which markers make a contribution to the variance”, however the applicant fails to recognize that it is not the prior art alone being taken into consideration. The knowledge of those skilled in the art is also taken into context and those skilled in the art can recognize the markers making a contribution to variance. The applicant’s claims are so broad that they do not describe any particular “marker” and they believe that since the prior art is not worded in such a manner that is being claimed that the art is somehow different than what is being claimed. Batch production is the process the applicant is claiming in a manner of using their own language. The process is obvious given the art and the knowledge to persons having ordinary skill in the art (PHOSITA). Determining outliers is common and conventional in the art.
The applicant argues that it is unclear why a person would combine Brem with Franklin and Kuenle. Kuenle teaches as previously discussed that the least important variables can be dropped while still retaining the most valuable parts of all of the variables and as an added benefit, each of the new variables after PCA are all independent of one another.
This is a benefit because the assumptions of a linear model require our independent variables to be independent of one another. If we decide to fit a linear regression model with these new variables this assumption will necessarily be satisfied. Furthermore it is a tool that is commonly used and one in which Franklin and Kuenle could use PCA as a statistical means for displaying variance in batches and for processing a large amount of data obtained by sampling with a small amount of calculation.
Conclusion
Currently no claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB ANDREW BOECKELMAN whose telephone number is (571)272-0043. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anand Desai can be reached at 571-282-0947. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JACOB A BOECKELMANExaminer, Art Unit 1655
/ANAND U DESAI/Supervisory Patent Examiner, Art Unit 1655
Prosecution Insights