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 .
Claim Objections
Claim 9 is objected to because of the following informalities: minor incomplete sentence.
Claim 9 recite “wherein the method comprises refining, by the second machine learning model, the secondary formula using the …” It seems the sentence does not finish.
Appropriate correction is required.
Claim 18 is objected to because of the following informalities: redundant limitations.
Claim 17 and claim 18 have the same limitations.
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 1-19 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-9, 10-18, 19 are directed to a method, system and medium of suggesting a formula for a product. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 10 and 19 these claims recite
obtaining a plurality of ingredients with at least one desired function inputted by a user associated with a user device for a primary formula or at least one first ingredient selected by the user from a set of ingredients suggested by a first machine learning model for a primary formula;
suggesting, by a second machine learning model, a list of related ingredients that have a causal relationship with the primary formula, wherein the second machine learning models trained based on causal relationships between features of historical ingredients or combinations of the historical ingredients using a second set of rules, for a product associated with a product category;
processing a selection, of at least one second ingredient from the list of related ingredients by the user, to obtain a secondary formula that comprises either the at least one first ingredient or the plurality of ingredients, along with the at least one second ingredient; and
suggesting, using a third machine learning model, a concentration for the secondary formula that performs the at least one desired function for the product category, wherein the third machine learning model is trained by correlating the historical ingredients with historical concentrations and historical desired functions based on a third set of rules, thereby refining the primary formula.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0049]-[0056], Fig. 1, etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 10 and 19 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 10 and 19: The claims do not include additional elements, alone or in combination, 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 amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options).
For example, claim 1 recites the additional elements of “obtaining…”, “suggesting…”, “processing…”, “suggesting…” etc. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 11
Claim 2, 11 merely recite other additional elements that receiving user input and determining primary function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 12
Claim 3, 12 merely recite other additional elements that define validating the formula which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 13
Claim 4, 13 merely recite other additional elements that define ranking the ingredients which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 14
Claim 5, 14 merely recite other additional elements that define ranking the concentration of each ingredients which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 15
Claim 6, 15 merely recite other additional elements that define refining the primary formula which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 16
Claim 7, 16 merely recite other additional elements that define refining the primary formula which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 17, 18
Claim 8, 17, 18 merely recite other additional elements that define validating the secondary formula which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9
Claim 9 merely recite other additional elements that define refining the secondary formula which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hutchinson et al. (Hutchinson) US 10984145.
In regard to claim 1, Hutchinson disclose A processor-implemented method for automatically suggesting a formula for a product based on a user preference and a desired function using machine learning models, comprising: (col. 2, line 32-49. Col. 5, line 7-col. 48, Method of predicting a formulation desired by the user based on user input and desired performance specification and target properties and target constraints using ML model)
obtaining a plurality of ingredients with at least one desired function inputted by a user associated with a user device for a primary formula or at least one first ingredient selected by the user from a set of ingredients suggested by a first machine learning model for a primary formula; (col. 2, line 32-49. Col. 5, line 7-col. 48, user input candidate formulation recipes with a client device with desired performance specification that use the ingredients)
suggesting, by a second machine learning model, a list of related ingredients that have a causal relationship with the primary formula, wherein the second machine learning models trained based on causal relationships between features of historical ingredients or combinations of the historical ingredients using a second set of rules, for a product associated with a product category; (col. 2, line 10-30, col. 3, line 26-col. 6, line 59, col. 10, line 12-35, training the ML model to predict ingredients that have associated properties with the formulation, and the ML model is trained based on the associated properties between the features of the historical training data of the ingredients using its role in formulation for a product with a category (such as mechanical properties with tear strength, etc. chemical property, with colorant, etc.)
processing a selection, of at least one second ingredient from the list of related ingredients by the user, to obtain a secondary formula that comprises either the at least one first ingredient or the plurality of ingredients, along with the at least one second ingredient; (col. 6, line 39-col. 8, line 49, col. 9, line 60- col. 10, line 35, selecting ingredients from the list of ingredients by the user based on the target constraints to obtain a new recipe that have the ingredients maybe with different percentage amount)
and
suggesting, using a third machine learning model, a concentration for the secondary formula that performs the at least one desired function for the product category, wherein the third machine learning model is trained by correlating the historical ingredients with historical concentrations and historical desired functions based on a third set of rules, thereby refining the primary formula. (col. 4, line 4-col. 6, line 3, col. 6, line 40-col. 8, line 49, col. 9, line 60- col. 10, line 35, using the ML model, predicting percentages of the ingredients for the new recipe that meet the property constraint for the mechanical or chemical properties, the model is trained by using the historical training data with the percentages and properties of ingredients based on its role or physical, chemical, mechanical properties to define the formula. Note: please further define the ML models and the set of rules, etc. to help move forward the prosecution, call to discuss if necessary)
In regard to claim 2, Hutchinson disclose The processor-implemented method of claim 1,
Hutchinson disclose wherein the set of ingredients is suggested by
(i) receiving, using a user device, an input from the user, wherein the input of the user comprises a product category, at least one user preference, and the at least one desired function; (col. 2, line 10-49-col. 3, line 44, col. 5, line 7-col. 48, user input candidate formulation recipes with a client device with desired performance specification that uses the ingredients with target constraints and properties (chemical, mechanical or physical, etc.)
(ii) determining, using the first machine-learning model, at least one primary function for the product category and suggesting the set of ingredients for the product category based on the at least one user preference and the at least one primary function, (col. 2, line 32-49. Col. 3, line 26-col. 6, line 3, determine, using the ML model, the target constraints for the target properties and predicting the ingredients based on the user inputted desired performance specification that use the ingredients and the target properties constraints) wherein the first machine learning model is trained by correlating historical products associated with historical product categories with the historical ingredients associated with the historical products based on a first set of rules; (col. 2, line 32-49. Col. 3, line 26-col. 6, line 3, the model is trained by the historical recipes associated with chemical, physical, mechanical, etc. categories with the ingredients associated with the recipes based on the target properties with target constraints and role of the ingredients, etc.) and
(iii) processing the selection of the at least one first ingredient from a set of suggested ingredients, to obtain the primary formula that comprises the at least one first ingredient. (col. 2, line 10-30, col. 3, line 26-col. 6, line 59, col. 10, line 12-35, selecting the ingredients from the predicted ingredients to obtain the target recipe what include the desired ingredients)
In regard to claim 3, Hutchinson disclose The processor-implemented method of claim 2,
Hutchinson disclose wherein the method comprises validating the primary formula by determining a performance rate of the primary formula based on the at least one user preference, a safety score, a stability index, a claim association, or an innovation index of the at least one first ingredient or the plurality of ingredients of the primary formula by applying a fourth set of rules on the primary formula. (col. 5, line 7-col. 6, line 34, evaluating the formula by determining a performance rate of the formula based on user inputted target properties, target constraints, for the ingredients of the recipe)
In regard to claim 4, Hutchinson disclose The processor-implemented method of claim 1,
Hutchinson disclose wherein the method further comprises ranking the list of related ingredients that are matched with the primary formula based on the causal relationship with the primary formula. (col. 5, line 7-col. 6, line 34, ranking the ingredients in each formula that by determining a performance rate of the formula corresponding to the target recipe)
In regard to claim 5, Hutchinson disclose The processor-implemented method of claim 1,
Hutchinson disclose wherein the method further comprises ranking the concentration of each ingredient in the secondary formula to perform the at least one desired function of the product category. (col. 5, line 7-col. 6, line 34, ranking the percentage of each ingredient in the formula to perform the desired properties for the category)
In regard to claim 6, Hutchinson disclose The processor-implemented method of claim 3,
Hutchinson disclose wherein the method comprises refining, by the second machine learning model, the primary formula by suggesting the list of related ingredients if the performance rate of the primary formula is below a threshold level. (col. 5, line 7-col. 7, line 6, using the ML model, adjust the formula based on the user desire with amount of different ingredients, properties and roles in the formulations, by listing ingredients and checking the performance rate whether meets the requirement or confidence level or not)
In regard to claim 7, Hutchinson disclose The processor-implemented method of claim 6,
Hutchinson disclose wherein the method comprises refining, by the second machine learning model, the primary formula by suggesting the list of related ingredients if the at least one first ingredient of the primary formula is not relevant to the at least one user preference or if the user does not satisfy with the at least one first ingredient of the primary formula that is validated. (col. 5, line 7-col. 7, line 6, using the ML model, adjust the formula based on the user desire if one ingredient’s performance rate not meet the requirement or user confidence level)
In regard to claim 8, Hutchinson disclose The processor-implemented method of claim 1,
Hutchinson disclose wherein the method comprises validating, by applying the fourth set of rules, the secondary formula by determining the performance rate of the secondary formula based on the at least one of the user preference, the safety score, and the stability, the claim association, and the innovation index of either the at least one first ingredient or the plurality of ingredients, along with the at least one second ingredient. (col. 5, line 7-col. 6, line 34, evaluating the formula by determining a performance rate of the formula based on user inputted target properties, target constraints, for the ingredients of the recipe)
In regard to claim 9, Hutchinson disclose The processor-implemented method of claim 8,
Hutchinson disclose wherein the method comprises refining, by the second machine learning model, the secondary formula using the if the user is not satisfied with the secondary formula that is validated or if the performance rate of the secondary formula is below the threshold level. (col. 5, line 7-col. 7, line 6, using the ML model, adjust the formula based on the user desire with amount of different ingredients, properties and roles in the formulations, if one ingredient’s performance rate not meet the requirement or user confidence level until with high confidence or user desired target properties and target constraints)
In regard to claims 10-17, 18, claims 10-17, 18 are system claims corresponding to the method claims 1-8, 8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-8, 8.
In regard to claim 19, claim 19 is a medium claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20210010993 A1 2021-01-14 SHIBATA et al.
USE OF SOIL AND OTHER ENVIRONMENTAL DATA TO RECOMMEND CUSTOMIZED AGRONOMIC PROGRAMS
SHIBATA et al. disclose systems and methods for classifying a microbiome at a geographic site where agricultural activity is, or will be, conducted in order to improve and/or promote agricultural productivity at the site. The subject invention utilizes a large sample set of diverse DNA sequencing input data collected from soil, plant, water and/or air samples, as well as environmental information, to determine relationships between environmental factors and the identities, quantities and distributions of microbial species, taxonomies, and/or groupings thereof, at a chosen site. Machine learning and/or artificial intelligence classifier tools use this information to generate various output data, such as, for example, recommendations for customized soil and/or crop treatment compositions, irrigation practices, and/or other agricultural activity, to enhance plant health and crop productivity at the site... see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights