Detailed Action
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The Preliminary Amendment filed 1/30/24 has been entered. Claims 1-15 are pending.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more and thus is directed to non-patentable subject matter. Specifically, the claims are directed toward the judicial exception of an abstract idea without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. 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 amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts.
STEP 1:
Per Step 1 of the two-step analysis, the claims are determined to include an apparatus (independent claim 1), a method (independent claim 14), and a non-transitory storage medium (independent claim 15) respectively and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
Step 2A, Prong 1:
The independent claims recite:
“reception process of receiving a request pertaining to new cosmetic product development” (A person can read, hear or receive a message requesting to make or develop some type of product including a cosmetic product);
“an inference process of inferring a production method for producing a new product conforming to the request based on the request and a learned model which has learned a relation between a component and efficacy of an existing product which is an existing cosmetic product, and a method for producing the existing product” (A person can mentally infer a method to produce some product based on certain specification details and a particular learned procedure that one has learned between a working component of an existing product and method for producing it);
“an output process of outputting information indicating the production method which has been inferred in the inference process” (A person can say or write with a pen and paper information describing the production method they inferred).
Claims 14 and 15 recite the same features and the same analysis applies.
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls under the mental process grouping of abstract ideas. Accordingly, these claims recite an abstract idea.
Regarding dependent claim 2, in addition to that mentioned for claim 1, a person can receive specification information in the request including a level of efficacy demanded of the product; a person can infer “basis” information about an existing product that has the same efficacy that is demanded in the request and say or write down the basis information.
Regarding dependent claims 3, in addition to that mentioned for claim 2, a person can infer information for producing an existing product which has the same efficacy as that demanded in the request.
Regarding dependent claims 4, in addition to that mentioned for claim 1, a person can use a pen and paper to draw a graph in which products are represented by nodes which indicate qualities such as production method, component, or efficacy (note the alternative language and even just labeling one of these suffices) for an existing product (note also one or more existing products is recited and this may just be one), and edges which indicate a relationship between the nodes. A person may mentally study this to learn a relationship in a given quality between two existing products.
Regarding dependent claim 5, in addition to that mentioned for claim 4, the request received by a person may include information such as efficacy that is demanded of the product, and a person can see in the existing product graph elements corresponding to a node and link which indicate efficacy of an existing product, and infer from that a production method to produce a new product having the same efficacy. For example, a person can infer to use the same production method that was used in making the existing product which has that efficacy.
Regarding dependent claim 6, 9, 12, and 13, in addition to that mentioned for claim 4, note that the link prediction may be performed by statistical calculations to determine correlation predictions. A person can mentally perform these calculations with pen and paper, especially given that the claim doesn’t specify more than two nodes for each of the two graphs. It need not matter the label of the graph such as base graph, existing graph, etc. or what particular property represented by a node is being correlated. Furthermore, a person can infer a production method or a product based on the one with the highest probability, or based on a predicted node with the highest calculated correlation value.
Regarding dependent claims 7, in addition to that mentioned for claim 6, a person can mentally identify an existing product that has a property conforming to the request, by analysing the nodes and links in the existing product graph which represent an existing product, and seeing which has that property.
Regarding dependent claims 8, as mentioned for claim 6, a person can mentally with pen and paper carry out statistical calculations to determine which existing product has a minimum correlation to a new product and categorize that existing product as being similar. A person can then predict a property of a new product by seeing which property the similar existing product has in the existing product graph.
Regarding dependent claims 10, in addition to that mentioned for claim 9, a person can mentally with pen and paper carry out statistical calculations to determine which existing product has a minimum correlation to a base product and categorize that existing product as being similar. A person can then identify which similar existing product has at least one property demanded of the new product.
Regarding dependent claim 11, in addition to that mentioned for claim 9, a person can mentally group the elements which are included in any production method (such as the one for producing the existing product that was identified in the link statistical prediction calculation) and then see which element from that group is not also included in the group of elements used for producing the base product. This may be performed by seeing which elements are in both groups (the intersection) and seeing which of the elements in the existing product group is not in the intersection. A user can then infer or label that element an element of a production method for the new product.
All these claim features may be accomplished by applying particular calculations, groupings, inspection, and general manipulation of data. The invention is thus directed to 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.
Step 2A, Prong 2
This judicial exception is not integrated into a practical application. This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The processor carrying out the process steps, the computer carrying out the method steps, and the computer readable non-transitory storage medium storing a program causing a computer to carry out the process steps, are using a generic computer to gather data and thus are mere instructions to apply the judicial exception using generic computer.
In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. See MPEP 2106.05. It is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using any generic computer. See MPEP 2106.05(f). The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Thus, under Step 2A, the Examiner holds that the claims are directed to concepts identified as abstract ideas.
STEP 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Regarding the processor carrying out the process steps, the computer carrying out the method steps, and the computer readable non-transitory storage medium storing a program causing a computer to carry out the process steps, these insignificant extra solution activities are well understood routine and conventional activities. See 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.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 112
5. 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.
6. Claim 4 is rejected under 35 U.S.C. 112(b) 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 4 recites “the learned model is an existing product graph” but a product graph is not a learned model. A product graph is a data structure that maps out items as nodes and how they relate to each other as edges. The learning model may use a product graph, and that is how this feature will be interpreted for purposes of examination. Appropriate correction however is still required.
Claim Rejections - 35 USC § 103
7. 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.
8. Claim(s) 1-5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Um et al “Um” (KR 20210086136 A) and Sunkle et al “Sunkle” (US 2022/0012287 A1).
(Please see the attached copy of Um which numbers paragraphs in the same manner as that used in this Action).
9. Regarding claim 1, Um shows a cosmetic production assistance apparatus, comprising at least one processor (para 69 shows the artificial neural network apparatus, including a processor, which helps in the manufacturing of cosmetics), the at least one processor carrying out a reception process of receiving a request pertaining to new cosmetic product development (para 82-83, 121, 158 show receiving data research information prompting/requesting to develop a new cosmetic product, para 147-148 show the user interface in which some information may be inputted to then develop the cosmetic product, and para 152 infers components of the product to be developed based on receiving target characteristics/features); an inference process of inferring the production materials for producing a new product conforming to the request based on the request and a learned model (para 121-123, 132 show an inference process to infer the materials/ingredients for producing a new cosmetic product based on the inputted information and a learned model, para 100-101 show how the model was trained, and para 152, 154, 157 show inferring the components and materials to generate the cosmetic product) which has learned a relation between (i) a component and efficacy of an existing product, which is an existing cosmetic product and (ii) the materials for producing the existing product (para 83, 121, 123, 125, 135 show the learned relation is between a component and efficacy of an existing cosmetic product and the materials for producing it); and an output process of outputting information indicating the production materials which have been inferred in the inference process (para 117, 147-148 show the display and interface for outputting cosmetic component and material information, para 139-140, 154, 158 also show outputting the materials to produce the cosmetic product). As explained above Um shows that the learned relationship, inference, and output is for production materials and components, but Um does not explicitly show this is for a production method per se. However, Um para 154, 157 does mention a process method may also be shown in addition to the materials and components. Furthermore, Sunkle para 24, 29, 73 shows determining production methods for new cosmetic products in addition to production components [which is shown in Sunkle para 21, 26, 37]; and Sunkle para 36, 54, 73 show how learning models and learned relationships are used to determine the production methods. Para 27-28 show how the determined production methods may be output). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to infer and output the production method in addition to the production materials like that determined in Sunkle, based on learned relationships regarding the production method, in the cosmetic production assistance apparatus of Um, because it would provide a complete and efficient way to use learned relationships to infer how to produce a cosmetic product.
10. Regarding claim 2, in addition to that mentioned for claim 1, the request includes information indicating efficacy that is demanded of the new product (Um para 83, 121, 125, 147 show the input and product characteristic information indicates an efficacy for the product); the at least one processor further carries out a basis generation process of generating, as a basis of inference, basis information including information pertaining to an existing product which has efficacy identical with the efficacy that is demanded of the new product (Um para 83, 122, 126, 152, 159 show accumulating raw material information, and extracting feature data to determine reference information for the inference process, which includes existing product information for a product that has the same efficacy as that input/requested for the new product); and in the output process, the at least one processor further outputs the basis information (Um para 117, 147-148 show the display and interface for outputting cosmetic component and material information, Um para 139-140, 154, 158 also show outputting the materials to produce the cosmetic product, Um para 83, 125 show the outputted information includes the reference information of the other products with the same efficacy as that input/requested).
11. Regarding claim 3, in addition to that mentioned for claim 2, the basis information includes information indicating the method for producing the existing product which has efficacy identical with the efficacy that is demanded of the new product (Um para 83, 123, 135-136 first shows the reference information includes information indicating the raw materials and components for producing an existing cosmetic product with the same efficacy as what is input and requested for the new product. Um para 154, 157 then shows a process/production method may also be determined and outputted in addition to the materials and components).
12. Regarding claim 4, in addition to that mentioned for claim 1, please also note the 112 rejection and the interpretation of this claim feature. Um does not explicitly show that the learned model uses an existing product graph in which one or more existing products are represented by nodes each indicating a component, efficacy, or a production method of each of the one or more existing products and edges each indicating a relationship between the nodes. Sunkle however does show that a learned model uses an existing product graph in which one or more existing products are represented by nodes each indicating a component, efficacy, or a production method of each of the one or more existing products and edges each indicating a relationship between the nodes (note the alternative language – Sunkle para 40, 54 show a learned model using a product graph in which existing cosmetic products are represented by nodes indicating the components/ingredients of the product, and the edges indicating a relationship between the nodes such as an operation or mixing action). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to use the product graph as described in Sunkle, in the cosmetic production assistance apparatus of Um, because it would provide an efficient data structure to use with a learned model to infer how to produce a cosmetic product.
13. Regarding claim 5, in addition to that mentioned for claim 4, the request includes information indicating efficacy that is demanded of the new product (Um para 83, 121, 125, 147 show the input and product characteristic information indicates an efficacy for the product); and in the inference means-process, the at least one processor infers a production method for producing a new product having the efficacy (Um para 83, 123, 135-136 first shows the inference process infers the raw materials and components for producing an existing cosmetic product with the same efficacy as what is input and requested for the new product. Um para 154, 157 then shows a process/production method may also be determined and outputted in addition to the materials and components). The product graph of Sunkle (which was cited in the rejection of claim 4 above) represents the production method that includes at least one of elements of an existing product production method that correspond to a node and a link which are included in the existing product graph (Sunkle para 29-31, 54, 67 show the product graph has nodes representing at least formulations and ingredients of an existing cosmetic product production and Sunkle para 40 shows the edges [which is another name for product graph links] representing actions or operations in the production method). Sunkle does not explicitly show that the node or edge indicates the efficacy, but given the combination of Um and Sunkle as explained for claim 4, and noting that the nodes and edges indicate characteristics of the products and their relationships respectively, then it further would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention for the node in the product graph to indicate efficacy and for the edge/link to indicate similarity between two efficacies, because it would provide an efficient way to use a product graph with a learned model that infers how to produce a cosmetic product based on efficacy.
14. Claim 14 shows the same steps as claim 1 and is rejected for the same reasons. In addition, Um para 181-182 show the computing device to carry out the steps.
15. Claim 15 shows the same steps as claim 1 and is rejected for the same reasons. In addition, Um para 181-182 show the computer readable non-transitory storage medium storing the program to cause the computing device to carry out the steps.
16. Claims 6-13 are not set forth in the prior art of record. Um and Sunkle and the other prior art alone and/or in combination do not show the link prediction features as specifically recited in these claims, in combination with the features of claim 4 and claim 1 through dependency.
Conclusion
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) Cao, “Machine Learning-aided Process Design for Formulated Products” shows using machine learning techniques to determine ingredient and production methods for formulated products.
b) Adachi (JP 2018139073 A) shows a cosmetic manufacturing system.
c) Feng (CN 112508636 B) shows a machine learning model which uses a product graph to determine cosmetic products.
18. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 Est.
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, Usmaan Saeed, can be reached at 571-272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN P SAX/Primary Examiner, Art Unit 2146
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