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 .
In response to the restriction requirement, Applicant elected claims 1, 5-21 for further examination. As a result, claims 2-4, 22-27 are withdrawn from further prosecution.
Claim Objections
Claims 1 and 5 are objected to because of the following informalities: The phrase “and/or” renders the claim indefinite because the claim does not clearly set forth the metes and bounds of the claimed invention, thereby rendering the scope of the claim unascertainable. 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.
Claim 1 is rejected under 35 U.S.C. 101 because:
The claim recites the limitations that predicting an environment condition and/or values for one or more properties by using one or more machine learning modes. The claim language does not specify about the one or more machine learning models, they are therefore considered as generic. As a result, the use of the one or more machine learning models as claimed is directed to the abstract idea of using a generic machine learning model/technique in a particular/new environment or to a particular field of use (Recentive Analytics, Inc v. Fox Corp (Fed Cir, 2023-2437, 4/18/2025)).
In addition, at Alice step two, the additional elements recited in the claim: gathering the environment condition data and the property data as the inputs of the machine learning models and displaying the output from the machine learning models, when viewed in combination of as a whole, adding insignificant extra-solution to the abstract idea of using the one or more machine learning (Insignificant solution activities (MPEP 2106.05(g)). As a result, these elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Furthermore, gathering the environment condition data and the property data as the inputs of the machine learning models and displaying the output from the machine learning models are well known in the field as evidenced by Fujita et al. (US 2020/0033838) (as addressed in the 102 rejection below); as a result, these elements do not amount the claim to significantly more to add an inventive concept to the claim.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fujita et al. (US 2020/0033838).
Fujita et al. discloses an integrated multifunctional environmental characterization system (IMECS) comprising:
a memory configured to store one or more machine learned models (FIG. 1, element 3: Learning processor) correlating one or more environmental conditions adjacent a thin film (FIG. 1: State data and control conditions. Paragraph [0033]: The temperature, humidity, and the like of the environment in which the film deposition installed are the state data. Paragraph [0057]: The control conditions the temperature, the degree of vacuum, the type of gas, the amount of gas in the film deposition chamber) with one or more properties of the thin film, where the one or more properties comprise one or more properties from at least one of gravimetric/viscoelastic, electrical or optical properties groups (FIG. 1: Film characteristic data and target film characteristic data and target. Paragraph [0067]: The film characteristic data may include data concerning electrical characteristics, optical characteristics);
one or more interfaces (FIG. 1, elements 31, 32, 33, 36);
a processor configured to:
predict an environment condition adjacent to the thin film using the one or more machine learned models from one or more measured properties of the thin film received via the one or
more interfaces; and/or predict values for one or more properties of the thin film using the one or more machine learned models from an environmental condition received via one of the one or more interfaces (paragraph [0072]: The model 35 outputs recommended control conditions in response to the input of target film characteristics); and
display the predicted environment condition and/or the predicted one or more properties (FIG. 9, elements 2216 and 2218).
Allowable Subject Matter
Claim 20-21 are allowed over prior art. Claims 5-19 and 28 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and the claim objection is corrected.
Regarding to claim 5: The primary reasons for the indication of the allowability of the claim is the inclusions therein, in combination as currently claimed, of the limitation that wherein the processor is configured to interface with two or more characterization modules, the two or more characterization modules are selected from a group consisting of an optical characterization
module, an electrical characterization module and a gravimetric/viscoelastic characterization
module, the optical characterization module being configured to measure at least an optical
spectra associate with the thin film, the electrical characterization module being configured to
measure at least one of I-V, C-V, or impedance associated with the thin film, and the
gravimetric/viscoelastic characterization module being configured to measure at least QCM
conductance spectra associated with the thin film, interface with one or more environmental control modules, wherein the system further comprises: a fluid-flow cell arranged to encompass the quartz crystal and the thin film, the fluid-flow cell being configured to maintain a controlled environmental condition around the quartz crystal and the thin film and circulate a fluid comprising gas, vapor, liquid
and/or a combination thereof adjacent to the thin film in conjunction with the one or more
environmental control modules, and wherein the processor is configured to acquire a dataset for
training and testing machine learning modules by controlling the one or more environmental
control modules to sequentially provide a plurality of different environmental conditions while
also acquiring values for one or more properties of the thin film is neither disclosed nor taught by the cited prior art of record, alone or in combination.
Regarding to claim 20: The primary reasons for the indication of the allowability of the claim is the inclusions therein, in combination as currently claimed, of the limitation that a user interface configured to receive acquisition parameters for two or more characterization modules, the characterization modules are selected from a group consisting of an optical characterization module, an electrical characterization module and a gravimetric/viscoelastic characterization module, and a processor configured to adjust the acquisition parameters used to acquire values of one or more properties of the thin film by each respective characterization module from the received acquisition parameters via the user interface based on measured values for the same properties is neither disclosed nor taught by the cited prior art of record, alone or in combination.
Claims 6-19, 21, and 28 are allowed because they depend directly/indirectly on claim 5 or 20.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853