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 .
DETAILED ACTION
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12 January 2024, 13 June 2024 and 03 September 2024 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 6 recites “An information processing method comprising: acquiring, by a first acquisition unit, total event status information obtained by synthesizing event status information representing a status of an event of each of a plurality of devices for two or more devices for an object manufacturing apparatus, the object manufacturing apparatus being configured to manufacture an object by operating each of the plurality of devices for each event; acquiring, by a second acquisition unit, time-series detection result information detected in a manufacturing process of manufacturing the object by the object manufacturing apparatus; and performing, by a machine learning processing unit, one or both of learning processing and determination processing, the learning processing being to generate a learning model by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit, the determination processing being to perform a determination on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit.”
Claim 6, in view of the claim limitations, recites the abstract idea of “acquiring, by a first acquisition unit, total event status information obtained by synthesizing event status information representing a status of an event of each of a plurality of devices for two or more devices for an object manufacturing apparatus, the object manufacturing apparatus being configured to manufacture an object by operating each of the plurality of devices for each event; acquiring, by a second acquisition unit, time-series detection result information detected in a manufacturing process of manufacturing the object by the object manufacturing apparatus; and performing, by a machine learning processing unit, one or both of learning processing and determination processing, the learning processing being to generate a learning model by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit, the determination processing being to perform a determination on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit.”
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited “acquiring, by a first acquisition unit, total event status information obtained by synthesizing event status information representing a status of an event of each of a plurality of devices for two or more devices for an object manufacturing apparatus, the object manufacturing apparatus being configured to manufacture an object by operating each of the plurality of devices for each event; acquiring, by a second acquisition unit, time-series detection result information detected in a manufacturing process of manufacturing the object by the object manufacturing apparatus; and performing, by a machine learning processing unit, one or both of learning processing and determination processing, the learning processing being to generate a learning model by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit, the determination processing being to perform a determination on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit.”; therefore, the claim recites mental processes. Accordingly, the claim recites a mental process, and thus, the claim recites an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of“[a] computer- implemented method” and “the method is carried out by one or more physical processors configured by machine-readable instructions” as recited in claim 1, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-5 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0287] (describing that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-5 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea. Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-6 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Crothers et al (US 11,256,231).
Crothers et al disclose the following claimed features:
Regarding claims 1 and 6, an information processing device and an information processing method (Figure 1) comprising: a first acquisition unit (120A) configured to acquire total event status information obtained by synthesizing event status information representing a status of an event of each of a plurality of devices for two or more devices for an object manufacturing apparatus, the object manufacturing apparatus being configured to manufacture an object by operating each of the plurality of devices for each event; a second acquisition unit (120B) configured to acquire time-series detection result information detected in a manufacturing process of manufacturing the object by the object manufacturing apparatus; and a machine learning processing unit (108) configured to perform one or both of learning processing and determination processing, the learning processing being to generate a learning model by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit, the determination processing being to perform a determination on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit (column 4, line 45 to column 6, line 13).
Regarding claim 2, wherein the total event status information comprises a bit value representing a status of an operation result of the event for each of the plurality of devices (column 8, lines 26-45).
Regarding claim 3, wherein the total event status information further comprises a bit value representing a status of the event during operation of each of the plurality devices (column 8, lines 26-45).
Regarding claim 4, wherein the two or more devices having the event status information included in the total event status information are devices which are a part of all the plurality of devices of the object manufacturing apparatus (column 6, line 65 to column 7, line 16).
Regarding claim 5, wherein the machine learning processing unit (108) determines a period of the time-series detection result information to be subjected to the learning processing or the determination processing on the basis of any of lots, substrates, carriers, recipes, and processing chambers (column 11, lines 1-46).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Isoyama et al (US 11,899,793) disclose an information processing apparatus that classifies each event that occurred in a target apparatus to be determined either as an event that also occurs in a standard apparatus or as an event that does not occur in the standard apparatus. Purpura et al (US 9,454,733) disclose a method that includes: receiving a complete set of training data; receiving instructions to train a predictive model having a plurality of parameters on an initial subset of the complete set of training data; training the predictive model on the initial subset; storing data representing a first state of the predictive model after training the predictive model on the initial subset; receiving updated parameter values and instructions to train the predictive model on a new subset of the complete set of training data; and training the predictive model on the new subset.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:5:30pm.
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/AN H DO/Primary Examiner, Art Unit 2853