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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed (i.e., a descriptive title that distinguishes the invention and is not a generic or general description). The new title should take into account any amendments to the claims to best indicate the claimed invention.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/11/2024 follows the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-5 and 7-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mogaki (US 2020/0322500).
Regarding claim 1, Mogaki teaches an information processing apparatus (information processing apparatus; ¶¶ 0027-0028, Fig. 1 and ¶¶ 0033-0039, Figs. 2B-3D),
comprising circuitry configured to infer a job to be executed (prediction process; ¶¶ 0095-0102, Fig. 9),
using a trained model that has learned a relation between image data and a job based on training data (learned model; ¶¶ 0070-0078, Fig. 5), the training data associating first image data that is input by a device with a job executed on the first image data according to an instruction from a user among two or more types of jobs executable by the device (data collection and learning; ¶¶ 0051-0081, Fig. 4B and 5), based on second image data that is newly input by the device (obtain image data; ¶ 0097, Fig. 9 S901).
Regarding claim 2, Mogaki teaches the information processing apparatus according to claim 1, wherein the trained model has learned a relation between image data and information indicating a situation in which a job has been executed on the first image data (performs machine learning by using the collected data and generates a learned model; ¶ 0052, Fig. 4A), and the job executed on the first image data based on the training data, the training data further associating the information indicating the situation with the first image data and the job executed on the first image data (collected data; ¶ 0058, Table 1), wherein the circuitry infers the job to be executed based on the second image data that is newly input by the device and the information indicating the situation when the second image data is input (data transmission destination prediction process; ¶¶ 0096-01012, Fig. 9).
Regarding claim 3, Mogaki teaches the information processing apparatus according to claim 2, wherein the information indicating the situation is identification information of a user who uses the device (execution user; ¶ 0058, Table 1).
Regarding claim 4, Mogaki teaches the information processing apparatus according to claim 2, wherein the information indicating the situation is information on an organization to which a user who uses the device belongs (groups; ¶ 0063).
Regarding claim 5, Mogaki teaches the information processing apparatus according to claim 2, wherein the information indicating the situation is information on a time at which the second image data is input (present time; ¶ 0058, Table 1 and ¶ 0098).
Regarding claim 7, Mogaki teaches the information processing apparatus according to claim 1, wherein the circuitry infers the job to be executed based on the second image data that is newly input by the device (data transmission destination prediction process; ¶¶ 0096-01012, Fig. 9 S901), using the trained model corresponding to a situation when the second image data is input (obtains learned model; ¶ 0098, Fig. 9 S902), the trained model having been selected from among trained models that have learned a relationship between the image data and the job for each of situations in which the job has been executed based on the training data associating the first image data with the job executed on the first image data (learned models; ¶ 0063).
Regarding claim 8, Mogaki teaches the information processing apparatus according to claim 1, wherein the circuitry is further configured to inquire of the user whether the inferred job is to be executed (displays the transmission destination of the prediction result on the operation panel 207; ¶ 0100, Fig. 9 S904 and ¶¶ 0109-0111, Fig. 10C).
Regarding claim 9, Mogaki teaches an information processing system (system; ¶¶ 0025-0028, Fig. 1), comprising:
an image processing apparatus including first circuitry (MFP 102; ¶¶ 0030-0031, Fig. 2A); and an information processing apparatus including second circuitry (information processing apparatus; ¶¶ 0027-0028, Fig. 1 and ¶¶ 0033-0039, Figs. 2B-3D), the first circuitry and the second circuitry being configured to operate in cooperation to: infer a job to be executed (prediction process; ¶¶ 0095-0102, Fig. 9), using a trained model that has learned a relation between image data and a job based on training data (learned model; ¶¶ 0070-0078, Fig. 5), the training data associating first image data that is input by the image processing apparatus with a job executed on the first image data according to an instruction from a user among two or more types of jobs executable by the image processing apparatus (data collection and learning; ¶¶ 0051-0081, Fig. 4B and 5), based on second image data that is newly input by the image processing apparatus (obtain image data; ¶ 0097, Fig. 9 S901); and execute the inferred job (data transmission destination prediction process; ¶¶ 0096-01012, Fig. 9).
Regarding claim 10, Mogaki teaches the information processing system according to claim 9, wherein the first circuitry is configured to execute the inferred job (transmit data; ¶ 0069, Fig. 4B and ¶ 0102, Fig. 9 S906).
Regarding claim 11, Mogaki teaches the information processing system according to claim 9, wherein the first circuitry is further configured to output a predetermined notification based on a comparison between a job designated by the user for the second image data and the inferred job (displays the transmission destination of the prediction result on the operation panel 207; ¶ 0068, Fig. 4B, ¶ 0100, Fig. 9 S904 and ¶¶ 0109-0111, Fig. 10C).
Regarding claim 12, Mogaki teaches the information processing system according to claim 9, wherein the first circuitry and the second circuitry are further configured to operate in cooperation to cause a model to learn the relation between the image data and the job based on the training data (data collection and learning; ¶¶ 0051-0081, Fig. 4B and 5).
Regarding claim 13, Mogaki teaches the information processing system according to claim 9, wherein the image processing apparatus includes a memory that stores the trained model (data transmission application 372 obtains the learned model; ¶ 0066, Fig. 4B) and the first circuitry infers the job to be executed based on the second image data that is newly input by the information processing apparatus, using the trained model (the data transmission application 372 inputs the scanned image data, the present date/time, and the information of the MFP 102, as input data, to the learned model obtained in step S421, and outputs the transmission destination prediction result; ¶ 0067, Fig. 4B S423).
Claim 14 is a method claim that corresponds to the apparatus of claim 1 thus, arguments similar to that presented above for claim 1 are equally applicable to claim 14.
Claim 15 recites a non-transitory recording medium storing a plurality of instructions which, when executed by one or more processors (¶ 0129 Mogaki), causes the one or more processors to perform similarly to the apparatus of claim 1. Thus, arguments similar to that presented above for claim 1 are equally applicable to claim .15
Claim Rejections - 35 USC § 103
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mogaki as applied to claim 1 above, and further in view of Umeizumi (US 2022/0116510)
Regarding claim 6, Mogaki teaches the information processing apparatus according to claim 1, wherein the circuitry is further configured to: and infer the job to be executed based on the second image data, using the trained model (the data transmission application 372 inputs the scanned image data, the present date/time, and the information of the MFP 102, as input data, to the learned model obtained in step S421, and outputs the transmission destination prediction result; ¶ 0067, Fig. 4B S423); but does not explicitly teach remove a blank page from the second image data; from which the blank page is removed.
However, Umeizumi teaches remove a blank page from the second image data; from which the blank page is removed (remove blank page; ¶¶ 0005-0006).
Mogaki and Umeizumi are in the same field of endeavor of an information processing apparatus that processes print jobs. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the formation processing apparatus of Mogaki to remove a blank page from the second image data as taught by Umeizumi. The combination improves the information processing apparatus by reducing cost by preventing unnecessary waste.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsuzawa et al. (US 2020/0285426) teaches an information processing apparatus that stores a learnt model obtained by learning the relationship between image information and operation information.
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/KENT YIP/Primary Examiner, Art Unit 2681
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