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 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 1-3, 7-13, 15-16
Claims 1-3, 7-13, 15-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims fall within at least one of the four categories of patent eligible subject matter. However, the claimed invention is directed to performing a mental process and statistical/mathematical calculations without significantly more.
The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG):
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-3, 7-10, 16 describe an apparatus, claims 11-13 describe a method, and claim 15 describes a non-transitory computer-readable recording medium, therefore satisfying Step 1 of the analysis.
Step 2 Analysis for Claims 1-3, 7-10, 16
Step 2A – Prong 1: Is a Judicial Exception Recited?
Claim 1 recites a sensor detecting information of a status of an apparatus, acquiring error type information by inputting the detected information into neural network models, checking an error category based on the type information, and controlling an operation of the apparatus based on the category. The limitations describe processes that, under their broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components (i.e., use of a processor or a generic computer, and generic AI models). That is, nothing in the claim elements preclude the steps from practically being performed in the mind using a computer or computing components as a tool. The claim is interpreted as data collection, data analysis and controlling an apparatus based thereon, which can be interpreted as merely displaying said analyzed data.
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
The applicant has amended the claim to control the operation of the apparatus according to one mode of a plurality of modes based on the category. The claim stops short of the “one mode” actually controlling the operation and merely cites the “limiting” mode as one of a plurality of possible modes. Therefore, the new language merely recites types of data collected as “to be operated” is not merely an idea of an operation and not an actual operation.
Claims 2-3, 8-9, 16 recite mathematical concepts. As explained in the October 2019 Update to the 2019 PEG, when determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), consideration must be given as to whether a claim recites a mathematical concept or merely includes limitations that are based on or involve a mathematical concept.
If a claim limitation, under its broadest reasonable interpretation, describes the performance of mathematical calculations (even if a formula is not recited in the claim), then it falls within the “Mathematical Concepts” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, claims 2-11 each recite an abstract idea.
Claims 7, 10 recite mere types of data being collected and types of data used for training.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
Claim 1 recites an electronic device, a memory storing neural networks and a processor. Even if the described methods are implemented on a computer, there is no indication that the combination of elements in the claim solves any particular technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the identified judicial exceptions. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s). The processor cited in the claim is described at a high level of generality such that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). This limitation can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Claims 1-3, 7-10 comprise learning data as used by neural network models. As currently written, the limitations in the amended claims describe certain types of data and mathematical calculations and evaluations performed on the data. The mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. Stating in the dependent claims that the learning comprises actions which describe a mental process and/or mathematical concepts is equivalent to merely specifying instructions to apply the judicial exception using learning. See MPEP 2106.05(f). There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s).
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
In the instant case, as detailed in the analysis for Step 2A-Prong 2, claim 1 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The processors and data storage devices recited in the claim describe a generic computer processor and/or computer components at a high level and do not represent “significantly more” than the judicial exception.
The limitations pertain to collecting data, analyzing data without applying a resolution to an identified problem describe insignificant extra-solution activity and are written at a high level in a generic manner without providing any details regarding a specific problem being solved or specific remedial actions being taken. Therefore, these limitations recite no additional elements that would amount to significantly more than the abstract ideas defined in the claim.
Claims 1-3, 7-10, 16 recite limitations regarding the use of learning models and the training of learning models. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component, or utilizing generic artificial intelligence technology to apply the identified judicial exception, does not describe an inventive concept.
As cited above, the applicant has amended the claim to control the operation of the apparatus according to one mode of a plurality of modes based on the category. The claim stops short of the “one mode” actually controlling the operation and merely cites the “limiting” mode as one of a plurality of possible modes. Therefore, the ne language merely recites types of data collected as “to be operated” is not merely an idea of an operation and not an actual operation.
Step 2 Analysis for Claims 11-13
Claims 11-13 contain limitations for a method which are similar to the limitations for the apparatus specified in claims 1-3, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 11-13 is similar to that presented above for claims 1-3.
In light of the above, the limitations in claims 11-13 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 11-13 are therefore not patent eligible.
Step 2 Analysis for Claim 15
Claim 15 contains limitations for a non-transitory computer-readable recording medium which are similar to the limitations for the methods specified in claim 1. As such, the analysis under Step 2A – Prong 1 and Step 2A – Prong 2 for claim 15 is similar to that presented above for claim 1.
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
Claim 15 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea.
Claim 15 recites the additional elements of a “non-transitory computer-readable recording medium.” The computer-readable medium and processors cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The enabling of the processors to troubleshoot a performance problem recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception.
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, 11, 15 is/are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Choung et al. US 11.514,316.
As per claim 1, Choung teaches an electronic apparatus comprising: a driving device (column 13, lines 53-61, wherein the processor controls the motor which drives the machine); a sensor configured to detect information indicating a status of the electronic apparatus (column 21, lines 37-41); a memory storing (i) a first neural network model and a second neural network model, pre-trained to classify an error type of the electronic apparatus, and (ii) one or more instructions (column 18, lines 46-56); and a processor operatively coupled to the memory and configured to execute the one or more instructions stored in the memory, wherein the one or more instructions, when executed by the processor, cause the electronic apparatus to: acquire first error type information and second error type information by inputting the information indicating the status of the electronic apparatus into each of the first neural network model and the second neural network model (column 18, lines 57-60), check an error category of the electronic apparatus based on the first error type information and second error type information (column 19, line 55 – column 20, line 9), and control an operation of the electronic apparatus to be operated in one mode of a plurality of modes based on the error category (column 22, lines 37-39), the plurality of modes including at least a mode that limits a performance of the driving device (column 13, lines 58-60; column 19, 10-39, wherein the motor is limited depending of the error category); wherein the first neural network model is pre-trained using first learning data for a plurality of error types (column 20, lines 44-52), and the second neural network model is pre-trained using second learning data processed from the first learning data to classify error categories to which the plurality of error types respectively correspond (column 21, lines 55-67).
As per claim 11, Choung teaches a method for controlling an electronic apparatus, the method comprising: detecting information indicating a status of the electronic apparatus (column 21, lines 37-41); acquiring first error type information and second error type information by inputting the information indicating the status of the electronic apparatus into each of a first neural network model and a second neural network model, pre-trained to classify an error type of the electronic apparatus (column 20, lines 44-52; column 21, 55-67); checking an error category of the electronic apparatus based on the first error type information and second error type information (column 19, line 55 – column 20, line 9); and controlling an operation of the electronic apparatus to be operated in one mode of a plurality of modes based on the error category (column 22, lines 37-39), the plurality of modes including at least a mode that limits a performance of the driving device (column 13, lines 58-60; column 19, 10-39, wherein the motor is limited depending of the error category); wherein the first neural network model is pre-trained using first learning data for a plurality of error types (column 20, lines 44-52), and wherein the second neural network model is pre-trained using second learning data processed from the first learning data to classify the error categories to which the plurality of error types respectively correspond (column 21, lines 55-67).
As per claim 15, Choung teaches a non-transitory computer-readable recording medium storing a program for executing a method for controlling an electronic apparatus, wherein the method comprises: receiving information indicating a status of the electronic apparatus (column 21, lines 37-41), acquiring first error type information and second error type information by inputting the information indicating the status of the electronic apparatus into each of a first neural network model and a second neural network model, pre-trained to classify an error type of the electronic apparatus (column 20, lines 44-52; column 21, lines 55-67), checking an error category of the electronic apparatus based on the first error type information and second error type information (column 19, line 55 – column 20, line 9), and controlling an operation of the electronic apparatus to be operated in one mode of a plurality of modes based on the error category (column 22, lines 37-39), the plurality of modes including at least a mode that limits a performance of the driving device (column 13, lines 58-60; column 19, 10-39, wherein the motor is limited depending of the error category); wherein the first neural network model is pre-trained using first learning data for a plurality of error types (column 20, lines 44-52), and wherein the second neural network model is pre-trained using second learning data processed from the first learning data to classify error categories to which the plurality of error types respectively correspond (column 21, lines 55-67).
Claim Rejections - 35 USC § 103
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 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(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choung in view of Darby U.S. Patent Application Publication US2004/0088797A1.
As per claim 7, Choung teaches the electronic apparatus as claimed in claim 1, wherein the driving device comprises a motor (column 13, 53-61); the electronic apparatus to check the error category for at least one of the apparatus based on the first error type information and the second error type information (column 18, lines 57-60). While Choung does a home appliance, he does not explicitly teach a driving device an inverter configured to provide driving power to the motor, wherein the sensor is configured to detect a plurality of current values in the driving device. Darby teaches a driving device comprising a motor and an inverter configured to provide driving power to the motor, wherein the sensor is configured to detect a plurality of current values in the driving device (page 5, claim 5). It would have been obvious to one of ordinary skill in the art to use the device of Darby in the process of Choung. One of ordinary skill in the art would have been motivated to use the device of Darby in the process of Choung because using the device of Darby would yield the predictable result of analyzing and correcting hardware failures of the apparatus.
Allowable Subject Matter
Claims 4-6, 14 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.
Response to Arguments
9. Applicant's arguments filed 6/30/26 have been fully considered but they are not persuasive.
With respect to the USC 101 rejection, the applicant has amended and has argued the new language overcomes the rejection. The examiner respectfully disagrees. As cited above, the applicant has amended the claim to control the operation of the apparatus according to one mode of a plurality of modes based on the category. The claim stops short of the “one mode” actually controlling the operation and merely cites the “limiting” mode as one of a plurality of possible modes. Therefore, the new language merely recites types of data collected as “to be operated” is not merely an idea of an operation and not an actual operation. Claim 4 is more directed to the “one mode” controlling the operation as may be intended by language of claim 1.
The applicant has also argued that Choung does not teach the claim language. The examiner respectfully disagrees. The applicant argues that Choung does not teach controlling the operation of the washer in a mode of the plurality of the modes. The examiner maintains that Choung teaches, in column 53-61, the motor is limited to certain RPMs based on the error category. This coincides with the current Specification of paragraph 0010, wherein a driving device provides power to the motor as power is limited on the motor of Choung as well.
The applicant also argues that Choung does not teach an error category based on the first and second error type information. The examiner maintains that Choung teaches, in column 20, lines 21-52, that both ANNs receive their respective input error data, one of a condition, and one of prior input learning data (20: 28-32). Both these inputs are used determine the further result of an error category of 0,1,2,3 correlating to the device state.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER S MCCARTHY whose telephone number is (571)272-3651. The examiner can normally be reached Monday-Friday 8:30-5:00.
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, Bryce Bonzo can be reached at (571)272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER S MCCARTHY/Primary Examiner, Art Unit 2113
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