DETAILED ACTION
This action is in response to the claims filed 12/22/2023 for Application number 18/394,939. Claims 1-19 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/22/203, 08/16/2024 and 05/15/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
The following title is suggested: Learning Model Relearning Based on a Result of Stratification.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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.
The term “high degree of importance” in claims 1, 18 and 19 is a relative term which renders the claim indefinite. The term “high degree of importance” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification fails to explicitly define what is a “high degree of importance”. Since the term “high” is a subjective definition which differs from person to person, the metes and bounds of the claim is not made clear and one of ordinary skill in the art would not be properly avoid infringing upon a claim when no definition of “high degree of importance” has been made.
Claims 2-17 are rejected as being dependent on a rejected base claim without curing any of the deficiencies.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
generating determination batch data including learned data and unlearned data, the learned data being learning data that has already been used to learn a first learning model for making a prediction based on the sensor data, the unlearned data corresponding to the sensor data can be considered to be an evaluation in the human mind
determining whether or not the first learning model is to be relearned by using a frequency of appearance of a critical layer and a frequency of appearance of critical data from a result of the stratification, the critical layer being a layer determined to have a high degree of importance out of the plurality of layers, the critical data being data determined to have a high degree of importance out of the unlearned data and the learned data can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas.
The limitation of calculating propensity scores for the learned data and the unlearned data by using a covariate affecting a result of the prediction to perform stratification by allocating the learned data and the unlearned data to a plurality of layers is a mathematical calculation.
This limitation as drafted, is a process that, under broadest reasonable interpretation, covers mathematical calculations which falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “An information processing device comprising: a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of…”. Thus, these elements in the claim are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites collecting sensor data from a plurality of sensors. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing a processor and memory to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitation of collecting sensor data from a plurality of sensors is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the processor determines that the first learning model is to be relearned when the frequency of appearance of the critical layer is equal to or higher than a predetermined threshold or when the frequency of appearance of the critical data is equal to or higher than a predetermined threshold. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the processor determines a layer having average prediction accuracy equal to or lower than a predetermined threshold or a layer whose difference between a prediction accuracy of the unlearned data and a prediction accuracy of the learned data is equal to or larger than a predetermined threshold to be the critical layer of the plurality of layers. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 2 is further incorporated, and further, the claim recites: wherein the processor determines a layer having average prediction accuracy equal to or lower than a predetermined threshold or a layer whose difference between a prediction accuracy of the unlearned data and a prediction accuracy of the learned data is equal to or larger than a predetermined threshold to be the critical layer of the plurality of layers. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the processor determines the unlearned data and the learned data contained in the critical layer to be the critical data. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding Claims 6-8, they recite features similar to claim 5 (with different dependencies) and are rejected for at least the same reasons therein.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the processor uses a second learning model to determine whether or not the unlearned data and the learned data contained in layers of the plurality of layers other than the critical layer is the critical data, the second learning model being a learning model different from the first learning model. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding Claims 10-16, they recite features similar to claim 9 (with different dependencies) and are rejected for at least the same reasons therein.
Regarding claim 17, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the processor relearns the first learning model when the processor determines that the learning model is to be relearned. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Claim 18 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 18 additionally requires analysis for “A non-transitory computer-readable medium that stores therein a program that causes a computer to execute processes of” however this is an additional element that amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding Claim 19, it recites features similar to claim 1 and 18 and is rejected for at least the same reasons therein.
Allowable Subject Matter
Claims 1-19 are objected to as being allowable over prior art if all outstanding rejections were withdrawn. None of the prior art, either alone or in combination, fairly discloses limitations of claims 1, 18 and 19 in particular:
calculating propensity scores for the learned data and the unlearned data by using a covariate affecting a result of the prediction to perform stratification by allocating the learned data and the unlearned data to a plurality of layers;
determining whether or not to the learning model is to be relearned by using a frequency of appearance of a critical layer and a frequency of appearance of critical data from a result of the stratification, the critical layer being a layer determined to have a high degree of importance out of the plurality of layers, the critical data being data determined to have a high degree of importance out of the unlearned data and the learned data.
The closest prior art uncovered was Autenrieth et al. (“Stratified Learning: a general-purpose statistical method for improved learning under Covariate Shift”) which discloses labeled (source) training data and unlabeled (target) training data resulting in covariate shift since the labeled data may not be representative of the unlabeled target data and further calculates propensity scores using the target/source training data by performing stratification by partitioning the data into subgroups. However, the prior art does not explicitly disclose collecting sensor data from sensors nor determining whether or not to the learning model is to be relearned by using a frequency of appearance of a critical layer and a frequency of appearance of critical data from a result of the stratification as required by the claims.
Liu et al. (“Concept Drift Detection via Equal Intensity k-Means Space Partitioning”) discloses calculating the differences of observed and expected frequencies in multiple sets of data however does not explicitly teach collecting sensor data from sensors nor determining whether or not to the learning model is to be relearned by using a frequency of appearance of a critical layer and a frequency of appearance of critical data from a result of the stratification as required by the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
Prosecution Insights