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 .
Claims 1, 3-5, 7-8, and 11 are presented for examination.
Response to Amendment
Applicant’s amendment has obviated most, but not all, of the objections to the specification and claims given in the first Office action. To the extent that an objection or rejection appears in the previous Office Action(s) but not this Office Action, that objection or rejection is withdrawn. To the extent that it appears both in a previous Office Action(s) and this Office Action, the objection or rejection is maintained.
Specification
Examiner objects to the specification for containing various grammatical informalities. Examiner has attached a marked-up copy of the specification indicating where errors have occurred. To the extent that the markings are not self-explanatory and are not corrected, Examiner will enumerate the remaining objections in a subsequent Office Action.
Claim Objections
Claims 1, 7, and 11 are objected to because of the following informalities: the meaning of the acronym “LOESS” should be spelled out the first time it is used.
Claim 5 is objected to because of the following informalities: “data … is used” should be “data … are used”.
All claims dependent on a claim objected to hereunder are also objected to for being dependent on an objected-to base claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-5, 7-8, and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As amended, independent claims 1, 7, and 11 are replete with limitations that are not disclosed by the specification as originally filed. These limitations include, but are not limited to, LOESS smoothing over sliding windows, minimizing within-cluster variance using Lloyd’s algorithm, computing discrete Fourier transform coefficients, performing element-wise vector addition of trend and seasonal/pattern vectors, performing matrix addition of forecast vectors using double-precision floating-point arithmetic, training a gradient-boosting regression tree by iterative gradient-descent optimization, using numerical linear algebra libraries optimized for parallel processing, z-score normalization of each time-series dimension, storing standardized datasets as floating-point arrays, and minimizing an L2-norm of prediction residuals. Examiner recommends that Applicant reread the claims to identify any further material not disclosed by the originally-filed specification. Note that, to the extent that Applicant is relying on the Japanese application to which the instant application claims priority for support for the limitations, the incorporation by reference in paragraph 1 is ineffective for this purpose because any such material relied upon would be essential material and essential material can only be incorporated by reference to U.S. patents and patent application publications. MPEP § 608.01(p).
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-5, 7-8, and 11 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.
Claims 1 and 11 recite the limitation "the provision target’s historical series". There is insufficient antecedent basis for this limitation in the claims.
Claim 7 recites the limitation "the L2-norm" and “the summation”. There is insufficient antecedent basis for these limitations in the claim.
Independent claims 1 and 11 use a slash in “seasonal/pattern vectors” and “re-clustering/re-extraction steps”. It is unclear whether the slash is to be interpreted as an “and” or as an “or”. For purposes of examination, “or” will be assumed.
Claims 1 and 11 recite that polynomial regression smoothing “reduce[s] discontinuities”. It is unclear what the baseline relative to which the reduction takes place is. Additionally, the term “dominant” in is a relative term which renders the claims indefinite. The term “dominant” 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. A similar observation applies to “optimized” in claim 7.
All claims dependent on a claim rejected hereunder are also rejected for being dependent on a rejected base claim.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-5, 7-8, and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a system comprising a processor; therefore, it is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
[E]xecut[ing] a time series clustering process on the input actual result data for each provision target, the clustering process including transforming each provision-target time series into a multidimensional vector of normalized demand magnitudes over discrete time intervals, computing pairwise Euclidean distances between vectors, and iteratively partitioning the vectors into k clusters by minimizing within-cluster variance using Lloyd's algorithm until a centroid-change threshold is met: This limitation is directed to a mathematical concept of vectorizing data, normalizing the vector, computing Euclidean distances, and performing iterative optimization.
[E]xecut[ing], for each cluster classified by the time series clustering process, an extraction process of extracting trend component data of the cluster from the actual result data, the extraction process including applying seasonal-trend decomposition (STL) to separate each time series into trend, seasonal, and residual components using LOESS smoothing over sliding windows: This limitation is directed to a mathematical concept of applying seasonal-trend decomposition and LOESS smoothing over sliding windows of the data.
[E]xecut[ing], for each cluster, a pattern calculation process of calculating pattern data indicating a demand pattern for a predetermined period by performing a statistical process on first data that are data remaining after the trend component data are extracted from the actual result data for the predetermined period, the statistical process including computing discrete Fourier transform coefficients to identify dominant periodicities and generating amplitude and phase parameters for each identified cycle for the cluster: This limitation recites the mathematical concept of performing a statistical process on data remaining after trend component data are extracted by computing discrete Fourier transforms and generating parameters.
[E]xecut[ing], for each cluster, a first forecasting process of obtaining first forecasting data indicating a representative demand forecasting value of the provision target belonging to the cluster for each cluster by calculating a sum of the pattern data and the trend component data: This limitation could encompass mentally performing the forecasting by adding the pattern and trend component data. The recitation of calculating a sum is also a mathematical concept.
[C]alculating the sum including element-wise vector addition of trend and seasonal/pattern vectors …, with polynomial regression smoothing applied to reduce discontinuities at period boundaries prior to the addition: This limitation is directed to the mathematical concept of performing elementwise vector addition and performing polynomial regression smoothing.
[E]xecut[ing] a second forecasting process of obtaining second forecasting data that are data indicating a forecasting value of the difference for each provision target: This limitation could encompass mentally performing the forecasting.
[O]btain[ing] third forecasting data that [are] data indicating a demand forecasting value for each provision target by calculating a sum of the first forecasting data and the second forecasting data for each provision target: This limitation could encompass mentally adding the first and second forecasting data to obtain third forecasting data. The recitation of calculating a sum is also a mathematical concept.
[C]alculating the sum including matrix addition of forecast vectors using double-precision floating-point arithmetic: This limitation is directed to the mathematical concept of performing matrix addition using floating-point arithmetic.
[E]xecut[ing], for each cluster, a determination process of determining whether a trend component is included in extracted data that are data obtained after the trend component data are extracted in the extraction process: This limitation could encompass mentally determining whether the trend component is included in extracted data.
[E]xecut[ing], when a determination is made that a trend component is included for at least one of the clusters as a result of the determination process, the time series clustering process and the extraction process on the extracted data instead of the actual result data, until a determination is made in the determination process that a trend component is not included: As noted above, the time-series clustering process and the extraction process are both mathematical concepts. Therefore, this limitation is also directed to a mathematical concept.
[T]he pattern calculation process: uses data remaining after the trend component data are extracted from the extracted data instead of the actual result data as the first data at a stage when a determination is made in the determination process that a trend component is not included, and calculates the pattern data by performing the statistical process on the first data for the predetermined period; wherein the determination process and re-clustering/re-extraction steps are iterated until a no-trend condition is met across the clusters: The pattern calculation process remains directed to a mathematical concept under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “ input[ting], regarding a provision target that is an automotive provision target that is a vehicle part, module, or service item, actual result data including the number of time series actual demand results for each of a plurality of types of provision targets”; “input[ting], for each provision target, a difference between the actual result data of the provision target and the first forecasting data for the cluster to which the provision target belongs, in a learning model in which a regression learning is performed on the difference for the provision target”; “storing a resulting forecast vector for each automotive provision target in non-transitory memory for downstream inventory-control operations”; and “output[ting] the third forecasting data for use in adjusting inventory”. However, these limitations recite the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). The claim further recites that the method is performed using a “processor”, that data are “stored in a memory”, that certain operations are performed by a “learning model comprising a gradient-boosting regression tree ensemble trained by iterative gradient-descent optimization of a mean-squared-error loss by executing repeated forward evaluations and weight updates until the loss converges below a specified threshold”, and that “the second forecasting data [are] generated by applying the trained ensemble to residual-difference feature vectors derived from the provision target's historical series”, which are mere instructions to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 1, with the exception that the inputting and outputting limitations, in addition to being insignificant extra-solution activity, also recite the well-understood, routine, and conventional activity of receiving or transmitting data over a network, MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), and the storing limitation recites the well-understood, routine, and conventional activity of storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). As an ordered whole, the claim is directed to a potentially mentally performable, mathematical algorithm for forecasting demand. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, that “the predetermined period is a period in which start date and end date are represented in days or months within a year”. The calculation and extraction of the data from the period remain mental processes and/or mathematical concepts under these further assumptions. The claim further recites that “the statistical process is a process of calculating a statistical value of the first data for the predetermined period in the first data for a predetermined number of years, for each cluster.” This limitation recites the mathematical concept of calculating a statistical value of data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “the first forecasting process includes an approximation process of approximating the trend component data, which are extracted in the extraction process, to a polynomial, and is a process of obtaining the first forecasting data for each cluster by calculating a sum of the pattern data and data indicated by the polynomial for each cluster.” This limitation recites the mathematical concepts of performing polynomial approximation of trend data and calculating sums of data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites that “data after a standardization for the types of provision targets are used in the time series clustering process, the extraction process, and the pattern calculation process as the actual result data, and data before the standardization [are] used in the second forecasting process as the actual result data.” The underlying manipulations of the data remain mental processes and/or mathematical concepts under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 1 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 1 analysis.
Claim 7
Step 1: The claim recites a system comprising a processor; therefore, it is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
[E]xecuting a time series clustering process on the actual result data for each provision target, for a cluster to which the provision target belongs: This limitation could encompass performing the clustering mentally.
[E]xecut[ing] a regression learning of the regression learning model and updat[ing] the regression learning model such that the regression learning model becomes a model that inputs the difference and outputs a forecasting value of the difference for a predetermined period during operation, the regression learning being performed using back-propagation of errors through a multilayer neural network or a gradient-boosted regression tree ensemble, with iterative adjustment of model parameters by stochastic gradient descent to minimize the L2-norm of prediction residuals across all training samples: This limitation is directed to the mathematical concept of training a regression model with backpropagation of errors and iterative adjustment of parameters by SGD and L2-norm minimization.
[T]he first forecasting data are obtained, as data indicating a representative demand forecasting value of the provision target belonging to the cluster for each cluster, by executing, for each cluster, an extraction process of extracting trend component data of the cluster from the actual result data, the extraction including performing seasonal-trend decomposition (STL) to separate each time-series into trend, seasonal, and residual components using LOESS smoothing over sliding time windows: This limitation is directed to the mathematical concept of obtaining forecasting data by extracting trend data using STL and LOESS smoothing.
[E]xecuting, for each cluster, a pattern calculation process of calculating pattern data indicating a demand pattern for a predetermined period by performing a statistical process on first data that [are] data remaining after the trend component data [are] extracted from the actual result data for the predetermined period, the statistical process including computing discrete Fourier transform coefficients to identify dominant periodicities and generating amplitude and phase vectors representing each periodic cycle of automotive-part demand: This limitation recites the mathematical concept of performing a statistical process on data remaining after the extraction by computing DFT coefficients.
[E]xecuting, for each cluster, a first forecasting process of calculating a sum of the pattern data and the trend component data by performing the summation as vectorized arithmetic addition of trend and pattern vectors: Calculating a vectorized sum is a mathematical concept.
[D]ata after a standardization for the types of provision targets are used in the time-series clustering process, the extraction process, and the pattern calculation process as the actual result data: As noted above, the clustering, extraction, and pattern calculation all recite mathematical concepts.
[T]he standardization includes z-score normalization of each time-series dimension: This limitation is directed to the mathematical concept of z-score normalization.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that that the method is executed using a “processor” and that the summation “us[es] numerical linear-algebra libraries optimized for parallel processing”. However, these are mere instructions to apply the judicial exception using a generic computer programmed with generically recited classes of computer algorithm. MPEP § 2106.05(f). The claim further recites “input[ting], regarding a provision target that is a commodity or a service, a difference between actual result data including the number of time series actual demand results for each of a plurality of types of provision targets and first forecasting data that are data indicating a representative demand forecasting value, which are obtained for each cluster classified …, the difference being represented as a numerical residual vector Δx(t) = x actual(t) - x predicted(t) stored in system memory and normalized to unit variance prior to regression training”; that “a difference, which is calculated by using data before the standardization, is input to the regression learning model, as the actual result data”; and “stor[ing] of standardized datasets as floating-point arrays indexed by provision-target identifiers within an automotive supply-chain database”. However, these limitations recite the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step is the same as in step 2A, prong 1, with the exception that the inputting limitations, in addition to being insignificant extra-solution activity, also recite the well-understood, routine, and conventional activity of receiving or transmitting data over a network, MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), and the storing limitation recites the well-understood, routine, and conventional activity of storing or retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). As an ordered whole, the claim is directed to a mathematical algorithm for time-series forecasting. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “the predetermined period is a period in which start date and end date are represented in days or months within a year”. Manipulating the data for the predetermined period remains a mental process and/or mathematical concept under these further assumptions. The claim further recites that “the statistical process is a process of calculating a statistical value of the first data for the predetermined period in the first data for a predetermined number of years, for each cluster.” This limitation recites the mathematical concept of calculating statistical values.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 7 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 7 analysis.
Claim 11
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step is the same as in claim 1, with the exception that this claim does not recite a processor.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step is the same as in claim 1, with the exception that this claim does not recite a processor.
Response to Arguments
Applicant's arguments filed November 28, 2025 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the withdrawal of a ground of rejection, not persuasive.
Applicant first argues that the claims as amended are allegedly eligible under 35 USC § 101 because (a) the limitations of the claim as a whole allegedly cannot be practically performed in the mind; (b) any judicial exception recited is allegedly integrated into a practical application insofar as the claims are directed to a non-conventional method for forecasting demand of automotive provision targets. Remarks at 10-11. However, regarding (a), the current rejection is analyzing most of the limitations at issue as mathematical concepts, thereby rendering the inquiry as to whether the limitations are practically mentally performable moot. Regarding (b), the use of the claimed method to perform automotive part demand forecasting is recited at a high level of generality and at most limits the judicial exception to the field of use of automotive part demand forecasting. MPEP § 2106.05(h).
The arguments regarding the art rejections, Remarks at 11, are moot in light of the withdrawal of that ground of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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, Kamran Afshar, can be reached at 571-272-7796. 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.
/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125