DETAILED ACTION
This office action is in response to communication filed on 29 January 2026.
Claims 1 – 20 are presented for examination.
The following is a FINAL office action upon examination of application number 18/540337. Claims 1 – 20 are pending in the application and have been examined on the merits discussed below.
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 .
Response to Amendment
In the response filed 29 January 2026, Applicant amended claims 1, 17, and 19.
Amendments to claims 1, 17, and 19 are insufficient to overcome the 35 USC § 101 rejection. Therefore, the 35 USC § 101 rejection of claims 1 – 20 are maintained.
Amendment to claims 17 and 19 is sufficient to overcome the 35 USC § 112 rejection. Therefore, the 35 USC § 112 rejection of claims 17 and 19 is withdrawn.
Response to Arguments
Applicant's arguments filed 29 January 2026 have been fully considered but they are not persuasive.
In the remarks regarding the 35 USC 101 rejection, Applicant argues that claims do not recite a judicial exception without significantly more. Examiner respectfully disagrees. Contrary to Applicant’s assertion, using trained sales data to change stock replenishment for a business application is not a practical application. It is not clear whether the stock replenishment entity is a person or not, so taking mathematical algorithm output of predicted future sales and providing to that entity with instructions to restock could amount to telling a human to order more items. This may be an improvement to the field of vending machine management, but it is not a technological improvement. Rather, this is an improvement to the business method. There are certainly business benefits to better methods of stocking vending machines, but the computing technology implemented into claims is generic and not necessary to perform the functional steps. This is merely an example of “apply it,” especially as the method claims only imply a computer system, as the entity performing the abstract functions of receiving data, filtering data, determining a ratio, comparing the ratio, filtering data, generating predicted sales data, and instructing to restock is not specified. There is no claim to controlling vending machines, only sending instructions, so that argument from Applicant is not relevant to current claim set. This is not a transformation of sales data to restock, as the data is merely calculated and determinations are made. The comparison to Subject Matter Eligibility Examples 47-49 are not applicable to instant claims as there is no improvement to technology as recited in Example 47, claim 3. Examiner maintains the 35 USC 101 rejection.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The independent claims recite receiving historical data comprising a plurality of time series of sales, each time series of sales associated with a product of the plurality of products and representing sales of the product from the vending machine on a plurality of days; filtering the historical data to form filtered historical data by, for each time series of sales of the plurality of time series of sale: determining a ratio between (i) a number of days of the plurality of days represented by the time series of sales on which the vending machine sold zero units of the product associated with the time series of sales; and (ii) a total number of days of the plurality of days represented by the time series of sales, such that the ratio is a proportion of the total number of days on which the product sold zero units; comparing the ratio to a predefined threshold ratio; and filtering from the historical data, conditional upon the ratio being less than the predefined threshold ratio, the time series of sales of the product; using at least parts of the filtered historical data for training data, generating a predicted sales data for the vending machine based on a recent sales data of the vending machine, and instructing a stock replenishment entity to restock the vending machine based on the predicted sales data. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance section 2106 of the MPEP (hereinafter, MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method, the system, and the computer readable media are directed to an eligible categories of subject matter. Step 1 is satisfied.
With respect to Step 2A prong 1 of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of marketing and sales, which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106. The claimed invention also recites an abstract idea that falls within the mental processes and mathematical concepts groupings, as claims describe receiving and filtering data (mental process) and calculating various ratios, averages, and mean errors (mathematical concept). The limitations reciting the abstract idea in independent claims are receiving historical data comprising a plurality of time series of sales, each time series of sales associated with a product of the plurality of products and representing sales of the product from the vending machine on a plurality of days; filtering the historical data to form filtered historical data by, for each time series of sales of the plurality of time series of sale: determining a ratio between (i) a number of days of the plurality of days represented by the time series of sales on which the vending machine sold zero units of the product associated with the time series of sales; and (ii) a total number of days of the plurality of days represented by the time series of sales, such that the ratio is a proportion of the total number of days on which the product sold zero units; comparing the ratio to a predefined threshold ratio; and filtering from the historical data, conditional upon the ratio being less than the predefined threshold ratio, the time series of sales of the product; using at least parts of the filtered historical data for training data, generating a predicted sales data for the vending machine based on a recent sales data of the vending machine, and instructing a stock replenishment entity to restock the vending machine based on the predicted sales data.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a machine learning system, processors, and non-transitory computer-readable media, to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application, because they are directed to the use of generic computing elements to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the MPEP 2106) and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a machine learning system, processors, and non-transitory computer-readable media. These elements have been considered, but merely serve to tie the invention to a particular operating environment, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. This does not amount to significantly more than the abstract idea, and it is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of further filtering historical data, determining threshold sales durations, by way of examples, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Allowable Subject Matter
Claims 1 – 20 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 112(b) and 35 U.S.C. 101, set forth in this Office action.
None of the prior art of record, taken individually or in any combination, teach, inter alia a method, system, and computer readable media for training a machine learning, ML, system to predict sales of a plurality of products available in a vending machine, the method comprising: receiving historical data comprising a plurality of time series of sales, each time series of sales associated with a product of the plurality of products and representing sales of the product from the vending machine on a plurality of days; filtering the historical data to form filtered historical data by, for each time series of sales of the plurality of time series of sale: determining a ratio between (i) a number of days of the plurality of days represented by the time series of sales on which the vending machine sold zero units of the product associated with the time series of sales; and (ii) a total number of days of the plurality of days represented by the time series of sales, such that the ratio is a proportion of the total number of days on which the product sold zero units; comparing the ratio to a predefined threshold ratio; and filtering from the historical data, conditional upon the ratio being less than the predefined threshold ratio, the time series of sales of the product; using at least parts of the filtered historical data for training the ML system; generating, via the ML system, a predicted sales data of the vending machine; and instructing a stock replenishment entity to restock the vending machine based on the predicted sales data. The closest prior art of Godwin (U.S. P.G. Pub. 2007/0136125) teaches prediction of vending machine sales with past operation history and sales history to calculate demand. However, Godwin does not disclose the particular method of time series of sales historical data, calculating a ratio of zero sales days to any sales history days, comparing to a ratio, filtering from the historical data, and using filtered data to train a machine learning model. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teaches.
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 AMANDA GURSKI whose telephone number is (571)270-5961. The examiner can normally be reached Monday to Thursday 7am to 5pm EST.
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, Brian Epstein can be reached at 571-270-5389. 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.
/AMANDA GURSKI/Primary Examiner, Art Unit 3625