DETAILED ACTION
Status of the Application
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 .
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 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
This action is a Final Action on the merits in response to the application filed on 09/24/2025.
Claims 1, 9, and 17 have been amended.
Claims 1-20 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 101 rejections was added.
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 1-8 are directed towards a method, claims 9-16 are directed towards computer-readable medium, and claims 17-20 are directed towards a system all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including applying an algorithm to a dataset. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-20, the independent claims (claims 1, 9, and 17) are directed to managing the interactions of a marketplace, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A method performed by a computer system comprising a processor and a computer-readable medium, the method comprising:
defining a plurality of sub-markets within a managed marketplace, each sub-market corresponding to a particular geographic region and a particular range of time;
for each sub-market of the plurality of sub-markets within the managed marketplace,
generating an optimal supply-demand ratio that is expected to optimize an objective function with respect to the sub-market, the determining generating comprising:
using a benchmark supply-demand model to generate a benchmark supply- demand ratio;
generating a plurality of candidate supply-demand ratios based on the benchmark supply-demand ratio;
for each of the plurality of candidate supply-demand ratios:
generating a plurality of metric values using a corresponding plurality of machine-learned prediction models, each prediction model trained to output an expected value of a corresponding metric based on the candidate supply-demand ratio, wherein each prediction model is trained by a process comprising:
accessing a plurality of training examples, wherein each of the plurality of training examples comprises a value for a supply-demand ratio within a geographic region and a label indicating a value for a metric for the geographic region, wherein the metric corresponds with the prediction model;
for each of the plurality of training examples:
applying the prediction model to the supply-demand ratio of the training example to generate an output value, wherein the output value is a predicted value for the corresponding metric;
comparing the output value to the value indicated by the label of the training example, wherein the comparing comprises generating a loss score by applying a loss function to the output value and the value indicated by the label;
updating parameters of the prediction model based on the generated loss score using gradient descent;
generating an objective function score for the candidate supply-demand ratio as a combination of the plurality of metric values;
for a first sub-market of the plurality of sub-markets:
generating a current supply-demand ratio within the first sub-market; and
adjusting a policy of one or more downstream sub-systems to adjust the current supply-demand ratio within the first sub-market to the generated optimal supply-demand ratio for the first sub-market.
these steps fall within the commercial interactions such as sales activities, business relation (See MPEP 2106.04(a)(2), subsection II).
Regarding steps of:
a method performed by a computer system comprising a processor and a computer-readable medium, the method comprising:
defining a plurality of sub-markets within a managed marketplace, each sub-market corresponding to a particular geographic region and a particular range of time;
for each sub-market of the plurality of sub-markets within the managed marketplace,
generating an optimal supply-demand ratio that is expected to optimize an objective function with respect to the sub-market, the determining generating comprising:
using a benchmark supply-demand model to generate a benchmark supply- demand ratio;
generating a plurality of candidate supply-demand ratios based on the benchmark supply-demand ratio;
for each of the plurality of candidate supply-demand ratios:
generating a plurality of metric values using a corresponding plurality of machine-learned prediction models, each prediction model trained to output an expected value of a corresponding metric based on the candidate supply-demand ratio, wherein each prediction model is trained by a process comprising:
accessing a plurality of training examples, wherein each of the plurality of training examples comprises a value for a supply-demand ratio within a geographic region and a label indicating a value for a metric for the geographic region, wherein the metric corresponds with the prediction model;
for each of the plurality of training examples:
applying the prediction model to the supply-demand ratio of the training example to generate an output value, wherein the output value is a predicted value for the corresponding metric;
comparing the output value to the value indicated by the label of the training example, wherein the comparing comprises generating a loss score by applying a loss function to the output value and the value indicated by the label;
updating parameters of the prediction model based on the generated loss score using gradient descent;
generating an objective function score for the candidate supply-demand ratio as a combination of the plurality of metric values;
for a first sub-market of the plurality of sub-markets:
generating a current supply-demand ratio within the first sub-market; and
adjusting a policy of one or more downstream sub-systems to adjust the current supply-demand ratio within the first sub-market to the generated optimal supply-demand ratio for the first sub-market.
The claim does not impose any limits on how the data is output or require any particular components that are used to output the data. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of computer system, processor, computer-readable medium, model. The claims recite the steps are performed by the computer system, processor, computer-readable medium, model.
The limitations of
a method performed by a computer system comprising a processor and a computer-readable medium, the method comprising:
defining a plurality of sub-markets within a managed marketplace, each sub-market corresponding to a particular geographic region and a particular range of time;
for each sub-market of the plurality of sub-markets within the managed marketplace,
generating an optimal supply-demand ratio that is expected to optimize an objective function with respect to the sub-market, the determining generating comprising:
using a benchmark supply-demand model to generate a benchmark supply- demand ratio;
generating a plurality of candidate supply-demand ratios based on the benchmark supply-demand ratio;
for each of the plurality of candidate supply-demand ratios:
generating a plurality of metric values using a corresponding plurality of machine-learned prediction models, each prediction model trained to output an expected value of a corresponding metric based on the candidate supply-demand ratio, wherein each prediction model is trained by a process comprising:
accessing a plurality of training examples, wherein each of the plurality of training examples comprises a value for a supply-demand ratio within a geographic region and a label indicating a value for a metric for the geographic region, wherein the metric corresponds with the prediction model;
for each of the plurality of training examples:
applying the prediction model to the supply-demand ratio of the training example to generate an output value, wherein the output value is a predicted value for the corresponding metric;
comparing the output value to the value indicated by the label of the training example, wherein the comparing comprises generating a loss score by applying a loss function to the output value and the value indicated by the label;
updating parameters of the prediction model based on the generated loss score using gradient descent;
generating an objective function score for the candidate supply-demand ratio as a combination of the plurality of metric values;
for a first sub-market of the plurality of sub-markets:
generating a current supply-demand ratio within the first sub-market; and
adjusting a policy of one or more downstream sub-systems to adjust the current supply-demand ratio within the first sub-market to the generated optimal supply-demand ratio for the first sub-market.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by computer system, processor, computer-readable medium, model. The computer system, processor, computer-readable medium, model are recited at a high level of generality. In limitation (a), the machine learning model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the computer system, processor, computer-readable medium, model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
a method performed by a computer system comprising a processor and a computer-readable medium, the method comprising:
defining a plurality of sub-markets within a managed marketplace, each sub-market corresponding to a particular geographic region and a particular range of time;
for each sub-market of the plurality of sub-markets within the managed marketplace,
generating an optimal supply-demand ratio that is expected to optimize an objective function with respect to the sub-market, the determining generating comprising:
using a benchmark supply-demand model to generate a benchmark supply- demand ratio;
generating a plurality of candidate supply-demand ratios based on the benchmark supply-demand ratio;
for each of the plurality of candidate supply-demand ratios:
generating a plurality of metric values using a corresponding plurality of machine-learned prediction models, each prediction model trained to output an expected value of a corresponding metric based on the candidate supply-demand ratio, wherein each prediction model is trained by a process comprising:
accessing a plurality of training examples, wherein each of the plurality of training examples comprises a value for a supply-demand ratio within a geographic region and a label indicating a value for a metric for the geographic region, wherein the metric corresponds with the prediction model;
for each of the plurality of training examples:
applying the prediction model to the supply-demand ratio of the training example to generate an output value, wherein the output value is a predicted value for the corresponding metric;
comparing the output value to the value indicated by the label of the training example, wherein the comparing comprises generating a loss score by applying a loss function to the output value and the value indicated by the label;
updating parameters of the prediction model based on the generated loss score using gradient descent;
generating an objective function score for the candidate supply-demand ratio as a combination of the plurality of metric values;
for a first sub-market of the plurality of sub-markets:
generating a current supply-demand ratio within the first sub-market; and
adjusting a policy of one or more downstream sub-systems to adjust the current supply-demand ratio within the first sub-market to the generated optimal supply-demand ratio for the first sub-market.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-8, 10-16, 18-20 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 5, 7, 8, 13, 15, 16, recite prediction models for indicating an importance of corresponding metric; using decision trees; retraining models. The dependent claims 2-8, 10-16, 18-20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-8, 10-16, 18-20 recites computer system, processor, computer-readable medium, model which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-8, 10-16, 18-20 recites computer system, processor, computer-readable medium, model, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-8, 10-16, 18-20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 9, and 17. Therefore claims 2-8, 10-16, 18-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Response to Arguments
Applicant’s arguments filed 09/24/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 09/24/2025.
Regarding the 35 U.S.C. 101 rejection, at pg. 11-12 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
a method performed by a computer system comprising a processor and a computer-readable medium, the method comprising:
defining a plurality of sub-markets within a managed marketplace, each sub-market corresponding to a particular geographic region and a particular range of time;
for each sub-market of the plurality of sub-markets within the managed marketplace,
generating an optimal supply-demand ratio that is expected to optimize an objective function with respect to the sub-market, the determining generating comprising:
using a benchmark supply-demand model to generate a benchmark supply- demand ratio;
generating a plurality of candidate supply-demand ratios based on the benchmark supply-demand ratio;
for each of the plurality of candidate supply-demand ratios:
generating a plurality of metric values using a corresponding plurality of machine-learned prediction models, each prediction model trained to output an expected value of a corresponding metric based on the candidate supply-demand ratio, wherein each prediction model is trained by a process comprising:
accessing a plurality of training examples, wherein each of the plurality of training examples comprises a value for a supply-demand ratio within a geographic region and a label indicating a value for a metric for the geographic region, wherein the metric corresponds with the prediction model;
for each of the plurality of training examples:
applying the prediction model to the supply-demand ratio of the training example to generate an output value, wherein the output value is a predicted value for the corresponding metric;
comparing the output value to the value indicated by the label of the training example, wherein the comparing comprises generating a loss score by applying a loss function to the output value and the value indicated by the label;
updating parameters of the prediction model based on the generated loss score using gradient descent;
generating an objective function score for the candidate supply-demand ratio as a combination of the plurality of metric values;
for a first sub-market of the plurality of sub-markets:
generating a current supply-demand ratio within the first sub-market; and
adjusting a policy of one or more downstream sub-systems to adjust the current supply-demand ratio within the first sub-market to the generated optimal supply-demand ratio for the first sub-market.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing the interactions of a marketplace, which constitutes methods related to commercial interactions such as sales activities, business relation which are still considered an abstract idea under the 2019 PEG. The models are comprised of generic computer elements to perform an existing business process. Using a trained machine learning model to adjust and managing of marketplace data, is considered an improvement to an existing business process and not an improvement to the functioning of a computer, or any other technology or technological field.
Regarding, the steps at pg. 11 and 12 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
“The use of multiple prediction models, each tied to a different metric, and the
combination of their outputs into an overall objective function score, reflects a carefully engineered approach to optimizing the managed marketplace in a way that was not routine or conventional. The models are not simply generic forecasting systems; they are tuned by the claimed training process to respond to variations in supply-demand ratios within particular geographic and temporal sub-markets. This results in improved computer functionality in the specific technical context of predictive modeling and optimization for marketplace management.”
and arguments at pg. 11 and 12 seems to describe a “particular way” of managing the interactions of a marketplace. “ Lastly, the general use of machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. The claims discloses the defining of machine learning models at a high-level of generality, without incorporating any updating (i.e. training) limitations. Therefore, currently, the machine learning recited in the claims is solely used a tool to perform the instructions of the abstract idea. The Examiner recommends further amending the claim to recite the training of the machine learning, as of right now; the Examiner would like to point the Applicant to the 2019 PEG, in which claims will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Additionally, please refer above to the 35 U.S.C. 101 rejection for further explanation and rationale. Furthermore, a revised 101 rejection is now presented. The Examiner recommends the Applicant reach out to the Examiner before responding to this office action or submitting amendments to possibly overcome the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Muller et al., U.S. Pub. 20230034820, (discussing the use of machine learning-enabled decision engine in the marketplace ).
Nayak et al., U.K. Pub. GB2527414, (discussing a system and method for dynamic pricing in a retail environment).
Bernstein et al., A General Equilibrium Model for Industries with Price and Service Competition, https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/4098/federgruen_equilibrium.pdf, Operations Research, Vol. 52, No. 6, November–December 2004, pp. 868–886 (discussing the modeling of inventory to determining demands and prices).
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.
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UCHE BYRD
Examiner
Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624