DETAILED ACTION
This office action is responsive to the response filed 1/27/2026. The application contains claims 1-20, all examined and rejected.
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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitations in amended claims 2-3, 8-9, and 16-17 have been interpreted under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, because it uses a non-structural term coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the non-structural term is not preceded by a structural modifier.
Claims 2-3, 8-9, and 16-17 recites the limitation "price optimizer” coupled with functional language without reciting sufficient structure to achieve the function.
Since these claim limitations invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, claims 2-3, 8-9, and 16-17 are interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation: Fig. 1, Paragraph [0020] states, “Computing environment 100 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as model optimizer 200 and price optimizer 300” Based on the guidelines announced from Federal Register Vol. 76, No. 27, this has been interpreted as encompassing a hardware or hardware in combination with software implementation of the module, but not a pure software implementation.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Claimed modules also trigger interpretation of the claim language under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph since they are considered a place holder for a corresponding structure in the specification.
If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claims 1-3, 6-10, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ettl et al. [US 2018/0005319 A1, hereinafter Ettl] in view of Verma et al. [US 10977711B1, hereinafter Verma].
With regard to Claim 1,
Ettl teach a method, comprising:
training, by machine learning implemented using a processor (¶¶3-4, ¶14, ¶¶21-23), and using historical data (¶21, “spot market price forecast model may be based at least in part on historical price information associated with the product”, ¶18, “ segmentation parameters may include … customer relationship management (CRM) data, historical request for quote (RFQ) data”), a total demand model configured to process current data (¶22, “ generate a demand forecast model that forecasts demand over the future period of time, or more specifically, an amount of the product purchased by the buyer under the long-term contract based on terms of the long-term contract and the spot market price“, ¶24, quote request include buyer, product, commitment inputs) and, based on processing the current data, output first data indicating a predicted future total demand for a product (¶22, “ generate a demand forecast model that forecasts demand over the future period of time “, ¶24, “submit a quote request 108 to the contract optimization system 104. The quote request 108 may include various inputs such as, for example, one or more attributes of the buyer 102, one or more attributes of the desired product, a minimum amount of product that the buyer 102 will commit to buying”); and
training, by the machine learning and using the historical data and the first data indicating the predicted future total demand for the product output by the total demand model, a target demand model configured to process the current data and, based on processing the current data (¶2, “a contract optimization model based at least in part on the spot market price forecast model and the demand forecast model”, ¶¶21-23, “optimization module(s) 118 may be executed to generate a multiple period contract optimization model using the spot market price forecast model and the demand forecast model. In certain example embodiments, the contract optimization model may be a dynamic program”), output a plurality of class demand models (¶17, “clustering algorithm to be executed to segment buyers into different buyer segments 112 with respect to one or more segmentation parameters”, ¶20, “In addition to clustering buyers into different buyer segments, the clustering module(s) 110 may further determine different product segments based on various product attributes … various types of segmentation described above may be used to determine an appropriate set of models to use for a given buyer segment, product segment, etc. in order to determine optimized terms”), each of the plurality of class demand models configured to predict demand, for each of a plurality of future time periods, for a plurality of classes of the product (¶20, “In addition to clustering buyers into different buyer segments, the clustering module(s) 110 may further determine different product segments based on various product attributes … various types of segmentation described above may be used to determine an appropriate set of models to use for a given buyer segment, product segment, etc. in order to determine optimized terms”), and the plurality of class demand models configured to optimize, for each of the plurality of future time periods (¶21, “ model generation module(s) 114 may be executed to generate a spot market price forecast model that forecasts the trajectory of a spot market price of a product over a future period of time”, ¶22, “demand forecast model that forecasts demand over the future period of time”, ¶23, “generate a multiple period contract optimization model“), a respective set of optimal prices for plurality of classes of the product (¶20, ¶23, “ contract optimization module(s) 118 may be executed to determine, using the contract optimization model, optimized terms including an optimized price for a long-term contract between a buyer 102 and a seller of a product “, ¶25, “optimized price 120 may include solving the following equation …”, ¶26, “constraint wc(t,t+T) may represent the minimum quantity “), wherein the machine learning comprising the total demand model and the target demand model (¶22, “demand forecast model that forecasts demand over the future period of time”, ¶23, “generate a multiple period contract optimization model“), and wherein the total demand model is a first-tier time series model (¶22, “the demand forecast model may be vector-based time series model”) and the target demand model is a second-tier time series model (¶23, “generate a multiple period contract optimization model using the spot market price forecast model and the demand forecast model”, ¶22, “the demand forecast model may be vector-based time series model”, ¶21, “spot market price forecast model may be a vector-based time series model”).
Ettl does not explicitly teach machine learning is a hierarchical machine learning.
Verma teach a method, comprising:
training, by machine learning implemented using a processor (Fig. 11, Col. 1, lines 50-52, “Various embodiments of methods and apparatus for utilizing a hierarchy of machine learning models“, “several of the models may be trained in parallel , thereby reducing the overall training time“) and using historical data (“an initial offline variable value (such as the identity of an item consumer interacting with an e-retail site) may be used to obtain other offline variable values (such as records of earlier interactions of that item consumer with the e-retail site.)”); and
training, by the machine learning and using the historical data and the first data output by [first] model, a [second] model configured to process the current data (Fig. 5, Col. 4, lines 26-30, “ intermediate results obtained from the offline analysis subsystems may be combined with a second set of properties of the interaction session and provided as input to another machine learning model”) and, based on processing the current data, output a plurality of class models (Fig. 5, Layer-1 models 510A-510D and layer-2 models 512A-512D),
wherein the machine learning is a hierarchical machine learning comprising the [first] model and the [second] model (Fig. 5, Col. 1, lines 50-52, “Various embodiments of methods and apparatus for utilizing a hierarchy of machine learning models“, Col. 12, lines 41-44, “an intermediate or layer-2 model 512A may consume output from offline models 510A and 510B”, Col. 13, lines 13-20, “a wide variety of model and algorithm categories 610 may be used at any of the different layers of a hierarchy, including for example regression models 612 (e.g., linear regression or logistic regression models), neural network based models 614, time series models 616 …”), and wherein the [first] is a first-tier time series model and the [second] model is a second-tier time series model (Figs. 5-6, Col. 12, lines 41-44, “an intermediate or layer-2 model 512A may consume output from offline models 510A and 510B”, Col. 13, lines 13-20, “a wide variety of model and algorithm categories 610 may be used at any of the different layers of a hierarchy, including for example regression models 612 (e.g., linear regression or logistic regression models), neural network based models 614, time series models 616 …”).
Ettl and Verma are analogous art to the claimed invention because they are from a similar field of endeavor of machine learning algorithms that analyze collected data sets for various types of predictions to increase the effectiveness of various services and applications. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ettl resulting in resolutions as disclosed by Verma with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ettl as described above to provide considerable efficiencies with respect to the amount of computational power, storage, and/or network traffic needed for the machine learning portion of the application by splitting up the machine learning workload among multiple layer models instead of using a single large model (Verma, Col. 3, lines 39-47). This simply applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
With regard to Claim 2,
Ettl-Verma teach the method of claim 1, wherein a price optimizer (Ettl, ¶23, “At block 208, computer-executable instructions of the contract optimization module(s) 118 may be executed to generate a multiple period contract optimization model“) adds to a price matrix, for the respective classes of the product for each of the plurality of future time periods (Ettl, ¶¶23-24, “ contract optimization module(s) 118 may be executed to determine, using the contract optimization model, optimized terms including an optimized price for a long-term contract between a buyer 102 and a seller of a product based at least in part on the buyer segment 112 to which the buyer 102 belongs and a minimum amount of product that the buyer 102 has committed to buy under the long-term contract.”, ¶21, “generate a spot market price forecast model that forecasts the trajectory of a spot market price of a product over a future period of time”,¶22, “generate a demand forecast model that forecasts demand over the future period of time”, the optimized price determined for each buyer segment over multiple forecasted periods is a matrix (multiple dimension matrix of price optimization outcome)) the respective set of optimal prices for the plurality of classes of the product that jointly maximize total expected revenue for the product (Ettl, ¶24, “ buyer segment 112 to which the buyer 102 belongs. The contract optimization module(s) 118 may then determine, based at least in part on the determined buyer segment 112, the appropriate contract optimization model to use to determine optimized terms including an optimized price 120”, ¶¶25-26, “above equation may be solved to determine the optimized price 120 that maximizes the sum of the respective probability of purchase of the product for each time period multiplied by the maximum amount of the product that may be purchased in each time period”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 3,
Ettl-Verma teach the method of claim 2, wherein the price optimizer adds (Ettl, ¶23, “At block 208, computer-executable instructions of the contract optimization module(s) 118 may be executed to generate a multiple period contract optimization model“) to the price matrix (Ettl, ¶22, “generate a demand forecast model that forecasts demand over the future period of time”, ¶23, “contract optimization module(s) 118 may be executed to generate a multiple period contract optimization model using the spot market price forecast model and the demand forecast model”, integrating the forecasted demand outputs into the optimizer models is adding the demand data to the optimization matrix), for each of the respective classes of the product for each of the plurality of future time periods, the predicted demand for a class of the product (Ettl, ¶17, “clustering algorithm to be executed to segment buyers into different buyer segments 112 with respect to one or more segmentation parameters”, ¶20, “In addition to clustering buyers into different buyer segments, the clustering module(s) 110 may further determine different product segments based on various product attributes … various types of segmentation described above may be used to determine an appropriate set of models to use for a given buyer segment, product segment, etc. in order to determine optimized terms”, ¶22, “ generate a demand forecast model that forecasts demand over the future period of time, or more specifically, an amount of the product purchased by the buyer under the long-term contract based on terms of the long-term contract and the spot market price “).The same motivation to combine for claim 2 equally applies for current claim.
With regard to Claim 6,
Ettl-Verma teach the method of claim 1, wherein the target demand model estimates price-sensitive market share changes to predict the demand (Ettl, ¶22, “ block 206, computer-executable instructions of the model generation module(s) 116 may be executed to generate a demand forecast model that forecasts demand over the future period of time, or more specifically, an amount of the product purchased by the buyer under the long-term contract based on terms of the long-term contract and the spot market price”, ¶¶25-26, “above equation, pc may represent the optimized contact price 120 to be determined; ps may represent the spot market price (which may be determined for each of T time periods); c may represent the cost of the product (which may be determined for each of the T time periods); w may represent the maximum quantity that can be purchased a given time period (which may be determined for each of the T time periods); x may represent a vector of environment variables indicative of an impact of industry factors on price/demand forecasts (which may be determined for each of the T time periods); and Pr may represent the probability (or share) of purchase of the product from the contract with a seller … above equation may be solved to determine the optimized price 120 that maximizes the sum of the respective probability of purchase of the product for each time period multiplied by the maximum amount of the product that may be purchased in each time period”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 7,
Ettl-Verma teach the method of claim 1, wherein each of the future time periods is a time period prior to a period of service for the product (Ettl, ¶21, “model generation module(s) 114 may be executed to generate a spot market price forecast model that forecasts the trajectory of a spot market price of a product over a future period of time”, ¶22, “ block 206, computer-executable instructions of the model generation module(s) 116 may be executed to generate a demand forecast model that forecasts demand over the future period of time, or more specifically, an amount of the product purchased by the buyer under the long-term contract based on terms of the long-term contract and the spot market price”, ¶¶25-26, “above equation, pc may represent the optimized contact price 120 to be determined; ps may represent the spot market price (which may be determined for each of T time periods); c may represent the cost of the product (which may be determined for each of the T time periods); w may represent the maximum quantity that can be purchased a given time period (which may be determined for each of the T time periods); x may represent a vector of environment variables indicative of an impact of industry factors on price/demand forecasts (which may be determined for each of the T time periods); and Pr may represent the probability (or share) of purchase of the product from the contract with a seller … above equation may be solved to determine the optimized price 120 that maximizes the sum of the respective probability of purchase of the product for each time period multiplied by the maximum amount of the product that may be purchased in each time period”). The same motivation to combine for claim 1 equally applies for current claim.
Regarding claim 8,
Claim 8 is similar in scope to claim 1; therefore it is rejected under similar rationale. In addition Ettl disclose a processor (¶¶3-4).
Regarding claim 9,
Claim 9 is similar in scope to claim 2; therefore it is rejected under similar rationale.
Regarding claim 10,
Claim 10 is similar in scope to claim 3; therefore it is rejected under similar rationale.
Regarding claim 13,
Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale.
Regarding claim 14,
Claim 14 is similar in scope to claim 7; therefore it is rejected under similar rationale.
Regarding claim 15,
Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale. In addition Ettl disclose a processor (claim 8, ¶4).
Regarding claim 16,
Claim 16 is similar in scope to claim 2; therefore it is rejected under similar rationale.
Regarding claim 17,
Claim 17 is similar in scope to claim 3; therefore it is rejected under similar rationale.
Regarding claim 20,
Claim 20 is similar in scope to claim 6; therefore it is rejected under similar rationale.
Claims 4-5, 11-12, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ettl et al. [US 2018/0005319 A1, hereinafter Ettl] in view of Verma et al. [US 10977711B1, hereinafter Verma] in view of Young et al. [KR20210063005A, hereinafter Young].
With regard to Claim 4,
Ettl-Verma disclose the method of claim 1, wherein the historical data comprises external data comprising historical decisions corresponding to the historical decisions pertaining to pricing of the product (Ettl, ¶21, “spot market price forecast model may be based at least in part on historical price information associated with the product”, ¶18, “ segmentation parameters may include … customer relationship management (CRM) data, historical request for quote (RFQ) data”, REQ is a decision to request data or offer regarding a specific product). The same motivation to combine for claim 1 equally applies for current claim.
Ettl-Verma does not explicitly teach results corresponding to the historical decisions pertaining to pricing of the product and results corresponding to the historical decisions.
Young teach historical data comprises external data (P.4, ¶¶8-9, “real estate price change prediction server of the present invention has specified three databases as collection objects in order to effectively collect and analyze the observed real estate-related events 99 first. The first is a database (1) about unstructured texts. The database 1 is a text data storage and stores unstructured texts such as news, reports, press releases, and SNS texts related to real estate. The second is the database (2) storing time series data. This database is a time-series data storage and stores real estate-related statistics and economic-related statistics with time-series data characteristics, such as stock index, interest rate, real estate index, and the like numerical data. Third, it is a database 3 for storing structured data. This database 3 is a storage for storing structured data such as real estate policy, base rate change, tax reform, and loan regulation”) comprising historical decisions (P.3, ¶¶2-3, “extract text unstructured events, time series numeric data events, and structured data events from the data store; The extracted events are classified into primary factors, secondary factors, and tertiary factors according to their influence on the real estate price, which is a target variable, but the primary factors including loan regulation items, base rate change items, and new sale items …”) and results corresponding to the historical decisions pertaining to pricing of the product and results corresponding to the historical decisions (P.4, ¶¶8-9, “real estate price change prediction server of the present invention”, P.6, ¶¶1-2, ¶4, “real estate price change prediction model learner 50 generates and learns a predictive model using the events classified in the event factor importance analysis step as input variables and real estate price index as output variables. The probability distribution of the causal relationship and temporal relationship of the collected event data is learned using the provided probabilistic model.”, “real estate price change prediction server of the present invention includes the steps of extracting an event from text, time series, and structured data, analyzing the importance of the factors of the event, learning a real estate price change prediction model, , predicting future real estate price changes using the observed real estate-related events, respectively”).
Ettl-Verma and Young are analogous art to the claimed invention because they are from a similar field of endeavor of predicting prices and demand. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ettl-Verma resulting in resolutions as disclosed by Young with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ettl-Verma as described above to find a method for objectively predicting in consideration of the time delay, the degree of influence, overlap, uncertainty, etc. applied between the various causes and results (Young, P.2, ¶6).
With regard to Claim 5,
Ettl-Verma disclose the method of claim 1, wherein the historical data (Ettl, ¶21, “spot market price forecast model may be based at least in part on historical price information associated with the product”, ¶18, “ segmentation parameters may include … customer relationship management (CRM) data, historical request for quote (RFQ) data”, REQ is a decision to request data or offer regarding a specific product). The same motivation to combine for claim 1 equally applies for current claim.
Ettl-Verma does not explicitly teach historical data comprises results of performing natural language processing on media content.
Young teach historical data comprises results of performing natural language processing on media content (P.4, ¶¶8-9, “real estate price change prediction server of the present invention has specified three databases as collection objects in order to effectively collect and analyze … database 1 is a text data storage and stores unstructured texts such as news, reports, press releases, and SNS texts related to real estate. The second is the database (2) storing time series data. This database is a time-series data storage and stores real estate-related statistics and economic-related statistics with time-series data characteristics, such as stock index, interest rate, real estate index, and the like numerical data. … database 3 for storing structured data”, P.5, ¶2, “event is extracted from an event sentence containing specific content on a specific topic from a text document expressing real estate-related psychology such as news, reports, press releases, and SNS comments. The term “event sentence” as used herein refers to a sentence in which specific content on a specific topic, ie, who, where, when, what, what, and the like is expressed”, ¶5, “text unstructured event extractor 10 extracts the unstructured event. A main entity is extracted from the real estate-related text 11 stored in the database 1 (S13). An entity is a word or text pattern that contains key information. In the case of the example sentence presented above, words, patterns, and phrases such as the Ministry of Land, Infrastructure and Transport, October 10, high-priced houses, single-homeowners, and limited public guarantees for jeonse loans are extracted. Next, meta information such as date and source is extracted from the text (S14). On the other hand, the document is classified using this meta information (S15). Classifying documents has the advantage of being able to extract events from only texts in the appropriate field by checking the type and source of the document”).
Ettl-Verma and Young are analogous art to the claimed invention because they are from a similar field of endeavor of predicting prices and demand. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ettl-Verma resulting in resolutions as disclosed by Young with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ettl-Verma as described above to predict psychological trends using text analysis, and statistical indicators such as interest rates, exchange rates, stock indices, and wages also become major factors in real estate price fluctuations (Young, P.2, ¶2, “analyzing the impact on the real estate market using online text data such as news, social networks, and blogs is being introduced. From a mid to long-term perspective, it is necessary to predict psychological trends using text analysis, and statistical indicators such as interest rates, exchange rates, stock indices, and wages also become major factors in real estate price fluctuations”).
Regarding claim 11,
Claim 11 is similar in scope to claim 4; therefore it is rejected under similar rationale.
Regarding claim 12,
Claim 12 is similar in scope to claim 5; therefore it is rejected under similar rationale.
Regarding claim 18,
Claim 18 is similar in scope to claim 4; therefore it is rejected under similar rationale.
Regarding claim 19,
Claim 19 is similar in scope to claim 5; therefore it is rejected under similar rationale.
Response to Arguments
Applicant’s arguments, see Remarks P. 9, filed 1/27/2026, with respect to the rejection(s) of claim(s) 1-3,6-10,13-17, and 20 under 35 USC 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Verma et al.. Verma et al. disclose machine learning is a hierarchical machine learning comprising the [first] model and the [second] model (Fig. 5, Col. 1, lines 50-52, “Various embodiments of methods and apparatus for utilizing a hierarchy of machine learning models“, Col. 12, lines 41-44, “an intermediate or layer-2 model 512A may consume output from offline models 510A and 510B”, Col. 13, lines 13-20, “a wide variety of model and algorithm categories 610 may be used at any of the different layers of a hierarchy, including for example regression models 612 (e.g., linear regression or logistic regression models), neural network based models 614, time series models 616 …”), and wherein the [first] is a first-tier time series model and the [second] model is a second-tier time series model (Figs. 5-6, Col. 12, lines 41-44, “an intermediate or layer-2 model 512A may consume output from offline models 510A and 510B”, Col. 13, lines 13-20, “a wide variety of model and algorithm categories 610 may be used at any of the different layers of a hierarchy, including for example regression models 612 (e.g., linear regression or logistic regression models), neural network based models 614, time series models 616 …”).
Ettl and Verma are analogous art to the claimed invention because they are from a similar field of endeavor of machine learning algorithms that analyze collected data sets for various types of predictions to increase the effectiveness of various services and applications. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ettl resulting in resolutions as disclosed by Verma with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ettl as described above to provide considerable efficiencies with respect to the amount of computational power, storage, and/or network traffic needed for the machine learning portion of the application by splitting up the machine learning workload among multiple layer models instead of using a single large model (Verma, Col. 3, lines 39-47).
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 2022/0351223 A1 filed by Vandkadaru et al. that disclose a method further includes predicting current demand value of the product based on the supply chain attributes using artificial intelligence-based models. Further, generating an optimum price value for the product based on the current demand value. Additionally, computing average price value at a regional level for the product based on regional product information retrieved from one or more local authority databases using artificial intelligence-based models. The method further includes determining a best suitable price value. Also, simulating the best suitable price value for the product in a simulation environment using one or more artificial intelligence-based models. Furthermore, the method includes generating a final price value See at least Abstract
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
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 MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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