Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/24/2025 has been entered.
3. The following is a non-Final Office Action. In response to Examiner’s Final Action of 09/03/2025, Applicant, on 11/24/2025, amended Claims 1-3, 5, 6, 11-13, 15 and 16; and cancelled Claims 7 and 17 . Claims 4, 8-10, 14 and 18-20 are as previously presented.
Claims 1-6, 8-16 and 18-20 are pending in the current application and have been rejected below.
Response to Amendment
4. Applicant’s amendments and arguments are acknowledged.
5. The prior 35 USC §101 rejection maintained despite Applicant’s amendments and arguments.
6. The prior 35 USC §103 rejection withdrawn, and new 35 USC §103 rejection added in light of Applicant’s amendments and arguments.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-6, 8-16 and 18-20 rejected under 35 U.S.C. 101 because, although they are drawn to statutory categories of method (process) or system (machine), they are also directed to a judicial exception (an abstract idea) without significantly more.
9. At Step 2A Prong One of the subject matter eligibility analysis, Claim 1 recites A .. method comprising: receiving .. a plurality of historic travel records for a plurality of upcoming dates relative to an upcoming travel date; generating .. a plurality of .. models .., in accordance with a plurality of configuration values .. associated with a corresponding upcoming date of the plurality of upcoming dates, each .. model of the plurality of models being associated with the corresponding upcoming date of the plurality of upcoming dates; receiving .. input travel data associated with a client .., the input travel data comprising one or more attributes including the upcoming travel date; and generating .. a report indicating a predicted demand for the upcoming travel date and each of upcoming dates of the plurality of upcoming dates before the upcoming travel date by applying one or more .. models of the plurality of .. models on the input travel data associated with the client, which is an abstract idea of Mental Processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), because generating models relating to future travel is a process that, under broadest reasonable interpretation, can be performed in the mind, since it involves observation, evaluation, judgment or opinion. Furthermore, under Broadest Reasonable Interpretation, it also falls under the abstract idea category of Certain Methods of Organizing Human Activity, particularly fundamental economic principles or practices (including mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; marketing or sales activities or behaviors; business relations) because generating a report indicating a future predicted demand is a practice of financial risk management for a business. Claim 11 recites the same abstract idea.
At Step 2A Prong Two of the analysis for independent Claims 1 and 11, the judicial exception (abstract idea) is not integrated into a practical application because the independent Claims, including additional elements such as computer-implemented, by a computer, one or more layers for each neural network, a machine-learning architecture, constructing the one or more layers, training, by the computer, each neural network model of the plurality of neural network models using (i) a set of one or more historic travel records for the corresponding upcoming date associated with the neural network model to train one or more configuration parameters of the neural network model and (ii) an optimization function to adjust the configuration values, associated with the corresponding upcoming date of the neural network model, based upon an accuracy of a prediction of the neural network model failing to satisfy a threshold value for a simulation operation, subsequent to training each neural network model of the plurality of neural network models, device, a computer comprising one or more processors, a graphical user interface, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, and the claims do not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant claims, is not indicative of integration into a practical application - see MPEP 2106.05(h); 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 using a computer as a tool to perform an abstract idea, as in the instant claims, is also not indicative of integration into a practical application - see MPEP 2106.05(f). Furthermore, display on a graphical user interface of the client device is merely post-solution activity (see MPEP 2106.05(g)). The Claims are therefore directed to the judicial exception.
At Step 2B of the analysis for independent Claims 1 and 11, the independent Claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because these additional elements such as those listed above, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figure 1 of the Drawings and paragraphs 26-28 and 63-66 of the Specification in the instant Application, and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. 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 using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant Claims, is not indicative of an inventive concept ("significantly more").
At Step 2A Prong One, dependent Claims 2-6, 8-10, 12-16 and 18-20 incorporate (and therefore recite) the abstract idea noted in independent Claims from which they depend, and further recite extensions of that abstract idea.
At Step 2A Prong Two, dependent Claims 2, 3, 5, 6, 9, 12, 13, 15, 16 and 19 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not integrate the judicial exception (abstract idea) into a practical application for the same reasons as stated above at Step 2A Prong Two for the independent Claims.
At Step 2A Prong Two, dependent Claims 4, 8, 10, 14, 18 and 20 do not integrate the judicial exception (abstract idea) into a practical application because the Claims, including additional elements such as those listed above for the independent Claims and at least one of a number of layers, a number of neurons, and an activation function of the each neural network model, a user interface, at least one of a convolutional neural network, a deep neural network, a recurrent neural network, a long short-term memory recurrent neural network, individually, and in combination, when viewed as a whole, are not an improvement to a computer or a technology, the Claims do not apply the judicial exception with a particular machine, and the Claims do not effect a transformation or reduction of a particular article to a different state or thing. Generally linking the use of the judicial exception to a particular technological environment or field of use, as in the instant claims, is not indicative of integration into a practical application - see MPEP 2106.05(h); 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 using a computer as a tool to perform an abstract idea, as in the instant claims, is also not indicative of integration into a practical application - see MPEP 2106.05(f).
At Step 2B, dependent Claims 2, 3, 5, 6, 9, 12, 13, 15, 16 and 19 do not include any additional elements beyond those included in the list above with respect to the independent Claims from which they depend. These dependent Claims therefore do not recite anything that is sufficient to amount to significantly more than the judicial exception for the same reasons as stated above at Step 2B for the independent Claims.
At Step 2B, dependent Claims 4, 8, 10, 14, 18 and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception (abstract idea), because these additional elements such as those listed above for the independent Claims and at least one of a number of layers, a number of neurons, and an activation function of the each neural network model, a user interface, at least one of a convolutional neural network, a deep neural network, a recurrent neural network, a long short-term memory recurrent neural network, individually or in combination, do not recite anything that is beyond conventional and routine activity or use of computers (as evidenced by Figure 1 of the Drawings and paragraphs 26-28 and 63-66 of the Specification in the instant Application and court decisions such as buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) discussed at 2106.05(d) of the MPEP), do not effect a transformation or reduction of a particular article to a different state or thing, nor do they apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular field of use or technological environment. 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 using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as in the instant Claims, is not indicative of an inventive concept ("significantly more").
Therefore, Claims 1-6, 8-16 and 18-20 are rejected under 35 U.S.C. 101 as being directed to non-eligible subject matter. See Alice Corp. v. CLS Bank International, 573__ U.S. 2014.
Claim Rejections - 35 USC § 103
10. The following is a quotation of 35 U.S.C. 103:
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.
35 U.S.C. 103 forms the basis for all obviousness rejections set forth in this Office action.
11. Claims 1-6, 8-16 and 18-20 rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US Patent Number 11,948,111 B1 - hereinafter Deng) in view of Candeli et al. (US Patent Publication 20200286106 A1 – hereinafter Candeli) in view of Acuna Agost et al. (US Patent Publication 20210398061 A1 - hereinafter Acuna Agost).
12. As per Claim 1, Deng teaches:
A computer-implemented method comprising: receiving, by a computer, a plurality of historic travel records for a plurality of upcoming dates relative to an upcoming travel date [DENG reads on: Abstract; Fig. 1 (system 10, database 15); Fig. 2 (FUTURE DEPARTURE DATES); Fig. 6 (CALCULATING, USING THE FORECASTING MODEL, A HISTORICAL PASSENGER DEMAND FORECAST FOR EACH KEY LEVEL IN A SET OF KEY LEVELS AND FOR EACH DEPARTURE DATE IN A FIRST SET OF DEPARTURE DATES 205); Col. 2, line 56-Col. 3, line 17 (historical data for different flights, dates); Col. 3, lines 18-51 (historical passenger demand, lines 60 and 62 represent demand, from 150 days to 331 days before departure); Col. 12, lines 4-61 (computer systems)];
generating, by the computer, one or more layers for each of a plurality of neural network models of a machine-learning architecture [DENG reads on: Fig. 1 (NEURAL NETWORK 30, TRAINED NEURAL NETWORK 45); Fig. 5, DEEP NEURAL NETWORK MODELS; Fig. 10 (forecasting model 300, CNN and LSTM models); Col. 8, lines 63-67; Col. 5 lines 23-39; Col. 7 line 20 - Col. 8 line 67; Col. 10, lines 38-40 (deep learning architecture)], in accordance with a plurality of configuration values for constructing the one or more layers associated with a corresponding upcoming date of the plurality of upcoming dates, each neural network model of the plurality of neural network models being associated with the corresponding upcoming date of the plurality of upcoming dates [DENG reads on: Fig. 2 (CURRENT SNAPSHOT); Fig. 6 (205, 210, 215); Fig. 9 (TIME SERIES FOR ALL FARE BANDS AND FOR EACH FORECAST PERIOD ACROSS DEPARTURE DATES); Fig. 10 (deep learning architecture 300); Col. 8, line 63 - Col. 9, line 22 (input data for the CNN model is a 5D tensor is a plurality of configuration values for constructing the one or more layers); Col. 9, lines 40-67 (The steps 205 and 210 are repeated [f]or all historical dates)];
training, by the computer, each neural network model of the plurality of neural network models using (i) a set of one or more historic travel records for the corresponding upcoming date associated with the neural network model to train one or more configuration parameters of the neural network model [DENG reads on: Fig. 6 (method 200); Col. 7, lines 1-19 (create a training sample at a step 210; training, using the historical demand forecasts and the training sample, the neural network 30)] and
(ii) an optimization function to adjust the configuration values [DENG reads on: Fig. 1 (OPTIMIZER 50); Col. 3, lines 1-11 (optimizer 50, uses future passenger demand and the approximated forecasting error of the future passenger demand to optimize inventory)], associated with the corresponding upcoming date of the neural network model [DENG, as above], …
… subsequent to training each neural network model of the plurality of neural network models [DENG reads on: Fig. 6 (225 USING THE TRAINED NEURAL NETWORK)],…
… generating, by the computer, a report indicating a predicted demand for the upcoming travel date and each of upcoming dates of the plurality of upcoming dates before the upcoming travel date by applying one or more neural network models of the plurality of neural network models [DENG reads on: Fig. 2; Col. 3, lines 18-51 (The system 10, specifically the forecasting model 20, uses the historical demand (indicated by the line 60) to predict the forecasted passenger demand (indicated by the line 62) and then the trained neural network 45 generates the approximated forecast error (indicated by the numeral 65) associated with that forecasted passenger demand)] …
Deng does not explicitly teach, but Candeli teaches:
… based upon an accuracy of a prediction of the neural network model failing to satisfy a threshold value for a simulation operation [CANDELI reads on: Fig. 4 (process to train an elasticity model); para 98 (a machine learning model such as a neural network); para 99; para 100 (system 102 reduces error by modifying parameters associated with the elasticity model 404); para 101 (repeats the training process, adjusting parameters to reduce the resultant error; Upon determining that the elasticity model 404 generates predicted functions within a threshold error, the transportation matching system 102 can implement the elasticity model 404 to generate a transportation metric function for a received transportation request)]; …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Deng to incorporate the teachings of Candeli in the same field of endeavor of transportation resource modeling to include based upon an accuracy of a prediction of the neural network model failing to satisfy a threshold value for a simulation operation. The motivation for doing this would have been to improve the resource management of Deng by efficiently managing transportation. See Candeli, Fig. 2 (Transportation Matching System 102).
Deng in view of Candeli does not explicitly teach, but Acuna Agost teaches:
… receiving, by the computer, input travel data associated with a client device, the input travel data comprising one or more attributes including the upcoming travel date [ACUNA AGOST reads on: Fig. 1, system 100; para 83 (computer); para 96 (incoming request 126 from a customer terminal 124 is received at the GDS 118, the information may include .. date of travel)]; and …
… on the input travel data associated with the client device [ACUNA AGOST, as above].
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Deng in view of Candeli to incorporate the teachings of Acuna Agost in the same field of endeavor of transportation resource modeling to include receiving, by the computer, input travel data associated with a client device, the input travel data comprising one or more attributes including the upcoming travel date. The motivation for doing this would have been to improve the transportation management of Deng in view of Candeli by efficiently managing inventory. See Acuna Agost, Abstract, "Methods of reinforcement learning for a resource management agent. Responsive to generated actions, corresponding observations are received. Each observation comprises a transition in a state associated with an inventory and an associated reward in the form of revenues generated from perishable resource sales. A randomized batch of observations is periodically sampled according to a prioritized replay sampling algorithm. A probability distribution for selection of observations within the batch is progressively adapted. Each batch of observations is used to update weight parameters of a neural network that comprises an approximator of the resource management agent, such that when provided with an input inventory state and an input action, an output of the neural network more closely approximates a true value of generating the input action while in the input inventory state. The neural network may be used to select each generated action depending upon a corresponding state associated with the inventory.".
13. As per Claim 2, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 1, wherein training each neural network model of the plurality of neural network models [AS ABOVE, CLAIM 1] includes
Deng further teaches:
determining, by the computer, for the particular neural network model the corresponding set of configuration parameters based upon the set of one or more historic travel records for the corresponding upcoming date [DENG reads on: Fig. 1 (FORECASTING MODEL 20, HISTORICAL PASSENGER DEMAND FORECAST 2, NEURAL NETWORK 30, TRAINED NEURAL NETWORK 45); Col. 2, line 56-Col. 3 line 20 (a forecasting model 20 that uses the historical data to create a historical passenger demand forecast)].
14. As per Claim 3, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 2, wherein training the particular neural network model [AS ABOVE, CLAIM 2] includes:
Deng further teaches:
determining, by the computer, the accuracy between the prediction and an expected output of the particular neural network model as applied to the set of one or more historic travel records for the corresponding upcoming date [DENG reads on: Fig. 1 (APPROXIMATED FORECAST ERROR OF FUTURE PASSENGER DEMAND 7); Col. 9, lines 55-63 (create a trained neural network 45 that approximates forecasting errors)]; and
Deng does not explicitly teach, but Candeli further teaches:
adjusting, by the computer, at least one configuration parameter of the one or more configuration parameters of the particular neural network model based upon the accuracy of the particular neural network model failing to satisfy the threshold value [CANDELI reads on: Fig. 1 (system 100, Server(s) 122); Fig. 4 (process to train an elasticity model); para 98 (a machine learning model such as a neural network); para 99; para 100 (system 102 reduces error by modifying parameters associated with the elasticity model 404); para 101 (repeats the training process, adjusting parameters to reduce the resultant error; Upon determining that the elasticity model 404 generates predicted functions within a threshold error, the transportation matching system 102 can implement the elasticity model 404 to generate a transportation metric function for a received transportation request)].
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Deng in view of Candeli in view of Acuna Agost to incorporate the further teachings of Candeli in the same field of endeavor of transportation resource modeling to include adjusting, by the computer, at least one configuration parameter of the one or more configuration parameters of the particular neural network model based upon the accuracy of the particular neural network model failing to satisfy the threshold value. The motivation for doing this would have been to improve the transportation resource modeling of Deng in view of Candeli in view of Acuna Agost by efficiently modeling transportation.
15. As per Claim 4, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 1 [AS ABOVE, CLAIM 1], further comprising
Deng further teaches:
constructing, by the computer, each neural network model of the plurality of neural network models according to a corresponding set of configuration values, wherein the corresponding set of configuration values includes at least one of a number of layers, a number of neurons, and an activation function of each neural network model [DENG reads on: Fig. 10 (forecasting model - CNN, LSTM are neural networks, each consisting of numbers of neurons); Col. 7, lines 20-45 (CNN model includes an input and an output layer; A Rectified Linear Unit ("RELU") function is commonly used in the activation function)].
16. As per Claim 5, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 1, wherein, for each neural network model, the computer [AS ABOVE, CLAIM 4]
Deng further teaches:
stores the adjusted configuration values associated with the corresponding upcoming date of the neural network model [DENG reads on: Col. 3, lines 1-5 (future set of dates and data is stored, with the forecasting model)].
17. As per Claim 6, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 4, wherein constructing each neural network model of the plurality of neural network models according to the corresponding set of configuration values [AS ABOVE, CLAIM 4] includes:
Deng further teaches:
determining, by the computer performing the simulation operation, the accuracy of the prediction of the particular neural network model by applying the particular neural network model on a candidate set of configuration values [DENG reads on: Abstract; Fig. 1 (FUTURE PASSENGER DEMAND 6, TRAINED NEURAL NETWORK 45, APPROXIMATED FORECAST ERROR OF FUTURE PASSENGER DEMAND 7); Col. 3 lines 7-11].
18. As per Claim 8, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 4 [AS ABOVE, CLAIM 4], further comprising
Deng in view of Candeli does not explicitly teach, but Acuna Agost further teaches:
receiving the set of configuration values via a user interface from the client device [ACUNA AGOST reads on: Fig. 1 (customer terminal 124); paras 95-96].
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Deng in view of Candeli in view of Acuna Agost to incorporate the further teachings of Acuna Agost in the same field of endeavor of transportation resource modeling to include receiving the set of configuration values via a user interface from the client device. The motivation for doing this would have been to improve the transportation resource modeling of Deng in view of Candeli in view of Acuna Agost by efficiently modeling transportation.
19. As per Claim 9, Deng in view of Candeli in view of Acuna Agost teaches:
The method according to claim 1, wherein the set of one or more historic travel records for training the neural network model [AS ABOVE, CLAIM 1] comprises
Deng further teaches:
at least one attribute, including at least one of: an event scheduled at the upcoming travel date, a particular day of a week, a particular week of a year, a holiday, or a pandemic status of a location [DENG reads on: Fig. 3 (DOW: FRIDAY)].
20. As per Claim 10, Deng in view of Candeli in view of Acuna Agost teaches:
The method of claim 1, wherein each neural network model of the plurality of neural network models [AS ABOVE, CLAIM 1] comprises
Deng further teaches:
at least one of a convolutional neural network, a deep neural network, a recurrent neural network, and a long short-term memory recurrent neural network [DENG reads on: Fig. 5 (DEEP NEURAL NETWORK MODELS)].
21. As per Claim 11, Deng teaches:
A system comprising: a computer comprising one or more processors [DENG reads on: Abstract; Fig. 1 (system 10, database 15); Col. 12, lines 4-61] configured to:
Deng does not explicitly teach, but Candeli more explicitly teaches:
.. for display on a graphical user interface of the client device [CANDELI reads on: Fig. 1 (Requester Device 116a); Fig. 7 (Communication Interface 710); …
At the time of filing, it would have been obvious to a person of ordinary skill in the art to have modified Deng to incorporate the teachings of Candeli in the same field of endeavor of transportation resource modeling to include for display on a graphical user interface of the client device. The motivation for doing this would have been to improve the transportation resource management of Deng by efficiently managing resources.
The remainder of the Claim rejected under the same rationale as Claim 1 above.
22. As per Claim 12, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 11, wherein when training each neural network model of the plurality of neural network models, the computer [AS ABOVE, CLAIM 11] is further configured to
The remainder of the Claim rejected under the same rationale as Claim 2 above.
23. As per Claim 13, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 12, wherein when training the particular neural network model, the computer [AS ABOVE, CLAIM 12] is further configured to:
The remainder of the Claim rejected under the same rationale as Claim 3 above.
24. As per Claim 14, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 11, wherein the computer [AS ABOVE, CLAIM 11] is further configured to
The remainder of the Claim rejected under the same rationale as Claim 4 above.
25. As per Claim 15, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 11, wherein, for each neural network model, the computer [AS ABOVE, CLAIM 14]
The remainder of the Claim rejected under the same rationale as Claim 5 above.
26. As per Claim 16, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 14, wherein when constructing each neural network model of the plurality of neural network models according to the corresponding set of configuration values, the computer [AS ABOVE, CLAIM 14] is further configured to:
The remainder of the Claim rejected under the same rationale as Claim 6 above.
27. As per Claim 18, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 14, wherein the computer [AS ABOVE, CLAIM 14] is further configured to
The remainder of the Claim rejected under the same rationale as Claim 8 above.
28. As per Claim 19, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 11, wherein the set of one or more historic travel records for training the neural network models [AS ABOVE, CLAIM 11] comprises
The remainder of the Claim rejected under the same rationale as Claim 9 above.
29. As per Claim 20, Deng in view of Candeli in view of Acuna Agost teaches:
The system according to claim 11, wherein each neural network model of the plurality of neural network models [AS ABOVE, CLAIM 11] comprises
The remainder of the Claim rejected under the same rationale as Claim 10 above.
Response to Arguments
30. Applicant's arguments filed 11/24/2025 have been fully considered but are found not persuasive and/or are moot in view of the new rejections.
31. Applicant argues (at pp. 8-9) that at Step 2A Prong One of the subject matter analysis, the amended claim language does not recite an abstract idea of Mental Processes or Certain Methods of Organizing Human Activity because, for example, the amended claims specify technical processes executed by a computer using configuration values for neural network models.
Examiner respectfully disagrees. The analysis at Step 2A Prong One (see MPEP 2106.04(II)A(1)), presented in detail at paragraph 9 above in this office action, clearly demonstrates the recitation of an abstract idea of Mental Processes, for example by “receiving .. a plurality of historic travel records for a plurality of upcoming dates relative to an upcoming travel date; generating .. a plurality of .. models .., in accordance with a plurality of configuration values”; and also demonstrates the recitation of Certain Methods of Organizing Human Activity, for example by “generating .. a report indicating a predicted demand for the upcoming travel date”, which is a business process under Broadest Reasonable Interpretation in light of the Specification (see paragraphs 5-7 of the Specification, for instance).
32. Applicant argues (at pp. 9-12) that the amended claim language is not directed to an abstract idea at Step 2A Prong Two of the subject matter eligibility analysis under 35 U.S.C. 101, but incorporates the abstract idea into a practical application because the claim language provides a technical improvement incorporating “a machine-learning architecture including a neural network architecture”.
Examiner respectfully disagrees. As explained in detail at paragraph 9 above in this office action, at Step 2A Prong Two the additional (computer) elements, including the neural network models, are merely used as a tool to implement the abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), and therefore do not integrate the judicial exception into a practical application of the abstract idea. The Claims are therefore directed to the judicial exception at Step 2A Prong Two; furthermore, the additional elements do not add an inventive concept (“significantly more”) at Step 2B, and the claims are thus ineligible under 35 U.S.C. 101.
33. Examiner notes that Applicant’s arguments are moot with regard to the 35 U.S.C. 103 obviousness rejection in light of the new combination of prior art references Deng, Candeli and Acuna Agost.
Conclusion
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/SARJIT S BAINS/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623