DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant amended claims and previously cancelled 8 and 11. Claims 1-7, 9-10, and 12-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The claims overcame the known prior art in the Final Office action dated 09/06/2024 and the 102/103 Rejections were withdrawn.
Amended claims introduce new matter facilitating the need for 112 Rejections.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that paragraph (para.) 24 of the specification supports a retraining function and further points to Fig. 1 wherein the user interface points back to the machine learning model.
Examiner respectfully disagrees. The cited paragraph merely recites a learning function but does not explicitly recite a retraining function with the output of the model. Examiner notes that the Specification explicitly recites training based upon historical weather conditions and historical construction project data (Specification, [24]; “a machine learning (ML) model is executed that is trained to predict weather conditions based on historical weather conditions and to predict activities that are likely affected by the predicted weather conditions. In one embodiment, the machine learning (ML) model may incorporate a weather predicting algorithm and be trained with historical construction project data”).
Applicant further contends that para. 81 describes running x number of iterations of a simulation for the machine learning process and thus supports a retraining process.
Examiner respectfully disagrees. The specification does not appear to provide support for the simulations being used to train/retrain the model. Para. 28 does simulate a sequence of construction activities based on weather predictions but running iterations of a simulation are not analogous to retraining a machine learning model.
Applicant contends that 35 USC 112 allows for alternative language not recited in the specification and that the fundamental factual inquiry conveys with reasonable clarity to those skilled in the art.
Examiner respectfully disagrees. The claim explicitly recite training but does not recite implicitly or explicitly the retraining of a model using the output of a machine learning model in a closed loop fashion.
Applicant contends that under 112(a) the input from user interface conveys with reasonable clarity that the user input is a retraining step.
Examiner respectfully disagrees. The input from a user interface could be any variety of things including selecting a timeframe to predict, selecting a location to predict for, or any other possible combinations of inputs.
Applicant contends that at para. 18 that the sequence of steps conveys retraining to a person skilled in the art of machine learning.
Examiner respectfully disagrees. The specification merely recites training based upon historic data. It would not be implicit that the user interface is used as a means for retraining a machine learning model. The specification does not appear to make any reference to retraining a machine learning model based upon the outputs of the model and therefore are not implicitly stated.
Applicant further points to para. 24 wherein the machine learning model may receive historical data from previous projects and as such implies retraining.
Examiner respectfully disagrees. The training step may be performed multiple times which constitutes as retraining but does imply the retraining based from the machine learning model and merely retrains the model using historic data.
Applicant further contends that the amended claims recite a retraining function and further an automated script which reduces errors and inefficiencies.
Examiner respectfully disagrees. The training/retraining of a machine learning model/automated script is recited at a high level of generality and as such the additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
The 101 Rejection is updated and maintained below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 9-10, and 12-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitations:
retraining the machine learning model with at least the predicted weather conditions, the predicted activities, and historical project data including activities that were delayed in projects
Claim 9 recites the limitations:
retrain the machine learning model with at least the predicted weather conditions, the predicted activities, and historical project data including activities that were delayed in projects
Claim 15 recites the limitations:
retrain the machine learning model with at least the predicted weather conditions, the predicted activities, and historical project data including activities…
Claim 2-7, 10, 12-14, and 16-20 inherit the deficiencies of the parent claim and is rejected similarly.
The Specification does discuss training the model based on historical weather and project data but does not appear to provide support for the more narrow limitation of training with at least the predicted weather conditions, the predicted activities, and historical project data including activities that were delayed in projects as predicted by the machine learning model. Examiner notes that the Specification explicitly recites training based upon historical weather conditions and historical construction project data (Specification, [24]; “a machine learning (ML) model is executed that is trained to predict weather conditions based on historical weather conditions and to predict activities that are likely affected by the predicted weather conditions. In one embodiment, the machine learning (ML) model may incorporate a weather predicting algorithm and be trained with historical construction project data”).
While Figure 1 does disclose an output of the model feeding into a GUI and further the GUI feeding back into the model; the Specification merely provides support for user input to be input into the model and does not appear to specify the predictions or output to be used for training purposes (Specification, [18]; A graphical user interface110 may be configured to provide access to functions of the prediction system110, receive input from a user, transmit input to the ML model 105, provide selectable options, and display information on a display device).
Appropriate correction is required.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
For the 112(b)/112(f) issues, Examiner suggests Applicant follow the USPTO policy on http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials, “112(f): Identifying Limitations That Invoke 112(f) Power Point,” posted August 2, 2013, slide 8, and recite that the generic placeholders, i.e. engine(s)”, “module(s)”, etc. are computer instructions stored in memory and executed by a processor to perform the claimed functions. This will overcome the 112(b) and 112(f) issues.
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 1-7, 9-10, and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1-7, 9-10, and 12-20 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 9, and 15 recite a series of steps for generating rearranged schedules for construction activities:
Regarding Claims 1 and 9.
A method performed by a computing device including at least one processor and memory, the method comprising: A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer cause the computer to:
training a machine learning model to predict weather conditions based on at least historical weather conditions and historical project data from projects with activities, wherein the machine learning model is trained to predict predicted activities that are likely affected by the predicted weather conditions;
executing the machine learning model with a set of activities to predict activities that are likely affected by predicted weather conditions;
retraining the machine learning model with at least the predicted weather conditions, the predicted activities, and historical project data including activities that were delayed in projects, to learn and prior failure patterns in the projects and to improve solutions on a next schedule to predict activities that are likely affected by the predicted weather conditions;
receiving, from the memory, a sequence of construction activities that comprises a plurality of activities;
identifying, by the computing device, a location and time periods for performing the plurality of activities;
executing the machine learning model;
executing an automated script with a plurality of URLs to access and search for historical weather conditions associated with the location and time periods for performing the plurality of activities;
establishing a network connection to a remote computing system that contains historical weather conditions based on the plurality of URLs from the automated script;
generating a request based on the location and the time periods identified,
and submitting the request, via the network connection to the remote computing system, for a set of historical weather conditions that are associated with the location and the time periods identified;
retrieving, from the remote computing system, the set of historical weather conditions that are associated with the location and the time periods for the plurality of activities, and automatically inputting, by the computing device, the set of historical weather conditions into the machine learning model;
generating, by the machine learning model, predicted weather conditions for the time periods for performing the plurality of activities based on at least the set of historical weather conditions;
simulating, by the machine learning model, a performance of the sequence of construction activities and identifying one or more target activities that cannot be performed during the predicted weather conditions;
generating a recommendation, by the machine learning model, to replace the one or more target activities in the sequence of construction activities with one or more replacement activities contained in the sequence;
wherein a replacement activity includes a different activity selected from the same sequence of construction activities, wherein the machine learning model predicts that the replacement activity can be performed during the time periods of the target activity being replaced and can be performed during the predicted weather conditions at the time periods;
replacing, by the machine learning model, the one or more target activities with the one or more replacement activities from the sequence of construction activities that can be performed during the predicted weather conditions;
generating, by the machine learning model, a rearranged sequence of the construction activities based on the replacing.
Regarding Claim 15.
A computing system, comprising:
at least one processor connected to at least one memory;
a non-transitory computer readable medium including instructions stored thereon that when executed by at least the processor cause the processor to:
train a machine learning model to predict weather conditions based on historical weather conditions and to predict activities that are likely affected by the predicted weather conditions based on at least historical project data that includes data describing weather data at an associated time and that identifies activities that were delayed;
executing the machine learning model with a set of activities to predict activities that are likely affected by predicted weather conditions;
retrain the machine learning model, with at least the predicted weather conditions, the predicted activities, and historical project data including activities that were delayed in projects, to learn based on at least the predictions of the machine learning model and prior failure patterns in the historical project data and to improve solutions on a next schedule to predict activities that are likely affected by the predicted weather conditions;
receive, from the at least one memory, a sequence of activities that comprises a plurality of activities;
identify, by the computing system, a location and time periods that are assigned for performing each activity in the sequence of activities;
executing an automated script with a plurality of URLs to access and search for historical weather conditions associated with the location and time periods for performing the plurality of activities;
establishing a network connection to a remote computing system that contains historical weather conditions based on the plurality of URLs from the automated script;
generating a request based on the location and the time periods identified,
and submitting the request, via the network connection to the remote computing system, for a set of historical weather conditions that are associated with the location and the time periods identified;
retrieve, from the remote computing system, the set of historical weather conditions that are associated with the location and the time periods for the plurality of activities, and automatically input, by the processor, the set of historical weather conditions into the machine learning model;
generate, by the machine learning model, predicted weather conditions for the location and the time periods for performing the plurality of activities based on at least the set of historical weather conditions;
predict, by the machine learning model, one or more target activities that cannot be performed during their assigned time period and during the predicted weather conditions;
wherein the one or more target activities are predicted based on an activity- weather matrix of activity types mapped to weather conditions that have associated condition thresholds that indicate whether an associated activity type cannot be performed when a condition threshold is met;
generate a recommendation, by the machine learning model, to replace the one or more target activities in the sequence of activities with one or more replacement activities contained in the sequence;
replace, by the processor, the one or more target activities with the one or more replacement activities from the sequence of activities that can be performed during the predicted weather conditions; and
generate, by the processor, a rearranged sequence of the activities based on the one or more target activities that are replaced.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed to receiving construction activities; identifying a time and period of activities, retrieving weather data, simulating data, replacing target activities with different activities and generating a rearranged sequence of activities all of which are capable of being performed in the human mind (i.e. via pen and paper) and “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claim are directed towards planning and rearranging business activities (construction work).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an: a computing device including at least one processor and memory, the method comprising: A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer cause the computer to; A computing system, comprising: at least one processor connected to at least one memory; a non-transitory computer readable medium including instructions stored thereon that when executed by at least the processor cause the processor to; training a machine learning model; retraining the machine learning model based on at least… (Examiner notes that the training/retraining steps are recited at a high level of generality. Further the retraining is based on the output/predictions and as such could include any iterative training of data and does not recite an improvement to the machine learning model); executing the machine learning model; establishing a network connection to a remote computing system; submitting the request, via the network connection to the remote computing system; retrieving, from the remote computing system; automatically inputting, by the computing device… into the machine learning model (automatically implies the use of a general purpose computer); generating, by the machine learning model, predicted weather conditions; simulating, by the machine learning model, a performance; generating a recommendation, by the machine learning model; and replacing, by the machine learning model (Examiner notes the machine learning functions are recited a high level of generality). The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Further the elements of submitting the request, via the network connection to the remote computing system and retrieving, from the remote computing system is an activity that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Regarding Claim(s) 2, 4, 5, 6, 10, 12, 13, 16, 17, 18, and 19 , the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims (i.e. machine learning model, etc.) .
Regarding Claim(s) 3, the claim further recite the additional element(s) of an activity database. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 7, 14, and 20, the claim further recite the additional element(s) of a visually displaying the rearranged sequence on a graphical user interface and providing a selectable option on the graphical user interface. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JEREMY L GUNN/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624