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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites the steps of generating a plurality of sequences for a workflow, the workflow including a plurality of steps (which falls into the mental processes grouping of abstract ideas); deploying each respective sequence of the plurality of sequences for the workflow to a respective set of users (which falls into the certain methods of organizing human activity grouping of abstract ideas, as it consists of instructing a group of people how to perform work); calculating a reward metric for each respective sequence based on a performance metric for users who complete the workflow and a performance metric for users who abandon the workflow or who have not executed the workflow (a mental process of judgement); and to predict an optimal workflow for a user (a mental process of judgement). Thus, the claim recites an abstract idea of predicting an optimal workflow for a user based on performances of users executing sequences of steps of the workflow.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
the fact that the method is processor-implemented, and the performance of an abstract idea on generic computer components cannot integrate the abstract idea into a practical application (see MPEP 2105.05(f)(2), “apply it on a computer”)
the requirement of training a machine learning model, based on a training data set including the plurality of sequences and the reward metric for each respective sequence, the machine learning model being trained to maximize the reward metric to perform the step of predicting an optimal workflow, because this limitation merely recites to “use a computer or other machinery as a tool” to perform the mental process step of predicting the optimal workflow (see MPEP 2106.05(f)(2), “apply it”).
Therefore, the claim is directed to the abstract idea of predicting an optimal workflow for a user based on performances of users executing sequences of steps of the workflow.
Finally, the additional elements, taken alone or in combination, cannot provide significantly more than the abstract idea itself, because mere instructions to apply the abstract idea cannot do so (MPEP 2106.05(f)(2)). Thus, the claim is subject-matter ineligible.
Claims 2-4 and 6, dependent upon Claim 1, merely recite details about the information being processed in the execution of the mental process steps, e.g. they specify a particular technological environment or field of use in which the abstract idea is performed, which by MPEP 2106.05(h) can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 5 and 7-10, dependent upon Claim 1, merely recite additional details of the steps of the mental processes (Claims 5 and 7-9: calculating the reward metric comprises calculating [certain specific values]; Claim 10: generating the plurality of sequences comprises generating …).
Claim 11 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites the steps of generating a workflow sequence that maximizes a reward metric for the user of the software application using features associated with the user of the software application (which falls into the mental processes grouping of abstract ideas); and executing the generated workflow sequence (which falls into the certain methods of organizing human activity grouping of abstract ideas, as it consists of instructing a group of people how to perform work). Thus, the claim recites an abstract idea of predicting an optimal workflow for a user and executing that workflow.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
the fact that the method is processor-implemented, and the performance of an abstract idea on generic computer components cannot integrate the abstract idea into a practical application (see MPEP 2105.05(f)(2), “apply it on a computer”)
receiving, from a user of a software application, a request to execute a workflow in the software application, which is insignificant extra-solution activity of data-gathering, necessary for all uses of the abstract idea (see MPEP 2106.05(g))
the requirement of using a predictive model to perform the step of predicting an optimal workflow, because this limitation merely recites to “use a computer or other machinery as a tool” to perform the mental process step of predicting the optimal workflow (see MPEP 2106.05(f)(2), “apply it”).
Therefore, the claim is directed to the abstract idea of predicting an optimal workflow for a user and executing that workflow.
Finally, the additional elements, taken alone or in combination, cannot provide significantly more than the abstract idea itself, because mere instructions to apply the abstract idea cannot do so (MPEP 2106.05(f)(2)) and because receiving a request is well-understood, routine, and conventional (see MPEP 2106.05(d), “transmitting or receiving data over a network”), and because there is no nexus between the additional elements which could constitute an inventive concept. Thus, the claim is subject-matter ineligible.
Claims 12-14, dependent upon Claim 11, merely recite details about the information being processed in the execution of the mental process steps, e.g. they specify a particular technological environment or field of use in which the abstract idea is performed, which by MPEP 2106.05(h) can neither integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 15-20 recite a processing system comprising: at least one memory and one or more processors configured to perform precisely the methods of Claims 1, 4-7, and 10, respectively. As performance of an abstract idea on generic computer components can neither integrate the abstract idea into a practical application nor provide an inventive concept (see MPEP 2106.05(f)(2)), Claims 15-20 are rejected for reasons set forth in the rejections of Claims 1, 4-7, and 10, respectively.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6-9, 11-13, 15, 18, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cruz-Benito et al., “Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning.”
Regarding Claim 1, Cruz-Benito teaches a processor-implemented method (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github” denotes that the inventors perform their method on a computer), comprising: generating a plurality of sequences for a workflow, the sequences including a plurality of steps (Cruz-Benito, Abstract, “the aim of improving users’ performance in completing large questionnaires through adaptability in web forms” and pg. 2252, 1st column, 2nd paragraph, “these web forms … typically include between 30 and 70 questions” denotes a workflow including a plurality of steps & pg. 2255, Fig. 1, “A/B tests” and “Different versions of web forms” and pg. 2253, 2nd column, last paragraph, “three different variations, called verticals A, B, and C” denotes a plurality of sequences for the workflow); deploying each respective sequence of the plurality of sequences for the workflow to a respective set of test users (Cruz-Benito, pg. 2255, Fig. 1, “A/B tests”); calculating a reward metric for each respective sequence based on a performance metric for users who complete the workflow and a performance metric for users who abandon the workflow or have not executed the workflow (Cruz-Benito, Abstract, “users’ performance in completing large questionnaires” & pg. 2258, “the finalization rates of the questionnaires of all clusters were calculated” i.e. counting whether the user completes the survey or not are reward metrics); and training a machine learning model, based on a training data set including the plurality of sequences and the reward metric for each respective sequence, to predict an optimal workflow for a user, the machine learning model being trained to optimize the reward metric (Cruz-Benito, pg. 2255, 2nd paragraph, “heuristic rules obtained at the end of the machine-learning workflow inferred from the machine learning results … These rules were applied to redirect users within the different verticals of the A/B tests” denotes that the trained machine-learning model is used to predict which of the three forms/workflows is most appropriate for any individual user, to optimize the finalization rate/reward metric).
Regarding Claim 2, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches wherein the plurality of sequences for the workflow includes a statically defined baseline sequence for the workflow (Cruz-Benito, pg. 2253, 2nd column, last paragraph, “three different variations, called verticals A, B, and C” where “A” is a baseline versions and the web forms are one of three fixed versions, i.e. statically defined).
Regarding Claim 3, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches wherein the training data set further includes one or more features associated with each user of the workflow (Cruz-Benito, pg. 2256, 1st column, last paragraph, “tablet_or_mobile” etc. describe features of the device with which the user accesses the webform).
Regarding Claim 6, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches wherein the reward metric comprises a different between a performance metric for users who complete the workflow and a performance metric for users who abandon the workflow or have not executed the workflow (Cruz-Benito, Abstract, “users’ performance in completing large questionnaires” & pg. 2258, “the finalization rates of the questionnaires of all clusters were calculated” i.e. determining a difference between users who complete and do not complete the questionnaires).
Regarding Claim 7, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches wherein the reward metric comprises calculating a cumulative reward metric for a user over multiple instances of executing the workflow (Cruz-Benito, pg. 2261, 1st column, “After the experiment took place … all of the users who entered or returned to the questionnaire … were sought to obtain the results regarding the application of redirection criteria … the study [using redirection] achieved a questionnaire completion rate of 77.38%, improving the previous rate” i.e. the participants were presented the questionnaire twice and finalization rates of both instances were computed).
Regarding Claim 8, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches wherein calculating the reward metric comprises calculating a metric based on a number of times a user completed the workflow (Cruz-Benito, pg. 2258, “the finalization rates of the questionnaires of all clusters were calculated” i.e. whether the user completed the workflow zero or one times; also, a total number of times any user completed the workflow vs the number of times the workflow was presented to a user).
Regarding Claim 9, Cruz-Benito teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). Cruz-Benito further teaches wherein calculating the reward metric comprises calculating the metric based further on a defined value associated with completing the workflow (Cruz-Benito, pg. 2258, 2nd column, 3rd paragraph, “the finalization rates of the questionnaires of all clusters were calculated” i.e. a total number of times any user completed the workflow vs the number of times the workflow was presented to a user, each of these values is a defined value associated with completing the workflow).
Regarding Claim 11, Cruz-Benito teaches a processor-implemented method (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github” denotes that the inventors perform their method on a computer), comprising: receiving, from a user of a software application, a request to execute a workflow in the software application (Cruz-Benito, Abstract, “the aim of improving users’ performance in completing large questionnaires through adaptability in web forms” & pg. 2260, 2nd column, last paragraph, “These rules were implemented in the OEEU’s ecosystem to apply them whenever a new user enters or resumes the questionnaire” where entering a questionnaire denotes the ecosystem receiving a request to execute the webform/workflow); generating, using a predictive model and features associated with the user of the software application, a workflow sequence that maximizes a reward metric for the user of the software application (Cruz-Benito, pg. 2260, 2nd column lists a set of derived rules, i.e. a predictive model based on features associated with the user to choose a particular sequence/web form in order to maximize finalization of the questionnaire, see Abstract, “the aim of improving users’ performance in completing large questionnaires”); and executing the generated workflow sequence (Cruz-Benito, pg. 2260, 2nd column, last paragraph, “These rules were implemented in the OEEU’s ecosystem to apply them” i.e. to provide the web form vertical A or B to the user to complete/execute the workflow sequence).
Regarding Claim 12, Cruz-Benito teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). Cruz-Benito further teaches wherein the features associated with the user comprise at least one of static features defining characteristics of the user (Cruz-Benito, pg. 2260, 2nd column lists the rules based on features including device operating system, i.e. which device the user connects to the application with, etc., are characteristics of the user).
Regarding Claim 13, Cruz-Benito teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). Cruz-Benito further teaches wherein the reward metric comprises a cumulative reward metric calculated over each step in the generated workflow (Benito-Cruz, pg. 2258, 2nd column, 3rd paragraph, “the finalization rates” where “finalization rate” is cumulative over each step¸ i.e. the questionnaire is only completed if each and every step is completed).
Claims 15, 18, and 19 recite a processing system comprising: at least one memory and one or more processors configured to perform precisely the methods of Claims 1, 6, and 7, respectively. As Cruz-Benito performs their method on a computer (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github”) in which memory and a processor are inherent, Claims 15, 18, and 19 are rejected for reasons set forth in the rejections of Claims 1, 6, and 7, respectively.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz-Benito et al., “Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning.”, in view of Xiang, US PG Pub 2023/0252499.
Regarding Claim 4, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito is silent regarding how many users are assigned to each vertical, but Xiang teaches wherein a number of users in the respect set of test users is based on a number of sequences in the plurality of sequences and a total number of test users participating in the workflow (Xiang, [0012], “For example, A/B tests generally assign users to the experiences under test at random and with equal probability” thus the number in any set will be based on the total number of test users and the probability 1/(number of sequences i.e. verticals). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to randomly assign users with equal probability, as teaches Xiang, in the method of Cruz-Benito. The motivation to do so is that this how “A/B tests generally assign users” (Xiang, [0012]), i.e. this is how it is normally done.
Claim 16 recites a processing system comprising: at least one memory and one or more processors configured to perform precisely the method of Claim 4. As Cruz-Benito performs their method on a computer (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github”) in which memory and a processor are inherent, Claim 16 is rejected for reasons set forth in the rejections of Claim 4.
Claims 5, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz-Benito, in view of Quin et al., “A/B Testing: A Systematic Literature Review.”
Regarding Claim 5, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Cruz-Benito further teaches the reward metric comprises calculating a … difference between the users who complete the workflow and the users who abandon the workflow or have not executed the workflow (Cruz-Benito, Abstract, “users’ performance in completing large questionnaires” & pg. 2258, “the finalization rates of the questionnaires of all clusters were calculated” i.e. determining a difference between users who complete and do not complete the questionnaires). However, Cruz-Benito teaches web forms in an educational environment, and thus does not teach where the reward metric comprises revenue differences. However, Quin, in the analogous art of A/B software testing of web forms for increasing user participation, teaches revenue as a reward metric (Quin, pg. 15, 2nd paragraph, “The third group of A/B metrics we identified are metrics related to monetization, i.e. revenue and cost”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use revenue as a metric to optimize via A/B testing for online tasks such as filling out web forms. The motivation to do so is because increased revenue is a desirable outcome.
Regarding Claim 14, Cruz-Benito teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). Cruz-Benito teaches web forms in an educational environment, and thus does not teach where the reward metric comprises a total revenue associated with completion of the workflow. However, Quin, in the analogous art of A/B software testing of web forms for increasing user participation, teaches revenue as a reward metric (Quin, pg. 15, 2nd paragraph, “The third group of A/B metrics we identified are metrics related to monetization, i.e. revenue and cost”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use revenue as a metric to optimize via A/B testing for online tasks such as the completion of the workflow web forms of Cruz-Benito. The motivation to do so is because increased revenue is a desirable outcome.
Claim 17 recites a processing system comprising: at least one memory and one or more processors configured to perform precisely the method of Claim 5. As Cruz-Benito performs their method on a computer (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github”) in which memory and a processor are inherent, Claim 17 is rejected for reasons set forth in the rejections of Claim 5.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz-Benito, in view of Schmidt-Karaca, US PG Pub 2018/0157987.
Regarding Claim 10, Cruz-Benito teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). In Cruz-Benito, the questionnaires appear to have the same numbers of questions, and as such Cruz-Benito does not teach wherein generating the plurality of sequences for the workflow comprises generating one or more sequences including a set of steps including a number of steps less than a number of steps in the plurality of steps. However, Schmidt-Karaca, in the analogous art of custom fillable forms, teaches this limitation (Schmidt-Karaca, Fig. 7, where both a) the questions come in “blocks”, i.e. a sequence includes a set of steps including a number of steps less than a number of the total plurality of steps, i.e. a ”block” is a set including less than all the steps; and b) not all blocks are included, see Fig. 7, element 740, and thus a sequence includes a set of steps including a number of steps less than a number of the total plurality of steps). It would have been obvious to one of ordinary skill in the art before the effective filing date to present workflows with sets with less than the total number of steps, as does Schmidt-Karaca, in the invention of Cruz-Benito. The motivation to do so is a) some sets of questions naturally fall into blocks and b) not all blocks are relevant for all users (Schmidt-Karaca, [0001]).
Claim 20 recites a processing system comprising: at least one memory and one or more processors configured to perform precisely the method of Claim 10. As Cruz-Benito performs their method on a computer (Cruz-Benito, pg. 2252, 1st column, 3rd paragraph, “this paper provides all the code used in the analysis process … in Github”) in which memory and a processor are inherent, Claim 20 is rejected for reasons set forth in the rejections of Claim 10.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 11-14, and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 8-11, and 12 of copending Application No. 18/680,861 (reference application), respectively. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the reference application anticipate the claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Mascaro, US PG Pub 2017/0200087, teaches A/B testing for fillable web forms, with a goal of increasing revenue.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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