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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/15/2026 has been entered.
Response to Arguments
Applicant's arguments filed 01/15/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC 103 rejection of claims 1-22 are applied in light of Applicant's amendments. Additionally, a 35 USC 101 rejection has been added. In regards to the newly added 101 rejection, the Examiner suggests looking at elements in paragraph 0060 from the spec as a possible avenue to overcome the 101 rejection.
Applicant’s arguments with respect to the rejection to claim 1 of 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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, 3-12, 14-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1, 3-12, 14-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1, 3-10, and 21-22), computer program product (claim 11), and system (claims 12 and 14-19) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and Certain methods of organizing human activity), as set forth in exemplary claim 1, are: determining attributes of an event log relating to a process; determining attributes of an end task of the process based on a comparison of a vector representation of the end task to vector representations to a set of criteria relating to process representations; tuning hyper-parameters of a plurality of process mining models, based on the attributes of the event log and the attributes of the end task, using reinforcement learning to identify a set of hyper-parameters for each of the plurality of process mining models that maximizes a respective reward based on a set of criteria; ranking the plurality of process mining models based on the attributes of the event log and the attributes of the end task; mining the event log using a top-ranked process mining model of the plurality of process mining models to generate a process representation; identifying an inefficiency of the process based on the process representation.
Independent claims 11 and 12 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: and automatically correcting the inefficiency by modifying a software program that performs the process to improve an efficiency of the software program…A computer program product for process analysis, the computer program product comprising a computer readable storage medium having program instructions embodied there with, the program instructions executable by a hardware processor to cause the hardware processor to…A system of process analysis, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor … (as recited in claims 1, 11, and 12). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: and automatically correcting the inefficiency by modifying a software program that performs the process to improve an efficiency of the software program…A computer program product for process analysis, the computer program product comprising a computer readable storage medium having program instructions embodied there with, the program instructions executable by a hardware processor to cause the hardware processor to…A system of process analysis, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor… (as recited in claims 1, 11, and 12). for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (3-10, 14-19, and 21-22) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 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, 4, 8-9, 11, 15, 19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 8407081 (hereinafter “Raj”) et al., in view of U.S. PGPub 20210201334 to (hereinafter “Walters”) et al., in further view of U.S. Patent 9304776to (hereinafter “Dice”) et al., in even further view of U.S. PGPub 20230096654 (hereinafter “Salameh”) et al.
As per claim 1, Raj teaches a computer-implemented method of process analysis, comprising:
determining attributes of an event log relating to a process; determining attributes of an end task of the process based on a comparison of a vector representation of the end task to vector representations to a set of criteria relating to process representations; tuning hyper-parameters of a plurality of process mining models based on the attributes of the event log and the attributes of the end task; mining the event log using a top-ranked process mining model of the plurality of process mining models to generate a process representation; identifying an inefficiency of the process based on the process representation; Raj 015-019: “In accordance with the present invention, systems, and apparatuses examine a process and its components, perform statistical analyses on the resources used in the process, and identify proposed process component/resource allocations likely to result in overall process improvement… a TIMSA-DAP (Time, Information, Motivation, Skill, Authority-Daily Action Plan) methodology, as described in the above-referenced related applications, is used to not only prevent process breakdown, but to also facilitate best process adoption… a labor arbitrage methodology, as also described in the above-referenced related applications, is used to iteratively develop, implement, and redesign best practices, focusing not necessarily on shift labor, but on the labor used within a process ("process labor")… process mining techniques analyze event logs, ontology and tagging data to provide inputs to TIMSA-DAP and labor arbitrage processing.”0133: “the DAP structure, distribution, and content need not be static, but can rather change based on the current situation (i.e., what sensors need addressed, what roles are available today, etc.) to overall minimize the risk of breakdown (e.g., auto-bypass of failing role-action nodes). Process dependent evaluation is used for tuning of the process when it is known that inter-related tactics will affect one another and the resources used to accomplish them. In alternate embodiments, TIMSA process dependent could be based on modeling simulations, feedback on time spent, adequacy of the information, role-interactions and personality conflicts, policy constraints or conflicts.065-067: “ Process mining in a preferred embodiment takes this information and processes it in combination with information from a knowledge base in order to provide probabilistic causality predictions (i.e., cause-effect relationships), as well as ascribing "comparability" factors to actions performed, such as by use of tagging and ontology analysis…Exploratory analysis is used to identify the most relevant variables and the complexity of the models needed to identify relationships and patterns in the database. Next, a validation stage of analysis is undertaken by applying analysis to historical data and checked to see which of a pool of potential forms of analysis provide the best results in predicting other known historical data. For example, certain characteristics of particular caregivers may be highly important to efficiency in one process but not in another. By testing to see how a particular past process could have been predicted by one of several models, a best model for predicting performance is selected. A third phase of process mining is to use the identified patterns that are selected as best and apply them to new data to create predictions of expected outcomes... process mining identifies best paths by examining key metrics in a case evolotion timeline or CET, i.e., a correspondence among a set of capabilities (e.g., role-action pairs), their "deltas" or the additions/subtractions applicable to them, the comparable capabilities surrounding them.”020, 0164, 0260: “FIG. 1 is a high-level block diagram of a system 100 for identifying and correcting process breakdowns according to an embodiment of the present disclosure…TIMSA-DAP analysis is not limited to only identifying and preventing process breakdowns; in a preferred embodiment it is also used to reduce inefficiencies as part of overall process improvement. As introduced above, DAPs are used to bridge gaps in knowledge and ability between demands of a work process and the supply of roles to accomplish the work process, and TIMSA-informed DAPs are self-reinforcing in that efficiency is increased via residual effects on a role with repeated DAPs as well as "halo" effects from roles educating and informing other roles…Then we move onto factor analysis based on a priori knowledge and "concepts" mining for correlations and potential causality. Here, the a priori knowledge knows to look for changes (deviations) between schedule data and timeclock data…It appears as if these two implied actions (both found in the a priori knowledge, but they could be labeled by the method arbitrarily, like action1, action2, etc.) are the culprits to the inefficiency... 0281: Look at PCPs to determine deal value by looking at each PCP to determine its expected path Look for worst expected paths and their opportunity costs Opportunity costs initiate when projection for current Best Path based on indicators or possible better actions deviate from the current projection of path based on current most likely actions (by seeing this, we can do "course corrections" and adjust to get back onto Best Path)”
Raj may not explicitly teach the following. However, Walters teaches:
ranking the plurality of process mining models based on the attributes of the event log and the attributes of the end task;;Walters 0041-0043: “FIG. 4 illustrates more details of an embodiment of the operation of the model management system 110. In addition to the criterion based clustering routine 308, and acceptability prediction routine 202, the model management system 110 may include a model rank ordering routine 402. The model rank ordering routine 402 may operate to generate and output a set of models, such as a set of higher ranked models than the model. The model rank ordering routine 402 may further output a list of models according to a rank ordering, as defined below. The model rank ordering routine 402 may include a reviewed model model accuracy component 406, providing an accuracy metric for reviewed models of the reviewed model collection 302. The model rank ordering routine 402 may also include a reviewed model acceptability component 408, providing an acceptability probability for reviewed models. As such, using the reviewed model model accuracy component 406 and the reviewed model acceptability component 408, the model rank ordering routine 402 may generate a rank order of model type for a plurality of reviewed models by calculating an approval metric based upon an acceptability probability and a model accuracy for a plurality of reviewed models... When an approval metric is determined for each of a plurality of reviewed models, the model rank ordering routine 402 may perform a rank ordering of a plurality of model types according to the approval metric for each reviewed model, such as from low to high or high to low.”
Raj and Walters are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj with the aforementioned teachings from Walters with a reasonable expectation of success, by adding steps that allow the software to utilize ranking with the motivation to more efficiently and accurately analyze data [Walters 0041].
Raj and Walters may not explicitly teach the following. However, Dice teaches:
and automatically correcting the inefficiency by modifying a software program that performs the process to improve an efficiency of the software program; Dice, Abstract: “A computer system may recognize a busy-wait loop in program instructions at compile time and/or may recognize busy-wait looping behavior during execution of program instructions. The system may recognize that an exit condition for a busy-wait loop is specified by a conditional branch type instruction in the program instructions. In response to identifying the loop and the conditional branch type instruction that specifies its exit condition, the system may influence or override a prediction made by a dynamic branch predictor, resulting in a prediction that the exit condition will be met and that the loop will be exited regardless of any observed branch behavior for the conditional branch type instruction. The looping instructions may implement waiting for an inter-thread communication event to occur or for a lock to become available. When the exit condition is met, the loop may be exited without incurring a misprediction delay…028: the existence of a PAUSE type instruction may provide a suggestion to the processor that execution of a busy-wait loop may be in progress. Some processors (e.g., some processors that support out-of-order execution) may be configured to avoid memory order violations in response to recognizing a PAUSE type instruction in a code sequence, which may improve processor performance.”
Raj, Walters, and Dice are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj and Walters with the aforementioned teachings from Dice with a reasonable expectation of success, by adding steps that allow the software to improve efficiency with the motivation to more accurately analyze data [Dice 028].
Raj, Walters, and Dice may not explicitly teach the following. However, Salameh teaches:
using reinforcement learning to identify a set of hyper-parameters for each of the plurality of process mining models that maximizes a respective reward based on a set of criteria; Salameh, Abstract: “An actor neural network, as part of a continuous action reinforcement learning (RL) agent, generates a randomized continuous actions parameters to encourage exploration of a search space to generate candidate architectures without bias. The continuous action parameters are discretized and applied to a search space to generate candidate architectures, the performance of which for performing the particular task is evaluated. Corresponding reward and state are determined based on the performance. A critic neural network, as part of the continuous action RL agent, learns a mapping of the continuous action to a reward using modified Deep Deterministic Policy Gradient (DDPG) with quantile loss function by sampling a list of top performing architectures. The actor neural network is updated with the learned mapping…0114: Where |R| is the number of samples in the replay buffer 326 and C.sub.max is a hyperparameter set to denote a maximum number of cycles. For example, C.sub.max may be constrained by financial, computational, or time considerations. In some embodiments, the training of actor network 322 and critic network 324 begins when the replay buffer 326 has |B.sub.R| samples…claim 1: (i) generating, by an actor neural network having actor parameters in accordance with current values of the actor parameters, a set of continuous neural network architecture parameters comprising score distributions over possible values for configuring a plurality of architecture cells of a trained search space; (ii) discretizing the set of continuous architecture parameters into a set of discrete neural network architecture parameters; (iii) generating a candidate architecture by configuring the trained search space using the discrete neural network architecture parameters, which specify a subset of the plurality of architecture cells to be active; (iv) evaluating a performance of the candidate architecture at performing the task; (v) determining a reward and a state for the discrete neural network architecture parameters based on the performance; (vi) storing an experience tuple comprising the continuous neural network architecture parameters, the reward, and the state in a buffer storage; (vii) learning a mapping, by a critic neural network, between network architectures and performance; and (viii) updating the actor neural network with the learned mapping from the critic neural network …claims 5-6: a list of top performing candidate architectures into an architecture history storage; comparing the performance of the candidate architecture with a performance of a worst stored architecture; if the performance of the candidate architecture is better than the performance of the worst stored architecture, replacing the worst stored architecture with the candidate architecture; and sorting the list of top performing architecture based on performance.”
Raj, Walters, Dice, and Salameh are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj, Walters, and Dice with the aforementioned teachings from Salameh with a reasonable expectation of success, by adding steps that allow the software to improve efficiency with the motivation to more accurately analyze data [Salameh 0049].
As per claim 2, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Raj teaches:
wherein tuning the hyper-parameters includes using reinforcement learning to identify a set of hyper-parameters for a process mining model that maximizes a reward based on a set of criteria; Raj 024-026: “ The list of role-actions varies depending upon a number of input factors, typically based upon a correlation of the role-actions with objectives, benchmarks, and sensors. Objectives represent overall business goals such as maximizing profit for the organization. Benchmarks represent goals specific to the type of organization… A process breakdown is prevented whenever a role-action is either facilitated by a daily action plan that lowers the barriers to execute the role-action, or when the role-action is supported by a backup role-action. For example, if the barrier to executing a role-action is a lack of information, a daily action plan providing the required information will facilitate the role-action. Similarly, if the barrier is a lack of motivation to execute the role-action, a daily action plan detailing the rewards for execution (or penalties for failure of execution) may facilitate the role-action. Other daily action plans may lower barriers due to insufficient time, lack of authority, or a lack of skill/unfamiliarity with an action… a daily action plan assigns another role-action (performed by a different role, for instance) that can accomplish the same objective in the event the first role-action fails to occur…0167: the better performing roles are provided fewer DAP assignments and left to promote their own best paths, while worse-performing roles are given more guidance to direct them to their highest and best uses. Thus, individuals are rewarded for good performance by independence and empowerment, as well as implicit recognition that by being left to their own devices, they are making optimal decisions on their own.”
As per claim 4, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Raj teaches:
wherein the reinforcement learning further uses a user-defined key performance indicator to identify the set of hyper-parameters; Raj 083: “As previously discussed, in a preferred embodiment iterative monitoring and tracking overall performance is employed with a feedback loop for iterative learning. For instance, if everyone except person 1 is successful with a certain profile claim, then it is likely there is a TIMSA factor involved with that person 1 role. So then the learning mechanism, initially starting with heuristics, looks at patterns over time for that person as it tries adjusting the DAP. Here is an illustrative progression for a preferred embodiment: If the action portion of a tactic fails consistently across all roles, then that action is likely ineffective, but if it is just one role, then likely a problem with skill. If there are questions from a single role who is failing with a tactic, then an information or authority issue is indicated. If, after skill, authority, and information angles are all addressed and monitoring shows that the role is still not responding to the requested action, a motivation issue is indicated. If many tactics fail for that person on the same day that historically worked in the past (i.e. see fall-off in success rate as quantity of tactics assigned to role rises), then time is likely an issue (especially if there were a greater than normal number of tactics that day). Time is also indicated in situations where a tactic fails randomly...0166: Rather than starting from scratch to generate new allocations, process arbitrage uses heuristics and statistical analysis on historical performance to determine suboptimal performance and reposition potential improvements to appeal to related demand. For example, where multiple markets that traditionally have tracked one another well begin to diverge, the markets can be realigned by selling more valuable items to the market that needs the improvement the most. The best under-utilized supply of work roles are thus matched with the most under-performing demand for work roles.”
As per claim 8, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Raj teaches:
wherein the plurality of process models include a process discovery method selected from the group consisting heuristic net, heuristic miner, fuzzy miner, alpha miner, and inductive miner; Raj 083: “As previously discussed, in a preferred embodiment iterative monitoring and tracking overall performance is employed with a feedback loop for iterative learning. For instance, if everyone except person 1 is successful with a certain profile claim, then it is likely there is a TIMSA factor involved with that person 1 role. So then the learning mechanism, initially starting with heuristics, looks at patterns over time for that person as it tries adjusting the DAP… Process arbitrage is used to determine those appropriate TIMSA levels. Rather than starting from scratch to generate new allocations, process arbitrage uses heuristics and statistical analysis on historical performance to determine suboptimal performance and reposition potential improvements to appeal to related demand. For example, where multiple markets that traditionally have tracked one another well begin to diverge, the markets can be realigned by selling more valuable items to the market that needs the improvement the most. The best under-utilized supply of work roles are thus matched with the most under-performing demand for work roles… The term "TMS (Time & Motion Study) mode" means a higher sampling rate is used to determine the results changes (or "deltas"), which in turn can better determine the sequence of implied actions and potential causality. KSR is an acronym for KeyStroke Recorder, and Interactive Mode means the automated use of email or wiki to interact with roles that can offer clarification of a process, using a set of templates (like multiple choice, etc.) filled with specific findings to confirm. In a preferred embodiment, fuzzy logic probabilities are used for representing "maybes", then trying to improve these probabilities by cross-checking with other data and trying to justify findings with what is known from other sources.”
As per claim 9, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Raj teaches:
wherein the plurality of process models include a representation selected from the group consisting of business process modeling notation, place/transition nets, process trees, and directly follows graphs; Raj 066, 0133: “Process mining commences with review of existing data. Pre-processing may be called for in some instances where data transformation, selection of data subsets, or variable selection is needed to identify the most appropriate data for processing. For example, in typical situations it is appropriate to pre-process patient data by stripping out variables relating to the patient's home address, as that information may be unrelated to the process being improved. In other circumstances, patient address data may be critical (e.g., for processes where patient "no-shows" are disruptive to practice efficiency, knowing how far patients are traveling may be important). Exploratory analysis is used to identify the most relevant variables and the complexity of the models needed to identify relationships and patterns in the database. Next, a validation stage of analysis is undertaken by applying analysis to historical data and checked to see which of a pool of potential forms of analysis provide the best results in predicting other known historical data. For example, certain characteristics of particular caregivers may be highly important to efficiency in one process but not in another. By testing to see how a particular past process could have been predicted by one of several models, a best model for predicting performance is selected. A third phase of process mining is to use the identified patterns that are selected as best and apply them to new data to create predictions of expected outcomes… Process dependent evaluation is used for tuning of the process when it is known that inter-related tactics will affect one another and the resources used to accomplish them. In alternate embodiments, TIMSA process dependent could bebased on modeling simulations, feedback on time spent, adequacy of the information, role-interactions and personality conflicts, policy constraints or conflicts.
Claims 11 are directed to the CRM and system for performing the method of claim 1 above. Since Raj, Walters, Dice, and Salameh teach the CRM and system, the same art and rationale apply.
Claims 15 and 19 are directed to the system for performing the method of claims 4 and 8 above. Since Raj, Walters, Dice, and Salameh teach the system, the same art and rationale apply.
As per claim 21, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Dice teaches:
wherein modifying the software program includes modifying the software program to eliminate a point at which a process waits or loops; Dice, Abstract: “A computer system may recognize a busy-wait loop in program instructions at compile time and/or may recognize busy-wait looping behavior during execution of program instructions. The system may recognize that an exit condition for a busy-wait loop is specified by a conditional branch type instruction in the program instructions. In response to identifying the loop and the conditional branch type instruction that specifies its exit condition, the system may influence or override a prediction made by a dynamic branch predictor, resulting in a prediction that the exit condition will be met and that the loop will be exited regardless of any observed branch behavior for the conditional branch type instruction. The looping instructions may implement waiting for an inter-thread communication event to occur or for a lock to become available. When the exit condition is met, the loop may be exited without incurring a misprediction delay…028: the existence of a PAUSE type instruction may provide a suggestion to the processor that execution of a busy-wait loop may be in progress. Some processors (e.g., some processors that support out-of-order execution) may be configured to avoid memory order violations in response to recognizing a PAUSE type instruction in a code sequence, which may improve processor performance.”Note: The system can influence or override a prediction made by dynamic brand predictor, resulting in a prediction that the loop will be exited immediately.
Raj, Walters, and Dice are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj and Walters with the aforementioned teachings from Dice with a reasonable expectation of success, by adding steps that allow the software to improve efficiency with the motivation to more accurately analyze data [Dice 028].
As per claim 22, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
In addition, Dice teaches:
herein modifying the software program includes optimizing the software program to perform another task while waiting for a stalled file access to complete; Dice, Abstract: “A computer system may recognize a busy-wait loop in program instructions at compile time and/or may recognize busy-wait looping behavior during execution of program instructions. The system may recognize that an exit condition for a busy-wait loop is specified by a conditional branch type instruction in the program instructions. In response to identifying the loop and the conditional branch type instruction that specifies its exit condition, the system may influence or override a prediction made by a dynamic branch predictor, resulting in a prediction that the exit condition will be met and that the loop will be exited regardless of any observed branch behavior for the conditional branch type instruction. The looping instructions may implement waiting for an inter-thread communication event to occur or for a lock to become available. When the exit condition is met, the loop may be exited without incurring a misprediction delay…012: In some embodiments, waiting for an inter-thread communication event (e.g., a message from another thread, an indication that another thread has performed a particular task, or an indication of a particular state or state change for another thread) may include spinning in a busy-wait loop while waiting for an event notification, waiting for the value stored in a particular memory location to change, waiting for the value stored in a particular memory location to be equal to a certain value (or to a value within a certain range of values), waiting for the release of a lock (or an ownership record thereof), waiting for a signal on a condition variable, monitor, or semaphore, or waiting for a barrier or entry condition to be met. If program execution encounters one of these busy-wait loops, it may enter the loop and waits for a specified exit condition (e.g., an exit condition specified using a conditional branch type instruction) to be met. If and when the exit condition is met, the execution flow may be transferred (e.g., by branching) to program instructions outside of the loop.”Note: The system can influence or override a prediction made by dynamic brand predictor, resulting in a prediction that the loop will be exited immediately.
Raj, Walters, and Dice are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj and Walters with the aforementioned teachings from Dice with a reasonable expectation of success, by adding steps that allow the software to improve efficiency with the motivation to more accurately analyze data [Dice 12].
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 8407081 (hereinafter “Raj”) et al., in view of U.S. PGPub 20210201334 to (hereinafter “Walters”) et al., in further view of U.S. Patent 9304776to (hereinafter “Dice”) et al., in even further view of U.S. PGPub 20230096654 (hereinafter “Salameh”) et al., and in even further view of U.S. PGPub 20180074836 to (hereinafter “Sole”) et al.
As per claim 3, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
Raj, Walters, Dice, and Salameh may not explicitly teach the following. However, Sole teaches:
wherein automatically correcting the inefficiency includes modifying a software program that performs the process to improve an efficiency of the software program; Sole 002-004: “Quality of a process model can be described in terms of fitness, simplicity, precision, and generalization. Fitness of a process model refers to how closely the process model aligns with an event log. If all traces in an event log can be replayed by a process model, then that model has perfect fitness. Perfect fitness, however, is generally not the goal because the process model should be able to generalize and capture behaviour beyond that expressed in the event log and not be limited to only reproducing the event log. If a process model captures most behavior expressed in the event log while also generalizing beyond the event log, then the process model is considered to be a good fit for the event log with some generalization. The “precision” of a process model quantifies the fraction of behavior allowed by a process model beyond the event log. Finally, a simple process model may be sought for reasons relating to efficient implementation and/or use of the process model. However, a simple model may be underfitting, which would be a process model that generalizes “too much.”
Raj, Walters, Dice, Salameh, and Sole are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj, Walters, Dice, and Salameh with the aforementioned teachings from Sole with a reasonable expectation of success, by adding steps that allow the software to utilize models with the motivation to more efficiently and accurately analyze data [Sole 002].
Claim 14 are directed to the system for performing the method of claim 3 above. Since Raj, Walters, Dice, Salameh, and Sole teach the CRM and system, the same art and rationale apply.
Claims 5-7 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 8407081 (hereinafter “Raj”) et al., in view of U.S. PGPub 20210201334 to (hereinafter “Walters”) et al., in further view of U.S. Patent 9304776to (hereinafter “Dice”) et al., in even further view of U.S. PGPub 20230096654 (hereinafter “Salameh”) et al. and in further view of U.S. Patent 11282020 to (hereinafter “Pan”) et al.
As per claim 5, Raj, Walters, Dice, and Salameh teach all the limitations of claim 1.
Raj, Walters, Dice, and Salameh may not explicitly teach the following. However, Pan teaches:
wherein ranking the plurality of process mining models includes matching text of the attributes of the end task to text of a knowledge base that describes attributes of a plurality of end tasks as they relate to process models; Pan 0148: “Curation engine module 1200 also includes vocabulary generation module 1214 that determines alternate wording options for the data and/or information being curated. For example, various natural language processing algorithms and/or models can be employed to identify similar wording, such as sematic matching algorithms, approximate string matching, text classifier algorithms, word2vec algorithms, latent semantic analysis, clustering algorithms, bag-of-words models, document-term matrices, automatic summarization algorithms, tagging operations, etc. Curation engine module 1200 applies the alternate wordings in the curation process as a way to identify similar data and/or entities, and then adds the information generated using the alternate wordings into the various facets of curating data. As one example, a company entitled “My Big Company” can alternately be referred to as “MBG”, “My Big Co.”, “Big Co.”, and so forth. Vocabulary generation module 1214 discerns when information with alternate naming conventions apply to a same entity, and builds corresponding attributes and/or relationship information to combine and/or associate the information from different sources of information to a same data point and/or entity, thus further enriching the information about that entity.”
Raj, Walters, Dice, Salameh, and Pan are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj, Walters, Dice, and Salameh with the aforementioned teachings from Pan with a reasonable expectation of success, by adding steps that allow the software to utilize matching with the motivation to more efficiently and accurately analyze data [Pan 0148].
As per claim 6, Raj, Walters, Dice, Salameh, and Pan teach all the limitations of claim 5.
Raj, Walters, Dice, and Salameh may not explicitly teach the following. However, Pan teaches:
wherein the attributes of the end task and the attributes of the plurality of process mining models are represented as vectors and wherein matching includes identifying similarities between the vectors; Pan 0133: “The newly generated queries and/or the original input query are then used by insight engine module 1114 to extract information from the curated data. Insight engine module 1114 analyzes the extracted information to identify one or more insights, such as by applying various machine-learning algorithms to the extracted information. An insight can include any suitable type of information, such as a trend, a pattern, an anomaly, an outlier, predictive behavior, a contradiction, connections, benchmarks, market segments, etc. Accordingly, an insight sometimes corresponds to an actionable finding that is based upon data analysis. For example, a rate of growth in sales for a product corresponds to a factual insight that a user can base future actions off of, such as a low rate of growth indicating a change is needed, a high rate of growth indicating that current solutions are working, and so forth. Insight engine module 1114 can apply any suitable type of machine-learning model and/or algorithm to discover an insight, such as cluster analysis algorithms, association rule learning, anomaly detection algorithms, regression analysis algorithms, classification algorithms, summarization algorithms, deep learning algorithms, ensemble algorithms, Neural Network based algorithms, regularization algorithms, rule system algorithms, regression algorithms, Bayesian algorithms, decision tree algorithms, dimensionality reduction algorithms, Instance based algorithms, clustering algorithms, K-nearest neighbors algorithms, gradient descent algorithms, linear discriminant analysis, classification and regression trees, learning vector quantization, supporting vector machines, Bagged Decision Trees and Random Forest algorithms, boosting, etc. While the various algorithms described here are described in the context of being utilized to generate insights by the insight engine module 1114, it is to be appreciated that these algorithms can alternately or additionally be employed in other modules of the personalized analytics system 1100, such as a curation engine module 1102, a parser module 1110, query magnifier module 1112, a story narrator module 1116, an animator module 1118, and so forth.”
Raj, Walters, Dice, Salameh, and Pan are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Raj, Walters, Dice, and Salameh with the aforementioned teachings from Pan with a reasonable expectation of success, by adding steps that allow the software to utilize matching with the motivation to more efficiently and accurately analyze data [Pan 0148].
As per claim 7, Raj, Walters, Dice, Salameh, and Pan teach all the limitations of claim 5.
In addition, Raj teaches:
wherein the attributes of the plurality of end tasks include whether activity executions are important to the end task, whether key process indicators or metric overlays are needed for the end task, whether particular organizational information is needed for the end task, and whether process replay and simulations are needed for the end task models; Raj 030: “In a healthcare billing example, the role-action of reviewing the physicians' charts would receive a very low scores in available time (for reviewing charts). This role-action might also receive a low score for available skill, since the physicians lack the training to use the exact wording necessary to follow standardization rules. Other role-actions may also score low in the process, such as the CDCs. For instance, although they have expertise in the "exact wording," the gap that still needs bridged is there indeed something necessitating that wording, as the CDCs lack the medical clinical expertise to efficiently review the physicians' charts. Note that these low scores not only indicate a PBP, they also may provide insight for an appropriate solution. The appropriate DAP needs to reduce the time requirements of the CDCs and needs to enhance their skill. Thus, an appropriate solution is to request that the physicians provide specific pieces of additional documentation. This is within the skill of the physicians and reduces (or eliminates) the need for the CDCs to review the charts as the information for determining the correct billing code will be readily apparent. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure...0118: To resolve discrepancies, processing begins by looking at more recent historicals and weighting them heavier, then rechecking, since the trend may be improving. However, if there is still discrepancy, then processing follows this heuristic: If the historical is higher than the predicted, then the actions are likely harder than detailed, so need to adjust the action rankings to more important ranks (e.g. 2's become 1's). If the historical is lower than the predicted, then the actions are likely easier than detailed, so need to adjust the action rankings to less important ranks (e.g. 1's become 2's, or higher numerical values). This step leads to a preliminary process that can be "visualized" by an Event Trace, as described in connection with meta-level management, and also indicates whether the roles or tactics set (i.e., strategy) are sufficient to avoid future breakdowns.”
Claims 16-18 are directed to the system for performing the method of claims 5-7 above. Since Raj, Walters, Dice, Salameh, and Pan teach the system, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beddo; Michael Ervin. SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR FORECASTING PRODUCT SALES, .U.S. PGPub 20140108094 The present invention relate to systems, methods, and computer program products for determining forecasting data relating to a product using a neural network and accessing that forecasting data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus. In some embodiments, the forecasting apparatus is configured to determine an initial sales forecast using at least a portion of the product information and the neural network, modify the initial sales forecast to generate a final sales forecast, and present the final sales forecast to the computing system via the web portal.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625
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