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 .
Detailed Action
This action is in response to the claims filed 11/16/2026:
Claims 1 – 20 are pending.
Claims 1, 8, and 15 are independent.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “processing device” recited in the claims is not defined at all in the instant specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processing device […] to perform the steps of” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 11, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, there are two separate recitations of claim 2 with different limitations such that the scope of claim 2 is indefinite. This appears to be a drafting issue in which case “7. The system of claim 1”, is recommended.
Regarding claims 4, 11, and 18, "the analytics data" lacks antecedent basis. “Analytics data” is recommended.
Claim limitation “processing device […] to perform the steps of” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The MPEP explicitly defines “device” as a nonce term and the instant specification does not define a “processing device” otherwise. In fact, the instant specification does not define a “processing device” at all. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
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.
Claims 3-7 are rejected with respect to their dependence on rejected claim 1.
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.
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.
Claims 1-20 are rejected under U.S.C. §103 as being unpatentable over the combination of Akella (US20190138967A1) and Blonski (US20240232214A9).
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FIG. 1 of Akella
Regarding claim 1, Akella teaches A system for optimizing workflow management and response systems in a distributed network, the system comprising:([¶0011] "FIG. 1 shows an action recognition and analytics system, in accordance with aspect of the present technology." [¶0044] "The action recognition and analytics system 100 can also include one or more engines 170 and one or more data storage units 175. The one or more interfaces 135-165, the one or more data storage units 175, the one or more machine learning back-end units 180, the one or more analytics units 185, and the one or more front-end units 190 can be coupled together by one or more networks 192" See FIG. 1)
a processing device;([¶0146] " the system 2300 includes at least one processing unit 2302 and memory 2304")
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:([¶0149] "the memory 2304 includes computer-readable instructions" [¶0009] "one or more non-transitory computing device-readable storage mediums storing instructions executable by one or more computing devices")
integrating with a plurality of enterprise systems through application programming interfaces (APIs) to aggregate workflow task data;([¶0047] "The action recognition and analytics system 100 can also be communicatively coupled to additional data sources 194, such as but not limited to a Manufacturing Execution Systems (MES), warehouse management system, or patient management system" [¶0087] "The action recognition and analytics system can include a plurality of sensor layers 702, a first Application Programming Interface (API) 704, a physics layer 706, a second API 708, a plurality of data 710, a third API 712, a plurality of insights 714, a fourth API 716 and a plurality of engine layers 718" See FIG. 7)
receiving, via a data acquisition module, workflow-related data from the plurality of enterprise systems;([¶0047] "The action recognition and analytics system 100 can receive additional data, including one or more additional sensor streams, from the additional data sources 194. The action recognition and analytics system 100 can also output data, sensor streams, analytics result and or the like to the additional data sources 194" [¶0049] "configured to receive a stream of frame-based sensor data 250 from sensors 120-130" Akella's local computing devices, streaming media server, format converter, stream processor, stream queue, and ML back-end units acquire and receive workflow related sensor/process data. For enterprise sources, Akella explicitly receives additional data from MES sources.)
storing the workflow-related data in a time-series database for subsequent retrieval and analysis;([¶0087] "The insights layer 714 can provide for video search 744, time series data 746, standardized work 748, and spatio-temporal 842" [¶0007] "one or more data storage units configured to store information for the engine, including information associated with one or more stations" [¶0072] "The initial stream processor 550 can be configured to store the sensor stream segments in one or more data structures for storing sensor streams 555 [...] each new segment can be appended to the previous sensor stream segments stored in the one or more data structures for storing sensor streams 555." Sensor stream interpreted as time series data such that database for storing sensor stream interpreted as a time-series database)
employing a machine learning engine to analyze the workflow-related data to detect patterns, anomalies, and inefficiencies and storing the patterns, the anomalies, and the inefficiencies as historical efficiency data;([¶0073] "machine learning back-end units 520 can be configured to recognize, in real time, one or more cycles, processes, actions, sequences, objects, parameters and the like in the sensor streams received from the plurality of sensors 505-515." [¶0074] "the one or more machine learning back-end units 520 can recognize cycles, processes, actions, sequences, objects, parameters and the like in sensor streams utilizing [...] reinforcement learning" [¶0096] "The analytics can be directed at various aspects of an activity (e.g., validation of actions, abnormality detection, training, assignment of actor to an action, tracking activity on an object, determining replacement actor, examining actions of actors with respect to an integrated activity, automatic creation of work charts, creating ergonomic data, identify product knitting components, etc.) Additional descriptions of the analytics are discussed in other sections of this detailed description" [¶0086] "The data set can also include judgements based on performance data, such as does a given person perform better or worse that average." [¶0072] "The initial stream processor 550 can be configured to store the sensor stream segments in one or more data structures for storing sensor streams 555. In one implementation, as sensor stream segments are received, each new segment can be appended to the previous sensor stream segments stored in the one or more data structures for storing sensor streams 555." abnormality interpreted as synonymous with anomalies. Performing worse than average interpreted as inefficiencies.)
applying a reinforcement learning algorithm to adjust a task prioritization within a workflow based on real-time analytics and the historical efficiency data;([¶0009] "analyzing the information in real time, including analyzing activity of the first actor with respect to a second actor; and forwarding respective feedback in real time based on the results of the analysis." [¶0048] " The one or more machine learning back-end units 180 can recognize cycles, processes, actions, sequences, objects, parameters and the like in sensor streams utilizing deep learning, decision tree learning, inductive logic programming, clustering, reinforcement learning, Bayesian networks, and or the like." [¶0074] " a plurality of processes including one or more actions arranged in one or more sequences" Arranging actions in sequences interpreted as adjusting a task prioritization. Akella explicitly discloses that the machine learning system includes reinforcement learning and recognizes sequences.)
predicting, via the machine learning engine, delegation needs and recommending proxy assignees using the reinforcement learning algorithm; and([¶0006] "The feedback can be a suggested assignment of a type of actor to an activity." [¶0100] "the action recognition and analytics system 100, 500 can also be utilized to create programmatic job assignments based on skills, tasks, ergonomics and time" [¶0048] "he one or more machine learning back-end units 170 can be configured to recognize, in real time, one or more cycles, processes, actions, sequences, objects, parameters and the like in the sensor streams received from the plurality of sensors 135-145. The one or more machine learning back-end units 180 can recognize cycles, processes, actions, sequences, objects, parameters and the like in sensor streams utilizing [...] reinforcement learning")
presenting a dashboard to end-users displaying prioritized tasks, delegated assignments, real-time alerts, and actionable insights derived from a machine learning engine analysis.([¶0043] "the one or more front-end units 190 can output one or more graphical user interfaces to present training content, work charts, real time alerts, feedback and or the like on one or more interfaces 165" [¶0085] "The one or more analytics units 525 can also be coupled to one or more front-end units 580 [...] The management port 585 can also be utilized to control operation of the one or more analytics units 525 for such functions as generating training content, creating work charts, performing line balancing analysis, assessing ergonomics, creating job assignments, performing causal analysis, automation analysis, presenting aggregated statistics, and the like").
However, Akella does not explicitly teach automating access management by dynamically assigning and adjusting user permissions and access levels within the plurality of enterprise systems based on predefined security policies and real-time workflow requirements;.
Blonski, in the same field of endeavor, teaches automating access management by dynamically assigning and adjusting user permissions and access levels within the plurality of enterprise systems based on predefined security policies and real-time workflow requirements;([¶0067] "A key benefit of the subject Service Management Cloud (44) configurations is an enhanced ability to bring people into collaborative processes with specific and controlled levels of access, whether they are within a particular organization, department, generalized security, or not." [¶0068] "participants or different entities involved in a workflow may share processes. For example, supply chain team may collaborate, working directly with partners on the same data within the same workflows via the platform herein. The platform may provide an interface for view, access and manage process-data such as tasks, assignments, reminders, all secured within the data cloud." [¶0068] 'the Service Management Cloud (44) preferably may be configured to allow pre-established, or in-app defined, (134) log-in permissions which may provide specific access roles or levels (for example, total global access" [¶0062] "assuring that the data continues to be updated, such as in real-time or near-real-time, and continues to reside entirely, or at least primarily, on the data cloud (34).").
Akella as well as Blonski are directed towards enterprise workflow automation and management systems. Therefore, Akella as well as Blonski are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Akella with the teachings of Blonski by using Blonski’s disclosure to supply the enterprise identity, authorization, and policy-management layer that automatically grants, revokes, and adjusts user permissions in response to workflow driven assignment changes generated by Akella's analytics engine.. Blonski provides as additional motivation for combination ([¶0067] “A key benefit of the subject Service Management Cloud (44) configurations is an enhanced ability to bring people into collaborative processes with specific and controlled levels of access, whether they are within a particular organization, department, generalized security, or not.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Akella, and Blonski teaches The system of claim 1, wherein employing the machine learning engine further comprises utilizing an unsupervised learning algorithm.(Akella [¶0048] "utilizing deep learning, decision tree learning, inductive logic programming, clustering, reinforcement learning, Bayesian networks, and or the like" clustering interpreted as unsupervised learning algorithm).
Regarding claim 3, the combination of Akella, and Blonski teaches The system of claim 1, wherein the system is further configured to: execute a feedback module configured to capture user feedback regarding system functionality and workflow efficiency.(Akella [¶0043] "the one or more front-end units 190 can output one or more graphical user interfaces to present training content, work charts, real time alerts, feedback and or the like on one or more interfaces 165, such displays at one or more stations 120-130, at management portals [...] The one or more front-end units can also receive responses on a touch screen display device, keyboard, one or more buttons, microphone or the like from one or more actors. Accordingly, the interfaces 135-165 can implement an analysis interface, mentoring interface and or the like of the one or more front-end units 190" [¶0097] "results of the analysis can be forwarded as feedback. The feedback can include directions to entities in the station. In one embodiment, the information accessing, analysis, and feedback are performed in real time").
Regarding claim 4, the combination of Akella, and Blonski teaches The system of claim 3, wherein the system is further configured to: retrain the machine learning engine using captured user feedback and the analytics data to refine the machine learning engine.(Blonski [¶0106] "the platform may develop an AI model to generation predictions about when to initiate an action (e.g., location in the workflow), what action to take, or other features in the initial workflow. The AI model may be trained and developed using training datasets collected within the platform. For example, past pattern of actions may be extracted from the action or processes data defined within the platform and the data may be utilized as training data to develop the AI model. In some cases, the data fields involved in a workflow may also be predicted by an AI model." [¶0107] "the pre-trained model may undergo continual training or refinement with custom data that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment or use application over time (e.g., changes in the user data, insight data, model performance, third-party data, etc.).").
Regarding claim 5, the combination of Akella, and Blonski teaches The system of claim 1, wherein the system is further configured to: update a dynamic flow designer tool based on a retrained machine learning engine to reflect an optimized workflow path. (Blonski [¶0097] "the Data-driven workflow platform provide a no-code configuration interfaces for creating cloud applications […] user may further customize the pre-built application via a “drag-and-drop” GUI" [¶0102] "an initial workflow for a stage may be recommended by the system and displayed on the GUI then a user may choose to accept, modify or rejection any components of the workflow" [¶0107] "Artificial intelligence, including machine learning algorithms, may be used to train a predictive model for predicting a recommendation (e.g., automation, workflow etc.), extracting the data relationship, normalizing data, performing impact analytics as described above [...] a machine learning algorithm trained model may be pre-trained and implemented on the provided system, and the pre-trained model may undergo continual training or refinement with custom data that may involve continual tuning of the predictive model or a component of the predictive model (e.g., classifier) to adapt to changes in the implementation environment or use application over time").
Regarding claim 6, the combination of Akella, and Blonski teaches The system of claim 1, wherein the system is further configured to: generate a report and alert based on the analysis performed by the machine learning engine; and provide the report and the alert via the APIs. (Akella [¶0043] "the one or more front-end units 190 can output one or more graphical user interfaces to present training content, work charts, real time alerts, feedback and or the like on one or more interfaces 165, such displays at one or more stations 120-130, at management portals on tablet PCs, administrator portals as desktop PCs or the like" [¶0078] "one or more engines 170, such as the one or more machine learning back-end units 520 and or the one or more analytics units 525, can create a data structure including a plurality of data sets, the data sets including one or more indicators of at least one of one or more cycles, one or more processes, one or more actions, one or more sequences, one or more object and one or more parameters. The one or more engine 170 can build the data structure based on the one of one or more cycles, one or more processes, one or more actions, one or more sequences, one or more object and one or more parameters detected in the one or more sensor streams. ").
Regarding claim 2, the combination of Akella, and Blonski teaches 2. The system of claim 1, wherein the system is further configured to: utilize a data streaming service to capture and process workflow events as they occur, facilitating an immediate identification and response to workflow incidents. (Akella [¶0048] "The one or more machine learning back-end units 170 can be configured to recognize, in real time, one or more cycles, processes, actions, sequences, objects, parameters and the like in the sensor streams received from the plurality of sensors 135-145. The one or more machine learning back-end units 180 can recognize cycles, processes, actions, sequences, objects, parameters and the like in sensor streams utilizing deep learning, decision tree learning, inductive logic programming, clustering, reinforcement learning, Bayesian networks, and or the like.").
Regarding claims 8-14, claims 8-14 are substantially similar to claims 1-6 and the second claim 2, respectively. Therefore, the rejections applied to claims 1-6 and the second claim 2 also apply the claims 8-14.
Regarding claims 15-20, claims 15-20 are directed towards the method performed by the system of claims 1-6, respectively. Therefore, the rejections applied to claims 1-6 also apply to claims 15-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kintsakis (“Reinforcement Learning based scheduling in a workflow management system”, 2019) is directed towards reinforcement learning on time series data for workflow scheduling.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
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