DETAILED ACTION
This is a non-final, first office action on the merits. Claims 21-40 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 21-40 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over respective corresponding Claims 21-40 of Patent No. 12,165,088, Claims 1-20 of Application No. 19/251,094 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the current application claims as mapped above can be anticipated by the reference application claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 21-40 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 21, 32, and 40 recite an abstract idea. Claims 21, 32, and 40 include “ access development resource reference unit data representative of a development resource reference unit; generate a development resource reference unit impact evaluation factor set based at least in part on the development resource reference unit data, the development resource reference unit impact evaluation factor set comprising at least one development resource reference unit evaluation factor; generate a development resource reference unit impact value for the development resource reference unit based at least in part on the development resource reference unit impact evaluation factor set; generate a development resource reference unit impact comprising the development resource reference unit impact value; and output the development resource reference unit impact for rendering to a development resource reference unit”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for outputting the development resource reference unit impact interface. As a result, claims 21, 32, and 40 recite an abstract idea under Step 2A Prong One.
Claims 22-31 and 33-39 further describe the process for outputting the development resource reference unit impact interface. As a result, claims 22-31 and 33-39 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 21, 32, and 40.
With respect to Step 2A Prong Two of the framework, claims 21, 32, and 40 do not include additional elements that integrate the abstract idea into a practical application. Claims 21, 32, and 40 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 21, 32, and 40 include a processor, a memory, interface component, program code, and a non-transitory computer readable medium. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 21, 32, and 40 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 22-25, 27-31, and 33-39 do not include any additional elements beyond those recited with respect to claims 21, 32, and 40. As a result, claims 22-25, 27-31, and 33-39 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 21, 32, and 40.
Claim 26 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 26 includes interface component to visually distinguish. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 26 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 21, 32, and 40 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 21, 32, and 40 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 21, 32, and 40 include a processor, a memory, interface component, program code, and a non-transitory computer readable medium. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 21, 32, and 40 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 22-25, 27-31, and 33-39 do not include any additional elements beyond those recited with respect to claims 21, 32, and 40. As a result, claims 22-25, 27-31, and 33-39 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 21, 32, and 40.
Claim 26 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 26 includes interface component to visually distinguish. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 26 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 21-40 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claim 21-25, 27-30, and 32-40 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bridges et al. (US Pub No. 2018/0373578) (hereinafter Bridges et al.), hereinafter Bridges.
Regarding claims 21, 32, and 40, Bridges discloses an apparatus for outputting a development resource reference unit impact interface component to a development resource reference unit interface, the apparatus comprising at least one processor, and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor (see Bridges, para [0082]), cause the apparatus to at least:
at least one non-transitory computer-readable storage medium having computer-readable program code portions stored (see Bridges, para [0082])
access development resource reference unit data representative of a development resource reference unit (see Bridges, para [0089], wherein
obtain further instructions or to access and use remote memory stores; and paras [0075]-[0076], wherein a library of algorithms (e.g., supervised machine learning models)…each algorithm may be trained and tested on the training and testing datasets (i.e., development resource reference unit data) using the most predictive features found in the step 415);
generate a development resource reference unit impact evaluation factor set based at least in part on the development resource reference unit data, the development resource reference unit impact evaluation factor set comprising at least one development resource reference unit evaluation factor (see Bridges, para [0004], wherein analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; (3) predicting a probability of failure and an impact of the proposed change using a model; (4) in response to a low predicted probability of failure, or a high predicted probability of failure with a low predicted impact: approving the proposed change; and implementing the proposed change; and (5) in response to a high predicted probability of failure and a high predicted impact, rejecting the proposed change; paras [0075]-[0076], wherein a library of algorithms (e.g., supervised machine learning models)…each algorithm may be trained and tested on the training and testing datasets (i.e., development resource reference unit data) using the most predictive features found in the step 415; and para [0050], wherein factor set such as stability and resiliency management data 138 may include, for example, one or more of KPIs and metrics for stability threats, exposures, known vulnerabilities and workarounds; stability action plans; component reliability; component maintainability and serviceability; system security; system availability; system resiliency (e.g., including service continuity management test results and compliance); capacity management ( e.g., including performance management), etc.);
generate a development resource reference unit impact value for the development resource reference unit based at least in part on the development resource reference unit impact evaluation factor set (see Bridges, para [0011], wherein monitoring an actual failure and an actual impact of the implemented proposed change; and scoring the model based on the actual failure and the actual impact; and para [0012], wherein selecting a new model in response to the score being below a predetermined threshold. The new model may be selected by: using unsupervised or semi-supervised machine learning to a dataset (i.e., development resource reference unit data) to determine a feature set for a new model; retrieving a plurality of supervised machine learning models);
generate a development resource reference unit impact interface component comprising the development resource reference unit impact value (see Bridges, para [0041], wherein change preparation and entry module 122 may include system(s) to raise and record details of the required change. It may provide a user interface to input current change data; para [0011], wherein monitoring an actual failure and an actual impact of the implemented proposed change; and scoring the model based on the actual failure and the actual impact; and para [0012], wherein selecting a new model in response to the score being below a predetermined threshold. The new model may be selected by: using unsupervised or semi-supervised machine learning to a dataset (i.e., development resource reference unit data) to determine a feature set for a new model; retrieving a plurality of supervised machine learning models); and
output the development resource reference unit impact interface component for rendering to a development resource reference unit interface (see Bridges, para [0068], wherein the change may be checked for adverse cross impacts. If there are no adverse cross impacts, in step 360, the change screening processor may apply the selected model to key fields, and in step 362, may update the probability of failure and impact fields on the basis of the output of the model; and para [0041], wherein change preparation and entry module 122 may include system(s) to raise and record details of the required change. It may provide a user interface to input current change data).
Regarding claims 22 and 33, Bridges discloses the apparatus of claim 21, wherein generating the development resource reference unit impact evaluation factor set comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
generate an acceptance development resource reference unit evaluation factor by performing an acceptance operation using the development resource reference unit data (see Bridges, para [0071], wherein the learning engine may use a scoring mechanism to keep track of its successes. If the score falls below an acceptable threshold, then the system actively recalibrates itself to determine the best algorithm/model and re-educates itself; para [0004], wherein analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; (3) predicting a probability of failure and an impact of the proposed change using a model; (4) in response to a low predicted probability of failure, or a high predicted probability of failure with a low predicted impact: approving the proposed change; and implementing the proposed change; and (5) in response to a high predicted probability of failure and a high predicted impact, rejecting the proposed change; and para [0050], wherein factor set such as stability and resiliency management data 138 may include, for example, one or more of KPIs and metrics for stability threats, exposures, known vulnerabilities…., etc.).
Regarding claims 23 and 34, Bridges discloses the apparatus of claim 21, wherein generating the development resource reference unit impact evaluation factor set comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
generate a resolution event development resource reference unit evaluation factor by performing a resolution event operation using the development resource reference unit data (see Bridges, para [0032], wherein machine learning and/or the wider field of Artificial Intelligence (AI) to identify and prevent or reduce the likelihood that a technology incident will occur. This is a pro-active approach rather than a re-active approach; and para [0049], wherein overall incident recovery and problem resolution costs; incident and problem histories and audit trails; repeat incident and problem flags; parent incident and problem IDs; child incident and problem IDs; incident recovery contacts and participants; problem resolution contacts and participants; incident recovery tasks; problem root cause analysis actions; incident recovery KPIs and metrics; problem resolution KPIs and metrics, etc.).
Regarding claims 24 and 35, Bridges discloses the apparatus of claim 21, wherein generating the development resource reference unit impact evaluation factor set comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
generate a recency development resource reference unit evaluation factor by performing a recency operation using the development resource reference unit data (see Bridges, para [0066], wherein in step 316, the change record is updated, and if there is a low probability of failure in step 318, or if there is not a low probability of failure and not a high impact if the change fails in step 320, the change may be ready for approval in step 322; and para [0070], wherein the system may need to periodically recalibrate or update the selected algorithm or model based on the accuracy of predictions compared with actual outcomes).
Regarding claim 25, Bridges discloses the apparatus of claim 21, wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
determine that the development resource reference unit impact value is below a development resource reference unit impact value threshold (see Bridges, para [0071], wherein the learning engine may use a scoring mechanism to keep track of its successes. If the score falls below an acceptable threshold, then the system actively re-calibrates itself to determine the best algorithm/model and re-educates itself).
Regarding claims 27 and 36, Bridges discloses the apparatus of claim 21, wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
initiate performance of one or more impact value responsive actions based at least in part on the development resource reference unit impact value (see Bridges, para [0021], wherein monitor an actual failure and an actual impact of the implemented proposed change and scores the model based on the actual failure and the actual impact; and para [0034], wherein identify anticipated, or otherwise expected change events that may have risk above a predetermined threshold…..a notification may be provided informing the sponsor of the change event of the risk and one or more actions to take to minimize the risk. In one embodiment, the action(s) may be based on successful actions taken for similar change events).
Regarding claims 28 and 37, Bridges discloses the apparatus of claim 27, wherein initiating performance of one or more impact value responsive actions comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
initiate performance of one or more impact value responsive alert actions (see Bridges, para [0021], wherein monitor an actual failure and an actual impact of the implemented proposed change and scores the model based on the actual failure and the actual impact; and para [0034], wherein….a notification may be provided informing the sponsor of the change event of the risk and one or more actions to take to minimize the risk. In one embodiment, the action(s) may be based on successful actions taken for similar change events).
Regarding claims 29 and 38, Bridges discloses the apparatus of claim 27, wherein initiating performance of one or more impact value responsive actions comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
initiate performance of one or more impact value responsive disablement actions (see Bridges, referring to Fig. 3B shows Alert user; input continues until impact not high or change is cancelled (370); and para [0021], wherein monitor an actual failure and an actual impact of the implemented proposed change and scores the model based on the actual failure and the actual impact).
Regarding claims 30 and 39, Bridges discloses the apparatus of claim 27, wherein initiating performance of one or more impact value responsive actions comprises the program code being further configured to, with the at least one processor, further cause the apparatus to:
initiate performance of one or more impact value responsive positioning actions (see Bridges, and para [0046], wherein priority, urgency, impact and risk of making the change; category of change with associated lead time (e.g., indicating whether normal, expedited or emergency)).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Bridges et al. (US Pub No. 2018/0373578) (hereinafter Bridges et al.) in view of Bodman et al. (US Pub No. 2018/0322434) (hereinafter Bodman et al.).
Regarding claim 26, Bridges discloses the apparatus of claim 25, wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
in response to determining that the development resource reference unit impact value is below the development resource reference unit impact value threshold, configure the development resource reference unit impact interface component (see Bridges, para [0071], wherein the learning engine may use a scoring mechanism to keep track of its successes. If the score falls below an acceptable threshold; para [0041], wherein change preparation and entry module 122 may include system(s) to raise and record details of the required change. It may provide a user interface to input current change data; and para [0043], wherein change screening processor 124 may flag alerts of varying severity against changes deemed to carry excessive risk by the change screening processor. These alerts may then be processed by change workflow management system 128).
Bridges et al. fails to explicitly disclose visually distinguish the development resource reference unit impact value.
Analogous art Bodman discloses visually distinguish the development resource reference unit impact value (see Bodman, para [0052], wherein server instance 114A color codes capability scores displayed in the GUI 134 based whether the capability scores indicate a gap…..A background of the second capability score 144 displayed in the screen 200 may be yellow indicating that the "change design" capability 202 has a medium gap. Similarly, a background of a capability score of the "marketing" capability may be red indicating that the marketing capability has a major gap. In other implementations, different combinations of colors may be used).
Bridges directed to a system for predicting a probability of failure and an impact of the proposed change. Bodman directed to indicating a health metric of the second capability within the customer network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Bridges, regarding the System for Predictive Technology Incident Reduction, to have included visually distinguish the development resource reference unit impact value because both inventions teach improving response to a low process score. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Bridges et al. (US Pub No. 2018/0373578) (hereinafter Bridges et al.) in view of Beck et al. (US Pub No. 2019/0260804) (hereinafter Beck et al.).
Regarding claim 31, Bridges discloses the apparatus of claim 21, wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
train a development resource reference unit assessment model using the one or more development resource reference unit training vectors (see Bridges, para [0075], wherein supervised learning (e.g., training) may be performed to train the system, and in step 425, supervised machine learning models may be loaded from a library of algorithms (e.g., supervised machine learning models); para [0071], wherein the learning engine may use a scoring mechanism to keep track of its successes. If the score falls below an acceptable threshold, then the system actively re-calibrates itself to determine the best algorithm/model and re-educates itself; and para [0076], wherein each algorithm may be trained and tested on the training and testing datasets using the most predictive features found in the step 415, above. Algorithms that may be used include, for example, Linear Regression, Logistic Regression, Decision Trees, Ensembles, Naive Bayes, Nearest Neighbor, Support Vector Machines (SVM)); and
generate a second development resource reference unit impact value by applying resource reference unit data to the development resource reference unit evaluation model (see Bridges, para [0021], wherein monitor an actual failure and an actual impact of the implemented proposed change and scores the model based on the actual failure and the actual impact; para [0056], wherein select and/or apply one or more algorithms or models to predict a technology incident. In one embodiment, machine learning from past technology incidents may be considered; and para [0012], wherein selecting a new model in response to the score being below a predetermined threshold. The new model may be selected by: using unsupervised or semi-supervised machine learning to a dataset (i.e., development resource reference unit data) to determine a feature set for a new model; retrieving a plurality of supervised machine learning models).
While Bridges discloses vector (see Bridges, para [0057]), Bridges et al. fails to explicitly disclose access one or more development resource reference unit training vectors.
Analogous art Beck discloses access one or more development resource reference unit training vectors (see Beck, para [0151], wherein retrieve the incident ticket containing the query; and para [0029], wherein Artificial Intelligence model trained on characteristics of vectors for malicious activity and related data); and
Analogous art Beck discloses train a development resource reference unit assessment model using the one or more development resource reference unit training vectors (see Beck, para [0028], wherein references machine-learning models that are trained on the normal behavior of network activity and user activity associated with a network. The cyber threat module determines a threat risk parameter that factors in 'the likelihood that a chain of one or more unusual behaviors of email activity, network activity, and user activity under analysis fall outside of derived normal benign behavior;' and thus, are likely malicious behavior; and para [0029], wherein Artificial Intelligence model trained on characteristics of vectors for malicious activity and related data).
Bridges directed to a system for predicting a probability of failure and an impact of the proposed change. Beck directed to collecting the visual data container from the threat-tracking. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Bridges, regarding the System for Predictive Technology Incident Reduction, to have included access one or more development resource reference unit training vectors because both inventions teach improving response to a low score. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2022/0122025; US Pat No. 7,672,921; US Pub No. 2011/0313949; US Pub No. 2022/0230090; US Pub No. 2020/0412757; and R Ruefle, A Dorofee, D Mundie (Computer security incident response team development and evolution)- IEEE Security & …, 2014 - ieeexplore.ieee.org.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 09/10/2026
