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
1. This Office Action is in response to the amendment filed on 03/28/2024. Claims 1-6, 8-13 and 15-20 are pending in this application. Claims 1, 8 and 15 are independent claims. Claims 7 and 14 are canceled. This Office Action is made Final.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 8 and 15 are corresponding to one of four statutory categories including method, system, and method respectively under step 1.
The claims 1, 8 and 15 similarly recite “an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, 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, cause the apparatus to at least: receive, from one or more sources, a plurality of data objects associated with a multi- layer service-oriented platform, wherein the one or more sources comprises at least one of an application or a service of the multi-layer service-oriented platform; identify, using a connected data graph structure representing relationships between the plurality of data objects, a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp; rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object; select, based on the ranking, one or more of the plurality of candidate code deployment data objects as one or more potential incident-causing deployments for the incident; and transmit, over a communication network, to a client computing device, one or more computer-readable instructions that causes the client computing device to render an incident mitigation interface configured to facilitate corrective action for mitigating the incident, wherein the incident mitigation interface comprises an interactive incident-mitigating deployment component that identifies the one or more potential incident-causing deployments”.
The limitation of the claims 1, 8 and 15 of “identify, using a connected data graph structure representing relationships between the plurality of data objects, a plurality of candidate code deployment data objects for an incident comprising an affected service data object;”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “identifying” in the context of this claim encompasses the user may identify a plurality of candidate code deployment data objects [in a human-readable format] for an incident comprising an affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “generate a temporal score for the candidate code deployment data object based on a current timestamp”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a temporal score for the candidate code deployment data object based on a current timestamp with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “ranking [ordering]” in the context of this claim encompasses the user may rank the plurality of candidate code deployment data objects [human-readable format] using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “select, based on the ranking, one or more of the plurality of candidate code deployment data objects as one or more potential incident-causing deployments for the incident”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “selecting” in the context of this claim encompasses the user may select one or more candidate code deployment data objects [in a human readable format] based on the ranking with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 15 recite additional elements such as “receive, from one or more sources, a plurality of data objects associated with a multi- layer service-oriented platform, wherein the one or more sources comprises at least one of an application or a service of the multi-layer service-oriented platform”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 15 recite additional elements such as “transmit, over a communication network, to a client computing device, one or more computer-readable instructions that causes the client computing device to render an incident mitigation interface configured to facilitate corrective action for mitigating the incident, wherein the incident mitigation interface comprises an interactive incident-mitigating deployment component that identifies the one or more potential incident-causing deployments”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data displaying under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the dependent claims 2, 9 and 16 recites additional elements such as “wherein the ranking model comprise a learning-to-rank model”
Examiner would like to point out that with the broad reasonable interpretation, this element amount to an insignificant extra-solution activity such as “field of use”, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Prong II step 2A and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 3, 10 and 17 recite additional elements such as “receive training input dataset comprising user feedback with respect to historical incident mitigating predictions”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
The limitation of the claims 3, 10 and 17 of “finetune the learning-to-rank model based on the training input dataset”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “finetuning [adjusting or changing parameters]” in the context of this claim encompasses the user may finetune the learning-to-rank model based on the training input dataset with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “extracting, using a feature extraction model, a code deployment description feature associated with the candidate deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “extracting [selecting]” in the context of this claim encompasses the user may extract, using a feature extraction model, a code deployment description feature associated with the candidate deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “extracting, using the feature extraction model, an incident description feature associated with an incident data object corresponding to the incident”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “extracting [selecting]” in the context of this claim encompasses the user may extract, using the feature extraction model, an incident description feature associated with an incident data object corresponding to the incident with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “generating the semantic similarity score for a pair comprising the code deployment description feature and the incident description feature”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate the semantic similarity score for a pair comprising the code deployment description feature and the incident description feature with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the claims 5, 12 and 19 recite additional elements such as “wherein the machine learning model comprises a large language model”.
Examiner would like to point out that with the broad reasonable interpretation, this element especially amounts to insignificant additional elements such as “field of use” as in MPEP § 2106.05(h): Field of Use and Technological Environment, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under step 2B.
The limitation of the claims 6, 13 and 20 of “traversing the topological graph structure”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “traversing” in the context of this claim encompasses the user may traverse the topological graph structure with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 6, 13 and 20 of “determining a distance between a first node associated with the candidate code deployment data object and second node associated with the affected service data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “determining” in the context of this claim encompasses the user may determine a distance between a first node associated with the candidate code deployment data object and second node associated with the affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
Dependent claims 2-6, 9-13 and 16-20 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-6, 9-13 and 16-20 are also rejected for incorporating the deficiency of their independent claims 1, 8 and 15 respectively.
Reasons for Allowance
4. The following is an examiner’s statement of reasons for allowance: the prior-art, the prior-art, Agarwal (US PGPub 20200153925), in view of Anderson (US Patent 8135775), in view of HajiahmadiFoomani (US PGPub 20240296310), in view of Kang (US PGPub 20130254209), and further in view of Li (US Patent 11093819) failed to disclose: an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, 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, cause the apparatus to at least: receive, from one or more sources, a plurality of data objects associated with a multi- layer service-oriented platform, wherein the one or more sources comprises at least one of an application or a service of the multi-layer service-oriented platform; identify, using a connected data graph structure representing relationships between the plurality of data objects, a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp; rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object; select, based on the ranking, one or more of the plurality of candidate code deployment data objects as one or more potential incident-causing deployments for the incident; and transmit, over a communication network, to a client computing device, one or more computer-readable instructions that causes the client computing device to render an incident mitigation interface configured to facilitate corrective action for mitigating the incident, wherein the incident mitigation interface comprises an interactive incident-mitigating deployment component that identifies the one or more potential incident-causing deployments, as recited by the independent claim 1.
Regarding Claim 1, the closest prior-art found, Agarwal, Anderson, HajiahmadiFoomani, Kang and Li
discloses of an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, 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, cause the apparatus to at least: identify a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp.
Individually, Agarwal teaches that the processor rolls back the deployment of the first software patch. The determination is made to determine that a second solution (150B) has the next highest ranking after a first solution (150A). The processor generates a second software patch that implements the second solution in response to the determination that the second solution has the next highest ranking after the first solution and deploys the second software patch. determine that the first software exception occurred previously in the plurality of desktops by querying a database using keywords from the first software exception; determine that the second software exception did not occur previously in the plurality of desktops by querying the database using keywords from the second software exception; determine a first solution and a second solution for resolving the first software exception; determine that the first solution has a higher ranking than the second solution; generate a first software patch that implements the first solution in response to the determination that the first solution has a higher ranking than the second solution; deploy the first software patch;
Anderson teaches of assigning at least one node in a network to a first deployment group and at least one other node in the network to a second deployment group; assigning a stage value to each of the deployment groups, wherein the stage value controls the order in which software is deployed to each deployment group; receiving a set of distribution criteria that specifies a triggering event which must occur before a software package is deployed to the second deployment group, wherein the distribution criteria is configurable to specify different triggering events based upon the severity level of the software package; distributing the software package to at least one node within the first deployment group; and deferring distribution of the software package to at least one node within the second deployment group until after the triggering event has occurred.
HajiahmadiFoomani teaches that in the testing/deployment phase 782, ML manager 416 operates or implements neural network 410 to generate deployment anomaly scores 714 for deployed printheads 104, and operates or implements recurrent neural network 412 to scale the deployment anomaly scores 714 generated by neural network 410. For example, ML manager 416 inputs or enters input samples 766 (i.e., first input samples) of deployment printhead data 704 for printheads 104 (i.e., deployed printheads) into neural network 410 to generate deployment anomaly scores 714 for printheads 104. One technical benefit of generating deployment anomaly scores 714 is that the magnitude of a deployment anomaly score 714 output by neural network 410 may be used to indicate a failure condition in an individual printhead. However, the ranges of anomaly scores may vary per printhead 104. For example, the range of anomaly scores for one printhead 104 may be “0-500”, while the range of anomaly scores for another printhead 104 may be “0-2000”. Thus, it is difficult to identify a threshold score indicative of a failure condition. Printhead maintenance supervisor 400 is configured to scale the anomaly scores nonlinearly via recurrent neural network 412 to determine whether to recommend replacement of certain printheads 104. One technical benefit of scaling is that scaled anomaly scores from different printheads 104 may be readily compared to each other or to a common threshold. ML manager 416 formats input samples 768 (i.e., second input samples) for recurrent neural network 412 based on the deployment anomaly scores 714 generated by neural network 410 for the printheads 104. ML manager 416 then inputs or enters the input samples 768 into recurrent neural network 412 to generate scaled anomaly scores 716 for printheads 104. One technical benefit is that the scaled anomaly scores 716 may be used to recommend replacement for certain printheads 104. A further operation of printhead maintenance supervisor 400 is described below.
Kang teaches upon the completion of the above process, the semantic search module 143 assigns relation scores to the extracted objects according to the number of the semantic segments for the each extracted object. The semantic search module 143 in accordance with an illustrative embodiment of the present inventive concept may calculate the relation score for each extracted object based on a topological distance method.
Li teaches that at sub-stage 304b, the pre-processing subsystem determines whether the target object to be classified at the current time step is the same or different from the target object that was classified at a preceding time step. For example, the pre-processing subsystem may use an object detector to process one or more frames of sensor data at a current time step and, based on the processing, to generate a signature for the target object at the current time step. The signature for a target object at the current time step can be compared to the signatures of target objects that were generated at one or more preceding time steps. If the compared signatures are sufficiently similar (e.g., a score that indicates a measure of similarity of the signatures meets a threshold similarity score), then the target object at the current time step is deemed to be the same as the target object from the one or more preceding time steps. If the compared signatures are dissimilar (e.g., a score that indicates a measure of similarity of the signatures does not meet a threshold similarity score), then the target object at the current time step is deemed to be different from the target object from the one or more preceding time steps.
However, the prior art, Agarwal, Anderson, HajiahmadiFoomani, Kang and Li failed to disclose the following subject matter such as “rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”.
Claim 8 is the method claim, similar to the claim 1, and claim 15 is the product claim, similar to the claim 1. Therefore, claims 1-6, 8-13 and 15-20 contain allowable subject matter.
5. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
6. Applicant's arguments with respect to the claims 1, 8 and 15 with their dependent claims have been fully considered but they are not persuasive.
Regarding the first argument of the remark on pages 8-9 that the current amendment to the claims 1, 8 and 15 such as receiving data objects from applications or services of a multi-layer service oriented platform is a machine operation that the human mind is not equipped to perform, the examiner agrees that it’s machine-operating. However, it’s considered as insignificant additional element, as the claim limitation is merely gathering the data in order to perform the mental process of identifying candidate code deployment data objects. Thus, the 101 Abstract Idea rejection is maintained over the amendment and the argument. Please see the updated 101 Abstract Idea rejection above.
Regarding the second argument of the remark on pages 10-11 that the current amendment reflect the improvement in their ordered combination that constitutes a specific technological solution, the examiner would like to point out that in order to determine if additional element is integrating the abstract idea into a practical application, 1) The specification should describe the claimed improvement to achieve the desired goal and 2) The claimed improvement should be reflected at least in the additional elements by specifying how the claimed improvement performs the additional element to improve functioning of a computer or existing technical field.
2106.05(a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022]
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.
I. RELEVANT CONSIDERATIONS FOR EVALUATING WHETHER ADDITIONAL ELEMENTS INTEGRATE A JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gross (US PGPub 20160360336): Gross teaches that for example, if the client provides a window specification that specifies a 4-hour time period of interest, the temporal forecast will only generate likelihood scores for the 15-minute timeslots that are in the 4-hour time period of interest. For example, if the time period of interest corresponds to 12:00-4:00 pm for each of the last 3 days, then 16 timeslots will be generated during the 4 hour period of interest and a score will be generated for each of the 16 15 minute timeslots. Scores will not be generated for timeslots outside the specified 4 hour time period of interest.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JAE U JEON/Primary Examiner, Art Unit 2193
