DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-3, 5-8, 10-35 have been presented for examination based on the amendment filed on 9/12/2025.
Claims 1, 14, 18 and 21-35 are amended.
Claims 4 and 9 are cancelled.
Claims 1, 3, 14 and 18 remain provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-3, 16, 18-19 of copending Application No. 18/144,105. The rejection is newly updated based on amendment presented in copending Application No. 18/144,105 with claim set filed 6/24/2024.
Claims 1-3, 5-8, 10-35 are newly rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 1-3, 5-8, and 10-35 are rejected under 35 U.S.C. 101 (Updated).
Claim(s) 1-3, 5-8, and 10-35 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20220376980 A1 by BHAT; Karthik Venkatesh, in view of US PGPUB No. US 20170103148 A1 by NATSUMEDA; Masanao (Updated).
In Alternate Claim(s) 1-3, 5-8, and 10-35 are rejected under 35 U.S.C. 103 as being unpatentable over BHAT, in view of NATSUMEDA, further in view of US PGPUB No. US 20150026521 A1 by Yabuki; Kentaro (to explicitly address applicant’s argument about displaying the data map).
This action is made Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5-8, 10-35 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Representative claim 1 now recites:
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The disclosure fails to show how the corrective actions are identified and how the repair or maintenance is performed (on what?) based on the data map and corrective actions. The “Exemplary Use Case 1” in specification shows no such details and these limitations are therefore new matter. E.g. the specification has no mentions “corrective action”. Claims 1, 14 and 18 recite similar limitations and are rejected. Respective dependent claims do not cure this deficiency and are rejected for inheriting from claims 1, 14 and 18.
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Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.13-15:
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(Response 1) Identifying links between the devices by determining correlation between their data is mathematical concept/mental step as mapped in the rejection below. The data map does not lead to generating a corrective action (see newly amended limitation). Data map is an abstract construct to show relationship and is construed as mental step.
The improvement in performance of the computer network is not pertinent to the step 2A prong 1 analysis.
(Argument 2) Applicant has argued in Remarks Pg.15-18:
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(Response 2) For arguments under step 2A Prong 2 and Step 2B, Generating the data map is considered as mental step addressed in Response 1 and in the rejection, under step 2A Prong 1. The identification of corrective action is an idea of solution, untethered to any specific application other than field of use (related to IoT device data). No specific device association, how their performance metric is determined and how it leads to corrective action (what is the corrective action, specific to any IoT device or device pair?, how is it determined?) is claimed. This is as stated earlier and in rejection an Idea of Solution and not in the improvement in the field of IoT devices. Further what/how device maintenance and repair is performed in view of corrective action is not defined and is further considered an idea of solution. These additional elements as mapped do not lead to practical application or contribute significantly more as they are done as field of use of data map and missing actual steps that integrates the use of data map to practical repair and maintenance of IoT network. See Exemplary Use Case 1 in specification Pg.38-39 as counter example where no details are presented how the corrective action/maintenance or repair is determined based on data map.
(Argument 3) Applicant has argued in Remarks Pg.:
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(Response 3) Bhat is shown to show the tabular (Fig.11) and graphical data map (Fig.5). Please see updated rejection. Natsumeda teaches graphical representation in more details for the user display. Applicant arguments are considered and are not found to be persuasive.
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Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 14 and 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-3, 16, 18-19 of copending Application No. 18/144,105 in view of mapping as presented below. Additional mapping may be made in future in view of US PGPUB No. US 20220376980 A1 by BHAT; Karthik Venkatesh, in view of US PGPUB No. US 20170103148 A1 by NATSUMEDA; Masanao. The rejection is incorporated from previous office action and modified in a manner similar to prior art rejection with Bhatt, and Namasuda (same combination as previously presented).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-8, 10-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1 and 18:
Step 1: the claims 1 are 18 are drawn to a method and article of manufacture claim respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: Taking method claim 1 as representative, however analysis is applicable to article of manufacture claim 18 as well. The claim 1 limitations recite (bolded for abstract idea identification):
Claim 1
Mapping under Prong 2 Step 1
1. A computer-implemented method comprising:
a) retrieving a first plurality of time-series data sets from a first plurality of data sources comprising one or more Internet of Things (IoT) devices associated with an automated environment;
b) retrieving a second plurality of time-series data sets from a second plurality of data sources comprising one or more Internet of Things (IoT) devices associated with the automated environment,
wherein the first plurality of time-series data sets and the second plurality of time-series data sets are stored in a storage, and
wherein there are one or more unknown links comprising data transmission between any of the first plurality of data sources and the second plurality of devices;
c) computing a magnitude score of one or more correlation over time between each of the first plurality of data sets and the second plurality of data sets to generate a first model, wherein the one or more correlation comprise a correlation matrix;
d) calibrating the first model by subtracting hyperparameters from the first model;
e) identifying a link between one or more of the first plurality of data sources and one or more of the second plurality of data sources based at least in part on the calibrated model, wherein the link indicates a data communication relationship comprising direct or indirect transmission of data between linked data sources;
f) repeating (e) to identify a plurality of links, and aggregating the plurality of links to generate a data map, wherein the data map comprises a series of nodes that each represent any of the first plurality of data sources and the second plurality of data sources and a series of edges that each represent a link between corresponding nodes,wherein the data map indicates the relationship between the linked data sources associated with the automated environment, and wherein each link further indicates a directionality of the relationship between linked data sources;
g) causing display, at a user device, the data map as a graphical structure;
f) determining performance of the first plurality of data sources or the second plurality of data sources based on the data map by ; determining a performance metric of each of the first plurality of data sources and the second plurality of data sources;
identifying that any performance metric of at least a first data source exceeds a threshold; and
responsive to identifying that the performance metric of at least the first data source exceeding the threshold, identifying one or more corrective actions to at least the first data source and
i) wherein maintenance or repair of any of the data sources in the automated environment is configured to be performed based on data map and one or more corrective actions..
See mapping under Step 2A Prong 2.
See mapping under Step 2A Prong 2.
See mapping under Step 2A Prong 2.
Abstract Idea/Mental step/Mathematical Concept: This may be considered as judgement/opinion (to identify unknown links between the two data sources) based on observation of data. No means or steps are claimed to determine the unknown links are identified. This may also be mathematical concept such as using neural network (e.g. as in Bhat [0147]-[0150]) to identify the unknown links.
Abstract Idea/Mathematical Concept: According to MPEP 2106.04(a)(2)(I)(C) and specification [024]1, computing correlation is a mathematical concept the requires calculating a score using Peasrson’s or Spearman’s correlation coefficient. Performing more than one correlation or over time simply repeats the use of mathematical concept and may be performed as mental step also. Computing a matrix remains a mathematical concept.
Abstract Idea/Mathematical Concept: In this step calibrating is a mathematical step to subtract a hyperparameter (a bias) (See Specification [025]).
Abstract Idea/Mental step: The selection of hyperparameter is matter of forming an opinion based on observation (See [025]2). See MPEP 2104(a)(2)(III).
Abstract Idea/Mental step: The creation of link between two data sources is matter of forming an opinion based on observation based on score. E.g. highest magnitude of correlation score between the any two time series data shows a link between them. See MPEP 2104(a)(2)(III). What link identifies only further assigns a name to the datum of the algorithm.
Abstract Idea/Mental step: The creation of plurality of link is repeating step e) and creating a data map from plurality of links is similar to forming a graph which is mental step of observation and forming an opinion based on the mathematical steps of generating a score. See MPEP 2104(a)(2)(III).
Further specifying the directionality in a graph is also mental step which can be performed with pen and paper, and/or using a computer as a tool.
See mapping under Step 2A Prong 2.
Abstract Idea Mental Step/Mathematical Concept: determining (opinion/judgement) based on data map (observation) remains an abstract idea/mathematical concept because the determining a metric (via mathematical steps) and identifying it exceeds a threshold can be performed as mental step (with computer as a tool).
Also see mapping under Step 2A Prong 2.
See mapping under Step 2A Prong 2.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper but for the recitation of generic computer and/or its components. Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper3.
Step 2A, Prong 2:
Claim 1
Mapping under Prong 2 Step 2
1. A computer-implemented method comprising:
a) retrieving a first plurality of time-series data sets from a first plurality of data sources comprising one or more Internet of Things (IoT) devices associated with an automated environment;
b) retrieving a second plurality of time-series data sets from a second plurality of data sources comprising one or more Internet of Things (IoT) devices associated with the automated environment;
wherein the first plurality of time-series data sets and the second plurality of time-series data sets are stored in a storage, and
wherein there are one or more unknown links comprising data transmission between any of the first plurality of data sources and the second plurality of devices;
c) computing a magnitude score of one or more correlation over time between each of the first plurality of data sets and the second plurality of data sets to generate a first model, wherein the one or more correlation comprise a correlation matrix;
d) calibrating the first model by subtracting hyperparameters from the first model;
e) identifying a link between one or more of the first plurality of data sources and one or more of the second plurality of data sources based at least in part on the calibrated model, wherein the link indicates a data communication relationship comprising direct or indirect transmission of data between linked data sources;
f) repeating (e) to identify a plurality of links, and aggregating the plurality of links to generate a data map, wherein the data map comprises a series of nodes that each represent any of the first plurality of data sources and the second plurality of data sources and a series of edges that each represent a link between corresponding nodes,wherein the data map indicates the relationship between the linked data sources associated with the automated environment, and wherein each link further indicates a directionality of the relationship between linked data sources;
g) causing display, at a user device, the data map as a graphical structure;
f) determining performance of the first plurality of data sources or the second plurality of data sources based on the data map by ; determining a performance metric of each of the first plurality of data sources and the second plurality of data sources;
identifying that any performance metric of at least a first data source exceeds a threshold; and
responsive to identifying that the performance metric of at least the first data source exceeding the threshold, identifying one or more corrective actions to at least the first data source and
i) wherein maintenance or repair of any of the data sources in the automated environment is configured to be performed based on data map and one or more corrective actions.
According to MPEP 2106.05(g)(3), retrieving a plurality of data sets is necessary data gathering step and therefore considered as insignificant extra-solution activity.
Further according to MPEP 2106.05(f)(2), retrieving time-series data from one or more IoT devices is invokes computers or other machinery merely as a tool to perform an existing process. I.e. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., using a computer to receive data from IoT devices) does not integrate a judicial exception into a practical application or provide significantly more.
According to MPEP 2106.05(g)(3), retrieving a plurality of data sets is necessary data gathering step and therefore considered as insignificant extra-solution activity. Also similarly under MPEP 2106.05.05(f)(2), as stated above, this us ordinary use of computer and IoT devices.
According to MPEP 2106.05(g) storage of data is considered as extra-solution activity.
See mapping under Step 2A Prong 1.
See mapping under Step 2A Prong 1.
See mapping under Step 2A Prong 1.
See mapping under Step 2A Prong 1.
See mapping under Step 2A Prong 1.
According to MPEP 2106.05(g)(3), displaying a plurality of data sets is post-solution/ insignificant extra-solution activity.
See mapping under Step 2A Prong 1.
As per MPEP 2106.05(f)(1), the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Specifically, without details of how the corrective action is achieved based on data source, the determining performance of data sources based on data map remains an idea of solution.
As per MPEP 2106.05(h) the use of data to perform maintenance or repair based on (non-specific) datum is at best field of use and parallel to fact pattern disclosed in In re Flook.
Further as per MPEP 2106.05(f)(1) the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". In this case how/what the maintenance or repair is performed based on the first plurality of data sources or the second plurality of data sources and the data map is neither evident from the claim nor the specification4.
In accordance with this step, the judicial exception is not integrated into a practical application. In claim 1, the recitation of computer in the preamble is generic recitation of a computer performing an abstract idea. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. See Alice Corp., 573 U.S. at 223 [full citation omitted] in MPEP 2106.05(f).
In claim 18, the claim recites the additional elements of a non-transitory computer-readable storage media and one or more processors. Their recitation at a high-level of generality amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(f).
Step 2B: As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer/ non-transitory computer-readable storage media/processor(s) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer/processing component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)). Also retrieving a plurality of data sets steps are extra-solution activity (MPEP 2106.05(g)) related to data gathering. Further, even if the displaying step is considered as additional element, this step under MPEP 2106.05(d) is well-understood, routine, conventional activities previously known to the industry US PGPUB No. US 20170103148 A1 by NATSUMEDA; Masanao and additionally US 20150026521 A1, US 8566070 B2 (per Berkheimer Memo). The claims 1 & 10 are therefore considered to be patent ineligible.
Claims 2 & 15 recite “wherein (d) further comprises computing a mean of the calibrated first model across the first plurality of data sources and the second plurality of data sources” is an abstract idea directed to mathematical concept of calculating mean. The claims do not disclose any additional limitations that integrate the judicial exception into practical element.
Claims 3, 16 & 19 recite “wherein the automated environment comprises a building, a warehouse, a factory, or a campus” merely confines the use of the abstract idea to a particular technological environment (adjusting performance/production of well based on simulation) and thus fails to add an inventive concept to the claims. MPEP 2106.05(g) & (h). The claims do not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 4 (Cancelled)
Claim 5 recite “wherein an algorithm may be utilized to calibrate the first model in (d), and wherein the algorithm comprises a ReLU function” is an abstract idea directed to mathematical concept of calculating ReLU function. See MPEP 2106.04(a)(2)(1)(C). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 6 recites “wherein (f) further comprises generating a correlation score for each of the plurality of links, and wherein the links with a correlation score greater than a pre-determined threshold are kept and aggregated to the data map” is an abstract idea directed to mathematical concept of calculating correlation score and then mental step of observation (of correlation score) and judgement/opinion of keeping and aggregating the link based on the score meeting the threshold. See MPEP 2106.04(a)(2)(1)(C) and 2106.04(a)(2)((III). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 7 recites “wherein each of the first plurality of data sources has a one-to-many relationship with one or more of the second plurality of data sources, and wherein a correspondence arrangement of the one-to-many relationship is unknown” and further add to the abstract idea directed to mental steps of observation (of relationship) and judgement/opinion (that there is no presumption of the relationship). See MPEP 2106.04(a)(2)((III). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 8 recites “wherein the correspondence arrangement of the one-to-many relationship is ascertained and presented by the data map” and further add to the abstract idea directed to mental steps of observation (ascertaining the relationship) and judgement/opinion (to present it on a map, which can be done with pencil and paper). See MPEP 2106.04(a)(2)((III). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 9 (Cancelled)
Claims 10 & 11 recites respectively “wherein (e) further comprises using a non-linear function to create the link” & “wherein (f) further comprises using a non-linear function to create the plurality of links” and further add to the abstract idea directed mathematical concept defining the type of mathematical function for one and more links requiring the calculation/computation (as inherited from claim 1). See MPEP 2106.04(a)(2)(I)(C). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 12 recite “wherein (f) further comprises computing a measure of centrality, and aggregating the plurality of links with aid of the measure of centrality” is an abstract idea directed to mathematical concept of calculating centrality. The claims do not disclose any additional limitations that integrate the judicial exception into practical element. See MPEP 2106.04(a)(2)(I)(C).
Claim 13 recites “wherein (e) further comprises: i) analyzing the calibrated model; ii) for each data source of the second plurality of data sources, identifying a correlation with the highest magnitude score with respect to the first plurality of data sources; and iii) creating the link representing the identified correlation.” and further add to the abstract idea directed mental step of observation/opinion (Step (i)-(iii)), which can be done with pencil and paper to create a data map (e.g. graph). See MPEP 2106.04(a)(2)((III). The claim does not disclose any additional limitations that integrate the judicial exception into practical element.
Claim 14:
Step 1: the claims 14 is drawn to a system claim, falling under one of the four statutory categories of invention.
Amended claim 14 is rejected in similar manner as claim 1 to address additional limitations pertaining to time-series data, IoT devices being source of data and map data having links as in claim 1.
Claim 21-25 recite “wherein the maintenance or repair of the automated environment comprises diagnosing/ maintaining / repairing / preventing disruption/ improving the automated environment based on the data map” in a method claim. These are at least abstract idea of forming judgement/opinion/evaluation of performing the above actions (sans the detail) based on observation of data map. If considered under the Step 2A Prong 2, these also present idea of solution, without detailing how the data map performs all the actions as claimed. See MPEP 2106.05(f)(1). The claim does not disclose any additional limitations that integrate the judicial exception into practical element or adds significantly more.
Claims 26-30 recite similar limitations for a system claim and are rejected for similar rationale as claims 21-25.
Claims 31-35 recite similar limitations for a non-transitory computer-readable storage media claim and are rejected for similar rationale as claims 21-25.
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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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5-8, and 10-35 are rejected under 35 U.S.C. 103 as being unpatentable over US PGPUB No. US 20220376980 A1 by BHAT; Karthik Venkatesh, in view of US PGPUB No. US 20170103148 A1 by NATSUMEDA; Masanao.
In Alternate Claim(s) 1-3, 5-8, and 10-35 are rejected under 35 U.S.C. 103 as being unpatentable over BHAT, in view of NATSUMEDA, further in view of US PGPUB No. US 20150026521 A1 by Yabuki; Kentaro.
Regarding Claims 1, 14 and 18 (Updated 12/11/25)
Bhat teaches (Claim 1) a computer-implemented method comprising (Bhat : Fig.3-6 showing components and flow of method, see detailed mapping of each limitation below) /(Claim 14) A computer implemented system, comprising at least one processor and instructions executable by the at least one processor to perform operations (Bhat : [0034]) comprising: a data set retrieving module… a magnitude score generation engine… a model calibration module… a link creation engine… (Bhat: system mapped as system 102, 106 & 300 in Figs. 1-3 where functionality of engine and modules are performed in software by the general purpose computer)/ (Claim 18) 18. One or more non-transitory computer-readable storage media coupled to one or more processors (Bhat : Fig.2 element 202 & 210) and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising :
a) retrieving a first plurality of time-series data sets (Bhat :"[0091]... In an example herein, the current operating context of the device (104a-104n) depicts the operation being performed by the device (104a-104n) at a current instance of time, based on its capability...." ; " [0149] The correlation identifier 306 may update the trained action continuity module 306a on detecting the action continuity feature between the first device 104a and the one or more second devices 104b-104n, each time...." ; “[0152]... The conflict training dataset may be information, which have been monitored while controlling the operations of one or more of the plurality of devices 104a-104n over a time...." –this implies data is collected for each current time instance and over time for correlation as shown in Figs. 3 & 11 between the pointed device (as first IoT device with data) and correlation device (as second IoT device with data) from a first plurality of data sources comprising one or more Internet of Things (IoT) devices associated with an automated environment (Bhat: [0087] "[0087] The plurality of devices 104a-104n may be IoT devices capable of exchanging information with each other and other devices (such as, the IoT cloud server 102, the electronic device 106, or the like) ..."; [0107] "[0107] The electronic device 106 collects the identified type of data from the first device 104a and the one or more second devices 104b-104n...." first plurality of data sources are mapped to one of the devices 104a-n in an automated environment where the devices are IoT devices; [0107]-[0109]) ;
b) retrieving a second plurality of time-series data sets (Bhat : [0091], [0149], [0152] as mapped above for all devices 104a-104n) from a second plurality of data sources comprising one or more Internet of Things (IoT) devices associated with the automated environment (Bhat: [0087] "[0087] The plurality of devices 104a-104n may be IoT devices capable of exchanging information with each other …” likewise second plurality of data sources are 104b-n in an IoT environment as in [0087] & [0107]-[0109]) wherein the first plurality of time-series data sets and the second plurality of time-series data sets are stored in a storage (Bhat: [0093]"... Also, the electronic device 106 maintains separate databases for storing the device information, the capabilities mappings, the location information, or the like of each device (104a-104n) present in the IoT environment....") , and wherein there are one or more unknown links comprising data transmission between any of the first plurality of data sources and the second plurality of devices (Bhat: [0147]-[0150] correlation identifier 306 identifying the unknown links between the devices; [0087] "[0087] The plurality of devices 104a-104n may be IoT devices capable of exchanging information with each other …” likewise second plurality of data sources are 104b-n in an IoT environment as in [0087] & [0107]-[0109])) ;
c) computing a magnitude score of [[a]] one or more correlations (Bhat : Fig.11 showing one or more correlation types for each set of device pairs, each with magnitude score as (continuity/conflict/failure) “confidence”) over time (Bhat: [0091], [0149], [0152] as mapped above for all devices 104a-104n, showing time series as over a time “[0152]... The conflict training dataset may be information, which have been monitored while controlling the operations of one or more of the plurality of devices 104a-104n over a time....") correlation between each of the first plurality of data sets and the second first plurality of data sources (Bhat : [0110] "[0110] Upon collecting the data from the first device 104a and the one or more second devices 104b-104n, the electronic device 106 determines the correlation between the first device 104a and each of the one or more second devices 104b-104n based on the data collected from the first device 104a and the one or more second devices 104b-104n and/or the capabilities of the first device 104a and the one or more second devices 104b-104n.... Examples of the correlation features with the correlation indices may be, but are not limited to, an action continuity feature with an action continuity correlation index, a conflict resolution feature with a conflict resolution correlation index, a failure resolution feature with a failure resolution correlation index, a screen intelligence feature with a screen intelligence correlation index, a setting suggestion feature with a setting suggestion correlation index, and so on… ") to generate a first model (Bhat: [0111]-[0114] disclosing various correlation related models or Examples of the correlation features with the correlation indices), wherein the one or more correlations comprise a correlation matrix (Bhat: [0151]-[0156] and correlation matrix as shown in Fig.11.) ;
d) calibrating the first model by subtracting hyperparameters from the first model (Bhat: [0104] "[0104]... On assigning the confidence score for each device, the electronic device 106 discards the one or more devices from the fetched list of devices 104a-104n that have been associated with the confidence score less than a device interaction threshold...." – here the hyperparameter is the threshold which is subtracted from the first model (score), thereby calibrating the model to discard associations/correlations below the threshold; also see [0116], [0144], [0168]) ;
e) identifying a link between one or more of the first plurality of data sources and one or more of the second plurality of data sources based at least in part on the calibrated model, wherein the link indicates a data communication relationship comprising direct and indirect transmission of data between linked data sources (Bhat: [0084] "...[0084] The IoT cloud server 102, the plurality of devices 104a-104n, and the electronic device 106 may be connected with each other. In an example, the IoT cloud server 102, the plurality of devices 104a-104n and the electronic device 106 may be connected with each other using a communication network 108. The communication network 108 may include at least one of, but is not limited to, a wired network, a value-added network, a wireless network, a satellite network, or a combination thereof...." [0087] "...[0087] The plurality of devices 104a-104n may be IoT devices capable of exchanging information with each other and other devices (such as, the IoT cloud server 102, the electronic device 106, or the like). Examples of the plurality of devices 104a-104n may be, but are not limited to, a smart phone, a mobile phone, a video phone, a computer, a tablet personal computer (PC), a netbook computer, a laptop, a wearable device, a vehicle infotainment system, a workstation, a server, a personal digital assistant (PDA), a smart plug, a portable multimedia player (PMP), a moving picture experts group (MPEG-1 or MPEG-2) audio layer 3 (MP3) layer, a mobile medical device, a light, a voice assistant device, a camera, a home appliance, one or more sensors, and so on. Examples of the home appliance may be, but are not limited to, a television (TV), a digital video disc (DVD) player, an audio device, a refrigerator, an air conditioner (AC), ..."; [0146] creating links as creating one or more device pairs 104a to 104b-n; Also see Fig.5-6 element D1 and filtering based on Threshold based dropout D1 U D2; Also see in Fig.8B first device data (e.g. TV) to second device data (speaker) with confidence of 80%; Also see Fig.11 showing link between the data sources in “pointed device” column against “Correlation device” column ) ; and
f) repeating (e) to identify a plurality of links (Bhat: [0146] creating links as creating one or more device pairs 104a to 104b-n; Also see Fig.5-6 element D1 and filtering based on Threshold based dropout D1 U D2; Also see in Fig.8B first device data (e.g. TV) to the action continuity correlation index computed for the speaker, the camera, the oven, and the smart plug with respect to the TV may be 80%, 15%, 5%, and 0, respectively), and aggregating the plurality of links (Bhat: Fig.11 see aggregated links in table format) (Bhat: Fig.11 see generated map as aggregated links in table format), wherein the data map comprises a series of nodes that each represent any of the first plurality of data sources and the second plurality of data sources and a series of edges that each represent a link between corresponding nodes (Bhat: See Fig,8B where the candidate device and pointed device form the nodes and edges of the neural network) ,wherein the data map indicates (Bhat: Fig.11 see Multi-model Correction Identifier with confidence scores, where the TV is first source and the other sources are listed as correction devices; data is associated with sensors in automated environment (IoT), [0021] [0027] [0110]) and wherein data map further indicates a directionality of the relationship between linked data sources (Bhat: Fig8A & Fig.11 directionality can be implied from the first column “pointed device” to second column “correlation device” – notice no specific implementation details are needed for visualizing the data map in this step; This step is additionally taught with Natsumeda below) indicating if data communication is unidirectional (Bhat: e.g. Fig.8B showing unidirection flow from input to output nodes) or bi-directional (Bhat : [0137] use of a bidirectional recurrent deep neural network (BRDNN)) between a first data source and a second data source identified in any link or a control of the first device over the second device(Bhat: [0034] "... The at least one processor is further configured to recommend at least one suggestion to control at least one operation of at least one of, the first device and the at least one second device, based on a correlation between capabilities of the first device and a current operating context of the at least one second device...."); See e.g., Fig.11 TV is correlated to Speaker with 80% confidence and TV would control the speaker, however TV would not control the oven (hence 4% continuity confidence) ;
h) determining a performance of the first plurality of data sources or the second plurality of data sources (Bhat: [0151]-[0156] and correlation matrix as shown in Fig.11.) By
determining a performance metric of each of the first plurality of data sources and the second plurality of data sources (Bhat: Fig.5 performance as confidence score between first and second plurality of data sources, e.g. TV and speaker in Fig.5) ;
identifying that any performance metric of at least a first data source exceeds a threshold (Bhat: Fig.5 “Threshold based Dropout”, [0037][0104][0116] & [0144] ) ; and
responsive to identifying that the performance metric of at least the first data source exceeding the threshold, identifying one or more corrective actions to at least the first data source (Bhat: [0144] "... The device interaction prediction module 302b compares the confidence score of each device with respect to the first device 104a with the interaction threshold. The device interaction prediction module 302b discards the set of devices associated with the confidence score of lesser than the interaction threshold. The device interaction prediction module 302b identifies the set of devices associated with the confidence score of equal to greater than the interaction threshold as the second set of relevant devices for the first device 104a...." – corrective action could be discarding the device pair if the confidence is low or lack of confidence exceeds the threshold; Fig.5),
Bhat teaches i) wherein maintenance or a repair of any of the data sources in the automated environment is configured to be performed based on data map and one or more corrective actions (Bhat: Fig.11 – [0093]-[0095] as maintaining the device information; Repair as in correlation matrix Fig.11 – as failure detection and resolution – [0112][0115][0152]-[0157], [0152]- "... The correlation identifier 306 assigns a positive reward to the first reinforcement module 306b if the generated conflict action is correct and assigns a negative reward to the first reinforcement module 306b if the generated conflict action is not correct...." correcting as repairing where the data map (such as Fig.5 and 11) are used to come to determination of corrective action; [0160] "... [0160] The recommendation manager 308 may also be configured to recommend the configurations for the first device 104a based on the one or more contents being rendered on the one or more second devices 104b-104n. In an example, the recommendation may recommend the configurations for a bulb (an example first device 104a) based on a type of content being played on a TV (an example second device (104b-104n) determined for the first device 104a). The configurations may include at least one of, turning ON the bulb, turning OFF the bulb, brightness settings, dimmer settings, or the like...."); Fig.9B [0182]-[0184] "... The electronic device 106 also updates the one or more conflict policies and the first reinforcement module 306b dynamically. The electronic device 106 updates the one or more conflicts based on the predicted action of the first reinforcement module 306b with respect to the received conflict training dataset. The electronic device 106 updates the first reinforcement module 306b by reinforcement the conflict observations into the first reinforcement module 306b for better generation of the conflict actions....") .
Bhat does not explicitly teach g) causing display, at a user device, the data map as a graphical structure; and f) optimizing performance of the first plurality of data sources or the second plurality of data sources based on the data map[Emphasis added on bolded].
Natsumeda teaches in general a)/b) retrieving a plurality of time series data sets (Natsumeda : [0055]) from a first/second plurality of data sources associated with an automated environment (Natsumeda: Fig.1 element 200 & [0053]); c) computing a magnitude score of a correlation between the first plurality of data sources and the second first plurality of data sources (Natsumeda: [0059] "... [0059] In the present invention, a model including at least a regression equation that defines a relationship between data items, and the permissible range of a prediction error for the regression equation is referred to as a correlation model, a correlation model including a regression equation containing two data items in the model is referred to as “cross-correlation model”, and a correlation model including a regression equation containing three or more data items is referred to as “many-body correlation model”. ..."& See Fig.12 S301-S302, S201-S202); d) calibrating the first model by subtracting hyperparameters from the first model (Natsumeda: [0061] showing prediction error and fineness degree for correlation model calibration); ; e) creating a link between one or more of the first plurality of data sources and one or more of the second plurality of data sources based at least in part on the calibrated model, wherein the link indicates a relationship between linked data sources (Natsumeda: Fig.6 show links as regression equation between one pair of data A and data B with score of F, many pairs are listed therein; Also see Fig.4 creating a link as annotated below).
Natsumeda teaches f) repeating (e) to create a plurality of links (Natsumeda: Fig.6 show links as regression equation between one pair of data A and data B with score of F, many pairs are listed therein), and aggregating the plurality of links to a data map, wherein the data map indicates the relationship between data sources of the automated environment (Natsumeda: Fig.6 shows one such map where the link between data A-D at least are shown and data map is shown as graph in representation 703F1 in view of disclosure in [0117]) and wherein each link further indicates a directionality of the relationship between linked data sources (Natsumeda: Fig.4 see element 701D [0113]-[0114] as detailed below with link’s directionality shown as directional arrows from exemplary Node A to Node B);
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Natsumeda teaches g) causing display, at a user device, the data map as a graphical structure; (Natsumeda: Fig.4 & [0114] "... In this example, the data-item classification unit 1211 expresses the data item A, the data item B, the data item C, and the data item D as nodes, respectively, and connects the nodes through a line with an arrow from the data item A to the data item B, a line with an arrow from the data item A to the data item C, a line with an arrow from a data item D to a data item B, and a line with an arrow from a data item D to a data item C, respectively, to obtain the graph structure 701D. ..." ; where each data item (node) is associated to data source such as a sensor as shown in [0053] "... A set of sensor values collected at a timing regarded as the same from each device 200 to be monitored is referred to as state information, and a set of data items corresponding to the sensor values contained in the state information is referred to as a data item group...."); [0085] "... [0085] The data-item classification unit 1211 classifies data items to associate each cluster of the graph structure with a data item group which is a group of one data item based on the graph structure of the fine cross-correlation model group. In such classification, the data items are associated with the nodes of the graph structure, and therefore, data items associated with nodes in each cluster become data items included in each data item group....").
Namasuda teaches h) optimizing performance of the first plurality of data sources or the second plurality of data sources based on the data map (Namasuda’s: [0085] "... [0085] The data-item classification unit 1211 classifies data items to associate each cluster of the graph structure with a data item group which is a group of one data item based on the graph structure of the fine cross-correlation model group. In such classification, the data items are associated with the nodes of the graph structure, and therefore, data items associated with nodes in each cluster become data items included in each data item group. In this case, data items excluded in such a cluster are not targeted for formulating an analysis model. ..." [0084]"... The graph structure is represented like a network chart with data items included in the regression equation of a cross-correlation model as nodes, and with the regression equation as a line...." —here the graph structure is data map, which is used to fine tune/exclude data items as discussed in [0085] to optimize performance/classification).
Namasuda also teaches i)performing maintenance or repair of the automated environment based on the performance of the first plurality of data sources or the second plurality of data sources and the data map (Namasuda: [0060] "... From the viewpoint of enhancing the sensitivity of abnormality detection, it is preferable that the predetermined period is a period that is as short as possible, to prevent an influence due to the change of the system to be monitored over time. For example, when the cycle of maintenance of the system to be monitored is one year,..."; Namasuda: Fig.4 showing graph (as data map) and, [0141]) Fig.15 & [0196] .
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showing the maintenance cycle is associated with analysis model which is build on the graph (data map)).
Namasuda also shows the correlation matrix in Fig.4 associated with data map (graph 701D).
In Alternate, despite Namasuda’s explicit Figs.4, 6 and 7 which show the data map with nodes and links, it is assumed that Namasuda does not teach displaying such a data map, alternate art of Yabuki; Kentaro is presented below.
Yabuki; Kentaro teaches displaying such a data map as shown in (Yabuki: Fig7-9 which states:
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-- here it is seen the information 130 is displayed and information 130 includes the map data along with the correlation –
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).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Natsumeda to Bhat to graphically represent the correlation present in the tabular form (such as mapping in Natsumeda Fig.6). The motivation to combine would have been that Natsumeda (2017) is prior art to Bhat (2021) and are analogous art to instant claim in the field of correlation determination between various collected data (scope of claim), to determine whether the state of the system is normal or abnormal or failure (intent of prior art: Natsumeda: [0008]; Bhat: [0020]). Natsumeda supplements Bhat in that such correlations can be graphically represented (Natsumeda: [0084]-[0085]).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to complement/apply the teachings of Yabuki to Natsumeda to graphically represent (specifically display) the correlation present in the graphical (as a nodes with links) as well as the tabular form (such as mapping in Natsumeda Fig.6; Yabuki: Fig.7-9, [0075]-[0079]) with to judge a fault cause correctly in the invariant analysis (Yabuki: [0013]). Further motivation to combine would have been that Yabuki and Namasuda are analogous art to the instant claim, in the field of correlation modeling including one or more correlation functions each of which indicates a correlation between two metrics different each other among a plurality of metrics in a system (Yabuke: Abstract; Namsuda: Abstract Also see Namasuda Fig.15 in view Yabuke Fig.7-9 --- and they have the same assignee).
Claims 2-20 are rejected similarly with Alternate grounds of rejection as presented below.
Regarding Claims 2 & 15
Natsumeda teaches wherein (d) further comprises computing a mean of the calibrated first model across the first plurality of data sources and the second plurality of data sources (Natsumeda: [0153]-[0154], [0077]-[0079]; [0100]-[0101], [0145]).
Regarding Claims 3, 16 & 19
Bhat teaches wherein the automated environment comprises a building, a warehouse, a factory, or a campus (Bhat: [0085] as “a factory unit”).
Regarding Claim 4 (Cancelled)
Regarding Claim 5
Bhat teaches wherein an algorithm may be utilized to calibrate the first model in (d), and wherein the algorithm comprises a ReLU function (Bhat: Fig.9A and 10A showing ReLU being used to calibrate).
Regarding Claim 6
Natsumeda teaches wherein (f) further comprises generating a correlation score for each of the plurality of links, and wherein the links with a correlation score greater than a pre-determined threshold are kept and aggregated to the data map (Natsumeda: Fig.2 showing the threshold based filtering; Fig.6 showing data map with plurality of aggregate links between data A-D, K-M; [0081]-[0085] shows exclusion based on fineness degree threshold in the flow of Fig.9 or Fig.12).
Regarding Claims 7, 17 and 20
Natsumeda teaches wherein each of the first plurality of data sources has a one-to-many relationship with one or more of the second plurality of data sources, and wherein a correspondence arrangement of the one-to-many relationship is unknown (Natsumeda: See the table 703B representation where each roe only shows one relationship and one to many is not known looking at only one row; One to many is later derived based on the graph 703D) .
Regarding Claim 8
Natsumeda teaches wherein the correspondence arrangement of the one-to-many relationship is ascertained and presented by the data map (Natsumeda: See the table 703B representation where each roe only shows one relationship and one to many is not known looking at only one row; One to many is later derived based on the graph 703D (data map)).
Regarding Claim 9 (Cancelled)
Natsumeda teaches wherein the link further indicates a directionality of the relationship between linked data sources (Natsumeda: Fig.4 see element 701D [0113]-[0114]).
Regarding Claim 10
Bhat teaches wherein (e) further comprises using a non-linear function to create the link (Bhat: Fig.9A and 10A showing ReLU to create link/action in view of Fig.3).
Regarding Claim 11
Bhat teaches wherein (f) further comprises using a non-linear function to create the plurality of links (Bhat: Fig.9A and 10A showing ReLU to create link/action in view of Fig.3; plurality of links as in Fig.11 TV to correlation device as plurality of links with associated (confidence) score(s)).
Regarding Claim 12
Natsumeda teaches wherein (f) further comprises computing a measure of centrality, and aggregating the plurality of links with aid of the measure of centrality (Natsumeda teaches: [0100] centrality as average value used to determine prediction error which is subtracted, in determination of plurality of links between the data; in [0077] calculation of F (threshold) using average value of objective variable, where F as shown in Fig.2 at least is used prune the links; Also in [0153]-[0154], [0077]-[0079]; [0100]-[0101], [0145]) .
Regarding Claim 13
Natsumeda teaches wherein (e) further comprises:i) analyzing the calibrated model (Natsumeda: Fig.1 element 12 & 13 creating and using the analysis model; calibration in [0061] with prediction error and fineness index) ; ii) for each data source of the second plurality of data sources, identifying a correlation with the highest magnitude score with respect to the first plurality of data sources (Natsumeda: [0080], [0088]-[0089],[0115], [0139]) ; and iii) creating the link representing the identified correlation (Natsumeda: [0144], [0139], [0115], [0151]).
Regarding Claims 21, 26, 31 (Updated 3/5/2025)
Bhat and Namasuda teach wherein the maintenance or repair of the automated environment comprises diagnosing the automated environment based on the data map (or correlation matrix as in Bhat: Fig.11 – as failure detection and resolution – [0112][0115] [0152]-[0157] Namasuda: Fig.4 showing graph (as data map) and, [0141]; Fig.15 & [0196]).
Regarding Claims 22, 27, 32 (Updated 3/5/2025)
Bhat and Namasuda teach wherein the maintenance or repair of the automated environment comprises maintaining the automated environment based on the data map (or correlation matrix as in Bhat: Fig.11 – [0093]-[0095] as maintaining the device information – [0112][0115] [0152]-[0157]; Namasuda: Fig.4 showing graph (as data map) and, [0141] ; Fig.15 & [0196]).
Regarding Claims 23, 28, 33 (Updated 3/5/2025)
Bhat and Namasuda teach wherein the maintenance or repair of the automated environment comprises repairing the automated environment based on the data map (or correlation matrix as in Bhat: Fig.11 – as failure detection and resolution – [0112][0115][0152] "... The correlation identifier 306 assigns a positive reward to the first reinforcement module 306b if the generated conflict action is correct and assigns a negative reward to the first reinforcement module 306b if the generated conflict action is not correct...." correcting as repairing; Namasuda: Fig.4 showing graph (as data map) ; Fig.15 & [0196]).
Regarding Claims 24, 29, 34 (Updated 3/5/2025)
Bhat and Namasuda teach wherein the maintenance or repair of the automated environment comprises preventing disruption the automated environment based on the data map (or correlation matrix as in Bhat: Fig.11 – as failure detection and resolution [0112][0115] [0152]-[0157];; Namasuda: Fig.4 showing graph (as data map) and [0141] "... In other words, the number of regression equations formulated for one objective variable is not limited to one, and therefore, a failure of detection of an abnormality, caused by the limitation of the objective variable, can be prevented...."; ; Fig.15 & [0196]). ).
Regarding Claims 25, 30, 35 (Updated 3/5/2025)
Bhat and Namasuda teach wherein the maintenance or repair of the automated environment comprises improving the automated environment based on the data map (Bhat: [0221] "... In an example, the semantic actions may include at least one of, a theme suggestion for the first bulb based on the content being played on the TV, and the settings (for example, brighter or dimmer settings) of the second bulb. The electronic device 106 recommends the created one or more semantic actions to the user for operating the first bulb. The electronic device 106 may recommend the created one or more semantic actions by rending the one or more semantic action as the keyboard overlayed content on the display/outputter 208 of the electronic device 106. Thus, controlling the devices 104a-104n in the IoT environment based on the correlation between the devices 104a-104n may enhance the user experience while controlling the devices...." and Namasuda: [0137] "... For example, methods described in PTLs 1 and 2 have a problem that specification of a data item influenced by an abnormality is precluded by increasing the number of data items contained as explanatory variables in one regression equation in order to enhance prediction accuracy....", [0009][0020]; Namasuda: ; Fig.15 & [0196]) .
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Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2147 Wednesday, March 5, 2025
1 Specification [024] "... In some embodiments, to compute the magnitude of the correlations between specific points, the systems and methods described herein may use a measure of the strength of the correlation such as Pearson's correlation coefficient or Spearman's rank correlation coefficient. These measures can be calculated based on the values of the points at different times in the time series data...."
2 Specification [025] "... Each bias is a hyperparameter of the model and it must be calibrated. In some embodiments, the bias (i.e., hyperparameter) may be a value that is set by the user or designer....".
3 See PTO 892 for wikipedia showing Pearson correlation coefficient and Spearman's rank correlation coefficient.
4 See specification [0025] & [0114] which cursorily recites such actions of maintenance and repair are performed based on datum (or use ML models) without going into any specifics what or how the first plurality of data sources or the second plurality of data sources and the data map lead to what needs to actions of maintenance and repair.
Prosecution Insights