DETAILED ACTION
Status of Claims
This is a final action in reply to the response filed on October 7, 2025.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1-20 under 35 USC § 101 is maintained. Please see the Response to Arguments.
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 1-20 are 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.
As per claim 1 recites “controlling at least one of the plurality of assets based at least in part on the improved operations data”. Applicant’s disclosure describes that the operation data is used at least to display/transmit/store the improved operations data as shown in ¶ 0077: “the one or more improved operations actions may include causing each improved operations data set of the plurality of improved operations data sets to be displayed on an improved operations data set interface of an associated asset of the plurality of assets 102. In this regard, for example, the operations processing system 140 and/or the one or more user devices 160 may be configured to transmit each improved operations data set to an associated asset of the plurality of assets 102.” See also ¶ 0081. Applicant’s disclosure does not describe that the plurality of assets are controlled based at least in part on the improved operations data. The same rationale applies to claims 11 and 20. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-10 falls within statutory class of a process, claims 11-19 falls within statutory class of a machine and claim 20 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
receiving registration data associated with a plurality of sensors, wherein each sensor of the plurality of the sensors are associated with a corresponding asset of a plurality of assets;
receiving operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors;
associating the operations data with the plurality of assets based at least in part on the registration data;
applying the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets;
initiating performance of one or more improved operations actions based at least in part on the improved operations data
and controlling at least one of the plurality of assets based at least in part on the improved operations data.
Claim 11:
An apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to:
receive registration data associated with a plurality of sensors, wherein each sensor of the plurality of the sensors are associated with a corresponding asset of a plurality of assets;
receive operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors;
associate the operations data with the plurality of assets based at least in part on the registration data
apply the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets;
initiate performance of one or more improved operations actions based at least in part on the improved operations data
and control at least one of the plurality of assets based at least in part on the improved operations data.
Claim 20:
receive registration data associated with a plurality of sensors, wherein each sensor of the plurality of the sensors are associated with a corresponding asset of a plurality of assets;
receive operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors;
associate the operations data with the plurality of assets based at least in part on the registration data
apply the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets;
initiate performance of one or more improved operations actions based at least in part on the improved operations data;
and control at least one of the plurality of assets based at least in part on the improved operations data.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor, memory, plurality of sensors and the improvement model is recited at a high level of generality, i.e., as a generic computing and processing system. This processor, memory, plurality of sensors and the improvement model is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. The plurality of sensors is used as a tool, in its ordinary capacity, to carry out the abstract idea i.e., collect data. The improvement model is used as a tool, in its ordinary capacity, to carry out the abstract idea. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a processor, memory, plurality of sensors and the improvement model. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor, memory, plurality of sensors and the improvement model type structure at paragraphs 0060: “Processor 202 or processor circuitry 202 may be embodied in a number of different ways. In various embodiments, the use of the terms “processor” should be understood to include a single core processor, a multi-core processor, multiple processors internal to the apparatus 200, and/or one or more remote or “cloud” processor(s) external to the apparatus 200.” Paragraph 0063: “Memory 204 or memory circuitry 204 may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In some embodiments, the memory 204 includes or embodies an electronic storage device (e.g., a computer readable storage medium).” Paragraph 0050: “each sensor of the plurality of sensors 110 is configured via hardware, software, firmware, and/or a combination thereof, to perform data reporting and/or other data output process(es) associated with monitoring or otherwise analyzing operations of one or more of the plurality of assets 102 or specific component(s) thereof. In some embodiments, each sensor of the plurality of sensors 110 may comprises one or more of a gas sensor, a temperature sensor, a humidity sensor, a material composition sensor, a vibration sensor, an acceleration sensor, location sensor, and/or the like.” And paragraph 0074: “an improvement model to generate improved operations data. In some embodiments, the optimization model may comprise one or more of a statistical model, an algorithmic model, and/or a machine learning model (e.g., using AI and machine learning circuitry 210). See also figure 2.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-10 and 12-19 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2 and 12 further limit the abstract idea that applying the operations data to the improvement model to generate improved operations data occurs in real-time (a more detailed abstract idea remains an abstract idea). Claims 3 and 13 further limit the abstract idea that causing each improved operations data set of the plurality of improved operations data sets to automatically be displayed on an improved operations data set interface of an associated asset of the plurality of assets (a more detailed abstract idea remains an abstract idea). Claims 4 and 14 further limit the abstract idea that transmitting the improved operations data to a database, wherein each improved operations data set of the plurality of improved operations data sets is transmitted to a database storage location associated with an associated asset of the plurality of assets (a more detailed abstract idea remains an abstract idea). Claims 5 and 15 further limit the abstract idea that receiving operations data representing operations of a plurality of assets is associated with a first bandwidth allocation and transmitting the improved operations data to a database is associated with a second bandwidth allocation (a more detailed abstract idea remains an abstract idea). Claims 6 and 16 further limit the abstract idea that the second bandwidth allocation is greater than the first bandwidth allocation (a more detailed abstract idea remains an abstract idea). Claims 7 and 17 further limit the abstract idea by determining a computing resource consumption for each of the plurality of assets based at least in part on the improved operations data (a more detailed abstract idea remains an abstract idea). Claims 8 and 18 further limit the abstract idea by transmitting a payment request to each of the plurality of assets based at least in part on the computing resource consumption for each of the plurality of assets (a more detailed abstract idea remains an abstract idea). Claim 9 further limit the abstract idea that the plurality of assets comprise at least one building, at least one plant, or at least one vehicle (a more detailed abstract idea remains an abstract idea). And claims 10 and 19 further limit the abstract idea that the operations data is associated with a first data size and the improved operations data is associated with a second data size, wherein the second data size is greater than the first data size (a more detailed abstract idea remains an abstract idea). The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 10/7/2025 have been fully considered but they are not persuasive.
With regard to the 35 U.S.C. 101 Rejection, Applicant argues that (1), “the claim is not directed to an abstract idea”; (2) “ The claims are directed toward enhancing the operational efficiency, scalability, and responsiveness of asset management systems through dynamic, real-time association of sensor- generated operations data with physical assets using registration metadata, and automated control of those assets based on improved operations data.” And (3) “Claim 1 provide an inventive concept and amounts to significantly more than the exception itself” (Remarks pages 7-11).
With regard to the 35 U.S.C. 102, Applicant argues that Bose (4) “does not disclose or suggest any methodology for receiving and utilizing explicit registration data that associates each sensor with a corresponding asset, nor does it teach a system that dynamically maps incoming operations data to assets based on such registration data.
Specifically, Bose lacks any teaching of system architecture that enables the association of operations data with assets at the system level using registration data, or the ability to perform asset-specific data mapping in real time without requiring sensors to transmit identification data. Furthermore, Bose does not disclose a system that applies an improvement model to the associated data and then automatically controls one or more assets based on the improved operations data.” (Remarks pages 11-13).
In response to Applicant’s argument (1). Examiner respectfully disagrees. Please see the 35 U.S.C. 112(a) rejection above with regard to the “controlling step”. In addition, claim 1 recites a computer-method for receiving registration data and operations data from a plurality of assets based on the registration data that is associated with a plurality of sensors which are associated with a corresponding asset of a plurality of assets and the operations data is associated with the plurality of assets based at least in part on the registration data. Associated operations data is applied to an improvement model to generate improved data in order to initiate performance of one or more operation actions and to control at least one of the plurality of assets based on the improved operations data. Claim 1 recites a concept related to Mental Processes, concepts performed in the human mind including observations (registration data associated with the plurality of sensors and corresponding asset of the plurality of assets), operations data i.e., sensor values associated with the plurality of assets based at least in part on the registration data), evaluation (improvement model to generate improved operations data), judgement (performance of one or more improved operations actions) and opinion (controlling at least one of the plurality of assets) and Certain Methods of Organizing Human Activity such as business relations i.e., asset management.. Therefore, claim 1 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of concept related to Mental Processes and certain methods of organizing human activity. Examiner notes that Applicant’s disclosure and the claims does not describe dynamically mapping the sensor-generated data to assets using structured registration metadata nor model-based inference and real-time orchestration of control signals. The same rationale applies to claims 11 and 20.
In response to Applicant’s argument (2). Examiner respectfully disagrees. Please see the Response to Applicant’s argument (1). In addition, per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor, memory, plurality of sensors and the improvement model. is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting/displaying. The recited processor, memory, plurality of sensors and the improvement model. merely links the abstract idea to a computer environment. In this way, the processor, memory, plurality of sensors and the improvement model. involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claim 1 uses the processor, memory, plurality of sensors and the improvement model.as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field of asset management. Further, a processor configured to cause receiving/determining/transmitting data to a device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The same rationale applies to claims 11 and 20.
In response to Applicant’s argument (3). Examiner respectfully disagrees, The claims each at most comprise additional elements of: a processor, memory, plurality of sensors and the improvement model.. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the functions/steps by a processor is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor, memory, plurality of sensors and the improvement model type structure at paragraphs 0060: “Processor 202 or processor circuitry 202 may be embodied in a number of different ways. In various embodiments, the use of the terms “processor” should be understood to include a single core processor, a multi-core processor, multiple processors internal to the apparatus 200, and/or one or more remote or “cloud” processor(s) external to the apparatus 200.” Paragraph 0063: “Memory 204 or memory circuitry 204 may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In some embodiments, the memory 204 includes or embodies an electronic storage device (e.g., a computer readable storage medium).” Paragraph 0050: “each sensor of the plurality of sensors 110 is configured via hardware, software, firmware, and/or a combination thereof, to perform data reporting and/or other data output process(es) associated with monitoring or otherwise analyzing operations of one or more of the plurality of assets 102 or specific component(s) thereof. In some embodiments, each sensor of the plurality of sensors 110 may comprises one or more of a gas sensor, a temperature sensor, a humidity sensor, a material composition sensor, a vibration sensor, an acceleration sensor, location sensor, and/or the like.” And paragraph 0074: “an improvement model to generate improved operations data. In some embodiments, the optimization model may comprise one or more of a statistical model, an algorithmic model, and/or a machine learning model (e.g., using AI and machine learning circuitry 210). See also figure 2. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The Examiner has adequately supported the finding that the recited a processor, memory, plurality of sensors and the improvement model is well-understood, routine and conventional. Further, Applicant’s claims are not similar to McRO because nor the claims and Applicant’s disclosure describe structure rules to transform raw sensor data into improved operations data to control the assets. The same rationale applies to claims 11 and 20.
In response to applicant's argument (4) that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., utilizing explicit registration data, dynamically maps incoming operation data to assets based on such registration data, perform asset-specific data mapping in real time without requiring sensors to transmit identification data; automatically controls one or more assets) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In addition, Examiner respectfully disagrees. Applicant’s disclosure paragraph 0070 describes that registration data may be the following “[i]n some embodiments, the registration data may be associated with the plurality of sensors 110. In some embodiments, for example, the registration data may indicate a device identification for each of the plurality of sensors 110 (e.g., a unique identification code for each sensor of the plurality of sensors 110 that uniquely identifies the sensor). As another example, the registration data may indicate an asset associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is associated with a first asset and a second sensor is associated with a second asset). As another example, the registration data may indicate a tenant (e.g., an operator and/or owner of an asset) associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is associated with a first tenant and a second sensor is associated with a second tenant). As another example, the registration data may indicate a sensor type associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is a temperature sensor and that a second sensor is an acceleration sensor).” Applicant’s disclosure paragraph 0073 describes that operations data may be the following “In some embodiments, for example, the operations data may include gas data (e.g., a flow rate of a gas associated with an asset captured by a gas sensor). As another example, the operations data may include temperature data (e.g., a temperature associated with the asset captured by a temperature sensor). As another example, the operations data may include humidity data (e.g., a humidity associated with an asset captured by a humidity sensor). As another example, the operations data may include material composition data (e.g., a composition of a material associated with an asset captured by the material composition sensor). As another example, the operations data may include vibration data (e.g., a vibration associated with an asset captured by a vibration sensor). As another example, the operations data may include acceleration data (e.g., an acceleration associated with an asset captured by the acceleration sensor). As another example, the operations data may include location data (e.g., a location associated with an asset captured by a location sensor).” Bose teaches receiving registration data associated with a plurality of sensors in ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.” wherein each sensor of the plurality of the sensors are associated with a corresponding asset of a plurality of assets in ¶ 0038: “the one or more software products 122 are operable to analyse the sensor data for determining an aggregate efficiency of operation of the asset 104 based upon a weighted combination of contributions from one or more apparatus 106 of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset. For example, the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104. Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” Bose also teaches receiving operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors in ¶ 0032: “The plurality of sensors 110 are optionally adjusted to monitor at given intervals for collection of appropriate amounts of data.” And ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” And associating the operations data with the plurality of assets based at least in part on the registration data Operation data: ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” With the plurality of assets: ¶ 0038: “the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104.” Based at least in part on the registration data ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors”. Bose disclose applying the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets; in ¶ 0038: “The one or more software products 122 are provided with simulation models of the one or more apparatus 106 of the asset 104 to which the configuration of sensors 110 is applied. The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104.” See also ¶ 0043: “the one or more software products 122 acquire data in real-time from the asset via a wireless communication network, analyses the acquired data to identify patterns and relationships in the acquired data, constructing a system model for the asset 104, applies simulation, for example Monte Carlo simulation, to determine where energy savings and/or increases in operating efficiency can be achieved and providing control information. The control information improves the efficiency of operation of the asset 104.” ¶ 0048: “The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104” and ¶ 0011: “identify adjustments that improve the efficiency of operation of the one or more assets and overall system.” And controlling at least one of the plurality of assets based at least in part on the improved operations data in ¶ 0044: “the cloud computing resource 124 generates response signals, namely containing adjustment data or recommendation, based on the analysis and/or simulation of the one or more software products 122. In addition, the one or more cloud computing resources 124 transmit the response signals and/or instructions to the control manager 108 to improve the efficiency of the operation of the asset 104.”
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4, 7, 9, 11-12, 14, 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bose et al., (US 2015/0170090 A1) hereinafter “Bose”.
Claim 1:
Bose as shown discloses a computer-implemented method, the method comprising:
receiving registration data associated with a plurality of sensors; (¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.”);
wherein each sensor of the plurality of the sensors are associated with a corresponding asset of a plurality of assets (¶ 0038: “the one or more software products 122 are operable to analyse the sensor data for determining an aggregate efficiency of operation of the asset 104 based upon a weighted combination of contributions from one or more apparatus 106 of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset. For example, the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104. Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.”);
receiving operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors; (¶ 0032: “The plurality of sensors 110 are optionally adjusted to monitor at given intervals for collection of appropriate amounts of data.” And ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.”);
associating the operations data with the plurality of assets based at least in part on the registration data (Operation data: ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” With the plurality of assets: ¶ 0038: “the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104.” Based at least in part on the registration data ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors”);
applying the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets; (¶ 0038: “The one or more software products 122 are provided with simulation models of the one or more apparatus 106 of the asset 104 to which the configuration of sensors 110 is applied. The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104.” See also ¶ 0043: “the one or more software products 122 acquire data in real-time from the asset via a wireless communication network, analyses the acquired data to identify patterns and relationships in the acquired data, constructing a system model for the asset 104, applies simulation, for example Monte Carlo simulation, to determine where energy savings and/or increases in operating efficiency can be achieved and providing control information. The control information improves the efficiency of operation of the asset 104.” ¶ 0048: “The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104” and ¶ 0011: “identify adjustments that improve the efficiency of operation of the one or more assets and overall system.”);
initiating performance of one or more improved operations actions based at least in part on the improved operations data ¶ 0036: “analysing the sensor data for determining an efficiency of operation of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset 104. The recommendations may include instructions for the operator to set particular controls like valves, switches to certain positions to improve the performance of the system 100 as a whole. The one or more software products 122 trigger proactive and predictive actions/responses that are transmitted to the asset 104, thereby allowing the asset 104 to run more efficiently and accurately. More or less continuous set point recommendation for the asset 104 is provided through the operation of the BRAINS.APP software product to the operator to ensure the process runs efficiently with minimal waste or energy consumption. For example, the operator of In Situ Recovery mining facility may get recommendations from BRAINS.APP to set a number of flow restricting valves to certain setpoints in order to achieve improved flow from the injection wells to extraction wells in ISR process.”);
and controlling at least one of the plurality of assets based at least in part on the improved operations data (¶ 0044: “the cloud computing resource 124 generates response signals, namely containing adjustment data or recommendation, based on the analysis and/or simulation of the one or more software products 122. In addition, the one or more cloud computing resources 124 transmit the response signals and/or instructions to the control manager 108 to improve the efficiency of the operation of the asset 104.”);Claims 11 and 20:
The limitations of claims 11 and 20 (¶ 0014) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 11 that differ from claim 1.
Claim 11:
Bose as shown discloses an apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to: (¶ 0035: Examples of one or more computing resources 124 include storage, processing, memory, network bandwidth, and virtual machines”);
Claim 2:
Bose as shown discloses the following limitations:
wherein applying the operations data to the improvement model to generate improved operations data occurs in real-time (¶ 0030: “The plurality of sensors 110 monitors and collects the data corresponding to the status/operating conditions of the plurality of apparatus 106 of the asset 104 in real time and transmits the data in real time in a form of signals to the server arrangement 112. ” see also ¶ 0031: “the system 100 has been applied to in-situ recovery mine. Combining real time flow rates and power consumption data of the submersible pumps allowed for identification of the pumps entering a “dry running” mode which is a damaging state for the pump. Real time identification of the dry running mode and addressing it by giving recommendations for the well workover timing in order to increase the well solution inflow would decrease the pump breakdown rate by about 15% and would lead to saving in energy up to about 35%.”);
Claim 12:
The limitations of claim 12 encompasses substantially the same scope as claim 2. Accordingly, those similar limitations are rejected in substantially the same manner as claim 2, as described above.
Claim 4:
Bose as shown discloses the following limitations:
wherein the one or more improved operations actions comprises: transmitting the improved operations data to a database, wherein each improved operations data set of the plurality of improved operations data sets is transmitted to a database storage location associated with an associated asset of the plurality of assets (¶ 0044: “the cloud computing resource 124 generates response signals, namely containing adjustment data or recommendation, based on the analysis and/or simulation of the one or more software products 122. […] the one or more cloud computing resources 124 transmit the response signals and/or instructions to the server arrangement 112 and/or back-up servers 128 to maintain the records.” See also figures 1 and 2);
Claim 14:
The limitations of claim 14 encompasses substantially the same scope as claim 4. Accordingly, those similar limitations are rejected in substantially the same manner as claim 4, as described above.
Claim 7:
Bose as shown discloses the following limitations:
determining a computing resource consumption for each of the plurality of assets based at least in part on the improved operations data (¶ 0045: “ the one or more cloud computing resources 124 transmit the response signals and/or instructions to one or more computing devices 130 of an administrator to take appropriate actions for increasing the efficiency of the asset 104. The analysis of the aggregate consumption data is performed online via the Internet or through wireless communication to the computing devices 130.”);
Claim 17:
The limitations of claim 17 encompasses substantially the same scope as claim 7. Accordingly, those similar limitations are rejected in substantially the same manner as claim 7, as described above.
Claim 9:
Bose as shown discloses the following limitations:
wherein the plurality of assets comprise at least one building, at least one plant, or at least one vehicle (¶ 0032: “Examples of the facility 102 include, but may not be limited to, micro-fabrication plants, manufacturing plants, steel mills, water treatment works, recovery assembly factories, power stations, oil and gas fields, quarries, mines, in-situ mining plants, water utilities, foundries, steel industry, petrochemicals industry, nuclear industry, transport facilities, water treatment works and food processing facilities. These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines.”);
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose” as applied to claims 1 and 11 above, further in view of Chambers et al., (US 2009/0132091 A1) hereinafter “Chambers”.
Claim 3:
Bose as explained above teaches a plurality of improved operations data sets. Bose is silent with regard to the following limitations. However, Chambers in an analogous art of asset management for the purpose of providing the following limitations as shown does:
wherein the one or more improved operations actions comprises: causing each improved operations data set of the plurality of improved operations data sets to automatically be displayed on an improved operations data set interface of an associated asset of the plurality of assets (Figures 5-8 illustrates a plurality of improved operations data sets i.e., cost based settings, temperature based settings on a user interface);
Both Bose and Chamber teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Chamber teaches in the Abstract “sending component configured to send a signal to the plurality of assets to implement the business rule.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Chamber would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chamber to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the one or more improved operations actions comprises: causing each improved operations data set of the plurality of improved operations data sets to automatically be displayed on an improved operations data set interface of an associated asset of the plurality of assets into similar systems. Further, as noted by Chamber “utilization of the control system 104 with the environment 102 may allow more efficient utilization of assets within the environment 102 by controlling usage based on user preferences.” (Chamber ¶ 0029).
Claim 13:
The limitations of claim 13 encompasses substantially the same scope as claim 3. Accordingly, those similar limitations are rejected in substantially the same manner as claim 3, as described above.
Claims 5-6, 10, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose” as applied to claims 1 and 11 above, further in view of Chet R. Douglas et al., (US 2005/0125563 A1) hereinafter “Douglas”.
Claim 5:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein receiving operations data representing operations of a plurality of assets is associated with a first bandwidth allocation and transmitting the improved operations data to a database is associated with a second bandwidth allocation (Figure 5 illustrates a plurality of devices with their respective bandwidths allocations);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as receiving operations data representing operations of a plurality of assets is associated with a first bandwidth allocation and transmitting the improved operations data to a database is associated with a second bandwidth allocation into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 15:
The limitations of claim 15 encompasses substantially the same scope as claim 5. Accordingly, those similar limitations are rejected in substantially the same manner as claim 5, as described above.
Claim 6:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein the second bandwidth allocation is greater than the first bandwidth allocation (Figure 4 illustrates when a device need extra bandwidth and Figure 5, note the total requested bandwidth for each device);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the second bandwidth allocation is greater than the first bandwidth allocation into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 16:
The limitations of claim 16 encompasses substantially the same scope as claim 6. Accordingly, those similar limitations are rejected in substantially the same manner as claim 6, as described above.
Claim 10:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein the operations data is associated with a first data size and the improved operations data is associated with a second data size, wherein the second data size is greater than the first data size (Figure 5, see device 504, Active Bandwidth [504] =20 i.e., first data size, and Total Requested Bandwidth [504] = 60 i.e., second data size which is greater than the first data size);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the operations data is associated with a first data size and the improved operations data is associated with a second data size, wherein the second data size is greater than the first data size into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 19:
The limitations of claim 19 encompasses substantially the same scope as claim 10. Accordingly, those similar limitations are rejected in substantially the same manner as claim 10, as described above.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose” as applied to claims 7 and 17 above, further in view of Dozortsev et al., (US 2020/0090109 A1) hereinafter “Dozortsev”.
Claim 8:
Bose is silent with regard to the following limitations. However, Dozortsev in an analogous art of asset management for the purpose of providing the following limitations as shown does:
transmitting a payment request to each of the plurality of assets based at least in part on the computing resource consumption for each of the plurality of assets (¶ 0091: “Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.”);
Both Bose and Dozortsev teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Dozortsev teaches in the Abstract “controlling an electronic device based on mapping sensors to a physical asset.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Dozortsev would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Dozortsev to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as transmitting a payment request to each of the plurality of assets based at least in part on the computing resource consumption for each of the plurality of assets into similar systems. Further, as noted by Dozortsev “ the capacity to improve the technical field of the field of information technology asset management by utilizing cognitive learning and a series of rules to make inferences regarding the correct matching of sensors to assets.” (Dozortsev ¶ 0012).
Claim 18:
The limitations of claim 18 encompasses substantially the same scope as claim 8. Accordingly, those similar limitations are rejected in substantially the same manner as claim 8, as described above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADJA CHONG whose telephone number is (571)270-3939. The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm ET, Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RUTAO WU can be reached on 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
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