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 .
In a Preliminary Amendment filed on March 11, 2024, the title, the abstract, claim 1 was cancelled, and new claims 2-21 were added.
Claims 2-21 are pending, of which claims 2, 9, and 13 are independent claims.
Priority
Applicant’s claim for the priority benefit of this application from CON of 16/143,354 09/26/2018 PAT 11,791,914, which is a CON of 15/973,406 05/07/2018 PAT 11,838,036, which is a CIP of PCT/US17/31721 05/09/2017, which claims benefit of 62/333,589 05/09/2016, and claims benefit of 62/350,672 06/15/2016, and claims benefit of 62/412,843 10/26/2016, and claims benefit of 62/427,141 11/28/2016, and said 16/143,354 09/26/2018 is a CON of PCT/US18/45036 08/02/2018, which claims benefit of 62/540,557 08/02/2017, and claims benefit of 62/540,513 08/02/2017, and claims benefit of 62/562,487 09/24/2017, and claims benefit of 62/583,487 11/08/2017 are acknowledged.
Information Disclosure Statement
The references cited in the information disclosure statement (IDS) submitted on 03/13/2024 has been considered by the examiner.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 6, line 2, replace “AR/VR” with “augmented reality/virtual reality (AR/VR)”.
Claim 9, line 6, replace “the error or the fault” with “the at least one of the error or the fault”.
Claim 13, line 14, replace “AR/VR” with “augmented reality/virtual reality (AR/VR)”.
Appropriate correction is respectfully requested.
35 USC § 112(f) Analysis
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claims 2, 4, 5, 13, 14, and 16-20 are interpreted under 35 U.S.C. 112(f), as reciting means for performing a specified function.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification, as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Referring to independent claim 2 and independent claim 13, these claims recite the claim limitations “a data collector” and “an expert system”.
Paragraph [0311] of the published Specification describes “Any of the sensor data types described throughout this disclosure can be fused in this manner and stored in a local data pool, in storage, or on an IoT device, such as a data collector, a component of a machine, or the like.”
For purposes of examination and in accord with paragraph [0311] of the Specification, as published, the “data collector” is construed as an IoT device.
Paragraph [0426] of the Specification, as published, describes “In embodiments, a relational database server (“RDS”) 5930 may be used to access all of the information from a MMP and PCSA information store 5932. As with the PARA server 5800 (FIG. 36), information from the information store 5932 may be used with an EP and align module 5934, a data exchange 5938 and the expert system 5940. In embodiments, a raw data stream archive 5942 and extract and process raw data archive 5944 may also be used by the EP align 5934, the data exchange 5938 and the expert system 5940 as with the PARA server 5800. In embodiments, new stream raw data 5950, new extract and process raw data 5952, and new data 5954 (essentially all other raw data such as overalls, smart bands, stats, and data from the information store 5932) are directed by the CDMS 5832.” Accordingly, for purposes of examination and in accord with FIG. 36 and paragraph [0426] of the Specification, as published, the “expert system” is construed as a functional block of a server to analyze, determine, and generate as recited in independent claims 2 and 13.
Referring to claim 4 and claim 16, claim 4 recites, “a learning feedback system configured to optimize an effectiveness of the heat map” and claim 16 recites “a learning feedback system configured to improve an effectiveness of the heat map interface”. However, the written description fails to disclose the corresponding structure, material, or acts for the claimed function associated with “a learning feedback system”.
Referring to claim 5, this claim recites “a user analyzer” and “the learning feedback system”. For purposes of examination and in accord with paragraph [0421], which describes “In embodiments, the [master raw data server] MRDS 5700 may include a stream data analyzer module with an extract and process alignment module 5068. The analyzer module 5068 may be shown to be a more robust data analyzer and extractor than may be typically found on portable streaming DAQ instruments although it may be deployed on the DAQ instrument 5002 as well. In embodiments, the analyzer module 5068 takes streaming data and instantiates it at a specific sampling rate and resolution similar to the local data control module 5062 on the DAQ instrument 5002. The specific sampling rate and resolution of the analyzer module 5068 may be based on either user input 5712 or automated extractions from a multimedia probe (“MMP”) and the probe control, sequence and analytical (“PCSA”) information store 5714 and/or an identification mapping table 5718, which may require the user input 5712 if there is incomplete information regarding various forms of legacy data similar to as was detailed with the DAQ instrument 5002.”, the “user analyzer” is construed as a functional block of a master raw data server to analyze the user data. In addition, claim 5 also refers to the “learning feedback system”. Therefore, for similar reasons as those provided with respect to claim 4, the Office submits that the written description fails to disclose the corresponding structure, material, or acts for the claimed function associated with “the learning feedback system”.
The recitations of claims 14 and 17-20 simply add more detail to or are cumulative to the “expert system” of independent claim 13 and the “learning feedback system” of claim 16.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4-8 and 16-21 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 4 and 16, claim 4 recites “a learning feedback system configured to optimize an effectiveness of the heat map”, claim 16 recites “a learning feedback system configured to improve an effectiveness of the heat map”, and claims 5 and 17-20 further refer to “the learning feedback system” of corresponding claims 4 and 16. The “learning feedback system” limitation invokes 35 U.S.C. 112(f), but the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed corresponding functions and to clearly link the structure, material, or acts to the corresponding functions of such limitation.
The corresponding structure for the means-plus-function limitations must disclose an algorithm for performing the claimed specific computer function that is sufficient to transform a general-purpose computer to a special purpose computer. The instant Specification appears to provide a description of an algorithm, but there is no mention of a computer, processor, controller, server, or microprocessor programmed with the algorithm. MPEP 2181 (“However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C. 112(b).”)
The published Specification describes in Paragraph [0307] provides “This may include optimization of input selection and configuration based on learning feedback from the learning feedback system 4012, which may include providing training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be de-selected, such as by powering down the other sensors).” (emphasis added)
However, there is no disclosure of any particular structure, such as computer programmed with an algorithm, either explicitly or inherently, of the operation the learning feedback system to perform the recited function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which computer structure or structures perform(s) the claimed functions. Therefore, claims 4, 5, and 16-20 are indefinite and are rejected under 35 U.S.C. 112(b).
In view of their dependencies to a rejected base claim, claims 7, 8, and 21 are also rejected as being indefinite.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
Claims 4-8 and 16-21 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.
Claims 4, 5, and 16-20 recite “a learning feedback system”, which is subject matter that is 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function of optimizing the heat map. The Specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
In view of their dependencies to a rejected base claim, claims 7, 8, and 21 are also rejected as being indefinite.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter, which the inventor or a joint inventor regards as the invention.
Claims 2-21 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding independent claim 2, the last function recites, “generate a heat map for at least a portion of the industrial environment, wherein the heat map is based, at least in part, on the at least one of the error or the fault…”. How is it possible to generate a heat map based on at least one of the error or the fault? By definition, a heat map is known to be an image or map or information that represents varying information, such as temperature or infrared radiation, etc., over an area during a period of time. It is indefinite and unclear as to how an error or a fault can be part of a heat map representation. Considering the description provided in paragraphs [0335]-[0369] and [0997]-[1002] of the published Specification, in an effort to advance examination of the present claim, the last function of the claim will be construed as “generate a heat map for at least a portion of the industrial environment based, at least in part, on where the at least one of the error or the fault occurred”. Appropriate correction through claim amendment is respectfully requested.
Regarding claim 4, this claim depends from claim 3 and recites “a learning feedback system configured to optimize an effectiveness of the heat map”. (emphasis added) How is a heat map optimized to be effective? How can the heat map, which is an image or map or information that represents varying information, be optimized to be effective? The Federal Circuit has affirmed a judgment invalidating patent claims for indefiniteness where the claims used “optimal” and “best” language without providing objective boundaries for those terms of degree. Akamai Technologies, Inc. v. MediaPointe, Inc., No. 2024-1571 (Fed. Cir. Nov. 25, 2025). For purposes of advancing examination and in accord with paragraph [0307] of the published Specification and claim 3, the features of claim 4 will be construed as “a learning feedback system for the heat map”.
Regarding claim 5, this claim recites “a user monitor configured to receive user data indicative of a behavior of a user; and a user analyzer configured to analyze the user data, wherein the learning feedback system is further configured to update at least one of the plurality of models used by the expert system to generate the heat map based on the analyzed user data.” This claim is indefinite because it is unclear how is it possible to generate the heat map based the analyzed user data which is indicative of a behavior of the user. Paragraph [0334] of the published Specification describes the following:
For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the heat map UI 4304. This may include rule-based or model-based feedback (such as feedback providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive heat map system may be provided, where selection of inputs or triggers for heat map displays, selection of outputs, colors, visual representation elements, timing, intensity levels, durations and other parameters (or weights applied to them) may be varied in a process of variation, promotion and selection (such as selection using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive heat map interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like. (emphasis added)
Thus, the Specification explains that the user behavior is more so a user input affecting the display on the heat map user interface, not that the heat map is based on a recorded behavior of the user. As recited, the claimed recitation is unclear and indefinite. In addition, claim 5 indirectly depends on claim 3, which recites that “the generation of the heat map is further based, at least in part, on at least one model of a plurality of models…” If the heat map has already been generated, then how is it possible for the heat map to be generated from the “update at least one of the plurality of models” as recited in claim 5. As a result, for purposes of examination, claim 5 will be construed as “a user monitor configured to receive user input; and a user analyzer configured to analyze the user input, wherein the learning feedback system is further configured to update at least one of the plurality of models used by the expert system to generate an updated heat map.” (emphasis added) Appropriate correction through claim amendment is respectfully requested.
Regarding claim 8, this claim recites “The monitoring system of claim 5, wherein the heat map is updated, based, at least in part, on the updated at least one of the plurality of models, to improve an effectiveness of an impact on a behavior of the user.” How can updating a heat map improves an impact on a behavior of the user? It is difficult to comprehend the intended scope of this feature. For similar reasons as those provided in claim 5, this claim is rejected as being indefinite and the feature “to improve an effectiveness of an impact on a behavior of the user” will be omitted from consideration. Appropriate correction through claim amendment is respectfully requested.
Regarding independent claim 9, this claim recites “generating a heat map for at least a portion of the industrial environment based, at least in part, on the error or the fault;…recording a behavior of the user; and updating the heat map based, at least in part, on the recorded behavior of the user. For same reasons as provided for independent claim 2, it is unclear as to how an error or a fault can be part of a heat map representation. In addition, how is it possible to update a heat map based on a recorded behavior of the user? The description of paragraph [0334] of the published Specification provided with respect to claim 5 is incorporated herein. The Specification explains that the user behavior is more so affecting the display on the heat map user interface, not that the heat map is based on a recorded behavior of the user. As recited, the claimed recitation is indefinite. In addition, the feature “the error or the fault” in line 6 of the claim should be “the at least one of the error or the fault”. As a result, for purposes of examination, the generating function will be construed as “generate a heat map for at least a portion of the industrial environment based, at least in part, on where the at least one of the error or the fault occurred” and the recording and updating functions will be construed as “recording a user input, and updating the heat map based, at least in part, on the recorded user input”. Appropriate correction through claim amendment is respectfully requested.
Regarding claim 12, this claim recites “The method of claim 11, wherein the heat map is updated to achieve an intended impact on a behavior of a user, and wherein the intended impact thereby results in an improvement in at least one of a desired system outcome, a data collection outcome, or an analytic outcome”. The claim is confusing. How is it possible for a heat map to be updated to then achieve an intended impact on a behavior of the user. As previously explained, the heat map is an image or map or information that represents varying temperature or infrared radiation over an area during a period of time. How can an image or map or information of temperature or infrared radiation “achieve an intended impact on a behavior of a user”? Also, the recitation “results in an improvement in at least one of a desired system outcome, a data collection outcome, or an analytic outcome” How is such improvement achieved? As written, a person of ordinary skill in the art would not be able to comprehend the intended scope of the claim. The claim is too confusing and indefinite. Thus, the Office is unable to properly examine the claim over prior art. Appropriate correction through claim amendment is respectfully requested.
Regarding independent claim 13, the last recitation provides “wherein the data collector is further configured to collect user data, representative of a behavior of the user, from the AR/VR device.” For same reasons as those provided in independent claim 9 and paragraph [0334] of the Specification, as published, the collected user data representative of a behavior of the user is found to be indefinite. For purposes of examination, the recitation is construed as follows: “wherein the data collector is further configured to collect user input from the AR/VR device.”
Regarding claim 14, this claim recites, in part “wherein the generated heat map further comprises a representation of the identified at least one of the error or the fault”. How is it possible for the generated heat map to represent the identified at least one of the error or the fault? For the same reasons as provided above, and in light of the description provided in paragraphs [0338]-[0372] and [0997]-[1002] of the published Specification, it is difficult to comprehend the intended scope of this claim and to clearly ascertain how the heat map includes a representation of the error or the fault. In view of the foregoing, the Office is unable to properly examine the claim over prior art. Appropriate correction through claim amendment is respectfully requested.
Regarding claim 16, this claim recites “further comprising a learning feedback system configured to improve an effectiveness of the heat map interface”. Improving effectiveness is a relative and subjective term, which the Office is unable to ascertain the intended scope. MPEP 2173.05(b)(IV) Paragraph [0336] of the published Specification explains that “user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the heat map UI 4304”, however, it does not explain how such effectiveness is done. The recitations of claim 16 are more conclusory than recitations defining concrete boundaries of the invention. In view of the foregoing, the Office will construe the claim to only recite “further comprising a learning feedback system for the heat map interface.” Appropriate correction through claim amendment is respectfully requested.
Regarding claim 17, this claim recites “wherein the expert system is further configured to analyze the user data, wherein the learning feedback system is further configured to update at least one of the plurality of rules used by the expert system to generate the heat map based on the analyzed user data, and wherein the update to the at least one of the plurality of rules results in at least one of a change in an output selection, a color, a visual representation element, a timing, an intensity level, or a duration of a signal in the generated heat map.” (emphasis added) For similar reasons as those presented in claim 13, the features of claim 17 to be construed as “wherein the expert system is further configured to analyze the user input, wherein the learning feedback system is further configured to update at least one of the plurality of rules used by the expert system to generate the heat map based on the analyzed user input, and wherein the update to the at least one of the plurality of rules results in at least one of a change in an output selection, a color, a visual representation element, a timing, an intensity level, or a duration of a signal in the generated heat map.” (emphasis added) Appropriate correction through claim amendment is respectfully requested.
Regarding claim 18, this claim recites “wherein the learning feedback system updates the at least one of the plurality of rules to improve the effectiveness of the heat map interface in impacting a behavior of a user to generate a desired outcome” (emphasis added). The highlighted features of claim 18 are found to be indefinite. How is it possible for the behavior of a user to be improved based on an effectiveness of an interface and based on updates to at least one of the plurality of rules? Paragraph [0334] of the published Specification provides that “The haptic interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the haptic user interface 4302. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed).” If the intent of the claim is to reflect what is described in Paragraph [0334], it is respectfully recommended that the claim be amended appropriately. However, as best understood, for purposes of examination, claim 18 is construed as “wherein the learning feedback system updates the at least one of the plurality of rules.” Appropriate correction through claim amendment is respectfully requested.
Regarding claims 19 and 20, claim 19 recites “wherein the learning feedback system is trained, at least in part, with analyzed user data collected in response to a corresponding simulated heat maps and a corresponding outcome.” (emphasis added) Claim 20 recites “wherein the learning feedback system is trained using historic data comprising at least two of analyzed user data, corresponding heat map data, or corresponding outcome”. (emphasis added) In view of their dependencies to independent claim 13 and for similar reasons as provided above, for purposes of examination, claim 19 is construed as reciting “wherein the learning feedback system is trained, at least in part, with the analyzed user input collected in response to a corresponding simulated heat maps and a corresponding outcome.” (emphasis added) Claim 20 is construed as “wherein the learning feedback system is trained using historic data comprising at least two of the analyzed user input, corresponding heat map data, or corresponding outcome”. (emphasis added)
In view of their dependencies to rejected base claims, claims 3, 6, 7, 10, 11, 15, and 21 are also rejected.
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 2-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Independent claim 2 recites, “... analyze the collected data resulting in analyzed collected data; determine at least one of an error or a fault, wherein the at least one of the error or the fault is based, at least in part, on at least a portion of the analyzed collected data;...”
Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0335]-[0336] and [0358], for instance, the analyze limitation is a mental process that can be performed through observation, evaluation and judgement. Also, paragraphs [0404] and [0660] of the Specification of the present application, as published, describe the determine limitation is a mental process that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 2 recites the additional elements of, “a data collector configured to collect data from at least one of a plurality of sensors in the industrial environment; an expert system configured to: …generate a heat map for at least a portion of the industrial environment, wherein the heat map is based, at least in part, on the at least one of the error or the fault; and a heat map interface to present the generated heat map to a user”.
The collecting limitation is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. In accord with MPEP 2105(g), “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional features including “a data collector”, “sensors”, and “a heat map interface to present the generated heat map to a user”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used as recited in claim 2, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic and/or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application.
The generating of the heat map limitation in response to the judicial exception (i.e., the determine function) identified is not applying or using the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment or field of use, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(h) In addition, the limitation amounts to no more than an idea of an outcome and does not recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
In view of the foregoing, the additional limitations, individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The limitations of obtaining data (user data and device data) amounts to no more than insignificant pre-activity of receiving data. Further, the obtaining steps simply append well-understood and conventional activity of receiving data over a network (see MPEP 2106.05(d)(II)(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
The additional features including “a data collector”, “sensors”, and “a heat map interface to present the generated heat map to a user”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used for the functions recited in claim 2, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
Furthermore, the limitation of “generate a heat map for at least a portion of the industrial environment, wherein the heat map is based, at least in part, on the at least one of the error or the fault” it amounts to no more than an idea of a solution or outcome and does not recite details of how a solution to a problem is accomplished; see MPEP 2106.05(f)(1)- “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". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).”
Additionally, the limitation of “generate a heat map for at least a portion of the industrial environment, wherein the heat map is based, at least in part, on the at least one of the error or the fault” simply appends well-understood and conventional activity of transmitting data (see MPEP 2106.05(d)(II)(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
Thus, when taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Therefore, the additional claimed features, individually or combined, do not amount to significantly more and the claim is not patent eligible.
Regarding claims 3, 4, and 8, claim 3 recites “the generation of the heat map is further based, at least in part, on at least one model of a plurality of models, and wherein a portion of the at least one model of the plurality of models utilizes at least a portion of the analyzed collected data”; claim 4 recites “a learning feedback system” for “the heat map”; and claim 8 recites “the heat map is updated, based, at least in part, on the updated at least one of the plurality of models, to improve an effectiveness of an impact on a behavior of the user”. These claims are simply applying a model to the heat map, including a learning feedback system, and updating the heat map without applying, relying on, or using the judicial exceptions of claim 2 in a manner that would impose a meaningful limitation on the judicial exceptions. Claims 3, 4, and 8 are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 3, 4, and 8 are not patent eligible.
Regarding claims 5, 6, and 7, claim 5 recites “a user monitor configured to receive user data indicative of a behavior of a user; and a user analyzer configured to analyze the user data, wherein the learning feedback system is further configured to update at least one of the plurality of models used by the expert system to generate the heat map based on the analyzed user data.” Claim 6 recites “wherein the heat map interface comprises an interface to an AR/VR device, and wherein the update to the at least one of the plurality of models comprises at least one of a change in an output selection, a color, a visual representation element, a timing, an intensity level, or a duration of a signal in the generated heat map” and claim 7 recites “the update to the at least one of the plurality of models comprises at least one of a change in an output selection, a change in timing, a change in an intensity level, or a change in a duration of a signal in the generated heat map.” The limitations of these claims are recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic and/or computer components (“user monitor”, “a user analyzer”, “an interface”, “an AR/VR device”), which is not indicative of integration into a practical application. Also, Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016). The updating function of claim 7 is not applying, relying on, or using the judicial exceptions of claim 2 in a manner that would impose a meaningful limitation on the judicial exceptions so as to integrate them into a practical application and amounting to significantly more. Thus, claims 5-7 are not patent eligible.
Independent claim 9 recites, “... analyzing data from a plurality of sensors in the industrial environment; determining at least one of an error or a fault in the industrial environment, wherein the at least one of the error or the fault is based, at least in part, on the analyzed data;...”
Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0335]-[0336] and [0358], for instance, the analyze limitation is a mental process that can be performed through observation, evaluation and judgement. Also, paragraphs [0404] and [0660] of the Specification of the present application, as published, describe the determine limitation is a mental process that can be performed through observation, evaluation and judgement. Therefore, a person may perform, through observation, evaluation and judgement, the features enunciated above.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 2 recites the additional elements of, “…generating a heat map for at least a portion of the industrial environment based, at least in part, on the error or the fault; providing the heat map to a user; recording a behavior of the user; and updating the heat map based, at least in part, on the recorded behavior of the user”.
The generating, the providing, and the updating of the heat map limitation in response to the judicial exception (i.e., the determine function) identified is not applying or using the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment or field of use, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(h) In addition, the limitation amounts to no more than an idea of an outcome and does not recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
The recording of the behavior of the user limitation does not integrate the abstract idea into a practical application and are insignificant extra-solution activities to the judicial exception, which are merely nominal or tangential additions to the claim. See MPEP 2106.05(g).
In view of the foregoing, the additional limitations, individually or combined, are not sufficient to demonstrate integration of a judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The generating, the providing, and the updating of the heat map limitation in response to the judicial exception (i.e., the determine function) identified amounts to no more than an idea of a solution or outcome and does not recite details of how a solution to a problem is accomplished; see MPEP 2106.05(f)(1)- “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". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).”
Additionally, the generating and the providing limitations simply append well-understood and conventional activity of transmitting data (see MPEP 2106.05(d)(II)(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
The recording of the behavior of the user limitation is an example of an activity that the courts have found to be well-understood, routine, and conventional activities when claimed in a generic manner. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (storing and retrieving information in memory).
Thus, when taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Therefore, the additional claimed features, individually or combined, do not amount to significantly more and the claim is not patent eligible.
Regarding claims 10-12, claim 10 recites “the heat map comprises an indicator of the determined at least one of the error or the fault”; claim 11 recites “the heat map further comprises at least one of a real world location coordinate, a location on a map, a time-based coordinate, or a frequency-based coordinate to indicate a location corresponding to at least a portion of the analyzed data used in the determination of the at least one of the error or the fault”; and claim 12 recites “the heat map is updated to achieve an intended impact on a behavior of a user, and wherein the intended impact thereby results in an improvement in at least one of a desired system outcome, a data collection outcome, or an analytic outcome”. These claims are simply further defining the heat map and updating the heat map without applying, relying on, or using the judicial exceptions of claim 9 in a manner that would impose a meaningful limitation on the judicial exceptions. Claims 10-12 are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application and that would be sufficient to amount to significantly more than the judicial exception. Thus, claims 10-12 are not patent eligible.
Claim Rejections - 35 USC § 102
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 2-5 and 7-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by LaComb et al. (US Patent Publication No. 2009/0100293 A1) (“LaComb”).
Regarding independent claim 2, LaComb teaches:
A monitoring system for data collection in an industrial environment, the monitoring system comprising: LaComb: Paragraph [0002] (“The system described herein relates generally to finding patterns in temporal data. More specifically, the system relates to the prediction of turbomachinery failure events by using statistical techniques and a genetic algorithm, to aggregate, identify and pattern outlying (i.e., anomalous) engineering or operational data when compared to small sets of related engineering or operational data.”)
a data collector configured to collect data from at least one of a plurality of sensors in the industrial environment; LaComb: Paragraph [0068] (“… operating system based application processor.”) Paragraph [0003] (“In the operation and maintenance of power generation equipment (e.g., turbines, compressors, generators, etc.), sensor readings corresponding to various attributes of the machine are received and stored. These sensor readings are often called “tags”, and there are many types of tags (e.g., vibration tags, efficiency tags, temperature tags, pressure tags, etc.).”) LaComb: Paragraph [0074] (“For example, the anomaly detection techniques, as embodied by the present invention, were applied to a set of turbines for which a significant failure event occurred. The failure event was rare, occurring in only 10 turbines during the 4-month period for which historical sensor data was available. For each turbine that experienced the event (event units), up to 2 months of historical data was collected. For the purposes of comparison, 4 months of historical data for 200 turbines that did not experience the event (non-event units) was obtained.”) [The processor reads on “a data collector”. Sensor readings or tags corresponding to various attributes of a machine collected reads on “collect data from at least one of a plurality of sensors in the industrial environment”.]
an expert system configured to: LaComb: Paragraph [0068] [As described above.] [The operating system of the processor reads on “an expert system”.]
analyze the collected data resulting in analyzed collected data; LaComb: Paragraph [0038] (“In order to account for unit/machine and environmental variations and determine whether or not a given value for a tag for a target unit is outside an expected range (i.e., anomalous), context information may be used to form a basis for the analysis of the target unit's tag data. This context information can be taken from two primary sources: the target unit's past performance, and the performance of the target unit's peers. By using such context information to quantify the typical amount of variation present within the group or within the unit's own performance, it is possible to systematically and rigorously compare current tag data to context data and accurately assess the level of anomalous data in the target unit's tag values.”) LaComb: Paragraph [0041] (“In addition to the context considerations stated above, context data also includes comparable operating conditions. For this implementation, and as one example only, comparable operating conditions can be defined to mean any time period in the past where the unit has the same OPMODE, DWATT and CTIM values within a window of 10. OPMODE can be defined as the operation mode (e.g., slow cranking, peak output, 50% output, etc.). DWATT can be a metric for power (e.g., megawatt output). CTIM can be defined as a temperature metric (e.g., inlet temperature). For example, if the target observation's value of OPMODE is equal to 1 and DWATT is equal to 95, only the historical periods where OPMODE=1 and DWATT was between 90 and 100 could be used. These comparable operating conditions are defined as part of the system configuration.”) LaComb: Paragraph [0043] (“For each unit, up to 8 or more other units with the same frame-size with similar configurations and in the same geographic region can be identified as peers.”) [The analysis of a target unit’s and target unit’s peers tag data and operating conditions reads on “analyze the collected data”.]
determine at least one of an error or a fault, wherein the at least one of the error or the fault is based, at least in part, on at least a portion of the analyzed collected data; and LaComb: Paragraphs [0038], [0041], [0043] and [0074] [As described above.] LaComb: Paragraph [0042] (“By establishing the appropriate context, both in time, geography, frame size, and operating conditions, … objective and automatic calculations can be made to detect and quantify anomalies.”) LaComb: Paragraph [0045] (“By using these techniques to detect anomalies, alerts can be created. An alert can be a rule-based combination of tag values against customizable thresholds.”) [The detected anomalies and alerts based on the tag values read on “determine at least one of an error or a fault, wherein the at least one of the error or the fault is based, at least in part, on at least a portion of the analyzed collected data”.]
generate a heat map for at least a portion of the industrial environment, wherein the heat map is based, at least in part, on the at least one of the error or the fault; and LaComb: Paragraph [0035] (“A heatmap is an outlier-detection-visualization tool that can be performed on each specified machine unit for a large number of selected tags across many different time points. A heatmap illustrates the anomaly-intensity and the direction of a `target observation.` A heatmap may also contain a visual illustration of alerts, and directs immediate attention to hot-spot sensor values for a given machine. Heatmaps can also provide comparison to peers analysis, which allows the operational team to identify leaders and lagers, as well as marketing opportunities on the fly with great accuracy across different time scales (e.g., per second, minute, hour, day, etc.).”) LaComb: Paragraph [0068] (“The anomaly detection process and heatmap tool can be implemented in software with two Java programs called the Calculation Engine and the Visualization Tool, according to one embodiment of the present invention. The Calculation Engine calculates exceptional anomaly scores, aggregates anomaly scores, updates an Oracle database, and sends alerts when rules are triggered.”) LaComb: Paragraph [0071] (“This instructs the Calculation Engine to perform the periodic update, utilize up to 7 or more simultaneous threads, and identify any new sensor data in the database prior to proceeding. The program begins by calculating rules for any new custom alerts and any new custom peers of machine units created by the users of the Visualization Tool. It then retrieves newly arrived raw sensor data from a server, stores the new data in the Oracle database, and calculates exceptional anomaly scores and custom alerts for the newly added data. It stores results of all these calculations in a database, enabling the Visualization Tool to display a heatmap of the exceptional anomaly scores and custom alerts. If the calculations trigger a custom alert with a mile that has a high possibility of detecting a machine deterioration event with lead time, the Calculation Engine can be configured to send warning signals to members of the Monitoring and Diagnostics team. Alerts could be audio and/or visual signals displayed by the team's computers/notebooks, or signals transmitted to the team's communications devices (e.g., mobile phones, pagers, PDA's, etc.).”) [The determined heatmap based on the sensor data/tag on each specified machine unit across may different time points reads on “generate a heat map for at least a portion of the industrial environment” and the heatmap including alerts indicative of anomalies reads on “the at least one of the error or the fault”.]
a heat map interface to present the generated heat map to a user. LaComb: Paragraph [0071] [As described above.] [The visualization tool displaying the heatmap of the anomalies reads on “a heat map interface to present the generated heat map to a user.”]
Regarding claim 3, LaComb teaches all the claimed features of claim 2, from which claim 3 depends. LaComb further teaches:
The monitoring system of claim 2, wherein the generation of the heat map is further based, at least in part, on at least one model of a plurality of models, and wherein a portion of the at least one model of the plurality of models utilizes at least a portion of the analyzed collected data. LaComb: Paragraphs [0038], [0041], [0043] and [0074] [As described in claim 2.] LaComb: Paragraph [0087] (“The term “tag” can be any of various sensor readings (e.g., vibration tags, efficiency tags, temperature tags, pressure tags, etc.), or “tag” could be an exceptional anomaly score…The terms “n” and “m” represent numerical values of specific time periods, such as seconds, minutes, hours, days, months, years, etc. This allows the genetic algorithm to make use of values, their changes over time, and how the changes in one value over time interact with the changes in another value over time. This essentially allows the model to vary over time and to consider changes over time as part of the classification solution. In other embodiments the alleles could be of the form <tag> <greater than or equal to or less than or equal to> <value> for <n> out of <m> time periods.”) LaComb: Paragraph [0088] (“The inputs and consideration of operating conditions are also very important. while the genetic algorithm can accept raw inputs, greater power can be achieved by utilizing Exceptional Anomaly Scores calculated using a bucketized correction model (i.e., only comparing an observation to times in its past when the unit was operating under similar conditions). These scores are standardized measures. The bucketized correction model eliminates much of the noise in the operational data.”)
Regarding claim 4, LaComb teaches all the claimed features of claim 3, from which claim 4 depends. LaComb further teaches:
The monitoring system of claim 3, further comprising a learning feedback system configured to optimize an effectiveness of the heat map. LaComb: Paragraph [0068] (“The Calculation Engine calculates exceptional anomaly scores, aggregates anomaly scores, updates an Oracle database, and sends alerts when rules are triggered. The Calculation Engine can be called periodically from a command-line batch process that runs every hour. The Visualization Tool displays anomaly scores in a heatmap (see FIG. 11) on request and allows users to create rules.”) LaComb: Paragraph [0071] (“This instructs the Calculation Engine to perform the periodic update, utilize up to 7 or more simultaneous threads, and identify any new sensor data in the database prior to proceeding. The program begins by calculating rules for any new custom alerts and any new custom peers of machine units created by the users of the Visualization Tool. It then retrieves newly arrived raw sensor data from a server, stores the new data in the Oracle database, and calculates exceptional anomaly scores and custom alerts for the newly added data. It stores results of all these calculations in a database, enabling the Visualization Tool to display a heatmap of the exceptional anomaly scores and custom alerts.”) [The calculation engine reads on “a learning feedback system”.]
Regarding claim 5, LaComb teaches all the claimed features of claim 4, from which claim 5 depends. LaComb further teaches:
The monitoring system of claim 4, further comprising:
a user monitor configured to receive user data indicative of a behavior of a user; and a user analyzer configured to analyze the user data, LaComb: Paragraph [0072] (“Users of the Visualization Tool can change the date range, change the peer (group, and drill into time series graphs of individual tags' data. The Visualization Tool may utilize Java Server Pages for its presentation layer and user interface.”)
wherein the learning feedback system is further configured to update at least one of the plurality of models used by the expert system to generate the heat map based on the analyzed user data. LaComb: Paragraphs [0068] and [0071] [As described in claim 4.] LaComb: Paragraph [0081] (“A genetic algorithm (GA) can be used to derive patterns of a specific form to predict turbomachine failure or trip events, as well as other trip events and/or anomalous behavior in turbomachinery. The training set for a failure event (or other trip events) in a turbomachine is very small and unbalanced given the rarity of occurrence. The estimated probability is approximately less than one percent. As a result, sample sizes for training models are unavoidably small. There are a large number of operational metrics that can be utilized in discriminating positive and negative cases. This data is available or can be calculated from operational data. The genetic algorithm, according to aspects of the present invention, can efficiently navigate this highly non-linear search space and perform feature selection.”) LaComb: Paragraph [0087] (“This essentially allows the model to vary over time and to consider changes over time as part of the classification solution. In other embodiments the alleles could be of the form <tag> <greater than or equal to or less than or equal to> <value> for <n> out of <m> time periods.”)
Regarding claim 7, LaComb teaches all the claimed features of claim 5, from which claim 7 depends. LaComb further teaches:
The monitoring system of claim 5, wherein the update to the at least one of the plurality of models comprises at least one of a change in an output selection, a change in timing, a change in an intensity level, or a change in a duration of a signal in the generated heat map. LaComb: Paragraph [0076] (“The low alert row has a cross-hatched pattern in specific cells. This is but one example of visually distinguishing between low, high and normal values, and many various patterns, colors and/or color intensities could be used.”) LaComb: Paragraph [0077] (“The cells of the heatmap can display different colors or different shading or patterns to differentiate between different levels or magnitudes and/or directions/polarities of data. In two-row embodiments, the top row could represent the magnitude of the Z-Between exceptional anomaly scores whereas the bottom row could represent the magnitude of the Z-Within exceptional anomaly scores. If the anomaly score is negative (representing a value that is unusually low), the cell could be colored blue. Smaller negative values could be light blue and larger negative values could be dark blue. If the anomaly score is positive (representing a value that is unusually high), the cell could be colored orange. Smaller positive values could be light orange and larger positive values could be dark orange. The user can specify the magnitude required to achieve certain color intensities. There can be as many color levels displayed as desired, for example, instead of three color levels, 1, 2 or 4 or more color intensity levels could be displayed. In this example the cutoffs were determined by the sensitivity analysis.”)
Regarding claim 8, LaComb teaches all the claimed features of claim 5, from which claim 8 depends. LaComb further teaches:
The monitoring system of claim 5, wherein the heat map is updated, based, at least in part, on the updated at least one of the plurality of models, to improve an effectiveness of an impact on a behavior of the user. LaComb: Paragraphs [0068] and [0071] [As described in claim 4.] LaComb: Paragraph [0081] (“A genetic algorithm (GA) can be used to derive patterns of a specific form to predict turbomachine failure or trip events, as well as other trip events and/or anomalous behavior in turbomachinery. The training set for a failure event (or other trip events) in a turbomachine is very small and unbalanced given the rarity of occurrence. The estimated probability is approximately less than one percent. As a result, sample sizes for training models are unavoidably small. There are a large number of operational metrics that can be utilized in discriminating positive and negative cases. This data is available or can be calculated from operational data. The genetic algorithm, according to aspects of the present invention, can efficiently navigate this highly non-linear search space and perform feature selection.”) LaComb: Paragraph [0087] (“This essentially allows the model to vary over time and to consider changes over time as part of the classification solution. In other embodiments the alleles could be of the form <tag> <greater than or equal to or less than or equal to> <value> for <n> out of <m> time periods.”)
Regarding independent claim 9, LaComb teaches:
A method for monitoring an industrial environment, the method comprising: LaComb: Abstract (“A method for predicting or detecting an event in turbomachinery includes the steps of obtaining operational data from at least one machine and at least one peer machine. The operational data comprises a plurality of performance metrics.”)
analyzing data from a plurality of sensors in the industrial environment; LaComb: Paragraph [0068] (“… operating system based application processor.”) Paragraph [0003] (“In the operation and maintenance of power generation equipment (e.g., turbines, compressors, generators, etc.), sensor readings corresponding to various attributes of the machine are received and stored. These sensor readings are often called “tags”, and there are many types of tags (e.g., vibration tags, efficiency tags, temperature tags, pressure tags, etc.).”) LaComb: Paragraph [0074] (“For example, the anomaly detection techniques, as embodied by the present invention, were applied to a set of turbines for which a significant failure event occurred. The failure event was rare, occurring in only 10 turbines during the 4-month period for which historical sensor data was available. For each turbine that experienced the event (event units), up to 2 months of historical data was collected. For the purposes of comparison, 4 months of historical data for 200 turbines that did not experience the event (non-event units) was obtained.”) LaComb: Paragraph [0038] (“In order to account for unit/machine and environmental variations and determine whether or not a given value for a tag for a target unit is outside an expected range (i.e., anomalous), context information may be used to form a basis for the analysis of the target unit's tag data. This context information can be taken from two primary sources: the target unit's past performance, and the performance of the target unit's peers. By using such context information to quantify the typical amount of variation present within the group or within the unit's own performance, it is possible to systematically and rigorously compare current tag data to context data and accurately assess the level of anomalous data in the target unit's tag values.”) LaComb: Paragraph [0041] (“In addition to the context considerations stated above, context data also includes comparable operating conditions. For this implementation, and as one example only, comparable operating conditions can be defined to mean any time period in the past where the unit has the same OPMODE, DWATT and CTIM values within a window of 10. OPMODE can be defined as the operation mode (e.g., slow cranking, peak output, 50% output, etc.). DWATT can be a metric for power (e.g., megawatt output). CTIM can be defined as a temperature metric (e.g., inlet temperature). For example, if the target observation's value of OPMODE is equal to 1 and DWATT is equal to 95, only the historical periods where OPMODE=1 and DWATT was between 90 and 100 could be used. These comparable operating conditions are defined as part of the system configuration.”) LaComb: Paragraph [0043] (“For each unit, up to 8 or more other units with the same frame-size with similar configurations and in the same geographic region can be identified as peers.”) [The analysis of a target unit’s and target unit’s peers tag data and operating conditions reads on “analyze data from a plurality of sensors”.]
determining at least one of an error or a fault in the industrial environment, wherein the at least one of the error or the fault is based, at least in part, on the analyzed data; LaComb: Paragraphs [0038], [0041], [0043] and [0074] [As described above.] LaComb: Paragraph [0042] (“By establishing the appropriate context, both in time, geography, frame size, and operating conditions, … objective and automatic calculations can be made to detect and quantify anomalies.”) LaComb: Paragraph [0045] (“By using these techniques to detect anomalies, alerts can be created. An alert can be a rule-based combination of tag values against customizable thresholds.”) [The detected anomalies and alerts based on the tag values read on “determining at least one of an error or a fault in the industrial environment, wherein the at least one of the error or the fault is based, at least in part, on the analyzed data”.]
generating a heat map for at least a portion of the industrial environment based, at least in part, on the error or the fault; providing the heat map to a user; LaComb: Paragraph [0035] (“A heatmap is an outlier-detection-visualization tool that can be performed on each specified machine unit for a large number of selected tags across many different time points. A heatmap illustrates the anomaly-intensity and the direction of a `target observation.` A heatmap may also contain a visual illustration of alerts, and directs immediate attention to hot-spot sensor values for a given machine. Heatmaps can also provide comparison to peers analysis, which allows the operational team to identify leaders and lagers, as well as marketing opportunities on the fly with great accuracy across different time scales (e.g., per second, minute, hour, day, etc.).”) LaComb: Paragraph [0068] (“The anomaly detection process and heatmap tool can be implemented in software with two Java programs called the Calculation Engine and the Visualization Tool, according to one embodiment of the present invention. The Calculation Engine calculates exceptional anomaly scores, aggregates anomaly scores, updates an Oracle database, and sends alerts when rules are triggered… The Visualization Tool displays anomaly scores in a heatmap (see FIG. 11) on request and allows users to create rules.”) LaComb: Paragraph [0071] (“This instructs the Calculation Engine to perform the periodic update, utilize up to 7 or more simultaneous threads, and identify any new sensor data in the database prior to proceeding. The program begins by calculating rules for any new custom alerts and any new custom peers of machine units created by the users of the Visualization Tool. It then retrieves newly arrived raw sensor data from a server, stores the new data in the Oracle database, and calculates exceptional anomaly scores and custom alerts for the newly added data. It stores results of all these calculations in a database, enabling the Visualization Tool to display a heatmap of the exceptional anomaly scores and custom alerts. If the calculations trigger a custom alert with a mile that has a high possibility of detecting a machine deterioration event with lead time, the Calculation Engine can be configured to send warning signals to members of the Monitoring and Diagnostics team. Alerts could be audio and/or visual signals displayed by the team's computers/notebooks, or signals transmitted to the team's communications devices (e.g., mobile phones, pagers, PDA's, etc.).”) LaComb: Paragraph [0073] (“Users of the Visualization Tool can view peer heatmaps; find machines with similar alerts; create custom peer groups; create custom alerts; and view several kinds of reports. Peer heatmaps merge each machine's heatmap into a single heatmap with adjacent columns showing peer machines' heatmap cells at the same instant in time instead of showing the machine's own heatmap cells at earlier and later times. Users can change the date; drill into time series graphs comparing peers' data for specific tags, and drill through to machine heatmaps.”) [The determined heatmap based on the sensor data/tag on each specified machine unit across may different time points reads on “generating a heat map for at least a portion of the industrial environment” and the heatmap including alerts indicative of anomalies reads on “the error or the fault”.]
recording a behavior of the user; and LaComb: Paragraph [0072] (“Users of the Visualization Tool can change the date range, change the peer (group, and drill into time series graphs of individual tags' data. The Visualization Tool may utilize Java Server Pages for its presentation layer and user interface.”)
updating the heat map based, at least in part, on the recorded behavior of the user. LaComb: Paragraph [0073] [As described above.] [The users changing the date; drill into time series graphs comparing peers' data for specific tags, and drill through to machine heatmaps reads on “updating the heat map based …on the recorded user input” (as construed in the indefiniteness rejection under 35 USC 112(b))]
Regarding claim 10, LaComb teaches all the claimed features of claim 9, from which claim 10 depends. LaComb further teaches:
The method of claim 9, wherein the heat map comprises an indicator of the determined at least one of the error or the fault. LaComb: Paragraphs [0068] and [0071] [As described in claim 9.] [The alerts read on “an indicator”.]
Regarding claim 11, LaComb teaches all the claimed features of claim 10, from which claim 11 depends. LaComb further teaches:
The method of claim 10, wherein the heat map further comprises at least one of a real world location coordinate, a location on a map, a time-based coordinate, or a frequency-based coordinate to indicate a location corresponding to at least a portion of the analyzed data used in the determination of the at least one of the error or the fault. LaComb: Paragraph [0035] and FIGS. 8, 9, and 11 (“Heatmaps can also provide comparison to peers analysis, which allows the operational team to identify leaders and lagers, as well as marketing opportunities on the fly with great accuracy across different time scales (e.g., per second, minute, hour, day, etc.).” ) LaComb: Paragraph [0073] (“Peer heatmaps merge each machine's heatmap into a single heatmap with adjacent columns showing peer machines' heatmap cells at the same instant in time instead of showing the machine's own heatmap cells at earlier and later times. Users can change the date; drill into time series graphs comparing peers' data for specific tags, and drill through to machine heatmaps.”) [The time scales and/or time periods read on “a time-based coordinate”.]
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over LaComb, in view of Brackney (US Patent Publication No. 20110115816 A1) (“Brackney”).
Regarding claim 6, LaComb teaches all the claimed features of claim 5, from which claim 6 depends. LaComb further teaches:
The monitoring system of claim 5, …wherein the update to the at least one of the plurality of models comprises at least one of a change in an output selection, a color, a visual representation element, a timing, an intensity level, or a duration of a signal in the generated heat map. LaComb: Paragraph [0076] (“The columns of the heatmap, shown in FIG. 11, represent time periods. The time periods could be days, hours, minutes, seconds or longer or shorter time periods. The rows represent metrics of interest, such as vibration and performance measures. For each metric, there can be two or more rows of colored cells, however, only one row is shown in FIG. 11 and the cells are shaded with various patterns for clarity. White cells can be considered normal or non-anomalous. The light vertical line filled cells in the AFPAP row could be considered as low negative values, while the heavy vertical line filled rows in the GRS_PWR_COR (corrected gross power) row could be considered as large negative values. The fight horizontal lines in the CSGV row could be considered as low positive values, while the heavy horizontal lines in the same row could be considered high positive values. The low alert row has a cross-hatched pattern in specific cells. This is but one example of visually distinguishing between low, high and normal values, and many various patterns, colors and/or color intensities could be used.”) LaComb: Paragraph [0077] (“The user can specify the magnitude required to achieve certain color intensities.”)
LaComb does not expressly teach “the heat map interface comprises an interface to an AR/VR device”. However, Brackney describes an augmented reality building operations tool. Brackney teaches:
wherein the heat map interface comprises an interface to an AR/VR device, and… Brackney: Paragraph [0011] (“Briefly, methods and systems are described that provide a building operator using a mobile client device, such as a smart phone, an augmented reality (AR) building operations tool (ARBOT). The building management systems include an ARBOT server that is communicatively linked with a building's energy management system (EMS), which acts in conventional manner to manage a large database with records for building system equipment that provide operating data for each piece of equipment (e.g., sensor-collected data) as well as other building management data (e.g., maintenance procedures, operating historical trends, and the like).”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of LaComb and Brackney before them, for the heat map interface to comprise an interface to an AR/VR device because the references are in the same field of endeavor as the claimed invention and they are focused on monitoring operation parameters in equipment.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would improve building system management through the use of AR techniques and tools and be able to interact with and access the building EMS database and its wealth of information for building management while onsite or at the location of the operating equipment. Brackney Paragraphs [0027]-[0029]
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter over Prior Art
The subject matter of claims 13-21 is found to be allowable over the prior art of record and would be considered allowable pending the indefinite rejection of these claims under 35 USC 112(b) given above. In relevance, WO2013049248 A2 to Haddick et al. describes in paragraph [00625] that Fig. 29 depicts an embodiment 2900 of an augmented reality eyepiece or glasses with a variety of sensors and communication equipment. One or more than one environmental or health sensors are connected to a sensor interface locally or remotely through a short range radio circuit and an antenna, as shown. The sensor interface circuit includes all devices for detecting, amplifying, processing and sending on or transmitting the signals detected by the sensor(s). The remote sensors may include, for example, an implanted heart rate monitor or other body sensor (not shown). The other sensors may include an accelerometer, an inclinometer, a temperature sensor, a sensor suitable for detecting one or more chemicals or gasses, or any of the other health or environmental sensors discussed in this disclosure. The sensor interface is connected to the microprocessor or microcontroller of the augmented reality device, from which point the information gathered may be recorded in memory.
The prior art to Dempski (US Patent Publication No. 2006/0244677 A1) describes a method for displaying data, which includes directing a wearable camera worn by a human operator towards one or more labeled objects within a field of view of the operator, detecting one or more visual markers within a field of view of the camera with at least one of the visual markers associated with and proximate to the labeled object, determining the environmental status, selecting data from a memory storage based on the environmental status with the data being associated with one of the objects associated with one of the visual markers, and then displaying the data on a wearable display worn by the operator. The data may be thought of as a "caption" in the augmented reality environment created by the system.
Also, WO 2007066166 A1 to Skourup et al. (“Skourup”) describes an optical mapping technique that involves identifying a series of objects or features in two or more repeated images taken by a camera or video camera of the equipment and its surroundings. An image processing software is used to identify objects detected optically, track the same objects over a succession of images, and calculate a position and orientation of the camera or sensor receiving and taking the images. In this way, the position and orientation of the user with a tracking apparatus 6, figures 1, 2 can be calculated. The image processing may be based on recognizing and natural features, especially features with relatively higher contrast to their background, and subsequently calculating, based on the change in a distance between those natural features in an image, a position of the viewer relative the equipment or other object. Skourup also describes that selected local or remote users or local or remote experts may share the composite image seen by the user 1 for the purpose of collaboration. The other users may add information in the form of notes or voice recordings or video clips which are attached to the composite image and shared by all selected users. Figure 3 shows schematically attached text information 9a, attached freehand notation 9b, attached diagram or flowchart 9c. The attached information which may have been contributed by other experts or users, called here annotations, may be used by user 1 as instructions or additional to carry out actions such as: carry out an inspection, adjust a set point, control a device or process, switch a device on or off. These actions are carried out by the user 1 by means of switching or operating buttons 37 or selection means 38 on the virtual control panel 30. This done by the user 1 moving the image 4' of pointer 4 viewed in the composite image 5 to select those buttons, by the action of moving the actual pointer 4 in the real world. As shown in Figure 3 a user may then inspect a condition of a motor and access or retrieve examine real time values for parameters such as speed, temperature, and load. On page 10, Skourup provides that a maintenance person requiring technical information picks up or preferably puts on a user AR equipment, which comprise be a PDA, wearable computer, headset based device, comprising a display 3, 3a, 3b. To deal with an incoming alarm, the control system 12 may generate information instructing the logged on maintenance person where in the plant to go and what the problem is (the system may indicate a new alarm via text, a visual or graphic display, or text-to- speech (TTS) . The maintenance person or user can ask for more info via text or voice. The maintenance person goes to a physical location and or a location of a plant or process section (functional location) indicated by the system, observes the problem, alarm or other event. The AR equipment may be used to recognize an equipment of interest 7 by means of: an asset number or similar, a wireless node mechanism, a marker 8 installed for the purpose of identifying the equipment to an AR system, a method of scanning equipment surroundings and processing the image to identify natural features to match to a predetermined location of the equipment, or a combination of equipment identity in the incoming alarm information together with any of the above. When the equipment of interest has been identified by one or other method to the control system the maintenance person or user can then access stored and/or real time information associated with the equipment through pre- configured associations of the control system.
However, the prior art of record cited above including LaComb et al. (US Patent Publication No. 2009/0100293 A1); Brackney (US Patent Publication No. 20110115816 A1); Lemieux et al. (US Patent Publication No. 2015/0132102 A1); Lee et al. (US Patent Publication No. 2013/0027561 A1); Redmond et al. (US Patent Publication No. 2012/0215366), individually or combined with does not teach or suggest “wherein at least a portion of the generated heat map is aligned in a view of the AR/VR device with at least one of the plurality of sensors whose corresponding collected data was at least partially a basis for the portion of the heat map”, as recited in independent claim 13. It is this concept that defines the present application over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lemieux et al. (US Patent Publication No. 2015/0132102 A1) describes an apparatus and method for determining a two-dimensional temperature distribution in a cross-sectional path of a hot-temperature flow in a turbine engine. As may be appreciated in FIG. 2, a comparison module 54 may be configured to compare the two-dimensional temperature distribution of the flow relative to a temperature distribution based on a model 56, e.g., computational flow dynamics (CFD). An adjuster module 58 may be configured to adjust model 56 based on a result of comparison module 54. It will be appreciated that modules 54, 58 need not be integrated in processor 50 since such processing functionality may be performed offline.
Lee et al. (US Patent Publication No. 2013/0027561 A1) describes area D3 shows the following sensor events: camera events C1, C2, C3, C4, C5, C6, C7, C8; POS events P1, P2, P3, P4; AC/RFID event A1; face recognition event F1, F2, F3, F4; and location/heat map events L1, L2.
Redmond et al. (US Patent Publication No. 2012/0215366) describes sensor data and thresholds display area 3816 comprising icons such as a rainfall indicator 3838, a rainfall threshold 3840, a rain trip indicator 3842, a temperature indicator 3844, a temperature threshold 3846, a temperature trip indicator 3848 and low temperature indicator 3850 (shown in FIG. 46d).
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117