DETAILED ACTION
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 .
Claims 1 – 20 are pending in this Office Correspondence.
Priority
This application is a continuation of U.S. Application No. 18/154,222 filed on January 13, 2023, which issues on September 23, 2025 as U.S. Patent No. 12,423,314, which claims priority to U. S. Provisional Application No. 63/299,539 filed on January 14, 2022.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, line 7, claim recited a term, “eh sensor node”. It is a typographical error and should be “the sensor node”.
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.
Step 1: The claim 8 recites a “method for querying, by an interpretation module, an admin database. . .., searching, by the interpretation module, the admin database. . . extracting, from the admin database, recent entries. . . , determining, by the interpretation module, that the entries indicate a pattern . . . , responsive to a detection of the pattern, searching an interpretation database . . . , extracting, by the interpretation module, interpretation data . . ., and providing the interpretation data to a user device” the claim(s) recites a series of steps and, therefore, is a process
Step 2A Prong One:
"searching, by the interpretation module, the admin database " as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0011] where one can mentally evaluate to perform searching the admin database.
“determining, by the interpretation module, that the entries indicate a pattern” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraph [0011] where one can mentally evaluate to perform determining the entries indicate a pattern.
“searching an interpretation database” as drafted recites a mentally performable process as an evaluation or judgement. Please see Instant paragraphs [0011] where one can mentally evaluate to searching an interpretation database.
These imitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “searching”, determining” and “searching” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, within the plurality of command sets, “searching, by the interpretation module, the admin database; determining, by the interpretation module, that the entries indicate a pattern; and responsive to a detection of the pattern, searching an interpretation database for a matching pattern.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements "querying, by an interpretation module, an admin database", “extracting, from the admin database, recent entries” and "extracting, by the interpretation module, interpretation data” and “providing the interpretation data to a user device.”
These limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
The limitations represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The limitations "querying”, “extracting” and “providing” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claims 1 and 15 are rejected for the same rational under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Therefore, claims 1, 8 and 15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 2 – 7, 9 – 14 and 16 – 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 9 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 9 recites the same abstract idea of claim 8. The claim recites the additional limitation of “identifying the pattern by a pattern matching algorithm”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 2 and 16, since they also recite limitations that further elaborate on the abstract idea.
Claim 10 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 10 recites the same abstract idea of claim 8. The claim recites the additional limitation of “the sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim. Same rationale applies to claim 3, since they also recite limitations that further elaborate on the abstract idea.
Claim 11 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 11 recites the same abstract idea of claim 8. The claim recites the additional limitation of “the interpretation data is created based on a machine learning process”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 4 and 17, since they also recite limitations that further elaborate on the abstract idea.
Claim 12 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 12 recites the same abstract idea of claim 8. The claim recites the additional limitation of “searching of the interpretation database for the matching pattern comprises using a pattern detection algorithm based on machine learning”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 5 and 18, since they also recite limitations that further elaborate on the abstract idea.
Claim 13 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 13 recites the same abstract idea of claim 8. The claim recites the additional limitation of “the interpretation module is connected to a temporary node connected to the sensor node and configured to repeat signals from the sensor node”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 6 and 19, since they also recite limitations that further elaborate on the abstract idea.
Claim 14 is dependent on claim 13 and includes all the limitations of claim 13. Therefore, claim 14 recites the same abstract idea of claim 13. The claim recites the additional limitation of “the temporary node is directly connected to the user device running a device management application”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 7 and 20, since they also recite limitations that further elaborate on the abstract idea.
Therefore, claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Double Patenting
Claim 1 – 20 of this application is patentably indistinct from claims 1 – 20 of Application No. 18/154,222, now U.S. Patent 12,423,314. Pursuant to 37 CFR 1.78(f) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
The subject matter claimed in the instant application is fully disclosed in the co-pending application and is covered by the co-pending application since the co-pending application and the application are claiming common subject matter, as follows:
Instant application 19/336,948
Co-pending Application 18/154,222
USP 12,423,314
1. A system for remote monitoring of an indicator of a device, comprising:
a processor of an interpretation module connected to a sensor node and to a user device over a remote network; a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
query an admin database for new data acquired from the sensor node,
search the admin database for a sensor ID associated with eh sensor node,
extract, from the admin database, recent entries corresponding to the sensor ID,
determine that the extracted entries indicate a pattern comprising a parameter,
responsive to a detection of the pattern, search an interpretation database for a matching pattern comprising the parameter,
extract interpretation data from an entry comprising the matching pattern, and
provide the interpretation data to the user device.
2. The system of claim 1, wherein the instructions further cause the processor to identify the pattern by a pattern matching algorithm.
3. The system of claim 1, wherein the at least one sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator.
4. The system of claim 1, wherein the interpretation data is created based on a machine learning process.
5. The system of claim 1, wherein a search of the interpretation database for the matching pattern comprises using a pattern detection algorithm based on machine learning.
6. The system of claim 1, wherein the interpretation module is connected to a temporary node connected to the sensor node and configured to repeat signals from the sensor node.
7. The system of claim 6, wherein the temporary node is directly connected to the user device running a device management application.
8. A method for remote monitoring of an indicator of a device, comprising:
querying, by an interpretation module, an admin database for new data acquired from a sensor node,
searching, by the interpretation module, the admin database for a sensor ID associated with the sensor node,
extracting, from the admin database, recent entries corresponding to the sensor ID by the interpretation module,
determining, by the interpretation module, that the entries indicate a pattern comprising a parameter,
responsive to a detection of the pattern, searching an interpretation database for a matching pattern comprising the parameter,
extracting, by the interpretation module, interpretation data from an entry comprising the matching pattern, and
providing the interpretation data to a user device.
9. The method of claim 8, further comprising identifying the pattern by a pattern matching algorithm.
10. The method of claim 8, wherein the sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator.
11. The method of claim 8, wherein the interpretation data is created based on a machine learning process.
12. The method of claim 8, wherein the searching of the interpretation database for the matching pattern comprises using a pattern detection algorithm based on machine learning.
13. The method of claim 8, wherein the interpretation module is connected to a temporary node connected to the sensor node and configured to repeat signals from the sensor node.
14. The method of claim 13,wherein the temporary node is directly connected to the user device running a device management application.
15. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform:
querying an admin database for new data acquired from a sensor node;
searching the admin database for a sensor ID associated with the sensor node; extracting, from the admin database, recent entries corresponding to the sensor ID; determining that the entries indicate a pattern comprising a parameter;
responsive to a detection of the pattern, searching an interpretation database for a matching pattern comprising the parameter;
extracting interpretation data from an entry comprising the matching pattern; and providing the interpretation data to a user device.
16. The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the processor, cause the processor to identify the pattern by a pattern matching algorithm.
17. The non-transitory computer readable medium of claim 15, wherein the interpretation data is created based on a machine learning process.
18. The non-transitory computer readable medium of claim 15, wherein the searching of the interpretation database for the matching pattern comprises using a pattern detection algorithm based on machine learning.
19. The non-transitory computer readable medium of claim 15, wherein an interpretation module comprising the processor is connected to a temporary node, which is in turn connected to the sensor node and configured to repeat signals from the sensor node.
20. The non-transitory computer readable medium of claim 19, wherein the temporary node is directly connected to the user device running a device management application.
1. A system for remote monitoring of an indicator mechanism for indicating a status of a device, comprising:
a processor of an interpretation module connected to a sensor node and to a user device over a remote network, wherein the sensor is selected from a group consisting of a photoelectric sensor, a camera, a microphone, and a sound sensor; a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
acquire new data, via the sensor node, related to the indicator mechanism, wherein the indicator mechanism is selected from a group consisting of a visual indicator, an audio indicator, and a haptic indicator,
query an admin database for the new data acquired from the sensor node,
search the admin database for a sensor ID associated with the sensor node,
extract, from the admin database, recent entries corresponding to the sensor ID,
determine that the extracted entries indicate a pattern comprising a parameter,
responsive to a detection of the pattern, search an interpretation database for an entry including interpretation data associated with a status of the device, the indicator mechanism, and the pattern comprising the parameter,
based on the indicator mechanism and the pattern comprising the parameter, extract interpretation data from the entry comprising the pattern, and provide the interpretation data to the user device.
2. The system of claim 1, wherein the instructions further cause the processor to identify the pattern by a pattern matching algorithm.
3. The system of claim 1, wherein the at least one sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator.
4. The system of claim 1, wherein the interpretation data is created based on a machine learning process.
5. The system of claim 1, wherein a search of the interpretation database for the pattern comprises using a pattern detection algorithm based on machine learning.
6. The system of claim 1, wherein the interpretation module is connected to a temporary node connected to the sensor node and configured to repeat signals from the sensor node.
7. The system of claim 6, wherein the temporary node is directly connected to the user device running a device management application.
8. A method for remote monitoring of an indicator of a device, comprising: acquiring new data, via a sensor node, related to the indicator, querying, by an interpretation module, an admin database for the new data acquired from the sensor node, searching, by the interpretation module, the admin database for a sensor ID associated with the sensor node, extracting, from the admin database, recent entries corresponding to the sensor ID by the interpretation module, determining, by the interpretation module, that the entries indicate a pattern comprising a parameter, responsive to a detection of the pattern, searching an interpretation database for an entry including interpretation data associated with a status of the device, an indicator mechanism, and the pattern comprising the parameter, based on the indicator mechanism and the pattern comprising the parameter, extracting, by the interpretation module, interpretation data from an entry comprising the pattern, and providing the interpretation data to a user device.
9. The method of claim 8, further comprising identifying the pattern by a pattern matching algorithm.
10. The method of claim 8, wherein the sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator.
11. The method of claim 8, wherein the interpretation data is created based on a machine learning process.
12. The method of claim 8, wherein the searching of the interpretation database for the pattern comprises using a pattern detection algorithm based on machine learning.
13. The method of claim 8, wherein the interpretation module is connected to a temporary node connected to the sensor node and configured to repeat signals from the sensor node.
14. The method of claim 13, wherein the temporary node is directly connected to the user device running a device management application.
15. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations for remote monitoring of an indicator of a device, the operations comprising: acquiring new data, via a sensor node, related to the indicator; querying an admin database for the new data acquired from the sensor node; searching the admin database for a sensor ID associated with the sensor node; extracting, from the admin database, recent entries corresponding to the sensor ID; determining that the entries indicate a pattern comprising a parameter; responsive to a detection of the pattern, searching an interpretation database for an entry including interpretation data associated with a status of the device, an indicator mechanism, and the pattern comprising the parameter, based on the indicator mechanism and the pattern comprising the parameter; extracting interpretation data from an entry comprising the pattern; and providing the interpretation data to a user device.
16. The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the processor, cause the processor to identify the pattern by a pattern matching algorithm.
17. The non-transitory computer readable medium of claim 15, wherein the interpretation data is created based on a machine learning process.
18. The non-transitory computer readable medium of claim 15, wherein the searching of the interpretation database for the pattern comprises using a pattern detection algorithm based on machine learning.
19. The non-transitory computer readable medium of claim 15, wherein an interpretation module comprising the processor is connected to a temporary node, which is in turn connected to the sensor node and configured to repeat signals from the sensor node.
20. The non-transitory computer readable medium of claim 19, wherein the temporary node is directly connected to the user device running a device management application.
Claims 1 – 20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1 – 20 of co-pending application No. 18/154,222, now U.S. Patent 12,423,3148. Although the conflicting claims are not identical, they are not patentably distinct from each other because of corresponding language that recites virtually all of the same elements and functions claimed in the claim 1 of instant application and claim 1 of the copending invention, e.g., “responsive to a detection of the pattern, search an interpretation database for a matching pattern comprising the parameter, extract interpretation data from an entry comprising the matching pattern, and provide the interpretation data to the user device..”
The claimed differences would be obvious to a programmer of ordinary skill because the instant claims are merely broader and/or alternate variations of the claims recited in the co-pending application.
Because the instant claims merely add/modify the additional elements from the set of elements and functions claimed in the parent application, such modifications would be readily apparent to a programmer of ordinary skill.
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to omit/add/modify the additional elements of claim 1 to arrive at the claim 1 of the instant application because the person would have realized that the remaining element would perform the same functions as before.
It would have been obvious to modify instant claims in order to providing more flexibility, immediacy of information transfer, increase productivity and enhance business intelligence for an enterprise.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over USPGPUB 2017/0261264 issued to Zhenbang Wang et al. (“Wang”) and in view of USPGPUB 2021/0374391 issued to James Jorasch et al. (“Jorasch”).
With respect to claims 1, 8 and 15, Wang teaches a system, a method
fault diagnosis, associated with a furnace utilizing, sensors (cameras), obtaining information and sending to a control center, analyzing the information by comparing a difference between with histograms, for shots segmentation, computing a set of characteristic values, computing color, texture, and motion vector information (based on the video data), evaluating importance via entropy, clustering, together by calculating similarity, generating, a multi-view video summarization with a multi-objective optimization model, and providing fault detection and diagnosis and displaying results of the fault detection and diagnosis on a host computer inter face of the control center.
Abstract
A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.
With respect to claims 1, 8 and 15, Wang teaches details directed to remote monitoring of an indicator of a device, comprising:
• a processor of an interpretation module (see Figs. 2) connected to a sensor node
SEE sensors as CCDs or cameras, connected to Control Center wherein the Sensors include Cameras, microphones, connected to the Control center, wherein the system, performs, “analyzing the sensor data including video information by a chip of the control center”
Wang teaches monitoring of an industrial process, associated video information for an electric-arc furnace (0002).
Wang also suggests as claimed, and teaches, query for new data acquired from the sensor node (see 0008 Real Time) and search to extract, recent entries (based on the sensors)
determine that the extracted entries indicate a pattern (SEE Figs. 4-10), comprising a parameter,
• responsive to a detection of the pattern, search an interpretation database for a matching pattern comprising the parameter,
• extract interpretation data from an entry comprising the matching pattern (see comparing), and
• provide the interpretation data (0132-), to the user device (see Figs. 6, 7, 8, 9, 10)
SEE abstract, from Cameras, Frame Histograms being Patterns and comparing SHOT segmentations (for, Color, texture and motion), directed to Fault detection and diagnosis.
Abstract
A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.
to a user device over a remote network
SEE 0133 (see On-Line Monitoring), suggests a communication Network, based on access On-Line.
[0133] Fault diagnosis device based on multi-view method has shown good performance in the process monitoring by using industrial video information. At the same time, the feature variables extracted by the multi-view video summarization method can effectively compress the raw video data and solve the problem that the video information is complicated and difficult to deal with. This solves the trouble of using the video information for online monitoring. Moreover, the constructed optical flow potential and the acceleration potential provide the possibility of predicting the occurrence of faults. All in all, the fault diagnosis device designed can solve the detection and diagnosis problems of furnace eruption fault and furnace leaking fault in the smelting process, effectively.
the processor to:
queries (Fig. 4, Data Collection), to compare (histograms), associated with, new data acquired from the sensor node, based on search of the sensor data associated with data from sensors, recent entries corresponding to the sensor data
SEE Compare Histograms
determine that the extracted entries indicate a pattern comprising a parameter (see Features and Information, in Fig. 4),
responsive to a detection of the pattern, search an interpretation database for a matching pattern comprising the parameter,
extract interpretation data from an entry comprising the matching pattern, and
provide the interpretation data (see Fig. 4, Fault Detection data), to the user device (Figs. 3, 6-10, output with Confidence)
Wang does teach Sensors in the form of different Cameras or other sensors, generating multi-view video associated with recent entries and storing (to Data collection) and accessing the data (extracting features and information) and providing interpretation data (associated with Fault diagnoses and confidence etc.), to the user (Figs. 7-10).
However, Wang does not explicitly teach sensors having, Sensor IDs associated with a user device connected over a remote network, facilitating query, extraction and interpretation (output), based on storing to a database (admin); and a memory storing machine-readable instructions that, when executed by the processor.
In view of Wang, one skilled in the art would realize the sensors vs. data is required to be correlated or to keep track of sensor readings vs. the sensors, to compare sensor the Histogram data, from plural cameras.
Jorasch discloses utilization of a memory storing machine-readable instructions that, when executed by the processor, based on algorithms (w/instructions), as a software process, is deemed obvious to implement algorithms in software vs. hardware based processes. Jorasch, is deemed to teach and render obvious any differences, in view of the teachings directed to capturing data and monitoring sensors where the sensors have IDs (Fig. 73, stored to data Tables), the IDs identify sensors and sensor data, facilitating data analysis and an associated admin database (0077, 0083, 0084, 0086), also teaching details directed to pattern analysis and interpretation, wherein the database includes IDs, associating Authorized Users (7308), correlated to the sensors, types and identifiers of the sensor devices (SEE Tables Fig. 73, w/Camera type 7304, w/Sensor ID, as well as correlated to User IDs, while table columns in a table (7310, 7312m 7314 and 7316), include Sensor Attributes (including subjects, resolutions, sampling rates and sensitivity, associated with a sensor (being a camera).
See Abstract: apparatus, interfaces, methods, and articles of manufacture are provided for providing information about objects, such as background information and task information, and for providing alerts related to objects. Data is captured about an object and about a user via a camera. Based on the data, information about the object may be provided to the user.
SEE Monitoring includes use of Sensors (0014)
[0104] Various embodiments may employ sensors (e.g., sensor 330; e.g., sensor 430). Various embodiments may include algorithms for interpreting sensor data. Sensors may include microphones, motion sensors, tactile/touch/force sensors, voice sensors, light sensors, air quality sensors, weather sensors, indoor positioning sensors, environmental sensors, thermal cameras, infrared sensors, ultrasonic sensors, fingerprint sensors, brainwave sensors (e.g., EEG sensors), heart rate sensors (e.g., EKG sensors), muscle sensors (e.g., EMG electrodes for skeletal muscles), barcode and magstripe readers, speaker/ping tone sensors, galvanic skin response sensors, sweat and sweat metabolite sensors and blood oxygen sensors (e.g., pulse oximeters), electrodermal activity sensors (e.g., EDA sensors), or any other sensors. Algorithms may include face detection algorithms, voice detection algorithms, or any other algorithms.
[0105] Motion sensors may include gyroscopes, accelerometers, Wi-Fi® object sensing (e.g. using Wi-Fi® signals that bounce off of objects in a room to determine the size of an object and direction of movement), magnetometer combos (inertia measurement units), or any other motion sensors. Motion sensors may be 6 or 9 axis sensors, or sensors along any other number of axes. Motion sensors may be used for activity classification. For example, different types of activities such as running, walking, cycling, typing, etc., may have different associated patterns of motion. Motion sensors may therefore be used in conjunction with algorithms for classifying the recorded motions into particular activities. Motion sensors may be used to track activity in a restricted zone of a building, identify whether an individual is heading toward or away from a meeting, as a proxy for level of engagement in a meeting, steps taken, calories burned, hours slept, quality of sleep, or any other aspect of user activity. Motion sensors may be used to quantify the amount of activity performed, e.g., the number of steps taken by a user. Motion sensors can also be used to track the movement of objects, such as the velocity or distance traveled of a user's mouse. Motion sensors may be used to identify whether an individual is approaching an entry to a house, and if so, trigger a doorbell within the house, and send an alert to a user device or peripheral devices of a user associated with the house.
Associated with, a process of interpreting data sensor at nodes to, user devices (SEE Fig. 1, User Device 106a, connected to the Network 109 {Local}, to Network 104) over a remote network.
Note the user devices at, nodes (106a-n), can be connected at different points any of, to the local network 109 to the {remote}, network 104) or, directly connects or even in-between (106b) the network 104 and user devices and peripheral devices, including related details of, including details of Query (see 0965), an admin database (Fig. 73, w/7308, and 0077, 0083, 0084, 0086), for new data acquired from the sensor node, search the admin database (Fig. 73), for a sensor ID (0165) associated with the sensor node (7302/7304), to extract, from the admin database
SEE Sensor ID (field 2302), to identify which sensor has captured the reading associated with, an Admin Table, correlated to USERs with IDs, as being Authorized).
[0165] Sensor reading ID field 2302 may store an identifier (e.g., a unique identifier) of a particular sensor reading. Peripheral ID field 2304 may store an indication of the peripheral device at which the sensor reading has been captured. Sensor field 2306 may store an indication of which sensor has captured the reading. For example, sensor field 2306 may explicitly identify a single sensor or type of sensor from among multiple sensors that are present on a peripheral device. The sensor may be identified, for example, as a heart rate sensor. In some embodiments, a sensor may have a given identifier, serial number, component number, or some other means of identification, which may be stored in field 2306. Start time field 2308 may store the time at which a sensor began to take a reading. End time field 2310 may store the time at which a sensor finished taking a reading. As will be appreciated, different sensors may require differing amounts of time in order to capture a reading. For instance, capturing a reading of a heart rate may require the reading to be taken over several seconds in order to allow for multiple heartbeats. Reading field 2312 may store the actual reading that was captured. For example, the field may store a graph of the acceleration of an accelerometer. In other embodiments, the reading may be a recording of an EKG signal from the start time to an end time.
See Para [0965]: if there are any new updates since the last query, that information is then transmitted to storage device 9445 of the user computer. In embodiments in which a mouse or keyboard autonomously logs into a user computer periodically in order to receive status updates relating to one or more other users, some functionality of the mouse may be disabled when a user is not present. For example, the xy positioning data generated by mouse movements may be disabled during these autonomous logins so that an unauthenticated person trying to use the mouse while it is logged into the user computer to get status updates will not be able to generate any xy data and will thus be unable to perform any actions with the user computer while it is activated by the autonomous logins.
Para [0627]: building 6802 represents a factory, laboratory, research laboratory, fabrication facility, experimental facility, sensing facility, monitoring facility, communications facility, storage facility, and/or any other industrial or scientific facility.
Building 6802 may include objects or environments at extreme temperatures (e.g., furnaces, e.g., cryogenic storage), which may be potentially hazardous. Building 6802 may include sharp objects.
Para [0211]: headset 4000 may facilitate the ability to sense smoke and provide safety warnings, with sensors used to detect smoke and alert the user or others around them. A user may be working in a warehouse or industrial setting in building 6802 with flammable substances. If a flammable substance ignites, the headset 4000 may detect the smoke and alert the user more quickly than human senses are possible. A smoke sensor may be attached to connector point 4037a-b by the user or as displayed in attachable sensor 4040. If a flammable substance ignites in an area away from the user, attachable sensor 4040 may detect the smoke, provide the information to processor 4055 and provide an alert to exit the area immediately. This alert from the processor may be in the form of a vibration from vibration generator 4080, an audible alert saying, ‘smoke detected, please exit immediately and call 9-1-1’ in speakers 4010a-b, lights 4042a-b flashing red to alert others around the user to evacuate and take the individual, boom lights 4044 on microphone boom 4016 may display a color or pattern (e.g. blinking red) and/or display 4046 may provide an image to alert the user to exit (e.g. a floor plan and path to the exit the room and building). Likewise, optical fibers 4072a-b may light up in orange for immediate visual alerts to others or emergency workers. The outward speaker 4074 may provide a high pitched burst of beeps to indicate the need to evacuate or a verbal warning that ‘smoke has been detected, please exit immediately’. Attachable sensor 4040 may detect the type of smoke (e.g. chemical, wood, plastic) based on information stored in data storage 4057 and interpreted by processor 4055. If the smoke detected is from a chemical fire, communications to company safety teams may occur through internal satellite, Bluetooth® or other communications mechanisms within headset 4000 and housing 4008a-b to alert them to the type of fire for improved response and specific location. Projector 4076 may display a message on the wall indicating that ‘smoke has been detected and it is a chemical fire—exit immediately—proceed to the wash station’. Also, the projector 4076 may display a map of building 6802 with the nearest exit or provide on display 4046.
Para [0212]: headset 4000 may facilitate the ability to sense various gases (e.g. natural gas, carbon monoxide, sulfur, chlorine) and provide safety warnings. In some embodiments, sensors (e.g. natural gas, carbon monoxide, sulfur) may be used to detect odors or gas composition (e.g. odorless carbon monoxide) and alert the user. A user may be working in their living room where a gas fireplace is located. During the day, the pilot light may go out, but the gas remains on due to a faulty fireplace gas sensor. The user's senses become saturated to a point they no longer smell the gas posing a danger to her family. The headset 4000 may detect the natural gas and alert the user more quickly than human senses are possible. A natural gas sensor may be attached to connector point 4037a-b by the user or as displayed in attachable sensor 4040. Attachable sensor 4040 may detect the natural gas, provide the information to processor 4055 and provide an alert to the user to exit the house immediately or open the windows and doors. This alert from the processor may be in the form of a headset vibration with vibration generator 4080, an audible alert saying, ‘natural gas detected, please exit immediately and call 9-1-1’ in speaker 4010a-b and/or outward speaker 4074, boom lights 4044 may display a color or pattern (e.g. blinking red) and/or display 4046 may provide an image to alert the user to exit (e.g. a floor plan and path to the exit the room and home). The attachable sensor 4040 may be used to detect the type of gas as well (e.g. natural gas, carbon monoxide, non-lethal sulfur, chlorine) based on information saved in data storage 4057 and interpreted by processor 4055. The headset 4000 may alert the fire department, other emergency agencies or family members with headsets through the communications mechanisms (e.g. antenna, satellite, Bluetooth®, GPS) within housing 4008a-b about the gas and composition and location of the user for more rapid response. Likewise, a research and development employee in building 6800 biohazard room 6870 may be working on an experiment to make chlorine gas. Instead of adding small amounts of concentrated hydrochloric acid to the potassium permanganate solution, the researcher adds too much hydrochloric acid, creating an unstoppable reaction and creating too much lethal chlorine gas. The headset 4000 may immediately detect elevated levels of chlorine gas through the attachable sensor 4040 based on values in data storage 4057 and interpreted by processor 4055 and immediately alerts the employee, safety teams, public emergency works and other employees. This alert sent from processor 4055 may be in the form of a buzz from cushion sensor 4050, an audible alert in speaker 4010a-b saying, ‘chlorine gas detected, please exit immediately and call 9-1-1’, boom lights 4044 or headband lights 4042a-b may display a color or pattern (e.g. blinking and solid red variation) and/or display 4046 may provide an image to alert the user to exit (e.g. a floor plan and path to the nearest exit the room). Headset 4000 may alert the fire department, other emergency agencies, local safety team members or employees in close proximity with headsets through the internal communications (e.g. antenna, satellite, Bluetooth, GPS) within housing 4008a-b about the chlorine gas for more rapid and accurate response (e.g. correct equipment to combat the chlorine gas). Alerts (e.g. chlorine gas detected in room 6870) may also be displayed on building 6802 walls using projectors 6850a-f and lights 6808a-g (e.g. red flashing) along with evacuation notices from speakers 6850a-e.
SEE Monitoring Facility, associated with a building 6802 may include machinery or equipment of a potentially dangerous nature e.g., furnaces
Para [0627]: building 6802 represents a factory, laboratory, research laboratory, fabrication facility, experimental facility, sensing facility, monitoring facility, communications facility, storage facility, and/or any other industrial or scientific facility. Building 6802 may include one or more areas where safety and/or intellectual property and/or property are of concern. Building 6802 may include machinery or equipment of a potentially dangerous nature (e.g., saws, lathes, lasers, industrial robots, etc.). Building 6802 may include irritants that may cause damage to organs/tissues (e.g. liver, heart, brain, stomach, eye, skin, nose, ears, lung) and/or dangerous chemicals, such as hydrofluoric acid. Building 6802 may include radioactive materials, radiation, biological hazards, pathogens, etc. Building 6802 may include controlled substances, such as opioids, drugs, drug precursors, etc. Building 6802 may include weaponizable materials. Building 6802 may include potentially hazardous gases, such as carbon monoxide, hydrogen, nitrogen, etc. Building 6802 may include objects or environments at extreme temperatures (e.g., furnaces, e.g., cryogenic storage), which may be potentially hazardous. Building 6802 may include sharp objects. Building 6802 may include dangerous heights, unguarded platforms, or other falling hazards. Building 6802 may include flammable, combustible, and/or explosive objects or materials. Building 6802 may include electrical components or equipment with dangerous voltage and/or current levels. Building 6802 may include high magnetic fields. Building 6802 may include vapors, breathing hazards (e.g., asbestos), etc. Building 6802 may include confined and/or unventilated areas. Building 6802 may include vats or liquids that present drowning or suffocation hazards. Building 6802 may include fragile, delicate, and/or otherwise sensitive items, such as clean rooms. Building 6802 may include sensitive plans, records (e.g., medical records), data, plans for controlled items (e.g., for rocket technology), and/or any other sensitive materials. Building 6802 may include commodities, valuables, and/or other items of value, such as currency, platinum, silver, computer chips, laptops, etc.
Note the data is also of, recent entries corresponding to, the sensor ID (see Fig. 73, 7302 (ID), 7304 (Type), 7306 (location)), determine that the extracted entries indicate a pattern comprising a parameter
Note, sensing Smoke (appears is directed to Recent Entries), also a Camera may detect the smoke, where the system provides warnings, alerts based in the information (being recent entries), wherein the Sensors have identifiers (see ID)
Para [0233]: camera 4100 may facilitate the ability to sense smoke and provide safety warnings, with sensors used to detect smoke and alert the user or others around them. A user may be working in a warehouse or industrial setting in building 6802 with flammable substances. If a flammable substance ignites, the camera 4100 may detect the smoke and alert the user more quickly than human senses are possible. A smoke sensor may be attached to attachment structure 4137 by the user or as displayed in attachable sensor 4140. If a flammable substance ignites in an area away from the user, attachable sensor 4140 may detect the smoke, provide the information to processor 4155 and provide an alert to exit the area immediately. This alert from the processor may be in the form of a vibration from vibration generator 4182, an audible alert saying, ‘smoke detected, please exit immediately and call 9-1-1’ from speaker 4110, camera lights 4142 flashing red to alert others around the user to evacuate and take the individual, and/or display 4146 may provide an image to alert the user to exit (e.g. a floor plan and path to the exit the room and building). Likewise, optical fibers 4172 may light up in orange for immediate visual alerts to others or emergency workers. The speaker 4110 may provide a high pitched burst of beeps to indicate the need to evacuate or a verbal warning that ‘smoke has been detected, please exit immediately’. Attachable sensor 4140 may detect the type of smoke (e.g. chemical, wood, plastic) based on information stored in data storage 4157 and interpreted by processor 4155. If the smoke detected is from a chemical fire, communications to company safety teams may occur through internal satellite, Bluetooth® or other communications mechanisms within camera 4100 to alert them to the type of fire for improved response and specific location. Projector 4176 may display a message on the wall indicating that ‘smoke has been detected and it is a chemical fire—exit immediately—proceed to the wash station’. Also, the projector 4176 may display a map of building 6802 with the nearest exit or provide on display 4146
responsive to a detection of the pattern, search an interpretation database for a matching pattern comprising the parameter
(The system also processes the sensor data over time to interpret the data is based on, Pattern analysis and Analyzing Patterns in the data to determine Motion (0105), activity (0107)and providing, interpretation data (output)).
Therefore, since 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 modify Wang with the teachings of Jorasch, to apply a database (admin), to store and access the sensor data of Wang and to store, Sensor IDs associated with a user device connected over a remote network, in the database, facilitating query, extraction and interpretation (output), based on storing to a database being an admin database in view of access user control, wherein the interpretations include applying ML processing, as taught by Jorasch.
As to claims 2, 9 and 16, the combination as applied is deemed to further render obvious, in view of comparing, the utilization of a pattern matching algorithm
Note, Wang (0015-, 0047-0048) and Jorasch (0104, 0252, 0254) teach pattern matching (by comparing) and also teach being, algorithm based analysis.
As to claims 3 and 10 the combination as applied further render obvious wherein the at least one sensor node is connected to the device, and wherein the indicator comprises one or more of: a visual indicator, an audio indicator, or a haptic indicator (SEE Wang, wherein the sensors include at least a Camera (Visual), microphone (audio)).
As to claims 4, 11 and 17, the combination as applied is deemed to further render obvious, wherein the interpretation data is created based on, a machine learning process
(Jorasch teaches machine learning (Model, 0024) or AL or (0185)
[0185]. Referring to FIG. 35, a diagram of an example of ‘AI models’ Table 3500 according to some embodiments is shown. As used herein, “AI” stands for artificial intelligence. An AI model may include any machine learning model, any computer model, or any other model that is used to make one or more predictions, classifications, groupings, visualizations, or other interpretations from input data. As used herein, an “AI module” may include a module, program, application, set of computer instructions, computer logic, and/or computer hardware (e.g., CPU's, GPU's, tensor processing units) that instantiates an AI model. For example, the AI module may train an AI model and make predictions using the AI model. AI Models Table 3500 may store the current ‘best fit’ model for making some prediction, etc. In the case of a linear model, table 3500 may store the ‘best fit’ values of the slope and intercept. In various embodiments, as new data comes in, the models can be updated in order to fit the new data as well.
As applied Jorasch teaches ML (modeling), directed to artificial intelligence, may include any machine learning model, directed to, predictions, classifications, groupings,
visualizations, or other interpretations from input data.
Therefore it is further obvious to utilize, a machine learning process, directed to generating, the interpretation data, as taught by Jorasch performing, predictions,
classifications, groupings, visualizations, or other interpretations from input data, based on an ML learning process.
As to claims 5, 12 and 18, the combination as applied above is deemed to further teach and render obvious, wherein a search of the interpretation database for the matching pattern comprises using a pattern detection algorithm based on machine learning (SEE Jorasch (0252, 0254, ML, AI based algorithms)).
As to claims 6, 13 and 19, the combination as applied above fails to address but, teaches and is deemed to render obvious, an interpretation module (based on permissions), is connected to, a temporary node (USER NODE), connected to the sensor node and configured to repeat signals from the sensor node.
SEE Jorasch teaches User permissions are adapted to be temporary (per time or event), wherein the User ACCESS NODES (by User devices), are temporary, can expire on time or event or controlled, for example, user 1 may enter a particular sequence of inputs that restore control of the peripheral device to user 2, where the User Nodes are temporary nodes by being access controlled per user at the node (end point).
Para [0294]: a control signal received from user 2 can be used directly (e.g., can be directly transmitted to the user device of user 1; e.g., can be directly used for controlling a game character of user 1), without modification. The peripheral device of user 1 would then be simply relaying the control signal received from user 2. In various embodiments, a hardware module or any other module or processor may be used for translating received control signals into signals usable by (or on behalf of) the peripheral device of user 1. In various embodiments, user 2 must have permission before he can control the peripheral device of user 1. User 1 may explicitly put user 2 on a list of users with permissions. User 1 may grant permissions to a category of users (e.g., to a game team) to which user 2 belongs. User 1 may grant permission in real time, such as by indicating a desire to pass control of a peripheral to user 2 in the present moment. Permissions may be temporary, such as a lasting a fixed amount of time, lasting until a particular event (e.g., until the current screen is cleared), lasting until ta are withdrawn (e.g., by user 1), or until any other suitable situation. In various embodiments, user 1 may signal a desire to regain control of his peripheral device and/or to stop allowing user 2 to control his peripheral device. For example, user 1 may enter a particular sequence of inputs that restore control of the peripheral device to user 2.
SEE Para [0568 and 0573]: authorized user field 7308 may store an indication of a user who will receive sensor data, view sensor data, view some result or transformation of sensor data, and/or who will otherwise be privy to sensor data.
Therefore, rendering obvious as claimed by an interpretation module (based on permissions), being connected to, a temporary node (USER NODE), connected to the sensor node and configured to repeat signals from the sensor node, upon access, as taught in view of Jorasch controlled by temporary access by user node/devices.
As to claims 7, 14 and 20, the combination as applied is deemed to render obvious, wherein the temporary node is directly connected to the user device running a device management application
[0350] Supervisor field 5010 may indicate the ID number of an employee's supervisor, manager, boss, project manager, advisor, mentor, or other overseeing authority. As will be appreciated, an employee may have more than one supervisor.
Therefore, rendering obvious in view of Jorasch, wherein temporary node is directly connected to the user device running a device management application, controlling access by temporary user nodes (access based on User ID) correlated to the sensor data (as shown in Fi. 73, 7302 Sensors vs. 7306, Users), as taught by Jorasch.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kadah (US 2015/0159887), teaches associated with LED visual fault indicator and store the fault indications directed to furnace operations.
JPAO (US 2021/00066623), teaches monitoring associated with security access by authorized users, to monitoring data, based on user location of the communication device.
Moriwaki (USPGPUB 2006/0242285), which discloses a directory server (DRS) sets a distributed data processing server (DDS) that is a home server to hold data of a mobile sensor node, for each sensor node. An identification process is executed in the DDS on receiving the data from the sensor node, so as to judge whether the data is sensor data to be managed by itself or another DDS. The data is transferred to the DDS which corresponds to the home server of the sensor data, based on setting of the DRS, when it is judged that the sensor data is to be managed by another DDS.
Chakraborty (USPGPUB 2016/0092501), which relates to the field of predictive analytics, event tracking and processing of data points that are severe outliers from a dataset. In particular, specific embodiments of the disclosure relate to the aggregation of large collections of data, performing processing on the aggregation and generating an improved predictive analytic analysis. Other embodiments relate to the processing of outliers from the dataset.
McSheffrey (USPGPUB 2017/0357926), which comprises a central station that is coupled with oxygen tanks in a health care facility for receiving data indicative of a location and state. A processor analyzes the data from the oxygen tank for predicting resource allocation needs based on a combination of received data and historical data. A user interface displays the data indicative of the location and state of the oxygen tanks, where user interface is deployed on a user device. The position of the oxygen tanks is received by using a Global positioning system(GPS).
Gupta (2019/0296979), which generally relates to inventory discovery of devices in a network of internet of things (IoT). In particular, the present application relates to systems and methods for discovery and identification of devices in an IoT network using a combination of machine learning and rule based pattern matching;
Zalewski (USPGPUB 2019/0363746), which described regarding devices that transmit data, including those that may also sense input, produce output, receive, process and exchange data with end-nodes or networks of end-nodes.
Roy (USPGPUB 2021/0375115), which relates generally to monitoring containers, and more particularly to method and system for performing real-time monitoring of a container containing a non-gaseous fluid substance.
Examiner Notes
The examiner has considered the applicant's claims in light of the disclosure. However, the examiner respectfully reminds the applicant that during prosecution before the USPTO, claims are to be given their broadest reasonable interpretation, and the scope of a claim cannot be narrowed by reading disclosed limitations into the claim. See In re Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997). The Office must apply the broadest reasonable meaning to the claim language, taking into account any definitions presented in the specification. In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004) (citing In re Bass, 314 F.3d 575,577(Fed. Cir. 2002)); “[i]t is the claims that measure the invention.” SRIInt’l v. Matsushita Elec. Corp. of Am., 775 F.2d 1107, 1121 (Fed. Cir. 1985) (enbanc). Written description may not be read into a claim when the claim language is broader than the embodiment. SuperGuide Corp. v. DirecTV Enters, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (citing Electro Med. Sys. S.A. v. Cooper Life Sci., Inc., 34 F.3d 1048, 1054 (Fed. Cir. 1994))
Note that “limitations appearing in the specification will not be read into the claims, and … interpreting what is meant by a word in a claim is not to be confused with adding an extraneous limitation appearing in the specification, which is improper.” Intervet Am., v. Kee-Vet Labs., 887 F.2d 1050, 1053, 12 USPQ2d 1474 1476 (fed. Cir. 1989).
“The ordinary and customary meaning of a claim term is the meaning that the term would have to a person of ordinary skill in the art in question at the time of the invention, i.e., as of the effective filing date of the patent application.” Phillips v. AWH Corp,. 415 F.3d 1303, 1313, 75 USPQ2d 1321, 1326 (fed. Cir. 2005).
“One purpose for examining the specification is to determine if the patentee has limited the scope of the claims.’… For example, an inventor may choose to be his own lexicographer is he defines the specific terms used to describe the invention’ with reasonable clarity, deliberateness, and precision.” Such a definition may appear in the written description, … or in the prosecution history, …” Teleflex, Inc. v. Ficosa N. Am Corp., 299 F.3d 1313, 1325, 63 USPQ2d 1374, 1381 (Fed. Cir. 2002).
Prior art pertinent to the disclosed invention is also cited and Applicants are reminded that they must consider all cited art under Rule 111(c) when amending the claims to conform with 35 U.S.C. 112.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached on M-F 8:00 AM-5:00 PM.
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July 11, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161
Prosecution Insights