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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitations “ configured to collect cyclic data”, “configured to store the collected cyclic data”, “configured to perform cloud processing of the stored cyclic data triggered by a cycle-start signal”, “configured to parse the stored cyclic data”, etc. invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is unclear whether the claim element such “ configured to” is insufficient disclosure of the corresponding structure, material, or acts for performing the entire claimed function or why there is no clear linkage between the structure, material, or acts and the function. There are no structure to support the function that can be found in the specification. Or the structure described in the specification does not perform the entire function in the claim,
This form paragraph must be preceded by form paragraphs 7.30.03.h, 7.30.03, and 7.30.05 (to set forth the claim interpretation and statutory basis for 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph), and then 7.30.02 or 7.103 and 7.34.01 (to set forth the statutory basis for the indefiniteness rejection and identify the claim at issue) and 7.30.06, if appropriate (invoked despite the absence of means).
When a rejection is made under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, because the disclosure is inadequate to support the limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description should also be considered. See MPEP § 2181, subsection IV.
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.
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 1-9 are rejected under 35 U.S.C. 101 because the claims 1-9 recite system claims. Further, specification is not clear to provide sufficiently the structure, material or acts for performing the claimed function. Therefore, the claims fail to define the physical structure as System claimed. The claim recites the components of the system are merely software per se. A System claims much recite physical structure thus enabling it to be properly categorized in one of the statutory categories of invention. Since the components of the system claims 1-9 are software per se and do not contain any physical components, the system cannot be categorized in one of the statutory categories of invention and is thus nonstatutory.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C 103 as being unpatentable over Cella et al. (U.S. Pub. 2020/0133254A1). in view of Shah et al. (U.S. 2021/0237276 A1) and further in view Malach et al. (U.S. Pub. 2020/0160126 A1)
With respect to claims 1 and 10, Cella et al. discloses a system for outlier detection based on process fingerprints (i.e., “The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, ”(0396)) from robot cycle data (i.e., “ the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like).”(0393)), comprising:
a data collection component (5054, fig. 22), which is configured to collect cyclic data (i.e., “the DAQ driver services 5054 may be configured so as to maintain an even larger (than the device) channel specific FIFO area 5152 that it fills with new data obtained from the device. In embodiments, the DAQ driver services 5054 may be configured to employ a further process in that the raw data server 5058 may take data from a FIFO 5110 …a FIFO end marker 5114 may be configured to mark the end of the most current data until it reaches the end of the spooler and then wraps around constantly cycling around.”(0500)), wherein the cyclic data comprises multiple vectors each of which comprises data from one individual cycle of the robot cycle data (i.e., “ smart band data collection templates may be configured for detecting and gathering data for smart band analysis covering vibration spectra, such as vibration envelope and current signature for spectral regions or peaks that may be combinations of absolute frequency or factors of machine related parameters, vibration time waveforms for time-domain derived calculations including, without limitation… RMS overall, peak overall, true peak, crest factor, and the like; vibration vectors, spectral energy humps in various regions: ”(0606));
a data storage component (5060, fig. 36), which is configured to store the collected cyclic data (i.e., “the DAQ driver services 5054 may be configured so as to maintain an even larger (than the device) channel specific FIFO area 5152 that it fills with new data obtained from the device. In embodiments, the DAQ driver services 5054 may be configured to employ a further process in that the raw data server 5058 may take data from a FIFO 5110 …a FIFO end marker 5114 may be configured to mark the end of the most current data until it reaches the end of the spooler and then wraps around constantly cycling around…It will be appreciated in light of the disclosure that many pieces of equipment and their components may contribute to the relative needed duration of the stream of gap-free data and those durations may be over four hours when relatively low speeds are present in large numbers, when non-periodic transient activity is occurring on a relatively long time frame, when duty cycle only permits operation in relevant ranges for restricted durations and the like”(0500)); and
a data processing component (5940, fig. 36), which is configured to store the collected cyclic data ((i.e., “the DAQ driver services 5054 may be configured so as to maintain an even larger (than the device) channel specific FIFO area 5152 that it fills with new data obtained from the device. In embodiments, the DAQ driver services 5054 may be configured to employ a further process in that the raw data server 5058 may take data from a FIFO 5110 …a FIFO end marker 5114 may be configured to mark the end of the most current data until it reaches the end of the spooler and then wraps around constantly cycling around…It will be appreciated in light of the disclosure that many pieces of equipment and their components may contribute to the relative needed duration of the stream of gap-free data and those durations may be over four hours when relatively low speeds are present in large numbers, when non-periodic transient activity is occurring on a relatively long time frame, when duty cycle only permits operation in relevant ranges for restricted durations and the like”(0500)),
a data processing component (5940, fig. 36) which is configured to perform cloud processing of the stored cyclic data cloud (i.e., “the CDMS 5832 may include a cloud data exchange 5902 configured to facilitate the transfer of data to and from the cloud network facility 5870. In embodiments, the CDMS 5832 may include a cloud plots/trends module 5904 that may be configured to show all plots via web apps including trend, waveform, spectra, envelope, transfer function, and the like”(0519))
wherein the data processing component (560,fig. 36) is configured to parse the stored cyclic data and to process the stored cyclic data based on a configuration file defining metadata of the stored cyclic data, wherein the data processing component is configured extract process fingerprints from the stored cyclic data using the metadata (i.e., “The locations of machine 2400 being close to machine 2600 can be included in the contextual metadata of both vibration surveys. ”(0370), “The many embodiments include hybrid database adaptation for harmonizing relational metadata and streaming raw data formats. Unlike older systems that utilized traditional database structure for associating nameplate and operational parameters (sometimes deemed metadata) with individual data measurements that are discrete and relatively simple, it will be appreciated in light of the disclosure that more modern systems can collect relatively larger quantities of raw streaming data with higher sampling rates and greater resolutions. At the same time, it will also be appreciated in light of the disclosure that the network of metadata with which to link and obtain this raw data or correlate with this raw data, or both, is expanding at ever-increasing rates.’(0371), “The analysis of the vibration data from the bearing or other components related to one another in the hierarchical data can use table lookups, searches for correlations between frequency patterns derived from the raw data, and specific frequencies from the metadata of the machine.”(0375), “Analysis of the vibration noise may be performed, such as filtering, signal conditioning, spectral analysis, trend analysis, and the like. Analysis may be performed on aggregate or individual sensor measurements to isolate vibration noise of equipment to obtain a characteristic vibration, vibration pattern or “vibration fingerprint” of the machine. The vibration fingerprints may be stored in a data structure, or library, of vibration fingerprints. The vibration fingerprints may include frequencies, spectra (i.e., frequency vs. amplitude), velocities, peak locations, wave peak shapes, waveform shapes, wave envelope shapes, accelerations, phase information, phase shifts (including complex phase measurements) and the like. Vibration fingerprints may be stored in the library in association with a parameter by which it may be searched or sorted. ”(1136), 1160 and “a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of at least one of an outcome and a state. In embodiments, the output data 10810 from the vibration sensors forms a vibration fingerprint. The vibration fingerprint may include one or more of a frequency, a spectrum, a velocity, a peak location, a wave peak shape, a waveform shape, a wave envelope shape, an acceleration, a phase information, and a phase shift. The data collection circuit 10808 may apply a rule regarding how many parameters of the vibration fingerprint to match or the standard deviation for the match in order to identify a match between the output data 10810 and the learned received output data pattern”(1162)).
But Cella et al. does not discloses each of the multiple vectors in the cyclic data comprises data from one individual cycle of the robot cycle data and the cloud processing of the stored cyclic data is triggered by a cycle-start signal and
However, Shah et al. (U.S. 2021/0237276 A1) discloses the cloud processing of the stored cyclic data is triggered by a cycle-start signal (i.e., “ the robotic system implements simultaneous localization and mapping (or “SLAM”) techniques to construct and update a (2D or 3D) spatial map of an unknown environment within the store while also tracking its location within this spatial map based on distance data collected via depth sensors in the robotic system throughout this mapping cycle… a depth sensor in the robotic system can capture depth images representing distances to nearby physical surfaces, and the robotic system can compile these depth images into a spatial map of the store, such as in the form of a 2D or 3D point cloud representing locations of inventory structures, displays, and counters throughout the store. …the robotic system can collect raw depth data during this mapping cycle and upload these data to the remote computer system, such as in real-time or upon conclusion of the mapping cycle. ”(0033) and “The remote computer system can then trigger the mobile robotic system to initiate a next scan cycle at this scan cycle start time”(0101))
It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Cella et al.’s features in order to easy to configure autonomously capture images or autonomously capture data and sent it to cloud for the stated purpose has been well known in the art as evidenced by teaching of Shah et al. Furthermore, Cella et al. and Shah et al. do not discloses each of the multiple vectors in the cyclic data comprises data from one individual cycle of the robot cycle data. However, Malach et al. (U.S. Pub. 2020/0160126 A1) discloses each of the multiple vectors in the cyclic data comprises data from one individual cycle of the robot cycle data (i.e., “At each cycle, the backbone may operate on a warp, such as a 64×64 warp (e.g., redness and gray). The backbone may store a sixty-four output vector in a cyclic buffer that holds the last 16 results.”(0041) and “automated control (e.g., autonomous cars, drones, robots, etc.), among others.”(0017)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Malach et al.’s features in order to easy to classify the data for the stated purpose has been well known in the art as evidenced by teaching of Malach et al
With respect to claims 2 and 11, Shah et al. (U.S. 2021/0237276 A1) discloses wherein the data processing component is configured to stop to perform cloud processing of the stored cyclic data triggered by a cycle-stop signal ((i.e., “ the robotic system implements simultaneous localization and mapping (or “SLAM”) techniques to construct and update a (2D or 3D) spatial map of an unknown environment within the store while also tracking its location within this spatial map based on distance data collected via depth sensors in the robotic system throughout this mapping cycle… a depth sensor in the robotic system can capture depth images representing distances to nearby physical surfaces, and the robotic system can compile these depth images into a spatial map of the store, such as in the form of a 2D or 3D point cloud representing locations of inventory structures, displays, and counters throughout the store. …the robotic system can collect raw depth data during this mapping cycle and upload these data to the remote computer system, such as in real-time or upon conclusion of the mapping cycle. ”(0033) and “The remote computer system can then trigger the mobile robotic system to initiate a next scan cycle at this scan cycle start time”(0101)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include stop signal since Shah et al. discloses mapping cycle that including the start cycle and stop cycle in order to have different feature for the stated purpose has been well known in the art as evidenced by teaching of Shah et al
With respect to claims 3 and 12, Cella et al. discloses wherein the data collection component is configured to collect cyclic data based on data communication over local area networks (i.e., “a system for data collection, processing, and utilization of signals from at least a first element in a first machine in an industrial environment includes a platform including a computing environment connected to a local data collection system having at least a first sensor signal and a second sensor signal obtained from at least the first machine in the industrial environment. The system includes a first sensor in the local data collection system configured to be connected to the first machine and a second sensor in the local data collection system. T”(0017) and “The data collection system 102 may include onboard sensors and may take input, such as through one or more input interfaces or ports 4008, from one or more sensors (such as analog or digital sensors of any type disclosed herein) and from one or more input sources 116 (such as sources that may be available through Wi-Fi, Bluetooth, NFC, or other local network connections or over the Internet)’(0399)).
With respect to claims 4 and 13, Cella et al. discloses wherein the data collection component comprises a network device “The data collection system 102 may include onboard sensors and may take input, such as through one or more input interfaces or ports 4008, from one or more sensors (such as analog or digital sensors of any type disclosed herein) and from one or more input sources 116 (such as sources that may be available through Wi-Fi, Bluetooth, NFC, or other local network connections or over the Internet)’(0399), 0489, 500). .
With respect to claims 5 and 14, Cella et al. discloses wherein the data collection component comprises a network edge device (i.e., “These methods and systems include methods, systems, components, devices, workflows, services, processes, and the like that are deployed in various configurations and locations, such as: (a) at the “edge” of the Internet of Things, such as in the local environment of a heavy industrial machine;”(0013)).
With respect to claims 6 and 15, Cella et al. discloses wherein the data processing component is configured to train an artificial intelligence system using the extracted process fingerprints of the stored cyclic data (i.e., “the expert system may make a comparison of the vibration noise with a stored vibration fingerprint. In other embodiments, the expert system may be seeded with vibration noise and initial feedback on states and outcomes in order to learn to predict other states and outcomes.”(1137)).
With respect to claims 7 and 16, Cella et al. discloses wherein the data processing component is configured to provide predictive maintenance for a robot system using the extracted process fingerprints of the stored cyclic data by discovering anomalies in the extracted process fingerprints of the robot system (i.e., “An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network.”(abstract) and “The library of vibration fingerprints may be stored as indicators with associated predictions, states, outcomes and/or events. Trend analysis data of measured vibration fingerprints can indicate time between maintenance events/failure event”(1136) and “the expert system may make a comparison of the vibration noise with a stored vibration fingerprint. In other embodiments, the expert system may be seeded with vibration noise and initial feedback on states and outcomes in order to learn to predict other states and outcomes”(1138)).
With respect to claims 8 and 17, Cella et al. discloses wherein the data processing component is configured to provide process monitoring by tracking changes to the fingerprints over time (i.e., “the library may be updated if a changed parameter resulted in a new vibration fingerprint, or if a predicted outcome or state did not occur in the absence of mitigation. In embodiments, the library may be updated if a vibration fingerprint was associated with an alternative state than what was predicted by the library. The update may occur after just one time that the state that actually occurred did not match the predicted state from the library”(1142-1143)).
With respect to claims 8 and 18, Cella et al. discloses the system according to claim 8, wherein the data processing component is configured to provide a summary of monitored cycles where changes in the fingerprints over time were tracked i.e., “the library may be updated if a changed parameter resulted in a new vibration fingerprint, or if a predicted outcome or state did not occur in the absence of mitigation. In embodiments, the library may be updated if a vibration fingerprint was associated with an alternative state than what was predicted by the library. The update may occur after just one time that the state that actually occurred did not match the predicted state from the library”(1142-1143)). .
Conclusion
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/HUNG T VY/Primary Examiner, Art Unit 2163 December 24, 2025