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 .
Drawings
1. The drawings are objected to under 37 CFR 1.83(a). The drawings must
show every feature of the invention specified in the claims. Therefore, the machine learning model set forth in claim 9; and mechanism that generates electricity from vibration of the guide bar as set forth in claims 15 and 27 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
2. 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second
paragraph:
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.
3. Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 14, “wherein the processing circuitry is configured to determine a replacement time or life remaining” is confusing as it is not clear replacement time or life remaining of what part or which item is evaluated.
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 8-11, 13-14, 21-23, 25, 26, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Baratta (2021/0402539 A1) in view of Azamfar et al. (2022/0187164 A1), hereinafter Azamfar, and in further view of D’Angelo et al.
(11,519,256 B1), hereinafter D’Angelo. Regarding claim 8, Baratta teaches a guide bar 2902 (Fig. 29) for a chainsaw (paragraph [0143]) comprising: a plate (2904, 2906); onboard circuitry (defined by the sensors and the processing circuitry) coupled to the plate, including: a sensor (1624, 1626, 1628,1630, 1632,1636); and a processing circuitry (1602) configured to receive measurements from the sensor. Baratta further teaches that the processing circuitry is programmed to determine a performance characteristic of the guide bar based on measurements from the sensor.
However, Baratta does not explicitly teach that: the processing circuitry is programmed to determine an event based on a batch of the measurements from the sensor. In particular, Baratta does not explicitly disclose processing a batch of measurements comprising multiple measurements collected over time.
Azamfar discloses a monitoring system 12 including: sensors 38 configured to generate operational signals (paragraph [0041]); a data acquisition module 48 configured to sample sensor signals and generate digital data (paragraph [0042]); collection of operational data over time, including vibration and other parameters (paragraphs [0041]-[0042]). Azamfar further discloses that signal samples from one or more windows of time may comprise a dataset (paragraph [0055]); datasets are processed (including filtering, outlier removal, and feature extraction) (paragraphs [0055]-[0056]); an analytic engine 44 applies analytic models 54, including machine learning models, to determine a health condition or state of the cutting tool (paragraphs [0043], [0045], [0062]). Therefore, Azamfar teaches collecting multiple measurements over time (i.e., a batch of measurements); and determining a tool condition (event/state) based on the batch using analytic models. Accordingly, it would have been obvious to one of ordinary skill in the art to modify Baratta’s processing circuitry to determine an event based on a batch of measurements from the sensor, as taught by Azamfar, since both references relate to monitoring cutting tools using sensor data, and processing time-based datasets improves the accuracy and robustness of condition determination.
However, the combination of Baratta and Azamfar does not explicitly teach storing, in memory, a log indicating an occurrence of the event while discarding the measurements from the sensor. D’Angelo teaches this limitation (steps 214, 218, 220, and 224; description of storing detected events as objects and backing up event history in a JSON file). D’Angelo discloses logging sensor data satisfying a predetermined condition as a count (step 214), adding subsequent qualifying data to the count log (step 220), discarding data when the predetermined condition is not met (step 218), and logging the resulting event (step 224). D’Angelo further discloses storing detected events as objects having, for example, an event type and start/end times, and continuously backing up the logged event history in a JSON file. See Figs. 1-11 in D’Angelo. It would have been obvious to modify the Baratta’s system, as modified by Azamfar, to retain event information in a log while discarding sensor data that does not satisfy the event condition, as taught by D’Angelo, to reduce unnecessary data retention while preserving information identifying detected events for subsequent analysis.
The combination of Baratta and Azamfar also does not explicitly teach communicating the log to an external computing device. D’Angelo teaches networked communication and remote storage (Fig. 11; computing system 1100, networking device 1110, network 1150, and memory 1120; description that memory may be implemented as network storage and that sensor/event data may be stored in remote storage accessible over network 1150). D’Angelo therefore provides a networked architecture in which stored data, including event data, can be made accessible through a network. It would have been obvious to communicate the stored event log to an external computing device using such network connectivity, particularly to permit the event information to be accessed, monitored, or analyzed remotely, as D’Angelo’s remote/network storage and computing architecture facilitates.
It is further noted that claim 8 does not expressly require that the particular sensor measurements used to determine the event themselves be deleted. Rather, the claim requires storing a log indicating occurrence of the event “while discarding the measurements from the sensor.” D’Angelo teaches retaining the event/count information while discarding sensor data when the predetermined condition is not met (step 218). Thus, the claim does not require an express disclosure that the exact measurements that resulted in the event determination are discarded.
Regarding claim 21, Baratta, as modified by Azamfar, teaches the claimed subject matter as set forth in claim 8 above including a plate (2904, 2906; defined by two layers or lamina adhered together) comprising a recess (defined by the recess that receives the processing circuit or electronic unit defined by the other microchip package 1602) and a channel (defined by the channels or openings within tools or adjacent layers or lamina of the tools that receive sensors 1624, 1626, 1628,1630, 1632,1636 of the microchip package unit 1602; Figs. 16, 18, 26, 29) extending from the recess; and onboard circuitry (defined by eth sensors and the processing circuitry) coupled to the plate and arranged so as not protrude from the plate, the onboard circuitry comprising: a sensor (1624, 1626, 1628,1630, 1632,1636) positioned in the channel; and processing circuitry (1602) configured to receive measurements from the sensor; wherein the processing circuitry is programmed to determine an event based on a batch of the measurements from the sensor. It should be noted that the sensors and processing circuitry do not protrude from the plate as two layers or lamina adhered together which houses the sensors and the processing circuitry.
However, the combination of Baratta and Azamfar does not explicitly teach storing, in memory of the processing circuitry and in response to determining the event based on the sensor measurements, a log indicating an occurrence of the event while discarding the measurements from the sensor. D’Angelo teaches this limitation (steps 214, 218, 220, and 224; event objects, JSON backup, and event-history storage), as discussed above. D’Angelo teaches logging qualifying sensor information and the resulting event while discarding sensor data when the predetermined condition is not satisfied. See Figs. 1-11 in D’Angelo. It would have been obvious to incorporate D’Angelo’s event logging and data-discarding technique into the Baratta’s system, as modified by Azamfar, to retain event information while reducing storage of sensor data that is not needed after event determination, thereby conserving memory and facilitating subsequent event analysis.
It is further noted that claim 21 likewise does not expressly require that the particular sensor measurements used to determine the event themselves be deleted or discarded. D’Angelo therefore teaches the recited relationship by storing the event log while discarding sensor data that does not satisfy the predetermined event condition, without requiring that the exact measurements that produced the event determination be discarded.
Regarding claims 9 and 22, Baratta, as modified by Azamfar, teaches everything noted above including that the processing circuitry (1602, taught by Baratta) is programmed to determine the event by applying the batch of the measurements (as taught by Azamfar) as inputs to a machine learning model, wherein the machine learning model is configured to output the event. See paragraphs [0078], [0091], [0128], and [0193] in Baratta. Azamfar explicitly teaches that analytic models 54 may include neural networks or other machine learning models (paragraph [0043]); and such models are trained using datasets generated from sensor data (paragraph [0062]). Thus, applying the batch of measurements to a machine learning model to output an event is taught.
Regarding claim 10, Baratta, as modified by Azamfar, teaches everything noted above including that the batch of the measurements (taught by Azamfar) comprises a timeseries of the measurements from the sensor over time. Bratta, as modified by Azamfar, teaches that the processing circuitry is programmed to determine the event by determining whether the batch of the measurements indicates the guide bar or a chain used with the guide bar should be replaced. Baratta, as modified by Azamfar, does not explicitly teach determining whether the guide bar or chain should be replaced. However, Azamfar teaches determining tool health condition and degradation levels, including different severity levels of tool condition (paragraphs [0052]-[0053]). Accordingly, it would have been obvious to a person of ordinary skill in the art to determine whether the guide bar or chain should be replaced based on the determined condition of the tool.
Regarding claim 11, Baratta teaches everything noted above including that the plate (2904, 2906; defined by two layers or lamina adhered together) comprises a recess (defined by the recess that receives the processing circuit or electronic unit defined by the other microchip package 1602), a channel (defined by the channels or openings within tools or adjacent layers or lamina of the tools that receive sensors 1624, 1626, 1628,1630, 1632,1636 of the microchip package unit 1602; Figs. 16, 18, 26, 29) formed in the plate as a valley extending from the recess. It should be noted that Baratta in Fig. 55 teaches that the grooves which inherently are in the form of a valley extend from the recess that receives the processing circuitry (5520, 5534). Baratta, also teaches additional channels (defined by the channels for additional sensors of the processing circuitry 1624) formed as additional valleys directly branching from the channel, wherein the onboard circuitry comprises additional sensors (1624, 1626, 1628,1630, 1632,1636) positioned in the additional channels. It should be noted that the additional channels or groves or valleys branching from one of the channels via the recess.
Regarding claim 23, Baratta teaches everything noted above including a plurality of additional channels extend directly from the channel to a plurality of region the guide bar.
Regarding claim 25, Baratta, as modified by Azamfar, teaches everything noted above including that the processing circuitry is programmed to determine the event by classifying the batch of measurements over time (as taught by Azamfar) between corresponding to normal cutting and corresponding adverse or reduced- performance cutting. Baratta, as modified by Azamfar, teaches classifying a batch of measurements over time. Azamfar teaches distinguishing between different health conditions and severity levels (paragraphs [0052]-[0053]); using analytic models to classify tool condition. Thus, classification between normal and adverse cutting conditions is suggested.
Regarding claims 13 and 26, Baratta, as modified by Azamfar, teaches everything noted above including wherein the plate (2904, 2906; defined by two layers or lamina adhered together) comprises a recess (defined by the recess that receives the processing circuit or electronic unit defined by the other microchip package 1602), a channel (defined by the channels or openings within tools or adjacent layers or lamina of the tools that receive sensors 1624, 1626, 1628,1630, 1632,1636 of the microchip package unit 1602; Figs. 16, 18, 26, 29) receiving the sensor (1624, 1626, 1628,1630, 1632,1636), and a plurality of additional channels (defined by more channels opening for more sensors) extending from the recess, wherein the onboard circuitry comprises a plurality of additional sensors positioned in the plurality of additional channels, wherein the batch of the measurements further comprises additional measurements of a plurality of conditions. It should be noted that Baratta teaches a plurality of channels and sensors positioned therein. Azamfar teaches that multiple sensors 38 may measure different operational parameters, including vibration, force, and motor characteristics (paragraph [0041]). Azamfar further teaches combining such measurements into datasets for analysis (paragraphs [0042], [0055]). Thus, the batch of measurements comprising additional measurements of a plurality of conditions is taught.
Regarding claim 14, as best understood, Baratta, as modified by Azamfar, teaches everything noted above including that the processing circuitry is configured to determine a replacement time or life remaining. It should be noted that Baratta, as modified by Azamfar, teaches determining tool condition. Azamfar teaches generating life cycle trajectories based on operational data (paragraph [0047]); monitoring tool condition over time and updating analytic models (paragraph [0047]), which reasonably corresponds to determining remaining useful life or replacement timing.
Regarding claim 29, the combination of Baratta and Azamfar also does not explicitly teach communicating the log to an external computing device. D’Angelo teaches networked communication and remote storage (Fig. 11; computing system 1100, networking device 1110, network 1150, and memory 1120; description that memory may be implemented as network storage and that sensor/event data may be stored in remote storage accessible over network 1150). D’Angelo therefore provides a networked architecture in which stored data, including event data, can be made accessible through a network. It would have been obvious to communicate the stored event log to an external computing device using such network connectivity, particularly to permit the event information to be accessed, monitored, or analyzed remotely, as D’Angelo’s remote/network storage and computing architecture facilitates.
6. Claims 15 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Baratta in view of Azamfar and D’Angelo, and in further view of Yin et al. (CN 108638036 A), hereinafter Yin, or unannounced (JP 7083212 B1), herein after 212. Regarding claims 15 and 27, Baratta teaches everything noted above except that that the power source is configured to generate electricity from vibration of the guide bar and provide the electricity to the processing circuitry. It should be noted that Baratta teaches a battery or other form of power could be supplied to the packaging units which also includes sensors. However, Yin teaches a chainsaw including a piezoelectric power generating sheet that converts vibration of the power tool inherently including the guide bar to electricity to charge the batter pack 1. The battery back also provide powers to all the electronic elements including the control system of the power tool. See Figs. 1-2 and pages 4-5 of the translated attached disclosure. 212 also teaches a power tool including a piezoelectric element 11 to convert vibration of the tool to a voltage for powering the tool. See Fig. 2 and translated specification. It would have been obvious to a person of ordinary skill in the art to provide Barrat’s chainsaw, as modified by Azamfar and D’Angelo, with the mechanism to generate electricity form the vibration of the chainsaw and the guide bar, as taught by Yin or 212, in order to harvest the vibration energy of the chainsaw and produce electricity for the chainsaw and its electronic components.
To the degree that it could be argued Baratta does not clearly and explicitly teach that the sensors and the processing circuitry are onboard the plate in a manner that they do not protrude out of the plate as they are placed in the recess and the channels, the rejection below is applied.
7. Claims 8-11, 13-14, 21-23, 25, 26, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Baratta in view of Azamfari and D’Angelo, and in further view of Mekid (2014/0022529 A1). Regarding claim 8, as stated above, Baratta in view of Azamfari and D’Angelo teaches the claim limitations. However, it further could be argued that Baratta does not explicitly teach that the sensors and processing circuitry are arranged such that they do not protrude from the plate. Mekid teaches a structural material 12 including embedded sensors 14 disposed within channels 42, 44 formed in the structural material (Figs. 4A-4B; paragraphs [0008], [0027]), wherein the sensors are embedded within the material and covered by a layer 40 such that the sensors do not protrude from the surface. Mekid further teaches leads 16 and nodes 24 for transmitting signals from the embedded sensors to processing components (Figs. 1-3A).
Accordingly, it would have been obvious to one of ordinary skill in the art to modify Baratta’s processing circuitry to determine an event based on a batch of measurements from the sensor, as taught by Azamfar and D’Angelo, and to further embed the sensors and associated circuitry within the plate such that they do not protrude, as taught by Mekid, since both references relate to monitoring conditions of structural or cutting elements using sensor data, and embedding sensors within a structure improves durability, protection, and measurement reliability while maintaining the structural profile of the component.
Regarding claim 21, Baratta, as modified by Azamfar, D’Angelo and Mekid, teaches the claimed subject matter as set forth in claim 8 above including a plate (2904, 2906; defined by two layers or lamina adhered together) comprising a recess (defined by the recess that receives the processing circuit or electronic unit defined by the microchip package 1602) and a channel (defined by the channels or openings within tools or adjacent layers or lamina of the tools that receive sensors 1624, 1626, 1628, 1630, 1632, 1636; Figs. 16, 18, 26, 29) extending from the recess, and onboard circuitry coupled to the plate. Mekid further supports that the sensors are positioned within channels (42, 44) formed in the structural material (Figs. 4A-4B), such that the sensors are embedded and do not protrude from the surface.
Regarding claims 9 and 22, Baratta, as modified by Azamfar, teaches everything noted above including that the processing circuitry (1602) is programmed to determine the event by applying the batch of the measurements (as taught by Azamfar) as inputs to a machine learning model, wherein the machine learning model is configured to output the event. See paragraphs [0043], [0055]-[0062] in Azamfar.
Regarding claim 10, Baratta, as modified by Azamfar, teaches everything noted above including that the batch of the measurements comprises a time series of the measurements from the sensor over time. Baratta, as modified by Azamfar, teaches that the processing circuitry is programmed to determine the event by determining whether the guide bar or a chain used with the guide bar should be replaced. Although Baratta does not explicitly teach determining replacement, Azamfar teaches determining tool health condition and degradation levels, including severity levels (paragraphs [0052]-[0053]). Accordingly, it would have been obvious to determine whether the guide bar or chain should be replaced based on the determined condition.
Regarding claim 11, Baratta teaches everything noted above including that the plate (2904, 2906) comprises a recess and a channel formed in the plate as a valley extending from the recess. Mekid further supports forming channels (42, 44) within the structural material to receive embedded sensors (Figs. 4A-4B), reinforcing the arrangement of sensors within non-protruding channels.
Regarding claim 13, Baratta, as modified by Azamfar and Mekid, teaches everything noted above including wherein the plate comprises a recess, a channel receiving the sensor, and a plurality of additional channels extending from the recess. Baratta teaches multiple channels and sensors positioned therein, and Mekid teaches an array of embedded sensors 14 arranged within the structural material (Figs. 1-3A), including multiple sensor placements within the structure. Azamfar teaches that multiple sensors may measure different operational parameters (paragraph [0041]) and that such measurements are combined into datasets (paragraphs [0042], [0055]). Thus, the batch of measurements comprising additional measurements of a plurality of conditions is taught.
Regarding claim 14, as best understood, Baratta, as modified by Azamfar, teaches everything noted above including that the processing circuitry is configured to determine a replacement time or life remaining. Azamfar teaches generating life cycle trajectories and monitoring tool condition over time (paragraph [0047]), which reasonably corresponds to determining remaining useful life or replacement timing.
Regarding claim 29, the combination of Baratta and Azamfar also does not explicitly teach communicating the log to an external computing device. D’Angelo teaches networked communication and remote storage (Fig. 11; computing system 1100, networking device 1110, network 1150, and memory 1120; description that memory may be implemented as network storage and that sensor/event data may be stored in remote storage accessible over network 1150). D’Angelo therefore provides a networked architecture in which stored data, including event data, can be made accessible through a network. It would have been obvious to communicate the stored event log to an external computing device using such network connectivity, particularly to permit the event information to be accessed, monitored, or analyzed remotely, as D’Angelo’s remote/network storage and computing architecture facilitates.
8. Claims 15 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Baratta in view of Azamfar, D’Angelo and Mekid, and in further view of Yin or 212. Regarding claims 15 and 27, Baratta teaches everything noted above except that that the power source is configured to generate electricity from vibration of the guide bar and provide the electricity to the processing circuitry. It should be noted that Baratta teaches a battery or other form of power could be supplied to the packaging units which also includes sensors. However, Yin teaches a chainsaw including a piezoelectric power generating sheet that converts vibration of the power tool inherently including the guide bar to electricity to charge the batter pack 1. The battery back also provide powers to all the electronic elements including the control system of the power tool. See Figs. 1-2 and pages 4-5 of the translated attached disclosure. 212 also teaches a power tool including a piezoelectric element 11 to convert vibration of the tool to a voltage for powering the tool. See Fig. 2 and translated specification. It would have been obvious to a person of ordinary skill in the art to provide Barrat’s chainsaw, as modified above, with the mechanism to generate electricity form the vibration of the chainsaw and the guide bar, as taught by Yin or 212, in order to harvest the vibration energy of the chainsaw and produce electricity for the chainsaw and its electronic components.
Response to Arguments
9. Applicant’s argument that the machine learning model is shown in the drawings, for example as onboard circuitry 910 and/or processor unit 1002, is not persuasive. The disclosure does not disclose that the circuitry 910 and/or processor unit 1002 as being a “machine learning model.” Th specification clearly is silent as to what constitutes a machine learning model or how such a model is implemented.
Regarding claim 14, applicant’s argument that the breadth of the claim is not indefiniteness is not persuasive because the rejection is not based on claim breadth, but rather on a lack of clarity regarding the scope of the limitation. Specifically, claim 14 recites “wherein the processing circuitry is configured to determine a replacement time or life remaining,” without clearly identifying the object or component for which the replacement time or remaining life is being determined. As such, it is unclear whether the limitation refers to a guide bar, chain, component, or other element of the system. While applicant relies on MPEP 2173.04 for the proposition that breadth is not indefiniteness, the present rejection is directed to ambiguity in the metes and bounds of the claim language itself. A claim is indefinite when it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Here, the claim does not reasonably apprise one of ordinary skill in the art as to what item’s life or replacement time is being determined, rendering the scope of the limitation unclear.
Applicant argues that the term “while” requires the log and the sensor measurements to be different data and to be stored and discarded concurrently, and that the claim therefore excludes an arrangement in which the same measurements are initially stored and later discarded. This argument is not persuasive. The claim expressly recites two distinct operations: “store . . . a log indicating an occurrence of the event” and “discard the measurements from the sensor.” The claim does not, however, require that the particular sensor measurements used to determine the event be the measurements that are discarded, nor does it require that every sensor measurement be discarded. Further, the term “while” does not expressly require that the storing and discarding operations occur at the exact same instant, or that the measurements first be stored and thereafter deleted. Rather, the limitation is satisfied where the event log is retained while sensor measurements are discarded, including where the respective operations occur concurrently or overlap in time.
D’Angelo discloses this claimed relationship by maintaining a count/event log while separately processing incoming sensor data and discarding sensor data when the predetermined condition is not met (steps 214, 218, 220, and 224). In particular, D’Angelo discloses logging qualifying data as a count, subsequently adding qualifying data to the count log, and discarding data that does not satisfy the predetermined condition, followed by logging the resulting event. Thus, D’Angelo does not require that the same sensor measurements constitute the event log and are later discarded. Rather, the event log and the sensor measurements are distinct information, and D’Angelo teaches retaining the event-related information while sensor data is discarded. Accordingly, the use of “while” in the claim does not exclude the arrangement disclosed by D’Angelo.
Applicant argues that the term “while” requires the log and the sensor measurements to be different data and to be stored and discarded concurrently, and that the claim therefore excludes an arrangement in which the same measurements are initially stored and later discarded. This argument is not persuasive. The claim expressly recites two distinct operations: “store … a log indicating an occurrence of the event” and “discard the measurements from the sensor.” The claim does not, however, require that the particular sensor measurements used to determine the event be the measurements that are discarded, nor does it require that every sensor measurement be discarded. D’Angelo discloses the claimed distinction by maintaining a count/event log while separately processing incoming sensor data and discarding sensor data when the predetermined condition is not met (steps 214, 218, 220, and 224). In particular, D’Angelo discloses logging qualifying data as a count, subsequently adding qualifying data to the count log, and discarding data that does not satisfy the predetermined condition, followed by logging the resulting event. Therefore, D’Angelo does not rely upon storing the same data that is later discarded as the event log; rather, the event log and sensor measurements constitute distinct information.
Applicant’s further argument that the juxtaposition of “the log indicating an occurrence of the event” and “the measurements from the sensor” requires the log to be something different from all of the sensor measurements is likewise not persuasive. The claimed log is information indicating occurrence of an event, whereas the sensor measurements are data received from the sensor and used to determine whether the event has occurred; the claim does not require the log to contain no information derived from those measurements. D’Angelo expressly teaches this type of event-based processing, including storing detected events as objects having an event type and start/end times, while sensor data that fails the predetermined condition is discarded. Accordingly, D’Angelo teaches storing event information while discarding sensor measurements in the same event-detection process, and the claim language does not impose the additional requirement that the measurements discarded must be the exact measurements that caused the event to be logged.
Applicant’s possible argument, in view of the rejection above, that D’Angelo is non-analogous art is not persuasive. D’Angelo is reasonably pertinent to the problem addressed by claim 8 because D’Angelo is directed to processing sensor measurements to detect an event, logging information corresponding to the detected event, and selectively discarding sensor data during the event-detection process (steps 214, 218, 220, and 224). D’Angelo teaches maintaining event information, including event type and start/end times, while sensor data that does not satisfy a predetermined condition is discarded. These teachings concern the same type of data-processing and storage-management operation recited in claim 8, notwithstanding that D’Angelo applies the technique to monitoring drilling operations rather than a chainsaw guide bar. The analogous-art inquiry does not require the reference to be directed to the same specific article or application as the claimed invention; a reference is reasonably pertinent when a person of ordinary skill would have reasonably consulted it in addressing the problem faced by the inventor.
Moreover, D’Angelo is in the same general technological field as Baratta and Azamfar with respect to sensor-based monitoring of equipment and processing sensor data to determine an operating condition. Baratta concerns sensor-based monitoring of a chainsaw guide bar, Azamfar concerns sensor-based monitoring and analysis of cutting-tool conditions, and D’Angelo concerns sensor-based monitoring and event detection for drilling equipment. Each uses sensor measurements, processing circuitry, condition/event determination, and storage of information associated with detected conditions. Thus, D’Angelo is not being relied upon merely because it discloses an isolated concept; it is relied upon for its teaching of event-based sensor-data processing and selective data retention, which is reasonably pertinent to the claimed operation of processing sensor measurements, storing an event log, and discarding sensor measurements.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to
applicant’s disclosure.
Shum et al. (2018/0267538 A1) and Hayahsi et al. (11,100,338 B2) teach deleting logged stored data to free storage space.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GHASSEM ALIE whose telephone number is (571) 272-4501. The examiner can normally be reached on 8:30 am-5:00 pm EST.
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/GHASSEM ALIE/Primary Examiner, Art Unit 3724
September 21, 2026
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