DETAILED ACTION
This is a response to Application # 18/974,966 filed on May 27, 2024 in which claims 1-20 were presented for examination.
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 .
Status of Claims
Claims 1-20 are pending, of which claims 1 and 9-14 are rejected under 35 U.S.C. § 101; claims 6, 11, and 18 are rejected under 35 U.S.C. § 112(b); and claims 1-20 are rejected under 35 U.S.C § 103.
Drawings
The drawings are objected to because Figs. 5 and 8 contain illegible text, and therefore, lack satisfactory reproduction characteristics as required by 37 C.F.R. § 1.84(I). Corrected drawing sheets in compliance with 37 C.F.R. § 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 C.F.R. § 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 Interpretation
Claims 2, 6-8, 15, and 18-20 refer to “sweeping” data. It is the examiner’s duty to give claims “their broadest reasonable interpretation consistent with the specification.” See MPEP § 2111, citing Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005). Further, if the specification is silent to the meaning of claim terminology, “words of the claim must be given their plain meaning.” See MPEP § 2111.01. Here, the present specification does not appear to explicitly define this term. Therefore, the plain and ordinary meaning must be used.
Based on the examiner’s research, it appears that the plain and ordinary meaning of the term “sweeping” as used in the context of the present claims is “[t]he process of trying different training parameter values in order to find a good set of neural network weight values.” (James McCaffrey, Parameter Sweeps, or How I Took My Neural Network for a Test Drive, November 10, 2015, Visual Studio Magazine, https://visualstudiomagazine.com/articles/2015/11/01/parameter-sweeps.aspx, Page 2).
If this is not Applicant’s intended interpretation, the examiner recommends Applicant provide evidence on the record of the intended interpretation or amending the claim to better align with the intended interpretation.
Claim 5 recites a method including the steps of “receiving the surface profile of the surface from the surface profile classifier in response to the confidence being not smaller than a predetermined threshold; and training the surface profile classifier using the preprocessed acceleration data comprises: training the surface profile classifier using the preprocessed acceleration data in response to the confidence being smaller than the predetermined threshold.” (Emphasis added). The broadest reasonable interpretation of this claim only requires the prior art to disclose, teach, or suggest one of these two actions as the confidence value cannot be both smaller and not smaller than the threshold. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Ex parte Heil (PTAB 2018) (App. S.N. 12/512,669), at 6; Ex parte Frost (PTAB 2018) (App. S.N. 12/785,052) at 7; Ex parte Dawson (PTAB 2018) (App. S.N. 12/103,472) at 6; and Ex parte Candelore (PTAB 2017) (App. S.N. 14/281,158) at 5 (supporting the interpretation that “in response to” limitations are conditional).
Claim 10 recites a method claim including the steps of “wherein the determined window size is 24 when the acceleration data is received from the accelerometer correspond to an X-axis, and the determined window size is 25 when the acceleration data is received from the accelerometer correspond to a Y-axis.”
Neither claim 10 nor any claim from which it descends require acceleration data to correspond to an X or Y axis. Therefore, the broadest reasonable interpretation of this limitation does not require the window size to be set to 24 or 25. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Reactive Surfaces v. Toyota Motor Corp., IPR2016-01914 (PTAB 2018) (“[t]he use of ‘when’ instead of ‘if’ does not change whether the method step is conditional”) (citing Ex parte Kaundinya, No. 2016-000917, 2017 WL 5510012, at *5-6 (PTAB Nov. 14, 2017) ("when" may indicate a conditional method step); Ex parte Zhou, No. 2016-004913, 2017 WL 5171533, at *2 (PTAB Nov. 1, 2017) (same); Ex parte Lee, No. 2014-009364, 2017 WL 1101681, at *2 (PTAB Mar. 16, 2017) (same)).
Claim 11 recites a method claim including the limitation “wherein the determined window size is 19 when the acceleration data is received from two accelerometers each corresponding to an X-axis and a Y-axis.” (Emphasis added).
Neither claim 11 nor any claim from which it descends require receiving data from two accelerometers, much less two that each correspond to an X or Y axis. Therefore, the broadest reasonable interpretation of this limitation does not require the window size to be 19. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Reactive Surfaces v. Toyota Motor Corp., IPR2016-01914 (PTAB 2018) (“[t]he use of ‘when’ instead of ‘if’ does not change whether the method step is conditional”) (citing Ex parte Kaundinya, No. 2016-000917, 2017 WL 5510012, at *5-6 (PTAB Nov. 14, 2017) ("when" may indicate a conditional method step); Ex parte Zhou, No. 2016-004913, 2017 WL 5171533, at *2 (PTAB Nov. 1, 2017) (same); Ex parte Lee, No. 2014-009364, 2017 WL 1101681, at *2 (PTAB Mar. 16, 2017) (same)).
Claim Objections
Claims 2, 5, 6, 13, 15, and 18 are objected to because of the following informalities: these claims appear to include limitations that are not separated by a new line character after a semi-colon, as is customary. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6, 11, and 18 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 6 and 18, these claim includes the limitation “sweeping the old acceleration data of the candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size comprises: sweeping the old acceleration data of the candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size.” This limitation appears to recursively define itself and thus, the examiner cannot determine if this limitation is intended to further narrow the limitation or not. If Applicant intends for this limitation to further narrow itself, the examiner recommends adding additional, limiting features. If Applicant did not intended for any further limitation to occur, the examiner recommends removing this limitation entirely. These claims should be compared to claims 7 and 19, which clearly further define the additional obtaining and updating steps as part of the sweeping step.
Regarding claim 11, this claim recites the limitation “when the acceleration data is received from two accelerometers each corresponding to an X-axis and a Y-axis.” (Emphasis added). This claim is subject to two mutually exclusive interpretations. First, this may be interpreted as “when the acceleration data is received from a first accelerometer corresponding to an X-axis and a second accelerometer corresponding to a Y-axis.” Second, this limitation may be interpreted as “when the acceleration data is received from a first accelerometer corresponding to an X-axis and a Y-axis and a second accelerometer corresponding to an X-axis and a Y-axis.”
“[I]f a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex parte Miyazaki, 89 USPQ2d 1207, 1211 (BPAI 2008) (precedential). See also Ex parte McAward, Appeal 2015-006416 (PTAB 2017) (precedential) (affirming the holding in Ex parte Miyazaki).
Therefore, this claim is indefinite.
Claim Rejections - 35 U.S.C. § 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 of this title, 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. Applicants are advised of the obligation under 37 C.F.R. § 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, 13, and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Hadj-Attou et al., Hybrid deep learning models for road surface condition monitoring, July 10, 2023, Measurement, Volume 220, 113267, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2023.113267, Pages 1-11 (hereinafter Hadj-Attou) in view of So, US Publication 2021/0331655 (hereinafter So).
Regarding claim 1, Hadj-Attou discloses a method for navigating a mobile machine having at least an accelerometer on a surface, comprising “receiving, from the accelerometer of the mobile machine, acceleration data while navigating the mobile machine on the surface” (Hadj-Attou 2, § 3.1. Data collection) where RSC data is collected from the accelerometer. Additionally, Hadj-Attou discloses “inputting the received acceleration data of a determined window size into an artificial neural network-based surface profile classifier” (Hadj-Attou 4, § 3.2.3. Segmentation; 6, § 3.3.2. LSTM and GRU) where the RSC data is segmented using a sliding window that is fed into a LTSM, which is a type of recurrent neural network. Further, Hadj-Attou discloses “receiving a surface profile of the surface from the surface profile classifier” (Hadj-Attou 6, § 3.3. Classification) where a road surface type is output by the classifiers.
Although Hadj-Attou discloses determining the system profile, it does not discuss how such a surface profile may be used and, thus, does not appear to explicitly disclose “determining at least a navigation parameter corresponding to the surface profile; and navigating the mobile machine according to the determined navigation parameter.”
However, So discloses a method for using surface profiles to navigate a vehicle including the step of “determining at least a navigation parameter corresponding to the surface profile” (So ¶ 303) where autonomous driving system 260 uses the road surface condition information in an AI model to generate navigation parameters. Additionally, So discloses “navigating the mobile machine according to the determined navigation parameter” (So ¶¶ 165, 303) where the driving control device controls the vehicle according to signals received from autonomous device 206 (So ¶ 165), which includes the signals that indicate unsafe driving parameters such as road surface condition information. (So ¶ 303).
Hadj-Attou and So are analogous art because they are from the “same field of endeavor,” namely that of determining road surface profile.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Hadj-Attou and So before him or her to modify the surface profile detection of Hadj-Attou to include the navigation based on the surface profile of So.
The motivation for doing so would have been to enhance vehicle safety. (So ¶ 22).
Regarding claim 13, the combination of Hadj-Attou and So discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Hadj-Attou and So discloses “wherein the navigation parameter includes an acceleration profile; determining the navigation parameter corresponding to the surface profile comprises: determining the acceleration profile according to the surface profile” (So ¶¶ 297, 360, and Fig. 13) where the safe braking performance model is chosen based on conditions such as the road surface (So ¶ 360) and the safe braking performance model includes an acceleration profile (So ¶ 297 and Fig. 13). Further, the combination of Hadj-Attou and So discloses “navigating the mobile machine according to the determined navigation parameter comprises: controlling the mobile machine to move according to the acceleration profile in the determined navigation parameter” (So ¶ 360) by creating a control command in response to a determination result based on the safe braking performance model.
Regarding claim 14, it merely recites a machine for performing the method of claim 1. The machine comprises computer hardware and software modules for performing the various functions. The combination of Hadj-Attou and So comprises computer hardware (Hadj-Attou 2, § 3.1. Data collection and So ¶ 17) and software modules for performing the same functions. Thus, claim 14 is rejected using the same rationale set forth in the above rejection for claim 1.
Claims 2-8 and 15-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Hadj-Attou in view of So, as applied to claims 1 and 14 above, and in further view of Lai et al., US Publication 2023/0367760 (hereinafter Lai).
Regarding claims 2 and 15, the combination of Hadj-Attou and So discloses the limitations contained in parent claims 1 and 14 for the reasons discussed above. In addition, the combination of Hadj-Attou and So discloses “wherein the surface profile classifier is implemented as a recurrent neural network.” (Hadj-Attou 6, § 3.3.2. LSTM and GRU). Additionally, the combination of Hadj-Attou and So discloses “before inputting the received acceleration data into the artificial neural network-based surface profile classifier, the method further comprises: preprocessing the received acceleration data.” (Hadj-Attou 4, § 3.2. Data preprocessing). Finally, the combination of Hadj-Attou and So discloses “training the surface profile classifier using the preprocessed acceleration data.” (Hadj-Attou 4, § 3.2.3. Segmentation).
The combination of Hadj-Attou and So does not appear to explicitly disclose “auto-tunning the trained surface profile classifier by sweeping old acceleration data of a plurality of candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size, wherein the old acceleration data is the preprocessed acceleration data previously received from the accelerometer that had been used to train the trained surface profile classifier.”
However, Lai discloses that it is well-known in neural networks, including LSTMs, to auto-tune a trained classifier by determining (i.e., sweeping) of a plurality of candidate window sizes of the classifier to choose a candidate window size for updating the determined window size, wherein the old data is the preprocessed data previously received from the device that had been used to train the trained classifier (Lai ¶ 108) by determining a new (i.e., updated) target window size based on the trained data.
Thus, a person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that when Lai was combined with Hadj-Attou and So, that the sweeping of Lai would be used with the surface profile classifier and acceleration data from the accelerometer of Hadj-Attou and So. Therefore, the combination of Hadj-Attou, So, and Lai at least teaches and/or suggests the claimed limitation “auto-tunning the trained surface profile classifier by sweeping old acceleration data of a plurality of candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size, wherein the old acceleration data is the preprocessed acceleration data previously received from the accelerometer that had been used to train the trained surface profile classifier,” rendering it obvious.
Hadj-Attou, So, and Lai are analogous art because they are from the “same field of endeavor,” namely that of neural networks.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Hadj-Attou, So, and Lai before him or her to modify the window size of Hadj-Attou and So to include the selection of a window size by sweeping of Lai.
The motivation for doing so would have been that such sweeping has the “advantages of the conventional method” of improving the accuracy of the neural network and eliminating the need for a domain expert. (Lai ¶ 64).
Regarding claims 3 and 16, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claims 2 and 15 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “wherein preprocessing the acceleration data comprises: cleaning the received acceleration data by selecting a data window between a starting part of the received acceleration data and an ending part of the received acceleration data” (Hadj-Attou 4, § 3.2.3. Segmentation) where the window is 1 second and there are 50 samples, meaning at least one sample window will be between a starting part and an ending part of the acceleration data.
Regarding claims 4 and 17, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claims 3 and 16 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “wherein preprocessing the acceleration data further comprises: normalizing all the received acceleration data to have values between 0 and 1.” (Hadj-Attou 4, § 3.2.4. Data normalization). Further, the combination of Hadj-Attou, So, and Lai discloses “adding a label to the acceleration data.” (Hadj-Attou 4, § 3.2.3. Segmentation).
Regarding claim 5, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “wherein after inputting the received acceleration data into the artificial neural network-based surface profile classifier, the method further comprises: obtaining a confidence of the surface profile classifier for the preprocessed acceleration data” (Lai ¶ 107) by calculating an anomaly score. Further, the combination of Hadj-Attou, So, and Lai discloses “receiving the surface profile of the surface from the surface profile classifier comprises: receiving the surface profile of the surface from the surface profile classifier in response to the confidence being not smaller than a predetermined threshold” (Lai ¶ 107) where the target data is labeled normal in response to the anomaly score A being equal to (i.e., not smaller than) a threshold value. Finally, the combination of Hadj-Attou, So, and Lai discloses “training the surface profile classifier using the preprocessed acceleration data comprises: training the surface profile classifier using the preprocessed acceleration data in response to the confidence being smaller than the predetermined threshold” (Lai ¶¶ 107-108) where when the anomaly score A is not greater than (i.e., smaller than) a threshold value, it is labeled normal, which is then used to train the classifier. This data being test for anomalies is the preprocessed data.
Regarding claims 6 and 18, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claims 2 and 15 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “wherein the received acceleration data is received after the old acceleration data while navigating the mobile machine on the surface” (So ¶ 341) where new data is used to update the learning model, requiring it to be received “after” the old data. Further, the combination of Hadj-Attou, So, and Lai discloses “training the surface profile classifier using the preprocessed acceleration data comprises: retraining the surface profile classifier using the old acceleration data” (So ¶ 197) by training the model again. Finally, the combination of Hadj-Attou, So, and Lai discloses “sweeping the old acceleration data of the candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size comprises: sweeping the old acceleration data of the candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size” (Lai ¶ 108) as discussed in the rejection to claims 2 and 15 above.
Regarding claims 7 and 19, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claims 2 and 15 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “sweeping the old acceleration data of the candidate window sizes of the trained surface profile classifier to choose the candidate window size for updating the determined window size comprises: sweeping the old acceleration data of each of the candidate window sizes of the trained surface profile classifier” (Lai ¶ 108) as discussed in the rejection to claims 2 and 15 above. Further, the combination of Hadj-Attou, So, and Lai discloses “obtaining an accuracy of the surface profile classifier at the candidate window size” Lai ¶ 87) by generating a loss value that represents how accurate the classifier was. Finally, the combination of Hadj-Attou, So, and Lai discloses “updating the determined window size as the candidate window size corresponding to the highest accuracy” (Lai ¶ 29) by using the loss values to optimize the model by minimizing the loss value.
Regarding claims 8 and 20, the combination of Hadj-Attou, So, and Lai discloses the limitations contained in parent claims 7 and 19 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Lai discloses “wherein sweeping the old acceleration data of each of the candidate window sizes of the trained surface profile classifier comprise: creating a helper function for sampling the preprocessed acceleration data, wherein the helper function has a window size variable” (Lai ¶ 107-108) by using the loss and reward calculator 230, which must necessarily have been created in order to be used, that includes the window size data as a variable as part of the context sampler. Further, the combination of Hadj-Attou, So, and Lai discloses “sweeping the old acceleration data using the helper function by using each of the candidate window sizes of the trained surface profile classifier as the window size variable of the helper function” (Lai ¶ 108) as discussed in the rejection to claims 2 and 15 above.
Claims 9-11 are rejected under 35 U.S.C. § 103 as being unpatentable over Hadj-Attou in view of So, as applied to claim 1 above, and in further view of Deciu et al., US Publication 2017/0316150 (hereinafter Deciu).
Regarding claim 9, the combination of Hadj-Attou and So discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Hadj-Attou and So does not appear to explicitly disclose “wherein the candidate window sizes of the trained surface profile classifier are from 2 to 32 data points.”
However, Deciu discloses that it is well-known in neural networks to include candidate window sizes “wherein the candidate window sizes of the trained … classifier are from 2 to 32 data points” (Deciu ¶ 248) where the widow sizes may include 1 to 25 data points, which is in the range of 2 to 32 data points. See MPEP § 2144.05.
A person of ordinary skill in the art prior to the effective filing date of the present application would have recognized that when Deciu was applied to Hadj-Attou and So, the window sizes of Deciu would be applied to the surface profile classifier of Hadj-Attou and So. Therefore, the combination of Hadj-Attou, So, and Deciu at least teaches and/or suggests the claimed limitation “wherein the candidate window sizes of the trained surface profile classifier are from 2 to 32 data points,” rendering it obvious.
Hadj-Attou, So, and Deciu are analogous art because they are from the “same field of endeavor,” namely that of neural networks.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Hadj-Attou, So, and Deciu before him or her to modify the window size of Hadj-Attou and So to include the window size of Deciu.
The motivation/rationale for doing so would have been that of simple substitution. See KSR Int’l Co v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(B). The combination of Hadj-Attou and So differs from the claimed invention by including a window size of 50 in place of the window size range of 2 to 32. Further, Deciu teaches that window size ranges of 1 to 25 were well known in the art. One of ordinary skill in the art could have predictably substituted window size of Deciu for the window size of Hadj-Attou and So because both merely define how much data to process at once and there is no indication that the window size of 50 was chosen for any particular reason.
Regarding claim 10, the combination of Hadj-Attou, So, and Deciu discloses the limitations contained in parent claim 9 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Deciu discloses “wherein the determined window size is 24 when the acceleration data is received from the accelerometer correspond to an X-axis, and the determined window size is 25 when the acceleration data is received from the accelerometer correspond to a Y-axis” (Deciu ¶ 248) where the window size can be set to 24 or 25 when the accelerometers correspond to any axis.
Regarding claim 11, the combination of Hadj-Attou, So, and Deciu discloses the limitations contained in parent claim 9 for the reasons discussed above. In addition, the combination of Hadj-Attou, So, and Deciu discloses “wherein the determined window size is 19 when the acceleration data is received from two accelerometers each corresponding to an X-axis and a Y-axis” (Deciu ¶ 248) where the window size can be set to 19 when the accelerometers correspond to any axis.
Claim 12 is rejected under 35 U.S.C. § 103 as being unpatentable over Hadj-Attou in view of So, as applied to claim 1 above, and in further view of Johnson et al., Stopping & Braking Distance | Definition, Formula & Examples, November 21, 2023, Study.com, https://study.com/learn/lesson/stopping-distance.html, Page 1-31 (hereinafter Johnson).
Regarding claim 12, the combination of Hadj-Attou and So discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, the combination of Hadj-Attou and So discloses “wherein the navigation parameter includes a braking distance; determining the navigation parameter corresponding to the surface profile comprises: calculating the braking distance corresponding to a variety of factors” (So ¶ 295). Further, the combination of Hadj-Attou and So discloses “navigating the mobile machine according to the determined navigation parameter comprises: detecting an obstacle while navigating the mobile machine on the surface” (So ¶ 144) by detecting an objection. Finally, the combination of Hadj-Attou and So discloses “controlling the mobile machine to brake according to the braking distance in the determined navigation parameter in response to having detected the obstacle” (So ¶¶ 300-301) where object detection is used as a factor affecting braking distance.
The combination of Hadj-Attou and So does not appear to explicitly disclose the exact formula for calculating the braking distance and, thus, does not appear to explicitly disclose “calculating the braking distance corresponding to the surface profile using an equation of: d = v2 / 2ug; where, d is the braking distance, v is the velocity of the mobile machine, u is a friction coefficient obtained based on the surface profile, and g is the gravitational acceleration constant.”
However, Johnson discloses that it is well known to perform the step of “calculating the braking distance corresponding to the surface profile using an equation of: d = v2 / 2ug; where, d is the braking distance, v is the velocity of the mobile machine, u is a friction coefficient obtained based on the surface profile, and g is the gravitational acceleration constant.” (Johnson 1).
Hadj-Attou, So, and Johnson are analogous art because they are from the “same field of endeavor,” namely that of motor vehicles.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Hadj-Attou, So, and Johnson before him or her to modify the braking distance calculation formula of Hadj-Attou and So to include the specific formula of Johnson.
The motivation/rationale for doing so would have been that of simple substitution. See KSR Int’l Co v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(B). The combination of Hadj-Attou and So differs from the claimed invention by including a generic formula of calculating braking distance in place of the formula d = v2 / 2ug. Further, Johnson teaches that calculating braking distance using the formula d = v2 / 2ug was well known in the art. One of ordinary skill in the art could have predictably substituted the specific formula of Johnson for the generic calculation of Hadj-Attou and So because both merely calculate a braking distance and the method used to calculate that distance would not affect the remainder of the system of Hadj-Attou and So.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Choi et al., US Publication 2019/0080232, System and method for sweeping the input of a neural network.
Cella et al., US Publication 2022/0187847, System and method for determining a ground profile.
Pavuluri et al., US Publication 2022/0245409, System and method for sweeping the input of a neural network.
Peri et al., US Publication 2023/0290153, System and method for sweeping the input of a neural network.
Affalo et al., US Publication 2024/0046071, System and method for sweeping the input of a neural network.
Qiu et al., US Publication 2024/0111956, System and method for determining a window size to be used in a neural network.
Ebrahimi Afrouzi et al., US Publication 2024/0310851, System and method for determining a ground surface profile.
Ferrés et al., US Patent 11,669,939, System and method for sweeping the input of a neural network.
Campbell et al., US Patent 11,901,915, System and method for using a quantum computer to execute a neural network.
Wu et al., Road Surface Recognition Based on DeepSense Neural Network using Accelerometer Data, December 3, 2020, 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, Pages 305-312, doi: 10.1109/IV47402.2020.9304737, System and method for determining a ground profile.
Lee, et al., Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer, January 14, 2021, Sensors 2021, 21, 561, https://doi.org/10.3390/s21020561, Pages 1-17, System and method for determining a ground profile.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662