DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 11-14, 16-30 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Henderson (U.S. Pub. No. 2023/0047444).
Regarding claim 11, 19, 20, Henderson discloses method for automatically adapting a traction control of a vehicle, comprising the following steps:
receiving current state variables (305) of the vehicle, which each indicates a current state of the vehicle;
determining a control action (fig. 3) using a traction controller (260) based on the received current state variables (305), wherein the control action includes increasing, or maintaining, or decreasing a control variable, wherein the control variable includes a torque of a motor of the vehicle (¶89) and/or a pressure of a brake cylinder (torque option addressed) of the vehicle;
determining a control gradient (¶84 “required global forces” and dFx, dFy) of the control variable using a value matrix, wherein the value matrix includes a plurality of parameters, which are each assigned to current value matrix state variables of the vehicle, wherein the control gradient is selected from the plurality of parameters as a function of the current value matrix state variables, wherein the current state variables include the current value matrix state variables;
Note: this is construed as requiring the acquisition and ordering of data. Nothing is being required of these data points.
carrying out the traction control of the vehicle, wherein the control variable is adapted by the determined control gradient according to the determined control action (¶187 ¶282-286 values outside the operational range triggers an intervention which uses current values to calculate an appropriate intervention. where this system adjusts for each situation, 305 and 310, and provides control, 320, for each of these situations this is construed as addressing the rules);
determining a change in the current state variables as a result of carrying out the traction control over a considered time period (continuous monitoring); and
adapting at least one parameter of the value matrix as a function of the determined change in the current state variables by triggering a first learning rule having a first learning value, wherein the adapting the at least one parameter of the value matrix by triggering the first learning rule comprises updating the at least one parameter of the value matrix based on the first learning value of the first learning rule (¶300 discloses that this data is observed and “filtered” over “time” to determine “capability range based on the filtering”. The “filtering” can be construed as the “learning rule” and what is left over is the “learning value”).
Note: “learning” is a complex limitation that requires more limitations than have been placed into this claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding claim 12 which depends from claim 11, Henderson discloses wherein the current value matrix state variables of the vehicle include a slip (¶185) and a wheel acceleration (¶290) of the vehicle.
Regarding claim 14 which depends from claim 13, Henderson discloses wherein the current value matrix state variables of the vehicle include a slip and a wheel acceleration of the vehicle, and wherein the learning value is adapted as a function of the wheel acceleration of the vehicle (¶185 and ¶290 discloses these variables).
Regarding claim 16 which depends from claim 11, Henderson discloses further comprising: adapting the at least one parameter of the value matrix as a function of the determined change in the current state variables by triggering at least two temporally successive learning rules; arbitrating the at least two temporally successive learning rules when the at least two temporally successive learning rules are triggered below a previously specified time interval between them (the system is always learning new information from the sensors which include more than two variables with the current data taking precedence over past data).
Regarding claim 17 which depends from claim 11, Henderson discloses further comprising: learning a response time between an evaluation of the change in the current state variables and the traction control (The sensors would feed the information to the controller when traction is regained).
Regarding claim 18 which depends from claim 11, Henderson discloses further comprising: ignoring triggered learning rules as a function of the current state variables (This is simply a way of stating that not all triggers are triggered and so this reference also accomplishes this task).
Regarding claim 21 which depends from claim 16, Henderson discloses wherein the at least two temporally successive learning rules include at least a first learning rule and a second learning rule, the second learning rule being triggered subsequent to the first learning rule, and wherein the arbitrating comprises ignoring the first learning rule (so effectively there is only one learning rule since the first learning rule is ignored making it simply a signal inside of a processor and so is considered extrasolution activity. Requiring only one learning rule to address this limitation).
Regarding claim 22 which depends from claim 16, Henderson discloses wherein the at least two temporally successive learning rules include at least a first learning rule and a second learning rule, the second learning rule being triggered subsequent to the first learning rule, and wherein the arbitrating comprises ignoring the first learning rule and the second learning rule (so effectively there is no learning rule since the both learning rules are ignored making it simply a signal inside of a processor and so is considered extrasolution activity. Requiring no learning rule to address this limitation).
Regarding claim 23 which depends from claim 16, Henderson discloses wherein the at least two temporally successive learning rules include at least a first learning rule and a second learning rule, the second learning rule being triggered subsequent to the first learning rule, the arbitrating including:
adapting a second set of parameters of the at least one parameter of the value matrix as a function of the determined change in the current state variables based on the second learning rule; and
adapting a first set of parameters of the at least one parameter of the value matrix as a function of the determined change in the current state variables based on the first learning rule (¶167 discloses how the control at the specific wheel, MSD control, is different from the global control, VMM, which is dealing with different values),
wherein each parameter of the first set of parameters is different than each parameter of the second set of parameters.
Regarding claim 24-26 which depends from claim 11, 19 and 20 respectively, Henderson discloses wherein the adjustment phase of the slip is defined by a first threshold value, the adjustment learning rules being applied during the adjustment phase of the slip when the slip is less than or equal to the first threshold value (310 disclosed in ¶83 is monitoring and adjusting values), and wherein the control learning rules are applied during control after the adjustment phase of the slip when the slip is greater than the first threshold value (320 the various thresholds are discussed in ¶167).
Regarding claim 27 which depends from claim 11, Henderson discloses wherein the triggering the first learning rule occurs in response to the determined change in the current state variables exceeding a threshold associated with the first learning rule (This citation is determining wheel slip when the variables suggest slip is occurring the system applies the “rules” for further processing).
Regarding claim 28-30 which depends from claim 11, 19 and 20 respectively, Henderson discloses wherein the first learning rule is selected from a plurality of learning rules, the learning rules including adjustment learning rules and control learning rules, wherein the adjustment learning rules are applied during an adjustment phase of a slip of the vehicle, and the control learning rules are applied during control after the adjustment phase of the slip (adjusting the variables also controls the variables there seems to be some arbitrary delineation that is not being claimed. As a result the reference will be construed as addressing the limitations by adjusting the variables gathering new variable information and then controlling new adjustments to the variables throughout the control process).
Response to Arguments
Applicant's arguments filed 01/14/26 have been fully considered but they are not persuasive.
Applicant argues on pages 8 and 9 that updating values based on the first learning rule is not being addressed by the reference. Without further limitations defining what is happening to these variables that effect the system, as opposed to being stored and never used, the limitations are being construed as functions inside of a processor and values are updated as they trigger other functions to take place. ¶300 discloses that the data on wheel slip is observed and “filtered” over “time” to determine “capability range based on the filtering”. The “filtering” can be construed as the “learning rule” and what is left over is the “learning value”.
Applicant argues on page 9 that “learning value” is being mischaracterized based on the amendments to the claims. These amendments have been addressed above in reference to paragraph 300.
Applicant argues on page 9 that the specification defines “learning rule” which is “a rule that defines how, on the basis of the current state variables of the vehicle, one or more parameters of the value matrix are to be changed”. The reference is using “current state variables” from the sensors of the vehicle which define more than one “parameter” (there are many sensors) to change the value of the capability range that will allow intervention control.
Conclusion
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GONZALO LAGUARDA
Primary Examiner
Art Unit 3747 email: gonzalo.laguarda@uspto.gov
/GONZALO LAGUARDA/Primary Examiner, Art Unit 3747