DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to the communication filed on 06/30/2023. The claims 1-20 are pending, of which the claim(s) 1, 11, & 20 is/are in independent form.
Claim Objections
Claims 4- 5 & 14- 15 objected to because of the following informalities:
Regarding claims 4- 5, the claim element “wherein determining the drift value” should be changed to “wherein the determining the drift value” to establish clear relationship with the limitation of “determining a drift value for the second time interval” recited in claim 1.
Regarding claims 14- 15, the claim element “wherein determining the drift value” should be changed to “wherein the determining the drift value” to establish proper relationship with the determine drift limitation of the claim 11 in line 10.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exception (“abstract idea”) without significantly more.
As to claim 1,
The claim is reproduced below for convenience.
1. A method comprising:
[a] receiving via a communication interface a first plurality of predictor values occurring during or before a first time interval for a mechanical process;
[b] determining an estimated outcome value for a second time interval by applying a prediction model via a processor to the first plurality of predictor values;
[c] receiving via a communication interface a designated outcome value detected by a sensor and occurring during the second time interval and a second plurality of predictor values for the mechanical process occurring during or before the second time interval, the first time interval preceding the second time interval;
[d] determining an error value via the processor based on the estimated outcome value and the designated outcome value;
[e] determining a drift value for the second time interval by fitting a function to the second plurality of predictor values;
[f] updating the prediction model when it is determined that the drift value exceeds a designated drift threshold or that the error value exceeds a designated error threshold;
[g] storing the updated prediction model on a storage device;
[h] determining an estimated outcome value for the mechanical process based on the updated prediction model; and
[i] transmitting an instruction to adjust a control parameter governing the mechanical process based on the estimated outcome value.
1. Step 1: Statutory Category? Yes. The claim recites a series of steps and, therefore, is a process.
2. Step 2A, Prong 1: Judicial Exception Recited? Yes. The claim recites various determining steps and a updating step as set forth in the reproduced claim 1 shown with italic emphasis. These limitations are considered mental processes based abstract idea exceptions because they can be practically performed in human’s mind via observation, evaluation, judgment, opinion at most with the aid of pen and paper.
For example, the limitation [b] requires determining an estimated output value for a second time (future time or after current time) by applying a prediction model to the already received first predictor values. Here, but for the recitation of “via a processor” nothing in the claim element precludes the step from practically being performed in the human mind. The mere nominal recitation of a generic computer/processor (see spec, page 15, lines 1- 10) does not take the claim limitation out of the mental processes grouping. PHOSITA knows that a prediction model (spec, 1page 6, lines 27- 31) can be a mathematical equation and human can input the numerical values of the received predictor values of limitations [a] to calculate an estimated outcome value that represent predicted value for the mechanical process for future/second time. Similarly, as to limitations [d] and [f], they require mere calculating of difference between already received designated value and estimated outcome value and comparing such difference/error value to determine need to modify/update the model (mathematical equation). The updating of the model covers every possible types of the updates including mere change in a parameter of a differential/mathematical equation. These steps clearly can be performed without the aid of a processor. The limitation [e] requires determining a drift value by fitting a function/curve. This limitation, under BRI, covers estimating a line or a curve by plotting already received second predictor values. When the collected second predictor values are plotted, human can observe and evaluate a drift/change in trend by using his/her judgement. Finally, the limitation [h] requires using of the updated prediction model (e.g., an updated mathematical equation being used with new predictor values to generate output) to determine new estimated outcome values (that can be used to determine transmitting an instruction to adjust a control parameter). Accordingly, all of the limitations shown above with italic emphasis, as drafted, are process that, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer component, namely “via a processor”. If claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
3. Step 2A, Prong 2: Integrated into a Practical Application? No. This judicial exception is not integrated into a practical application. In particular, the claim recites various additional elements shown above with bold emphasis (i.e., limitations [a], [c], [g], [j] and “via a processor”). The processor in both steps [b] and [d] is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components to automate the exceptions. The limitations [a] and [c] encompass “mere data gathering in conjunction with a law of nature or abstract idea” and are recited at high level of generality (for the first and second predictor values) hence are akin to adding insignificant extra-solution (pre-solution) activities to the judicial exception - see MPEP 2106.05(g)/2105.05(a). The limitation [g] recites 2storing of the updated model and the limitation [j] recites mere transmitting an instruction for an intended use of adjusting a control parameter of a mechanical process based on the estimated outcome value. Therefore, they too are akin to adding an insignificant (post-solution) extra-solution activity to the judicial exception. Even considering, additional elements in an ordered combination, they still do not go beyond mere using generic computer components (processor, a storage device, and a communication interface) as a tool to perform the mental processes and adding of insignificant extra-solution activities to the judicial exceptions. Such limitations do not impose any meaningful limits on practicing the above abstract idea other than merely collecting data and using generic computer elements to automate the mental steps. The claim is directed to an abstract idea.
4. Step 2B: Inventive Concept? No. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements go nothing beyond using computer elements (processor, network interface, a storage device) as a tool to perform an abstract idea - see MPEP 2106.05(f) and adding of pre-solution and post-solution insignificant extra-solution activities to the judicial exception - see MPEP 2106.05(g). Furthermore, these additional elements are recited at a very high level of generality and do not provide any improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a). Please note that the improvement of the mental processes limitations is not improvement of the technology. Additionally, the insignificant extra-solution activities of limitations [a], [c], [g], and [j] are well-understood, routine, conventional activities and examiner takes an Official notice to that effect by relying on the cited prior arts (e.g., see, figs. 9-10, US 20220227397 A1 to Jiang as an example evidence) -- Berkheimer memo. The steps of receiving data (as in limitations [a], [c]), storing data (limitation [g]), and transmitting data/command steps (limitation [j]) are widely prevalent or in common use in the networking industry as explained in MPEP § 2106.05(d)(I). Accordingly, the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception. For these reasons, there is no inventive concept in the claim, and thus it is ineligible under 101.
Regarding claim 11, this claim is to a system but recites similar functional and structural limitations (using a communication interface, a processor, a storage device) as in method claim 1. Therefore, the claim 11 also recites similar mental processes based abstract idea and additional elements set forth above in claim 1. Accordingly, for the similar reasons set forth above, the claim 11 also fails to provide a practical application in step 2A Prong 2 and an inventive step in Step 2B. Thus, the claim is not patent eligible.
Regarding claim 20, this claim is to “a non-transitory computer readable media” but recites similar structural and functional limitations as in method claim 1. Therefore, the claim 20 also recites similar mental processes based abstract idea and additional elements set forth above in claim 1. Accordingly, the claim 20 also fails to provide a practical application in step 2A Prong 2 and an inventive step in Step 2B. Thus, the claim is not patent eligible.
Regarding claims 2- 10 & 12- 19, these claims depend on claims 1 & 11 respectively. Therefore, these claims recite same abstract idea and additional elements already outlined above in claims 1 & 11 respectively. The claims 2- 10 & 12- 19 recite other new limitations. However, these new limitations only further define the abstract idea set forth above without reciting any new additional elements. That is, the claims 2- 10 & 12- 19 do not add any new additional elements (other than those already discussed above in claims 1 & 11).
For example, the claims 2- 3 define the used function for fitting of the second predictor values in step [e] of the claim 1 is polynomial function and third-order polynomial function respectively. However, human mind can perform these steps using pen and paper when the number of collected predictor values are minimal with simple mathematical operation. Performing of mathematical solution to 1st to 3rd polynomial are simple math that can be done with pen and paper and humans have been them for many years without the need of using a computer. The claims 4 -5 indicate calculation of derivatives value which represent simple calculus and hence can be performed in human’s mind. The claims 7- 8 describe the type of the variables used for predictor values and hence still can be performed in human’s mind. The claims 9- 10 also require using of the simple mathematical operation hence still can be performed in human’s mind. The claims 12- 19 are in similar scope as that of the claims 2- 10.
Accordingly, the claims 2- 10 & 12- 19 also do not integrate the judicial exception into a practical application and provide an inventive concept. These claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6 – 11, & 16- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al., (US 20220227397 A1) in view of Patankar et al., (US 20130173215 A1).
Regarding claim 1, Jian teaches a method [“control-in-the-loop simulator that uses a dynamic model to model… accuracy of the simulated or predicted output of the dynamic model with respect to the actual behavior…dynamic model may then be fine-tuned”] comprising: ([018, 093], Fig. 10);
receiving [“At operation 1001, process 1000 receives states of an ADV to be simulated and control commands to be applied to the ADV at a plurality of time points” as part of the driving statistics 123/313 of fig. 6] via a communication interface a first plurality of predictor values [control commands provided to the ADV and states reported by the sensors as part of the “captured by sensors of the vehicles”] occurring during or before a first time interval [before the current time] for a mechanical process ([033, 094], fig. 9);
determining an estimated outcome value [“operation 1002, the process 1000 generates a plurality of predicted positions of a simulated trajectory based on the received states and the control commands from the dynamic model”] for a second time interval [time period after current time/future time] by applying a prediction model [the “dynamic model” of “training the dynamic model 601 using the training data” or “example dynamic model 500” see fig. 5] via a processor [processor of the server 104/103, see para. 054] to the first plurality of predictor values ([051, 095]);
receiving via a communication interface a designated outcome value [“operation 1003, the process 1000 receives a plurality of actual positions of a ground truth trajectory generated based on the same control commands applied to the ADV.”] detected by a sensor and occurring during the second time interval and a second plurality of predictor values [available “driving statistics” after running for “current driving cycle” (driving commands plus responses/states of vehicles for the commands both) that includes “driving statistics” for the current and previous driving cycles both] for the mechanical process occurring during or before the second time interval, the first time interval preceding [current/first driving cycle comes before next/second driving cycle] the second time interval ([033, 064, 096]);
determining an error value [“the degree of error between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle”, e.g., values outputted from “performance evaluation module 901” of fig. 9 and step 1004 of fig. 10] via the processor based on [“compare each actual future state 606 and each expected future state 608”] the estimated outcome value and the designated outcome value ([019, 063-065, 085, 097]);
updating [“retrain the dynamic model” 601 when the error is abnormal] the prediction model when it is determined that the drift value exceeds a designated drift threshold or that the error value exceeds a designated error threshold; storing [the “use the fine-tuned” model for new prediction indicates storing of the updated prediction model at the servers] the updated prediction model on a storage device ([067-068, 092]);
determining an estimated outcome value [new estimated values (e.g., positions predicted) by the updated dynamic model 601 for the “next driving cycle”] for the mechanical process based on the updated prediction model; and transmitting [“the control commands for the second driving cycle” being transmitted to the vehicle after the model is fine-tuned after updating as part of using of the fine-tuned model to “more accurately predict the behavior of the ADV”] an instruction to adjust a control parameter governing the mechanical process based on the estimated outcome value ([018, 053, 058, 080]).
Jiang teaches receiving pluralities of the sensor data at the server’s communication interface as part of the driving statistics. However, Jiang fails to teach looking for drift on the sensor values of the received driving statistics. That is, Jiang fails to teach determining a drift value for the second time interval by fitting a function to the second plurality of predictor values as claimed.
Patankar relates to detecting trend-changes in collected data and the function fitting in electronic and mechanical systems to monitor various health characteristics of these monitored system using one or more data sources 102 that include numerous types of sensors ([002- 003, 017]). Specifically, Patankar teaches method comprising: receiving second plurality of predictor values [“the initial number of data points “ and “new set of data points” from “the initial number of data points is retrieved” and “new set of data points is retrieved”] for the mechanical process occurring during or before the second time interval and determining [“After the new set of data points is retrieved, the processor 104 detects whether a trend change has occurred 208”] a drift value for the second time interval by fitting a function [“trend change is detected by a metric that measures if and how the latest set of data points are departing from the initial curve fit that was made with the initial set of data points”] to the second plurality of predictor values ([006, 021-022]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Patankar and Jiang because they both related to monitoring collected timeseries sensor data of a monitored mechanical system (2) modify the system/method steps of Jiang to include an step of determining a drift value for the second time interval by fitting a function to the second plurality of predictor values (driving statistics) to identify data deviation. Furthermore, doing so would help to determine when the received driving metrics (of Jiang) started to change in trend thereby improving the probability of accurate fault detection (Patankar [004]).
Regarding claim 6, Jiang in view of Patankar further teaches the method recited in claim 1, wherein the second plurality of predictor values includes the designated outcome value (using of the ground truth as part of the vehicle statistics for the next driving cycle, Jiang [053, 080]).
Regarding claim 7, Jiang in view of Patankar further teaches the method recited in claim 1, wherein the first plurality of predictor values includes a first vector of variables [available “driving statistics 603” (that includes “various control commands” and “responses or states of the vehicles” until the “current driving cycle” provided to the server] observed during the first time interval (Jiang [0064, 077]).
Regarding claim 8, Jiang in view of Patankar further teaches the method recited in claim 7, wherein the second plurality of predictor values includes a second vector of variables [commands and vehicle states received at the server after the current driving cycle is over] observed during the second time interval (Jiang [053, 080]).
Regarding claim 9, Jiang in view of Patankar further teaches the method recited in claim 8, wherein the estimated outcome value [“predict the output for the next driving cycle” by using “output data 504 for the second driving cycle maybe fed back to the dynamic model 500”] for the second time interval is determined by applying the prediction model to the first vector of variables and the second vector of variables (Jian [053, 080]).
Regarding claim 10, Jiang in view of Patankar further teaches the method of the claim 9.
However, Jian in view of Patankar does not expressly teach wherein the drift value is determined by fitting the function to both the first vector of variables and the second vector of variables although both the first vector and second vector of the variables are calculated in Jian’s system and trend/drift is determined in Patankar.
Therefore, in combined teachings of Jian and Patankar, the driving statistics from current and previous cycles can be averaged/combined to avoid noisy/transient effect and the average/combined datasets for one parameter of the driving statics can be curve fitted to see the change in trend so that where the trend change is determined without impact of the noise to improve diagnostic capabilities. See, Jian [063], fig. 6 & Patankar [024] & MPEP 2144.01. In summary, Jiang in view of Patankar implicitly teaches this claimed feature and renders invention of this claim obvious to PHOSITA.
Regarding claims 11 & 16- 19, Jiang in view of Patankar teaches/suggests inventions of these system claims for the similar reasons set forth above in the method claims 1 & 6- 10.
Regarding claim 20, Jiang in view of Patankar teaches invention of this computer readable storage medium claim for the similar reasons set forth above in method claim 1.
Claim(s) 2- 3 & 12- 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Patankar, and further in view of Saito et al., (US 20070073511 A1).
The combination of Jiang, Patankar, and Saito is referred as JPS hereinafter.
Regarding claim 2, Jiang in view of Patankar teaches the method recited in claim 1 including receiving second plurality of predictor values for the mechanical process from one or more sensors and fitting a function to the second plurality of predictor values (Patankar [017]).
However, Jiang in view of Patankar fails to teach wherein the function is a polynomial function. That is, Jiang in view of Patankar fails to teach what type of the function can be utilized to perform fitting to the received second plurality of predictor values.
Saito in the field of monitoring of the sensor reading teaches a system/method comprising second plurality of predictor values for the mechanical process and fitting a function to the second plurality of predictor values, wherein the function is a polynomial function [“Preferably, a third order polynomial curve fit is utilized for high accuracy”] ([013, 038]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Saito and Jiang in view of Patankar because they both related to monitoring sensor data and (2) have the function used to fit the second plurality of predictor values to be of a polynomial function type in order to achieve high degree of accuracy (Saito [0013]). Saito teaches an exemplary type of the function that is known to have used to perform function fitting to the measured sensor data to identify the pattern on the received second plurality of predictor values in Jiang in view of Patankar.
Regarding claim 3, JPS further teaches the method recited in claim 2, wherein the polynomial function is a third-order polynomial function (Saito [0013]).
Regarding claims 12- 13, JPS teaches inventions of these claims for the similar reasons set forth above in claims 2- 3 respectively.
Claim(s) 4- 5 & 14- 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Patankar, and further in view of Dehaan et al. (US 20110078302 A1). The combination of Jiang, Patankar, and Dehaan is referred as JPD hereinafter.
Regarding claims 4- 5, Jiang in view of Patankar teaches the method recited in claim 1 as set forth above. However, Jiang in view of Patankar fails to teach determining the drift value for the second time interval includes
(1) determining one or more derivatives of the function as in claim 4 and
(2) wherein determining the drift value for the second time interval includes determining a first derivative, a second derivative, and a third derivative of the function as in claim 5.
Dehaan relates to a system/method for monitoring streams of network operation data for identifying various network conditions including trends, or patterns of behavior in the network (Abstract, [011]). Specifically, Dehaan teaches A method comprising:
receiving a second plurality of predictor values [“network operation data 118 can be captured and/or accessed via monitoring tool 120”] and determining a drift value for the second time interval to the second plurality of predictor values by fitting a function [“generate one or more trends lines”] to the second plurality of predictor values, (1) wherein the determining the drift value for the second time interval includes determining one or more derivatives [“generate or calculate one or more higher order derivatives of one or more trend lines derived from network operation data 118”] of the function and (2) wherein determining the drift value for the second time interval includes determining [“the second derivative (acceleration) or third derivative (jerk) of time series data for Web site hits”] a first derivative, a second derivative, and a third derivative of the function ([011, 024-025]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Dehaan and Jiang in view of Patankar because they both related to monitoring change in monitored sensor data and (2) modify the method/system of Jiang in view of Patankar to include missing limitations (including determining derivates (first to third) to the function to determine drift value for the second time intervals). Doing so would allow to determine one or more potential abnormal events in the received monitored mechanical system of Jiang in view of Patankar before such abnormal events actually occurring (Dehaan [025]).
Regarding claims 14- 15, JPD teaches inventions of these system claims for the similar reasons set forth above in method claims 4- 5 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Karale (US 20190145233 A1) teaches updating a model after determining a difference between the actual and estimated response exceeds an error tolerance threshold (Abstract).
2) Ananthanarayanan 20220414534 A1) teaches determining a drift value for the second time interval to the second plurality of predictor values and updating the prediction model when it is determined that the drift value exceeds a designated drift threshold ([025, 087]).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas Lee can be reached on 571-272-3667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 “any suitable prediction model may be used. Examples of suitable prediction models may include, but are not limited to: autoregression models, moving average models, exponential smoothing models, other types of models, or some combination thereof…”
2 See 2106.05(d) (II) (iv) states --“Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” is a courts recognized well‐understood, routine, and conventional functions.