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 .
Applicant is requested to amend the term “and/or” to either “and” or “or.”
Per the 2019 (PEG) guidance, claim(s) 1-20 were reviewed for abstract idea. Claims 1-20 not “fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)”. Claim(s) 1-20 fail to satisfy the subject matter eligibility requirement at step 2a prong 2, “practical application,” improvement to technology. Claim(s) 1-20 fail to satisfy the subject matter eligibility requirement at step 2B, “additional element(s) amount to more significantly more than the judicial exception.” Claim 20 is a system claim and claiming generic processor, sensor, and well-known memory in the prior art. Claims 1 and 20 do not pass the improvement to technology of accident monitoring system, step 2a prong 2. A human mental step can define a first characteristic of the sensor data reading based on a time period, the time period comprising at least a portion of a time window extending between the first time stamp and the fourth time stamp. A human mental step can predict the accident probability based on the characteristics of the sensor data previously defined by the human mental step. None of the dependent claims pass step 2B, “significantly more than an abstract idea” or step 2a prong 2 “improvement to technology of accident monitoring system.” See detailed 35 USC 101 rejections.
How would the applicant be able to overcome the 101 rejection? Examiner will give favorable consideration, if the independent claims are amended to claim the limitation of claim 19 and use the claimed “additional analysis” of the machine learning and evaluation process to detect and predict an accident probability. Examiner would consider making a case that the claims are directed to a practical application, and the improvement to technology.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-19 are directed to a process and claim 20 is directed to a machine. The claim(s) 1-20 not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed steps could be met by: Claim 1 have following additional elements “mobile device,” “sensor,” “memory,” claim 10 have following additional element: “photo sensor” and/or “proximity sensor.” Claim 20 have the following additional elements: “processor.”
Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes, because claims 1-19 are directed to a process and claim 20 is directed to a machine.
Can Analysis be streamlined? No, because when viewed claims 1-20, as a whole, the eligibility of the claim is not self-evident.
Step 2A Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). Yes, because the claims 1-20 recite a judicial exception:
The claims 1-20 to fall into the “Mental Process’” category of abstract ideas defined by the courts and 2019 PEG Guidance. Specifically, the claims to fall into the following subcategories:
Concepts Relating To Organizing Or Analyzing Information In A Way That Can Be Performed Mentally Or Is Analogous To Human Mental Work: For Example, consider claims 1 and 20.
“the mobile device being carried along with the vehicle,” i.e. human can imagine carrying the mobile device on the vehicle;
continuously acquiring, … , sensor data … and temporarily storing the sensor data …” i.e. ” (collecting/monitoring data) a data gathering activity
setting a first time stamp … in response to the sensor data passing a first threshold value defined for the at least one sensor, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update;
setting a second time stamp … in response to the sensor data passing a second threshold value defined for the sensor thereafter, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update;
setting a third time stamp in response to the sensor data passing the second threshold value again thereafter, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update;
setting a fourth time stamp in response to the sensor data passing the first threshold value again thereafter, wherein the second threshold value is above or below the first threshold value, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update;
if at least the first time stamp and the second time stamp and the fourth time stamp are present, defining a first characteristic of the sensor data based on a time period, the time period comprising at least a portion of a time window extending between the first time stamp and the fourth time stamp, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update, i.e. (portion/threshold logic implicates mathematical relationships), Mathematical concepts (mathematical relationships/thresholds);
feeding the characteristic to a machine learning and evaluation process for its evaluation of the characteristic, i.e. (portion/threshold logic implicates mathematical relationships), Mathematical concepts (mathematical relationships/thresholds);
detecting and/or predicting an accident probability based on the characteristic and at least one predefined characteristic, i.e. predicting the occurrence of the incident, i.e. (evaluative decision based on a condition) a human mental process, i.e. (setting a rule/definition) a human mental process (concepts performed in the human mind e.g., observation, evaluation, judgment) per Oct. 2019 Update, i.e. (portion/threshold logic implicates mathematical relationships), Mathematical concepts (mathematical relationships/thresholds); and
outputting… a result based on the accident probability, a human displaying output with any possible means;
In this case, examples of gathering information/data and mathematical concepts can be performed by a human with a pen and paper; examples of mental process can be performed by a human mental process See MPEP 2106.05(g) and Vanda Memo.
Analysis of dependent claims 2-19: dependents seem to narrow the information contained in the abstract idea and/or within human implementation; however, collecting, processing and characterizing, sensor data is general data gathering activity, and does not involve actually carrying out the process in a meaningful way. Therefore, Claims 2-19 are nothing more than, examples of gathering information/data can be performed by a human with a pen and paper; examples of mental process can be performed by a human mental process.
Step 2A Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the judicial exception into a practical application? No, because the claims do not recite any additional elements recited in the claim beyond the judicial exception, and those additional elements do not integrate the judicial exception into a practical application because:
It is Examiner’s position that claims 1-20 comprise following additional elements: “mobile device,” “sensor,” “memory,” “photo sensor” and/or “proximity sensor,” and “processor.” The additional elements do not integrate into a practical application of the exception. Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Furthermore, Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
“outputting, by the mobile device, a result” examples of insignificant extra solution activities have been ruled ineligible subject matter by the superior courts, See MPEP 2106.05(g) and Vanda Memo.
Furthermore, the additional elements “mobile device,” “sensor,” “memory,” “photo sensor” and/or “proximity sensor,” and “processor” perform no meaningful improvement or meaningful limitation: including (i) improvement to computer or (ii) improvement a non-computer technology in the field of accident detection. The additional elements “mobile device,” “sensor,” “memory,” “photo sensor” and/or “proximity sensor,” and “processor” do not “use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” The additional elements only add insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Analysis of dependent claims 2-19: dependents seem to narrow the information contained in the abstract idea and/or within human implementation; however, “outputting, by the mobile device, a result” examples of insignificant extra solution activities have been ruled ineligible subject matter by the superior courts, See MPEP 2106.05(g) and Vanda Memo.
Step 2B: Does the claim recite additional elements that amount to “significantly more” than the judicial exception? No, because the claims “as a whole” do not recite additional elements that amount to “significantly more” than the judicial exception because:
It is Examiner’s position that claims 1-20 comprise following additional elements: “mobile device,” “sensor,” “memory,” “photo sensor” and/or “proximity sensor,” and “processor.” However, these elements are identified as generic components and “as a whole” do not amount to “significantly more” than an abstract idea.
At best, the claimed subject matter requires the use of a generic “mobile device,” “sensor,” “memory,” “photo sensor” and/or “proximity sensor,” and “processor”, which are shown in the prior art cited, below. Examiner has cited sections of see cited Rosenbaum, that teach these elements; therefore, these elements alone and in combination do not qualify as something “significantly more” than an abstract idea. The claimed limitations are (i) routine and conventional in the field of accident detection, and (ii)
Limitations that are indicative of an inventive concept (aka “significantly more”):
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a): None.
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b): None.
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c): None.
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo: None.
Adding a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d): None.
Claim Rejections - 35 USC § 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.
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.
Claims 1-20 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.
Claim 1 recites, “feeding the characteristic to a machine learning and evaluation process for its evaluation of the characteristic…” There is lack of antecedent basis for the claimed, “the characteristic.” It is unclear whether the limitation is referring to “first characteristic” recited in the paragraph above. Furthermore, the term “its” is ambiguous and unnecessary. Claim 20 is rejected for the same reason. Dependent claims 2-19 are rejected by the virtue of dependency.
Claim 1 recites, “detecting and/or predicting an accident probability based on the characteristic and at least one predefined characteristic…” There is lack of antecedent basis for the claimed, “the characteristic.” Examiner has no educated guess as to what is the claimed “predefined characteristic,” although it cannot be the previously introduced “first characteristic.” Applicant’s cooperation is requested to make the limitation clear. Applicant is further requested to amend the term “and/or” to either “and” or “or.” Claim 20 is rejected for the same reason. Dependent claims 2-19 are rejected by the virtue of dependency.
Claims 2, 3, 5, 6, 11, 15, 16, recites, “the characteristic” and It is unclear whether the limitation is referring to “first characteristic” recited in the claim 1 or “predefined characteristic” recited in the claim 1. Please maintain the proper antecedent basis throughout the claims.
Prior Art
Rosenbaum, Walter Steven (US 2017/0309092 A1) teaches, “ determining driving characteristics of a vehicle, wherein during operation of the vehicle an acceleration of the vehicle is detected, preferably continuously detected or at random times, by an acceleration sensor in the vehicle and may be evaluated by an analyzing system in the vehicle. A driving parameter occurring during an acceleration event in which the vehicle acceleration is above a predetermined threshold is used to determine a driving characteristics value of the vehicle.” See ¶ 0020.
Rosenbaum teaches, “detect the acceleration of the vehicle, and to evaluate a driving parameter, like vehicle speed or an activity of a mobile communication device, during the acceleration event… from the moment on when the acceleration exceeds a positive or negative acceleration threshold and until the acceleration A falls below the threshold A.sub.T the driving parameter is evaluated” See ¶ 0033.
Rosenbaum teaches, “acceleration event is thus existent in the time window where A>A.sub.T, or in other words from t.sub.1 to t.sub.2, where t.sub.1 is the time when A exceeds A.sub.T, and t.sub.2 is the time when A falls below A.sub.T. In an alternative t.sub.2 is set differently, for example the time point where A falls to zero, thus the time window reaches to the end of the acceleration. In a further alternative the time window is fixed in time with t.sub.1 the time when A exceeds A.sub.T, and t.sub.2 is a fixed time span later, like 1 minute.” See ¶ 0034.
Rosenbaum teaches, “the acceleration event can be categorized into regular events and accidental events, the accidental events involving some kind of dangerous situation,” See ¶ 0037.
Rosenbaum teaches, “driving characteristic can be an accident risk of the vehicle, like over a driving timespan, driving distance or road section. The driving characteristic value can be a quantification of the driving characteristic, either a single numerical value, a multidimensional value, like a vector, or a function dependent on a one or more independent parameters. The value can be used for controlling a driving assist system. A driving assist system is an electronic system in the vehicle for supporting the driver in specific driving situations,” see ¶ 0160.
Rosenbaum teaches, “[a] driver assist system may intervene semi-autonomously or autonomously into the drivetrain—brake or acceleration, or another system control, or alert the driver through a human machine interface in safety critical situations.” See ¶ 0161.
Rosenbaum teaches, “the characteristics may comprise the number of times the driver of the subject vehicle has driven the respective driving route. Also it is fundamental to autonomous driving algorithms that repetitive driving allowing for self-learning to improve the autonomous handling of critical route autonomous driving obstacles.” See ¶ 0224
Rosenbaum does not teach, “setting a third time stamp in response to the sensor data passing the second threshold value again thereafter;
setting a fourth time stamp in response to the sensor data passing the first threshold value again thereafter, wherein the second threshold value is above or below the first threshold value;
if at least the first time stamp and the second time stamp and the fourth time stamp are present, defining a first characteristic of the sensor data based on a time period, the time period comprising at least a portion of a time window extending between the first time stamp and the fourth time stamp…”
Hergesheimer, Peter et al. (US 2014/0111354 A1) teaches, “an event reporting telematics unit configured to report the location of events includes a first sensor configured to determine sensor information, a storage device configured to store sensor information and an event reporting application, and a processor, wherein the event reporting application configures the processor to receive a first sensor information using the first sensor, calculate a first sensor information timestamp, where the first sensor information timestamp is associated with the first sensor information, determine the occurrence of a vehicle event” See ¶ 0005.
Hergesheimer teaches, “calculate a plurality of location timestamps corresponding to one or more of the plurality of locations, and estimate the location corresponding to the beginning of the determined event using at least one of the plurality of location timestamps and at least one of the plurality of locations.” See ¶ 00011.
“acceleration information for a vehicle can be measured using an accelerometer, which are often installed on a vehicle or mobile device” See ¶ 0033
Hergesheimer teaches, “the acceleration threshold and/or the acceleration duration window is determined dynamically. In several embodiments, the acceleration threshold and/or the acceleration duration window is pre-determined. A number of embodiments of the invention include a plurality of acceleration thresholds and/or acceleration duration windows. In many embodiments, the acceleration threshold and/or the acceleration duration window is provided by an event detection profile. In a variety of embodiments, detecting (410) acceleration exceeding a threshold value and/or detecting (418) acceleration below a threshold value is associated with a timestamp at the time the threshold value is exceeded.” See ¶ 0051.
Pal, Jayanta et al. (US 2017/0053461 A1) teaches, “detecting an accident of a vehicle, the method including: receiving a movement dataset collected at least at one of a location sensor and a motion sensor arranged within the vehicle, during a time period of movement of the vehicle, extracting a set of movement features associated with at least one of a position, a velocity, and an acceleration characterizing the movement of the vehicle during the time period, detecting a vehicular accident event from processing the set of movement features with an accident detection model, and in response to detecting the vehicular accident event, automatically initiating an accident response action.” See abstract.
Pal teaches, “collecting movement data whenever the mobile computing device is in a moving vehicle… collecting movement data continuously, at specified time intervals (e.g., every minute, every 15 minutes, every half hour, every hour, etc.), in response to satisfaction of conditions (e.g., movement thresholds, supplementary data conditions, etc.) and/or at any suitable time… receiving a first motion dataset collected at a motion sensor of the mobile computing device during the first time period; in response to a vehicle motion characteristic (e.g., extracted from at least one of the first location dataset and the first motion dataset) exceeding the motion characteristic threshold: receiving a second location dataset collected at the location sensor of the mobile computing device during a second time period of the movement of the vehicle, where the second time period is after the first time period, and receiving a second motion dataset collected at the motion sensor of the mobile computing device during the second time period.” See ¶ 0033.
Pal teaches, “extracting a vehicle motion characteristic (e.g., to compare against motion characteristic threshold). Vehicle motion characteristics and motion characteristic thresholds can typify motion characteristic types including any one or more of: speed characteristics (e.g., average speed, instantaneous speed, speed variability, change in speed, etc.), acceleration (e.g., average acceleration, instantaneous acceleration, acceleration variability, change in acceleration, etc.)… a vehicle motion characteristic, where the vehicle motion characteristic is a vehicular speed value, and where the threshold motion characteristic (e.g., to which the vehicle motion characteristic can be compared) is a vehicular speed threshold… where the vehicle motion characteristic describes the movement of the vehicle within a time window of the first time period.” See ¶ 0104.
Pal teaches, “in response to the vehicle motion characteristic exceeding the motion characteristic threshold: retrieving an accident detection model, receiving a location dataset collected at the location sensor of the mobile computing device during a second time period of the movement of the vehicle, where the second time period is after the first time period (e.g., a first time period where an initial location dataset and/or motion dataset were collected), and receiving a motion dataset collected at the motion sensor of the mobile computing device during the second time period... before the first time period: generating a first accident detection trained model from first training data characterized by a first training data motion characteristic below a motion characteristic threshold (e.g., vehicular speed of 30 MPH), and generating a second accident detection trained model from second training data characterized by a second training data motion characteristic exceeding the motion characteristic threshold. In other examples, Block S142 can include performing one or more comparisons to a stopping distance threshold (e.g., stopping distance must be less than the typical emergency stopping distance), a movement cessation threshold (e.g., vehicle must not move for thirty seconds after a detected accident), an acceleration threshold (e.g., deceleration must be greater than free-fall deceleration/9.8 ms.sup.−2), and/or any other suitable threshold.” See ¶ 0106.
Rishi, Sunija et al. (US 10,814,815 B1) teaches, “an improved collision detection and collision analysis system, which can minimize false negative and false positive generation.” Col. 1 line 65+, “training model may receive the plurality of acceleration data points corresponding to a plurality of time windows. The plurality of acceleration data points may be provided manually. The plurality of acceleration data points may comprise of certain anomalies, that may be fed to the encoder of the LSTM autoencoder 204. FIG. 4 illustrates a plot of plurality of input acceleration data points. The corresponding plurality of input acceleration data points are fed to the LSTM autoencoder 204. Depending upon the number of acceleration data points, a frequency “n” may be set to determine the time windows for the corresponding plurality of acceleration data points.” Col. 5 line 17+.
Rishi teaches, “a sensing system disposed in the automobile to detect acceleration of the automobile; at least one sensor data processor disposed in the automobile, wherein the sensor data processor is configured to: receive acceleration data points for a plurality of time windows, wherein each of the time windows comprises multiple acceleration data points; encode the multiple acceleration data points within each of the time windows to obtain feature encodings for each of the time windows; determine distance between the encodings of at least two of the time windows; and verify whether the distance meets a threshold value, wherein meeting of the threshold value indicates a possibility of an accident;” See claim 1.
Prior art does not teach, “setting a third time stamp in response to the sensor data passing the second threshold value again thereafter;
setting a fourth time stamp in response to the sensor data passing the first threshold value again thereafter, wherein the second threshold value is above or below the first threshold value;
if at least the first time stamp and the second time stamp and the fourth time stamp are present, defining a first characteristic of the sensor data based on a time period, the time period comprising at least a portion of a time window extending between the first time stamp and the fourth time stamp…”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Omer S. Khan whose telephone number is (571)270-5146. The examiner can normally be reached 10:00 am to 8:00 pm EST.
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, Brian A. Zimmerman can be reached at 571-272-3059. 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.
/Omer S Khan/Primary Examiner, Art Unit 2686
Prosecution Insights