DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 6/29/2023. Claims 21-42are pending in this Office Action.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The abstract of the disclosure is objected to because it exceeds 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21,32,39 and 42 are rejected on the ground of nonstatutory obviousness type double patenting as being unpatentable over claims 1-3 and 9-11 of US Patent 11,518,391 B1hereinafter “391 patent”. Although the claims at issue are not identical, they are not patentably distinct from each other because all the elements of the instant application claims 21,32,39 and 42 are to be found in the claims 1-3 and 9-11 of the 391 patent. Claims 21,32,39 and 42 are also rejected on the ground of nonstatutory obviousness type double patenting as being unpatentable over claims 1-3 and 9-11 of US Patent 11,518,392 B1 for the same rationale.
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 21-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 21,32,39 and 42:
Step 1: Statutory Category ?: Yes. claim 21 , 39, 42 recite a system (i.e., a “machine”) , and claim 32 recites a method (i.e., a “process”) which are statutory categories.
Claim 21:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The claim recites one or more processes or steps that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. The limitation(s) is/are:
“ wherein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events and passenger events represented by the user data”;
Except for the “using the train model” language, there is nothing in the claim that prevents the analyzing step to be performed in the mind .
Step 2A-Prong 2: Integrated into a practical application? No.
The claim recites one or more additional elements that is/are data gathering which is insignificant extra-solution activities. (See MPEP 2106.05(g))
“ wherein the event module is configured to process driving event records to determine potential distracted driving events”;
“wherein the machine learning module is configured to receive features input from a user computing device to cluster the potential distracted driving events determined by the event module into clustered data by processing the potential distracted driving events using a clustering algorithm based at least in part upon features defined in the features input;
wherein the machine learning module is further configured to receive clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters and define a trained model based at least in part upon the qualified clustered data”
The claim recites one or more additional elements that amount to amounts no more than using generic computer with generic machine learning / clustering to apply the abstract idea (See MPEP 2106.05(f)):
“machine learning” and “clustering algorithm”
The claim recites one or more additional element(s) that amount to amounts no more than using generic computer device/components to apply the abstract idea:
“device including one or more processors”; “an event module, a machine learning module and a trip analysis module”
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 21 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of using machine learning , cluster algorithm, device including one or more processors”; “an event module, a machine learning module and a trip analysis module is at best the equivalent of merely adding the words “apply it” to the exception. The additional element of receiving input and pressing event records is data gathering and is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 21 therefore is ineligible.
Claim 22 recites the additional limitation of “wherein the driving event records include historical driving data associated with operation of a vehicle and historical phone usage data associated with usage of a mobile computing device” which is mere data gathering which is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 22 therefore is ineligible.
Claim 23 recites the additional limitation of “wherein the historical driving data includes at least one of data collected by a vehicle sensor, data collected by a vehicle operating system, or data collected by the mobile computing device” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 23 therefore is ineligible.
Claim 24 recites the additional limitation of “wherein the historical phone usage data includes at least one of application-related data, texting data, or general phone usage data” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 24 therefore is ineligible.
Claim 25 recites the additional limitation of “wherein the event module determines potential distracted driving events based at least in part upon timestamps within the historical driving data and the historical phone usage data” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Except for the ”event module” language, there is nothing that prevent the determining step to be performed in the mind . Claim 25 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 25 is not patent eligible.
Claim 26 recites the additional limitation of “wherein the features input includes at least one of acceleration data, speedometer data, braking data, tap and swipe data, or texting data” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 26 therefore is ineligible.
Claim 27 recites the additional limitation of “wherein the features input includes compound features including at least one of a combination of two or more data types or a relationship between two or more data types” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 27 therefore is ineligible.
Claim 28 recites the additional limitation of “wherein the cluster qualifications include manual input denoting whether each clustered data likely indicates a distracted driving event or a passenger event” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 28 therefore is ineligible.
Claim 29 recites the additional limitation of “wherein the clustering algorithm is a semi- supervised machine learning algorithm” which amounts no more than using generic computer with generic learning algorithm to apply the abstract idea and at best the equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 29 therefore is ineligible.
Claim 30 recites the additional limitation of “wherein the trip analysis module is further configured to determine a confidence level that the user data indicates a distracted driving event or a passenger event” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 29 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 30 is not patent eligible.
Claim 31 recites the additional limitation of “wherein the one or more processors is further programmed to provide a profile module that receives identified distracted driving events and passenger events from the trip analysis module and generates a driver profile of a user associated with the user date” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 31 therefore is ineligible.
Claim 32:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The claim recites one or more processes or steps that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. The limitation(s) is/are:
“ analyzing user data using the trained model to identify distracted driving events and passenger events represented by the user data”;
Except for the “using the train model” language, there is nothing that prevent the analyzing step to be performed in the mind .
Step 2A-Prong 2: Integrated into a practical application? No.
The claim recites one or more additional elements that is/are data gathering which is insignificant extra-solution activities. (See MPEP 2106.05(g))
processing driving event records to determine potential distracted driving events;
receiving features input from a user computing device to cluster the potential distracted driving events into clustered data by processing the potential distracted driving events using a clustering algorithm based at least in part upon features defined in the features input;
receiving clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters.
The claim recites one or more additional elements that amount to amounts no more than using generic computer with generic machine learning / clustering to apply the abstract idea (See MPEP 2106.05(f)):
“defining a trained model based at least in part upon the qualified clustered data”
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 32 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of using trained model , cluster algorithm” is at best the equivalent of merely adding the words “apply it” to the exception. The additional elements of data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 32 therefore is ineligible.
Claim 33 recites the additional limitation of “wherein the driving event records include historical driving data associated with operation of a vehicle and historical phone usage data associated with usage of a mobile computing device” which is mere data gathering which is insignificant extra-solution activities. (See MPEP 2106.05(g)) and is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 33 therefore is ineligible.
Claim 34 recites the additional limitation of “wherein processing driving event records to determine potential distracted driving events includes evaluating timestamps within the historical driving data and the historical phone usage data” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Except for the ”event module” language, there is nothing that prevent the determining step to be performed in the mind . Claim 34 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 34 is not patent eligible
Claim 35 recites the additional limitation of “wherein the features input includes at least one of acceleration data, speedometer data, braking data, tap and swipe data, or texting data” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 35 therefore is ineligible.
Claim 36 recites the additional limitation of “wherein the cluster qualifications include manual input denoting whether each clustered data likely indicates a distracted driving event or a passenger event” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 36 therefore is ineligible.
Claim 37 recites the additional limitation of “wherein the clustering algorithm is a semi-supervised machine learning algorithm” which amounts no more than using generic computer with generic learning algorithm to apply the abstract idea and at best the equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 37 therefore is ineligible.
Claim 38 recites the additional limitation of “generating a driver profile of a user associated with the user data” which is insignificant extra-solution activities therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 38 therefore is ineligible.
Claim 39:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The claim recites one or more processes or steps that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. The limitation(s) is/are:
“ wherein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events and passenger events represented by the user data”
“analyze the labeled training data to identify features that are correlated with a label of a distracted driving event and a label of a passenger event”
Except for the “using the train model” language, there is nothing that prevent the analyzing step to be performed in the mind .
Step 2A-Prong 2: Integrated into a practical application? No.
The claim recites one or more additional elements that is/are data gathering which is insignificant extra-solution activities (See MPEP 2106.05(g)) :
receive labeled training data indicating a distracted driving event wherein usage of a mobile computing device is conducted by a user who is operating a vehicle or a passenger event wherein usage of the mobile computing device is conducted by a passenger in the vehicle
The claim recites one or more additional elements that amount to amounts no more than using generic computer with generic machine learning / clustering / computer components to apply the abstract idea (See MPEP 2106.05(f)):
“a distracted driving analysis device including one or more processors programmed to provide a machine learning module and a trip analysis module”
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 39 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of using “trained model , device including one or more processors “ is at best the equivalent of merely adding the words “apply it” to the exception. The additional elements of data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 39 therefore is ineligible.
Claim 40 recites the additional limitation of “wherein the trip analysis module is further configured to determine a confidence level that the user data indicates a distracted driving event or a passenger event” which is a mental process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 40 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 40 is not patent eligible.
Claim 41 recites the additional limitation of “wherein the one or more processors is further programmed to provide a profile module that receives identified distracted driving events and passenger events from the trip analysis module and generates a driver profile of a user associated with the user data” which is mere data gathering and is insignificant extra-solution activities (See MPEP 2106.05(g)) . Data gathering is well-understood, routine conventional activities previously known to the industry and therefore does not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional element does not provide an inventive concept, claim 41 therefore is ineligible.
Claim 42:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The claim recites one or more processes or steps that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. The limitation(s) is/are:
“ means for analyzing user data using the trained model to identify distracted driving events and passenger events represented by the user data”
Except for the “means for” language, there is nothing that prevent the analyzing step to be performed in the mind .
Step 2A-Prong 2: Integrated into a practical application? No.
The claim recites one or more additional elements that is/are data gathering which is insignificant extra-solution activities (See MPEP 2106.05(g)) :
means for processing driving event records to determine potential distracted driving events;
means for receiving features input from a user computing device;
means for receiving clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters
The claim recites one or more additional elements that amount to amounts no more than using generic computer with generic machine learning / clustering to apply the abstract idea (See MPEP 2106.05(f)):
means for clustering the potential distracted driving events into clustered data;
means for defining a trained model based at least in part upon the qualified clustered data
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 42 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of using “trained model , clustering” is at best the equivalent of merely adding the words “apply it” to the exception. The additional elements of data gathering is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) , Subsection II. Even when considered in combination, the additional elements do not provide an inventive concept, claim 42 therefore is ineligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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.
Claim 42 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 42:
Claim limitations “means for processing driving event records to determine potential distracted driving events; means for receiving features input from a user computing device; means for clustering the potential distracted driving events into clustered data; means for receiving clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters; means for defining a trained model based at least in part upon the qualified clustered data; and means for analyzing user data using the trained model to identify distracted driving events and passenger events represented by the user data” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure for performing the entire claimed function and to clearly link the structure to the function.
A claim that recites a means for performing a specific computer-implemented function and only discloses a general purpose computer as the structure designed to perform that function would not be an adequate disclosure of the corresponding structure to satisfy the requirements of § 112(b). See MPEP section 2181, subsection II (B).
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Due at least to their dependency upon Claims 22-25 and 27 are also indefinite.
Claim 42 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 42 recites “means for processing driving event records to determine potential distracted driving events; means for receiving features input from a user computing device; means for clustering the potential distracted driving events into clustered data; means for receiving clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters; means for defining a trained model based at least in part upon the qualified clustered data; and means for analyzing user data using the trained model to identify distracted driving events and passenger events represented by the user data”. However, the written description fails to disclose the corresponding structure for performing the entire claimed function and to clearly link the structure to the function.
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.
Claims 21-42 are rejected under 35 U.S.C. 103 as being unpatentable over Daniels.,( US Patent 11,590,982 B1,hereinafter “Daniels”) and further in view of Harish et a.,(US Patent Application Publication 2019/0019351 A1, hereinafter “Harish”)
As to claim 21, Daniels teaches a system for identifying distracted driving events comprising:
a distracted driving analysis device including one or more processors programmed to provide an event module, a machine learning module and a trip analysis module (Daniels col 2, lines 20-27 teaches system for characterizing driver behavior);
wherein the event module is configured to process driving event records to determine potential distracted driving events; (Daniels col 2, lines 41-45 teaches a vehicle event recorder is able to record sensor data which can be used to determine events related to a driver that characterize the driver’s behavior while operating the vehicle)
wherein the machine learning module is configured to receive features input from a user computing device to cluster the potential distracted driving events determined by the event module into clustered data by processing the potential distracted driving events using a clustering algorithm based at least in part upon features defined in the features input; (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
wherein the machine learning module is further configured to receive clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data) and
define a trained model based at least in part upon the qualified clustered data (Daniels col 9, lines 24-28 teaches model trainer 306 builds a model by training a machine learning model); and
wherein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events and passenger events represented by the user data.(Daniels col 10, lines 50-60 teaches long time scale embedded image vectors are used to determine the duration , and/or number of occurrences of driver behavior)
Daniels fails to expressly teach wherein the trip analysis module is configured to analyze user data using the trained model to identify passenger events represented by the user data.
However, Harish teaches wherein the trip analysis module is configured to analyze user data using the trained model to identify passenger events represented by the user data. (Harish par [0205] –[00206]teaches analyzing sensor data including phone handling events using a classification model to generate event detection data. Harish par [0215] teaches predicting whether a given user was a driver or a passenger during a given trip based on phone handling events detected during the trip)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Daniels and Harish to achieve the claimed invention. One would have been motivated to make such combination to provide functionality to various users that improves safety, convenience and financial savings.(Harish par [0060])
As to claim 22, Daniels and Harish teach the system of claim 21, wherein the driving event records include historical driving data associated with operation of a vehicle and historical phone usage data associated with usage of a mobile computing device. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 23, Daniels and Harish teach the system of claim 22, wherein the historical driving data includes at least one of data collected by a vehicle sensor, data collected by a vehicle operating system, or data collected by the mobile computing device. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 24, Daniels and Harish teach the system of claim 22, wherein the historical phone usage data includes at least one of application-related data, texting data, or general phone usage data. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 25, Daniels and Harish teach the system of claim 22, wherein the event module determines potential distracted driving events based at least in part upon timestamps within the historical driving data and the historical phone usage data. (Harish par [0101] teaches analyzing sensor data including timestamps)
As to claim 26, Daniels and Harish teach the system of claim 21, wherein the features input includes at least one of acceleration data, speedometer data, braking data, tap and swipe data, or texting data. (Daniels col 4, lines 62-67 teaches sensor data includes accelerometer, GPS and others)
As to claim 27, Daniels and Harish teach the system of claim 21, wherein the features input includes compound features including at least one of a combination of two or more data types or a relationship between two or more data types. (Daniels col 4, lines 62-67 teaches sensor data includes accelerometer, GPS and others)
As to claim 28, Daniels and Harish teach the system of claim 21, wherein the cluster qualifications include manual input denoting whether each clustered data likely indicates a distracted driving event or a passenger event. (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
As to claim 29, Daniels and Harish teach the system of claim 21, wherein the clustering algorithm is a semi- supervised machine learning algorithm. (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data. Clustering using semi-supervised algorithm is well known in the art )
As to claim 30, Daniels and Harish teach the system of claim 21, wherein the trip analysis module is further configured to determine a confidence level that the user data indicates a distracted driving event or a passenger event. (Harish par [0268] teaches the output of such supervised learning may be a confidence value representative of whether a trip is representative of a specific driver)
As to claim 31, Daniels and Harish teach the system of claim 21, wherein the one or more processors is further programmed to provide a profile module that receives identified distracted driving events and passenger events from the trip analysis module and generates a driver profile of a user associated with the user data. (Harish par [0060] teaches generating user profiles based on data gathered form various user devices such as trip information, sensor data and other information)
As to claim 32, Daniels teaches a computer-implemented method for identifying distracted driving events comprising:
processing driving event records to determine potential distracted driving events; (Daniels col 2, lines 41-45 teaches a vehicle event recorder is able to record sensor data which can be used to determine events related to a driver that characterize the driver’s behavior while operating the vehicle)
receiving features input from a user computing device to cluster the potential distracted driving events into clustered data by processing the potential distracted driving events using a clustering algorithm based at least in part upon features defined in the features input; (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
receiving clusters qualifications from the user computing device qualifying the clustered data as either vehicle operator clusters or passenger clusters; (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
defining a trained model based at least in part upon the qualified clustered data (Daniels col 9, lines 24-28 teaches model trainer 306 builds a model by training a machine learning model)) ; and
analyzing user data using the trained model to identify distracted driving events and passenger events represented by the user data. (Daniels col 10, lines 50-60 teaches long time scale embedded image vectors are used to determine the duration , and/or number of occurrences of driver behavior)
Daniels fails to expressly teaches analyzing user data using the trained model to identify passenger events represented by the user data.
However, Harish teaches analyzing user data using the trained model to identify passenger events represented by the user data. (Harish par [0205] –[00206]teaches analyzing sensor data including phone handling events using a classification model to generate event detection data. Harish par [0215] teaches predicting whether a given user was a driver or a passenger during a given trip based on phone handling events detected during the trip)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Daniels and Harish to achieve the claimed invention. One would have been motivated to make such combination to provide functionality to various users that improves safety, convenience and financial savings.(Harish par [0060])
As to claim 33, Daniels and Harish teach the computer-implemented method of claim 32, wherein the driving event records include historical driving data associated with operation of a vehicle and historical phone usage data associated with usage of a mobile computing device. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 34, Daniels and Harish teach the computer-implemented method of claim 33, wherein processing driving event records to determine potential distracted driving events includes evaluating timestamps within the historical driving data and the historical phone usage data. (Harish par [0101] teaches analyzing sensor data including timestamps)
As to claim 35, Daniels and Harish teach the computer-implemented method of claim 32, wherein the features input includes at least one of acceleration data, speedometer data, braking data, tap and swipe data, or texting data. (Daniels col 4, lines 62-67 teaches sensor data includes accelerometer, GPS and others)
As to claim 36, Daniels and Harish teach the computer-implemented method of claim 32, wherein the cluster qualifications include manual input denoting whether each clustered data likely indicates a distracted driving event or a passenger event. (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
As to claim 37, Daniels and Harish teach the computer-implemented method of claim 32, wherein the clustering algorithm is a semi-supervised machine learning algorithm. (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data. Clustering using semi-supervised algorithm is well known in the art )
As to claim 38, Daniels and Harish teach the computer-implemented method of claim 32, further comprising generating a driver profile of a user associated with the user data. (Harish par [0060] teaches generating user profiles based on data gathered form various user devices such as trip information, sensor data and other information)
As to claim 39, Harris teaches a system for identifying distracted driving events comprising: a distracted driving analysis device including one or more processors programmed to provide a machine learning module and a trip analysis module ;
wherein the machine learning module is configured to: receive labeled training data indicating a distracted driving event wherein usage of a mobile computing device is conducted by a user who is operating a vehicle or a passenger event wherein usage of the mobile computing device is conducted by a passenger in the vehicle; (Harris par [0203] teaches building labeled dataset by assigning various labels such as trip start, end, walking , driving, riding as passenger. Harish par [0205] teaches identifying phone handling events)
analyze the labeled training data to identify features that are correlated with a label of a distracted driving event and a label of a passenger event (Harish par [0203] teaches in building a labeled dataset, distributed data processing computing platform 110 may relate and/or assign various labels (e.g., trip start, trip end, walking, driving, riding as passenger, etc.) to different features and/or segments in the sensor data signal, and these labels may be used in helping to generate and/or recognize patterns and/or signatures that may be used by distributed data processing computing platform 110 in automatically classifying future trips); and
train a model based at least in part upon the identified features (Harish par [02023] teaches these labels may be used in helping to generate and/or recognize patterns and/or signatures that may be used by distributed data processing computing platform 110 in automatically classifying future trips); and
wherein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events and passenger events represented by the user data. arish par [0205] –[00206]teaches analyzing sensor data including phone handling events using a classification model to generate event detection data. Harish par [0215] teaches predicting whether a given user was a driver or a passenger during a given trip based on phone handling events detected during the trip)
Harish fails to expressly teach wherein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events.
However, Daniels teaches herein the trip analysis module is configured to analyze user data using the trained model to identify distracted driving events. (Daniels col 10, lines 50-60 teaches long time scale embedded image vectors are used to determine the duration , and/or number of occurrences of driver behavior)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Daniels and Harish to achieve the claimed invention. One would have been motivated to make such combination to improve accuracy.(Daniels col 3, lines 35-40)
As to claim 40, Harish and Daniels teach the system of claim 39, wherein the trip analysis module is further configured to determine a confidence level that the user data indicates a distracted driving event or a passenger event. (Harish par [0268] teaches the output of such supervised learning may be a confidence value representative of whether a trip is representative of a specific driver)
As to claim 41, Harish and Daniels teach the system of claim 39, wherein the one or more processors is further programmed to provide a profile module that receives identified distracted driving events and passenger events from the trip analysis module and generates a driver profile of a user associated with the user data. (Harish par [0060] teaches generating user profiles based on data gathered form various user devices such as trip information, sensor data and other information)
As to claim 42, merely recites a system with similar features of claim 21. Accordingly, Daniels and Harish teach every limitation of claim 42 as indicates in the above rejection of claim 21.
Conclusion
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/HIEN L DUONG/Primary Examiner, Art Unit 2147