DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendment filed on Dec. 17th, 2025. The amendments are linked to the original application filed on Dec. 5th, 2022.
Response to Amendment
The Examiner thanks the applicant for the remarks, edits and arguments.
Regarding Claim Rejections – 35 USC 112(b)
Applicant Remarks:
The applicant states they have made amendments to claim 19 to comply with 35 USC 112(b) to no longer recite indefinite subject matter. Therefore, the applicant requests the rejection under 35 U.S.C. 112(b) be withdrawn.
Examiner Response:
The examiner has considered the remarks and the amended claims. The examiner believes the submitted amended claims comply with 35 U.S.C. 112(b). Therefore, the rejection under 35 U.S.C. 112(b) is withdrawn.
Regarding Claim Rejections – 35 U.S.C. 101
Applicant Remarks:
The applicant states they have made amendments to no longer recite abstract ideas and the amened claims comply with 35 U.S.C. 101. The applicant states that the amended claims recite a technical process which uses technical equipment to perform technical process of the claimed method.
Next, the applicant states that claims recite a practical application which, “improves the functioning of a computer-implemented geographic information and map-based prediction system”. Further the applicant states the claims recite a definite technical process using computing devices to perform specified action which integrates machine learning to “improving how location-based prediction are created and stored.”.
Next, the applicant states, the independent claims recite the use of developing feature vectors from static geographic attributes with geographic features. This process allows for data to be transformed in a way so it can be used by a machine learning model. Further stating that this claimed process recites technical improvements and a novel system when compared to other similar inventions.
Therefore, for the reasons stated above and in the submitted remarks, the applicant requests that the rejection under 35 U.S.C. 103 be withdrawn.
Examiner Response:
The applicant argues that the independent claims have been amended to no longer recite abstract ideas. The examiner would like to point to MPEP 2106.04(a)(2)(III)(C): “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process”. When reviewing the current claims, they recite a process of retrieving information from a database and evaluating it. A human is able to, using a computer as a tool, access a database which contains known data, retrieve that data and evaluate it and produce an opinion or judgement from the evaluation. Further the examiner would like to point to Claim 1 which recites a process which is able to, “determine, from past vehicle trips, mood data associated with a plurality of locations, …” and “determine static map features associated with the plurality of locations …” to “generate, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features;”. As stated above this process can be carried out by a human using a computer as a tool. Specifically, using the broadest reasonable interpretation in light of the specification, a human would be able to, using a generic computer, retrieve information from a database, evaluate that data, and generate an opinion or judgement from the evaluated data. Therefore, the examiner believes that the current amended claims recite abstract ideas and concepts. This would require the examiner to further evaluate the claims using the Alice/Mayo test and proceed to step 2A prong 2.
Next the applicant states, in light of the specification, the claims recite a technical improvement to technology or technical field. The examiner would like to point to the MPEP 2106(d)(1) which states, “in short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel")” while considering this, the examiner would like to point that the specification fails to recite an improvement of technology or technical field in a non-conclusory manner. The examiner believes that the specification fails to recite a technical improvement where one of ordinary skill in the art would be able to recognize an improvement to a technical field or technology. Next the examiner believes the claims fail to recite the technical improvement, “… a technical workflow that integrates machine learning into the operation of a geographic database and map platform, thereby improving how location-based predictions are generated and stored.” as stated in the remarks. Specifically, the claims fail to recite a novel storage process for geographic data. Therefore, the examiner believes the claims fail to recite additional elements that integrate into a practical application and according to the Alice/Mayo test the claims are further evaluated in Step 2B.
Finally, the amended claims have been evaluated using Step 2B and the examiner believes they recite well-understood, routine, conventional processes. For example, the independent claims recite, “access a geographic database comprising road segment data records and point-of-interest data records;” and “store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” which would be considered per MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. See 101 rejection below for further examples and explanations.
Finally, after the amended claims and the remarks are considered, the examiner believes, for the reasons above, the amended claims recite patent ineligible subject matter and are rejected under 35 USC 101, see 101 rejection below.
Regarding Claim Rejections – 35 U.S.C. 103
Applicant Remarks:
The applicant states they have amended the independent claims to further define the invention from the proposed prior art. The applicant states that the art Zhao and Capineri fail to explicitly discloses each and every element of the amended claims. Therefore, the applicant requests the rejection under 35 U.S.C. 103 should be withdrawn.
Examiner Response:
The applicant has amended the claims and further evaluation of the previously proposed arts is required. After each amendment the examiner must also perform a full and complete search for prior art which may anticipate or disclose the amended subject matter. The examiner has noted that the combination of the previously proposed arts, Capineri et al and Zhao et al, fail to teach each and every element of the amended claims. However, after further search, the examiner has found art which is able to teach the amended claims. The proposed art discloses a system which is able to evaluate locations for a level of frustration for a particular roadway and able to evaluate the environments and sections of roadways. Therefore, the examiner believes, the combination of the new proposed art, Chintakindi et al. (US20220034678 A1), and the previously proposed art, Zhao et al, discloses the amended claims and are rejected under 35 USC 103, see 103 rejection below.
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 1-21 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1, recites “An apparatus comprising a processor; and a memory comprising computer program code for one or more programs, wherein the memory and the computer program code is configured to cause the processor of the apparatus to:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate previous trips and identify a mood or emotion for a specified location using data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine static map features associated with the plurality of locations by retrieving attributes from the geographic database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a map and evaluate is for static features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generate, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a map and from that map produce judgements or opinions. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “access a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “access a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computer as a tool to evaluate geographic locations and determine a mood or emotion for that location. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receive information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receive information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein causing the apparatus to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the apparatus to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein causing the apparatus to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the apparatus to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 8, recites “A non-transitory computer-readable storage medium comprising one or more instructions for execution by one or more processors of a device, the one or more instructions which, when executed by the one or more processors, cause the device to:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate previous trips and identify a mood or emotion for a specified location using data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine static map features associated with the plurality of locations by retrieving attributes from the geographic database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a map and evaluate is for static features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generate, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a map and from that map produce judgements or opinions. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “access a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “access a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computer as a tool to evaluate geographic locations and determine a mood or emotion for that location. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receive information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receive information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein causing the device to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the device to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein causing the device to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the device to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 15, recites “A method for training a machine learning model for mood prediction, the method comprising:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determining, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate previous trips and identify a mood or emotion for a specified location using data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining static map features associated with the plurality of locations by retrieving attributes from the geographic database;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a map and evaluate is for static features. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a map and from that map produce judgements or opinions. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “accessing a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“storing, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“training a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “accessing a geographic database comprising road segment data records and point-of-interest data records;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“storing, in the geographic database, the mood data as mood data records associated with the plurality of locations;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“training a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determining, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computer as a tool to evaluate geographic locations and determine a mood or emotion for that location. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receiving information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receiving information about an upcoming vehicle trip; and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, (iv) a type of landmark, (v) a dimension, (vi) a position of said landmark relative to one of the road segments, or (vii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, (iv) a type of landmark, (v) a dimension, (vi) a position of said landmark relative to one of the road segments, or (vii) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein training the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises training the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein training the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises training the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the mood data associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the mood data associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 21
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining mood data comprises determining mood data based on one or more visual aspects associated with one or more road segments associated with the plurality of locations based on past vehicle trips, one or more temporal elements corresponding to the one or more road segments of the plurality of the road segments based on past vehicle trips or a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining mood data comprises determining mood data based on one or more visual aspects associated with one or more road segments associated with the plurality of locations based on past vehicle trips, one or more temporal elements corresponding to the one or more road segments of the plurality of the road segments based on past vehicle trips or a combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Chintakindi et al, (Chintakindi et al, “AUTOMATED DRIVING BASED ON DRIVER FRUSTRATION”, US 20220034678 A1, filed Oct. 2020, hereinafter “Chintakindi”) in view of Zhao et al, (Zhou et al., "Smart Tour Route Planning Algorithm Based on Naive Bayes Interest Data Mining Machine Learning", Feb. 2020, hereinafter "Zhou").
Regarding claim 1, Chintakindi discloses, “An apparatus comprising a processor; and a memory comprising computer program code for one or more programs, wherein the memory and the computer program code is configured to cause the processor of the apparatus to:” (Detailed Description, pp. 9, [0063]; “FIG. 2 shows an illustrative block diagram of a system 200 for generating and using road frustration index information, such as by generating one or more road frustration index maps that may be used by one or more users in accordance with aspects of this disclosure. The system may include a vehicle 210, one or more user devices 220 associated with a user ( e.g., a driver, etc.) of the vehicle, and a remote computing system 240 that may be associated with a business entity ( e.g., an insurance provider, a vehicle manufacturer, a global positioning company, etc.) or governmental agency having an interest in assessing and/or minimizing a level of frustration being experienced by a driver when travelling on one or more segments of road upon which the user travels within the vehicle. The one or more user devices 220 may include a variety of personal computing devices including, but not limited to, a phone (e.g., a smart phone 220a), a personal computer 220b, a laptop computer 220c, a tablet computer 220d, a personal navigation device 110b, a vehicle's computer system, and/or the like.” Figure 2 discloses the system used in the application. This includes generic computing devices where processors are connected to memory which stores instructions.)
“access a geographic database comprising road segment data records and point-of-interest data records;” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use databases to access information about roadways. This includes being able evaluate data related to road construction data and points-of-interest.)
“determine, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” (Detailed Description, pp. 2, [0022]; “In some cases, one or more conditions experienced by the driver may have an effect on a level of frustration being experienced by the driver and may be included in a calculation of a road frustration index value for one or more road segments of a travel route. Such conditions may include, but not limited to, road conditions (e.g., potholes, standing water, turns, bridges, narrow lanes, darkness, etc.), a time of day, weather conditions (e.g., rain, fog, wind, storms, an angle of sunshine, etc.), environmental hazards (e.g., debris in the road, a threat of debris falling into the roadway, smoke from a nearby fire, etc. ), a particular human condition, and/or other people within the vehicle (e.g., a number of people in the vehicle, a noise level within the vehicle, a number of children being present, etc.), traffic flow, one or more traffic patterns, a traffic amount (e.g., heavy traffic), a time of day (e.g., night driving, rush hour, etc.), an event that may have an effect on traffic congestion (e.g., a concert, a sporting event, a political rally, etc.), pedestrian traffic (e.g., e.g., a crosswalk, a school zone, etc.), and the like. In some cases, the information may be gathered in near real-time, at time intervals during a trip, before a trip, after a trip, or the like.” The system in this application is able to evaluate road segments for a frustration level. This model is able to run in real time and use historical data to make decisions, predations or recommendations for the user. The system is able to evaluate subjective and objective risks which include road construction or locations of prior accidents.)
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” The system in this application is able to generate and store information to severs and other connected devices. As stated above data from databases of different types can be accessed by the system.) and (Detailed Description, pp. 2, [0021]; “Systems and methods in accordance with aspects of this disclosure may be provided to generate a road frustration index value corresponding to a level of frustration being experienced (or predicted to be experienced) by a driver along one or more road segments.” The system is able to generate a frustration index for a segment of a roadway. This index is a scale from “not frustrated” too “frustrated”.)
“determine static map features associated with the plurality of locations by retrieving attributes from the geographic database;” (Detailed Description, pp. 4, [0035]; “For example, this information may be analyzed using one or more mathematical algorithms to determine a location and/or a likelihood that a subjective risk may exist along a route. The road frustration risk analysis system may further incorporate objective risks (e.g., construction areas, wildlife areas, accident prone areas, dangerous intersections, etc.) and/or subjective risks (e.g., blind left-hand turns, bridges, etc.) when generating a map and/or routes for presentation to the driver.” This system is able to evaluate a section of a roadway for subjective hazards. This will generate a map of or route for the user where the static features are noted.) And (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” This teaches that this system is able to use databases connected via a network. Included in the database is a geographic database containing geographic data of roadways and locations.)
“generate, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features; and” (Detailed Description, pp. 17, [0104]; “In an illustrative example, the RFI model 800 used to compute an aggregate RFI value corresponding to multiple road segments of a travel route may take into account many variables including an actual total route travel time, an expected route travel time (e.g., total_base_time), a total time spent in traffic (e.g., total_traffic_time), a total distance, which may then be used along with a TID multiple.” This model is able to generate a route aggregate of all the possible road segments into an index or score. Each of the road segments would represent a vector, containing a direction and magnitude i.e. Road frustration index.) And (Detailed Description, pp. 18, [0128]-[0129]; “FIG. 6B illustrates an example composition of a segment. Each segment may have a starting point 602, a middle portion 603, and an ending point 604. The starting point 602 and the ending point 604 may be defined by 3D coordinates (e.g., GPS coordinates representing a latitude, longitude, and altitude). The middle portion 603 may be defined by a set of 3D coordinates or a vector. [0129] Each segment in FIGS. 6A and 6B may be associated with a risk score. The risk score may be a value within a predefined range (e.g., 0 to 100). In some embodiments, the risk score may change over time or be variable depending on various conditions (e.g., weather, traffic, etc.). In some embodiments, the risk score of a segment may be different for different portions of the segment. For example, the risk score at the starting point 602 of a segment may be different than the risk score at the ending point 604 of the segment.” This is their definition of a road segment. This teaches that each road segment is comparable to a vector, containing a direction and a magnitude or road frustration index.)
Chintakindi fails to explicitly disclose the remaining elements of this claim. However, Zhao discloses, “train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; “Definition 3. Tourist site classification vector C. According to tourist site characteristics and features, the main intentions of tourists visiting tourist sites and the actual situations of visitors can be used to group city tourist sites into m classifications. The m dimension of a vector that is composed by m tourist site classifications is called the tourist site classification vector C. Vector C contains tourist site classifications reflecting on tourists’ interest tendencies which are based on feature attribute vectors, and then
C
=
{
c
j
|
j
∈
(
0
,
m
]
∈
Z
+
}
. It contains tourist site of interest classifications
c
1
,
c
2
,
…
,
c
m
relating to tourist feature attributes, in which an arbitrary
∀
c
j
relates to one certain or more tourist feature attribute vectors X. We set m as the total quantity of tourist site classifications, and 0 < m << k. As to one certain tourism city, we define the specific tourist site of the No.
c
j
tourist site classification as
c
j
s
. The total quantity of tourist sites in
c
j
is
s
j
, and then
s
∈
(
0
,
s
j
]
Z
+
.” Each of the tourists in this system contains different attributes. Thes attributes are stored as vectors in this system. The attributes can contain information about mood or feelings of the tourist. For example, attributes can include how a client feels about a location, i.e. like or dislike.) and (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Chintakindi and Zhao. Chintakindi teaches a system that is able to access large databases and evaluate data to determine a level of frustration for prior traveled sections of a roadway and predicted frustration levels associated to locations and sections of a roadway. Zhao teaches a routing algorithm that uses machine learning to evaluate tourists wants, needs, and emotions to generate an ideal tour route. One of ordinary skill would have motivation to combine a system that is able to determine frustrations levels of sections of roadways on a planned route with a system that is able to take in data, such as location data, to plan an ideal route while taking into consideration a tourist/driver’s predicted emotions or location requirements, “The algorithm that is designed and developed in the study is based on the mining and learning tourism big data. In future research work, there is more work that could be carried out. First, tourists’ feature attributes can be subdivided to more precisely mine tourists’ needs and interests. Secondly, an interest tendency deviation correction method will be designed and developed to accurately predict and output tourists’ interests, the aim of which is to ensure that each tourist can get the best motive benefits and travel experience. Thirdly, on the aspect of tourist site mining and tour route planning, tourist sites accessibility will be studied, and more transportation and ferry ways will be considered to enrich the functions of the smart machine. Finally, real-time controlling and monitoring of tourists’ travelling process could be studied and developed to ensure the tourists’ motive benefits.” (Zhao, Conclusions and Future Work, pp. 33).
Regarding claim 2, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “receive information about an upcoming vehicle trip; and” (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
“determine, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” (Smart Tour Route Planning Algorithm Modeling, pp. 9; "The algorithm can output optimal tour routes, which conform to actual conditions, meet tourists' interests and motive benefits, and decrease travel expenditures. Meanwhile, sub-optimal tour routes are also provided for tourists." Once trained this model can determine an optimal route for a tourist. This will take into account the tourists want and needs of the trip as well as the geographic locations.)
Regarding claim 3, Chintakindi discloses “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” (Detailed Description, pp. 8, [0057]; “Some examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include, but are not limited to, information about characteristics of a corresponding location described by some location information: posted speed limit, construction area indicator (i.e., whether location has construction), topography type (e.g., flat, rolling hills, steep hills, etc.), road type (e.g., residential, interstate, 4-lane separated highway, city street, country road, parking lot, etc.), road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections, whether a roundabout is present, number of railroad crossings, whether a passing zone is present, whether a merge is present, number of lanes, width of road/lanes, population density, condition of road (e.g., new, worn, severely damaged with sink-holes, severely damaged with erosion, gravel, dirt, paved, etc.), wildlife area, state, county, and/or municipality. Geographic information may also include other attribute information about road segments, intersections, bridges, tunnels, railroad crossings, and other roadway features.” The system proposed in this application is able to use data from geographic databases to build static maps for the user. This is an example of the geographic data that can be used by the system to evaluate a section of a roadway for a frustration index.)
Regarding claim 4, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” (Detailed Description, pp. 9, [0061]; “For example, in accordance with aspects of this disclosure, a data source 104b may provide the computing device 102b with geographic information that is used to generate new roadway feature risk values in a database of risk values and/or update existing risk values; where the roadway feature may comprise intersections, road segments, tunnels, bridges, or railroad crossings. Attributes associated with roadways may also be used in part to generate risk values. The computing device 102b may use at least part of the received geographic information to calculate a value, associate the value with a road segment (or other location information), and store the value in a database format.” The system in this application is able to use data from different databases. This data can contain location information and roadway related features of the locations.)
Regarding claim 5, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” (Detailed Description, pp. 8, [0057]; “Some examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include, but are not limited to, information about characteristics of a corresponding location described by some location information: posted speed limit, construction area indicator (i.e., whether location has construction), topography type (e.g., flat, rolling hills, steep hills, etc.), road type (e.g., residential, interstate, 4-lane separated highway, city street, country road, parking lot, etc.), road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections, whether a roundabout is present, number of railroad crossings, whether a passing zone is present, whether a merge is present, number of lanes, width of road/lanes, population density, condition of road (e.g., new, worn, severely damaged with sink-holes, severely damaged with erosion, gravel, dirt, paved, etc.), wildlife area, state, county, and/or municipality. Geographic information may also include other attribute information about road segments, intersections, bridges, tunnels, railroad crossings, and other roadway features.” The system proposed in this application is able to use data from geographic databases to build static maps for the user. This is an example of the geographic data that can be used by the system to evaluate a section of a roadway for a frustration index.)
Regarding claim 6, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “wherein causing the apparatus to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the apparatus to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; "The foundation of the machine learning module is based on tourism interest big data. Text data that conforms to its function are collected and the valuable information is mined from the data. The valuable information is the training data that will be used in building the machine learning module. Through the process of data de noising, cleaning, integrating and grouping, etc., the training data is precisely processed. The valuable information data should be noted in text format and stored item by item in a database. Each item contains tourists' feature attribute information, and each feature attribute relates to one or more elements in the tourist site classification vector. The final output re suit of the machine learning module is the predicted descending order basic vector with the tourist interest tendency elements from highest to lowest. This system will determine a route for tourism based on many different factors. This will take in user information such as user preferences and the locations themselves. This system will take in static data such as landmarks or locations as well as dynamic data such as user wants and needs and/or financial constraints. It will use this information and use machine learning to determine an output route.)
Regarding claim 7, Chintakindi discloses, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use data from different databases to evaluate a section of a roadway. Included in data from car accidents, this data would have location data along with data/time, etc.)
Regarding claim 8, Chintakindi discloses “A non-transitory computer-readable storage medium comprising one or more instructions for execution by one or more processors of a device, the one or more instructions which, when executed by the one or more processors, cause the device to:” (Detailed Description, pp. 9, [0063]; “FIG. 2 shows an illustrative block diagram of a system 200 for generating and using road frustration index information, such as by generating one or more road frustration index maps that may be used by one or more users in accordance with aspects of this disclosure. The system may include a vehicle 210, one or more user devices 220 associated with a user ( e.g., a driver, etc.) of the vehicle, and a remote computing system 240 that may be associated with a business entity ( e.g., an insurance provider, a vehicle manufacturer, a global positioning company, etc.) or governmental agency having an interest in assessing and/or minimizing a level of frustration being experienced by a driver when travelling on one or more segments of road upon which the user travels within the vehicle. The one or more user devices 220 may include a variety of personal computing devices including, but not limited to, a phone (e.g., a smart phone 220a), a personal computer 220b, a laptop computer 220c, a tablet computer 220d, a personal navigation device 110b, a vehicle's computer system, and/or the like.” Figure 2 discloses the system used in the application. This includes generic computing devices where processors are connected to memory which stores instructions.)
“access a geographic database comprising road segment data records and point-of-interest data records;” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use databases to access information about roadways. This includes being able evaluate data related to road construction data and points-of-interest.)
“determine, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” (Detailed Description, pp. 2, [0022]; “In some cases, one or more conditions experienced by the driver may have an effect on a level of frustration being experienced by the driver and may be included in a calculation of a road frustration index value for one or more road segments of a travel route. Such conditions may include, but not limited to, road conditions (e.g., potholes, standing water, turns, bridges, narrow lanes, darkness, etc.), a time of day, weather conditions (e.g., rain, fog, wind, storms, an angle of sunshine, etc.), environmental hazards (e.g., debris in the road, a threat of debris falling into the roadway, smoke from a nearby fire, etc. ), a particular human condition, and/or other people within the vehicle (e.g., a number of people in the vehicle, a noise level within the vehicle, a number of children being present, etc.), traffic flow, one or more traffic patterns, a traffic amount (e.g., heavy traffic), a time of day (e.g., night driving, rush hour, etc.), an event that may have an effect on traffic congestion (e.g., a concert, a sporting event, a political rally, etc.), pedestrian traffic (e.g., e.g., a crosswalk, a school zone, etc.), and the like. In some cases, the information may be gathered in near real-time, at time intervals during a trip, before a trip, after a trip, or the like.” The system in this application is able to evaluate road segments for a frustration level. This model is able to run in real time and use historical data to make decisions, predations or recommendations for the user. The system is able to evaluate subjective and objective risks which include road construction or locations of prior accidents.)
“store, in the geographic database, the mood data as mood data records associated with the plurality of locations;” (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” The system in this application is able to generate and store information to severs and other connected devices. As stated above data from databases of different types can be accessed by the system.) and (Detailed Description, pp. 2, [0021]; “Systems and methods in accordance with aspects of this disclosure may be provided to generate a road frustration index value corresponding to a level of frustration being experienced (or predicted to be experienced) by a driver along one or more road segments.” The system is able to generate a frustration index for a segment of a roadway. This index is a scale from “not frustrated” too “frustrated”.)
“determine static map features associated with the plurality of locations by retrieving attributes from the geographic database;” (Detailed Description, pp. 4, [0035]; “For example, this information may be analyzed using one or more mathematical algorithms to determine a location and/or a likelihood that a subjective risk may exist along a route. The road frustration risk analysis system may further incorporate objective risks (e.g., construction areas, wildlife areas, accident prone areas, dangerous intersections, etc.) and/or subjective risks (e.g., blind left-hand turns, bridges, etc.) when generating a map and/or routes for presentation to the driver.” This system is able to evaluate a section of a roadway for subjective hazards. This will generate a map of or route for the user where the static features are noted.) And (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” This teaches that this system is able to use databases connected via a network. Included in the database is a geographic database containing geographic data of roadways and locations.)
“generate, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features; and” (Detailed Description, pp. 17, [0104]; “In an illustrative example, the RFI model 800 used to compute an aggregate RFI value corresponding to multiple road segments of a travel route may take into account many variables including an actual total route travel time, an expected route travel time (e.g., total_base_time), a total time spent in traffic (e.g., total_traffic_time), a total distance, which may then be used along with a TID multiple.” This model is able to generate a route aggregate of all the possible road segments into an index or score. Each of the road segments would represent a vector, containing a direction and magnitude i.e. Road frustration index.) And (Detailed Description, pp. 18, [0128]-[0129]; “FIG. 6B illustrates an example composition of a segment. Each segment may have a starting point 602, a middle portion 603, and an ending point 604. The starting point 602 and the ending point 604 may be defined by 3D coordinates (e.g., GPS coordinates representing a latitude, longitude, and altitude). The middle portion 603 may be defined by a set of 3D coordinates or a vector. [0129] Each segment in FIGS. 6A and 6B may be associated with a risk score. The risk score may be a value within a predefined range (e.g., 0 to 100). In some embodiments, the risk score may change over time or be variable depending on various conditions (e.g., weather, traffic, etc.). In some embodiments, the risk score of a segment may be different for different portions of the segment. For example, the risk score at the starting point 602 of a segment may be different than the risk score at the ending point 604 of the segment.” This is their definition of a road segment. This teaches that each road segment is comparable to a vector, containing a direction and a magnitude or road frustration index.)
Chintakindi fails to explicitly disclose the remaining elements of this claim. However, Zhao discloses, “train a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; “Definition 3. Tourist site classification vector C. According to tourist site characteristics and features, the main intentions of tourists visiting tourist sites and the actual situations of visitors can be used to group city tourist sites into m classifications. The m dimension of a vector that is composed by m tourist site classifications is called the tourist site classification vector C. Vector C contains tourist site classifications reflecting on tourists’ interest tendencies which are based on feature attribute vectors, and then
C
=
{
c
j
|
j
∈
(
0
,
m
]
∈
Z
+
}
. It contains tourist site of interest classifications
c
1
,
c
2
,
…
,
c
m
relating to tourist feature attributes, in which an arbitrary
∀
c
j
relates to one certain or more tourist feature attribute vectors X. We set m as the total quantity of tourist site classifications, and 0 < m << k. As to one certain tourism city, we define the specific tourist site of the No.
c
j
tourist site classification as
c
j
s
. The total quantity of tourist sites in
c
j
is
s
j
, and then
s
∈
(
0
,
s
j
]
Z
+
.” Each of the tourists in this system contains different attributes. Thes attributes are stored as vectors in this system. The attributes can contain information about mood or feelings of the tourist. For example, attributes can include how a client feels about a location, i.e. like or dislike.) and (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
Regarding claim 9, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “receive information about an upcoming vehicle trip; and” (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
“determine, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” (Smart Tour Route Planning Algorithm Modeling, pp. 9; "The algorithm can output optimal tour routes, which conform to actual conditions, meet tourists' interests and motive benefits, and decrease travel expenditures. Meanwhile, sub-optimal tour routes are also provided for tourists." Once trained this model can determine an optimal route for a tourist. This will take into account the tourists want and needs of the trip as well as the geographic locations.)
Regarding claim 10, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” (Detailed Description, pp. 8, [0057]; “Some examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include, but are not limited to, information about characteristics of a corresponding location described by some location information: posted speed limit, construction area indicator (i.e., whether location has construction), topography type (e.g., flat, rolling hills, steep hills, etc.), road type (e.g., residential, interstate, 4-lane separated highway, city street, country road, parking lot, etc.), road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections, whether a roundabout is present, number of railroad crossings, whether a passing zone is present, whether a merge is present, number of lanes, width of road/lanes, population density, condition of road (e.g., new, worn, severely damaged with sink-holes, severely damaged with erosion, gravel, dirt, paved, etc.), wildlife area, state, county, and/or municipality. Geographic information may also include other attribute information about road segments, intersections, bridges, tunnels, railroad crossings, and other roadway features.” The system proposed in this application is able to use data from geographic databases to build static maps for the user. This is an example of the geographic data that can be used by the system to evaluate a section of a roadway for a frustration index.)
Regarding claim 11, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, or (vi) a combination thereof.” (Detailed Description, pp. 9, [0061]; “For example, in accordance with aspects of this disclosure, a data source 104b may provide the computing device 102b with geographic information that is used to generate new roadway feature risk values in a database of risk values and/or update existing risk values; where the roadway feature may comprise intersections, road segments, tunnels, bridges, or railroad crossings. Attributes associated with roadways may also be used in part to generate risk values. The computing device 102b may use at least part of the received geographic information to calculate a value, associate the value with a road segment (or other location information), and store the value in a database format.” The system in this application is able to use data from different databases. This data can contain location information and roadway related features of the locations.)
Regarding claim 12, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a type of landmark, (ii) a dimension, (iii) a position of said landmark relative to one or more road segments, or (iv) a combination thereof.” (Detailed Description, pp. 8, [0057]; “Some examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include, but are not limited to, information about characteristics of a corresponding location described by some location information: posted speed limit, construction area indicator (i.e., whether location has construction), topography type (e.g., flat, rolling hills, steep hills, etc.), road type (e.g., residential, interstate, 4-lane separated highway, city street, country road, parking lot, etc.), road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections, whether a roundabout is present, number of railroad crossings, whether a passing zone is present, whether a merge is present, number of lanes, width of road/lanes, population density, condition of road (e.g., new, worn, severely damaged with sink-holes, severely damaged with erosion, gravel, dirt, paved, etc.), wildlife area, state, county, and/or municipality. Geographic information may also include other attribute information about road segments, intersections, bridges, tunnels, railroad crossings, and other roadway features.” The system proposed in this application is able to use data from geographic databases to build static maps for the user. This is an example of the geographic data that can be used by the system to evaluate a section of a roadway for a frustration index.)
Regarding claim 13, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “wherein causing the device to train the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises causing the device to train the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; "The foundation of the machine learning module is based on tourism interest big data. Text data that conforms to its function are collected and the valuable information is mined from the data. The valuable information is the training data that will be used in building the machine learning module. Through the process of data de noising, cleaning, integrating and grouping, etc., the training data is precisely processed. The valuable information data should be noted in text format and stored item by item in a database. Each item contains tourists' feature attribute information, and each feature attribute relates to one or more elements in the tourist site classification vector. The final output re suit of the machine learning module is the predicted descending order basic vector with the tourist interest tendency elements from highest to lowest. This system will determine a route for tourism based on many different factors. This will take in user information such as user preferences and the locations themselves. This system will take in static data such as landmarks or locations as well as dynamic data such as user wants and needs and/or financial constraints. It will use this information and use machine learning to determine an output route.)
Regarding claim 14, Chintakindi discloses, “wherein the dynamic features associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use data from different databases to evaluate a section of a roadway. Included in data from car accidents, this data would have location data along with data/time, etc.)
Regarding claim 15, Chintakindi discloses, “A method for training a machine learning model for mood prediction, the method comprising:” (Detailed Description, pp. 2, [0021]; “Systems and methods in accordance with aspects of this disclosure may be provided to generate a road frustration index value corresponding to a level of frustration being experienced (or predicted to be experienced) by a driver along one or more road segments.” This application discloses a method which is able to use machine learning to determine a frustration index for a driver along a route.)
“accessing a geographic database comprising road segment data records and point-of-interest data records;” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use databases to access information about roadways. This includes being able evaluate data related to road construction data and points-of-interest.)
“determining, from past vehicle trips, mood data associated with a plurality of locations, including associating respective portions of the mood data with one or more of (i) a road segment, (ii) a POI, or (iii) a map tile identified by a tile identifier;” (Detailed Description, pp. 2, [0022]; “In some cases, one or more conditions experienced by the driver may have an effect on a level of frustration being experienced by the driver and may be included in a calculation of a road frustration index value for one or more road segments of a travel route. Such conditions may include, but not limited to, road conditions (e.g., potholes, standing water, turns, bridges, narrow lanes, darkness, etc.), a time of day, weather conditions (e.g., rain, fog, wind, storms, an angle of sunshine, etc.), environmental hazards (e.g., debris in the road, a threat of debris falling into the roadway, smoke from a nearby fire, etc. ), a particular human condition, and/or other people within the vehicle (e.g., a number of people in the vehicle, a noise level within the vehicle, a number of children being present, etc.), traffic flow, one or more traffic patterns, a traffic amount (e.g., heavy traffic), a time of day (e.g., night driving, rush hour, etc.), an event that may have an effect on traffic congestion (e.g., a concert, a sporting event, a political rally, etc.), pedestrian traffic (e.g., e.g., a crosswalk, a school zone, etc.), and the like. In some cases, the information may be gathered in near real-time, at time intervals during a trip, before a trip, after a trip, or the like.” The system in this application is able to evaluate road segments for a frustration level. This model is able to run in real time and use historical data to make decisions, predations or recommendations for the user. The system is able to evaluate subjective and objective risks which include road construction or locations of prior accidents.)
“storing, in the geographic database, the mood data as mood data records associated with the plurality of locations;” (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” The system in this application is able to generate and store information to severs and other connected devices. As stated above data from databases of different types can be accessed by the system.) and (Detailed Description, pp. 2, [0021]; “Systems and methods in accordance with aspects of this disclosure may be provided to generate a road frustration index value corresponding to a level of frustration being experienced (or predicted to be experienced) by a driver along one or more road segments.” The system is able to generate a frustration index for a segment of a roadway. This index is a scale from “not frustrated” too “frustrated”.)
“determining static map features associated with the plurality of locations by retrieving attributes from the geographic database;” (Detailed Description, pp. 4, [0035]; “For example, this information may be analyzed using one or more mathematical algorithms to determine a location and/or a likelihood that a subjective risk may exist along a route. The road frustration risk analysis system may further incorporate objective risks (e.g., construction areas, wildlife areas, accident prone areas, dangerous intersections, etc.) and/or subjective risks (e.g., blind left-hand turns, bridges, etc.) when generating a map and/or routes for presentation to the driver.” This system is able to evaluate a section of a roadway for subjective hazards. This will generate a map of or route for the user where the static features are noted.) And (Detailed Description, pp. 7, [0048]; “In one embodiment, the computing device 102b may be a high-end server computer with one or more processors 114b and memory 116b for storing and maintaining the values generated. The memory 116b storing and maintaining the values generated need not be physically located in the computing device 102b. Rather, the memory (e.g., ROM, flash memory, hard drive memory, RAID memory, etc.) may be located in a remote data store (e.g., memory storage area) physically located outside the computing device 102b, but in communication with the computing device 102b.” This teaches that this system is able to use databases connected via a network. Included in the database is a geographic database containing geographic data of roadways and locations.)
“generating, for each of the plurality of locations, a feature vector comprising at least a subset of the static map features and” (Detailed Description, pp. 17, [0104]; “In an illustrative example, the RFI model 800 used to compute an aggregate RFI value corresponding to multiple road segments of a travel route may take into account many variables including an actual total route travel time, an expected route travel time (e.g., total_base_time), a total time spent in traffic (e.g., total_traffic_time), a total distance, which may then be used along with a TID multiple.” This model is able to generate a route aggregate of all the possible road segments into an index or score. Each of the road segments would represent a vector, containing a direction and magnitude i.e. Road frustration index.) And (Detailed Description, pp. 18, [0128]-[0129]; “FIG. 6B illustrates an example composition of a segment. Each segment may have a starting point 602, a middle portion 603, and an ending point 604. The starting point 602 and the ending point 604 may be defined by 3D coordinates (e.g., GPS coordinates representing a latitude, longitude, and altitude). The middle portion 603 may be defined by a set of 3D coordinates or a vector. [0129] Each segment in FIGS. 6A and 6B may be associated with a risk score. The risk score may be a value within a predefined range (e.g., 0 to 100). In some embodiments, the risk score may change over time or be variable depending on various conditions (e.g., weather, traffic, etc.). In some embodiments, the risk score of a segment may be different for different portions of the segment. For example, the risk score at the starting point 602 of a segment may be different than the risk score at the ending point 604 of the segment.” This is their definition of a road segment. This teaches that each road segment is comparable to a vector, containing a direction and a magnitude or road frustration index.)
Chintakindi fails to explicitly disclose the remaining elements of this claim. However, Zhao discloses, “training a machine learning model on the static map features using the (i) the feature vectors and (ii) the mood data associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; “Definition 3. Tourist site classification vector C. According to tourist site characteristics and features, the main intentions of tourists visiting tourist sites and the actual situations of visitors can be used to group city tourist sites into m classifications. The m dimension of a vector that is composed by m tourist site classifications is called the tourist site classification vector C. Vector C contains tourist site classifications reflecting on tourists’ interest tendencies which are based on feature attribute vectors, and then
C
=
{
c
j
|
j
∈
(
0
,
m
]
∈
Z
+
}
. It contains tourist site of interest classifications
c
1
,
c
2
,
…
,
c
m
relating to tourist feature attributes, in which an arbitrary
∀
c
j
relates to one certain or more tourist feature attribute vectors X. We set m as the total quantity of tourist site classifications, and 0 < m << k. As to one certain tourism city, we define the specific tourist site of the No.
c
j
tourist site classification as
c
j
s
. The total quantity of tourist sites in
c
j
is
s
j
, and then
s
∈
(
0
,
s
j
]
Z
+
.” Each of the tourists in this system contains different attributes. Thes attributes are stored as vectors in this system. The attributes can contain information about mood or feelings of the tourist. For example, attributes can include how a client feels about a location, i.e. like or dislike.) and (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
Regarding claim 16, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “receiving information about an upcoming vehicle trip; and” (The Design and Foundation of Naive Bayes Interest Data Mining Machine learning, pp. 3; "The design thought for smart tour route recommendation systems is in training a sufficient quantity of easily obtained tourism interest big data and setting up a machine learning module to obtain tourists' interest tendencies on tourist site classifications, and mining and recommending optimal tourist sites of interest according to their schedule." The system proposed in this article discloses a tourism-based route planning. This system will take into account different geographic locations and other data to determine a route for tourism.)
“determining, based on the machine learning model using the information about the upcoming vehicle trip, a predicted mood of an individual at one or more locations associated with the upcoming vehicle trip.” (Smart Tour Route Planning Algorithm Modeling, pp. 9; "The algorithm can output optimal tour routes, which conform to actual conditions, meet tourists' interests and motive benefits, and decrease travel expenditures. Meanwhile, sub-optimal tour routes are also provided for tourists." Once trained this model can determine an optimal route for a tourist. This will take into account the tourists want and needs of the trip as well as the geographic locations.)
Regarding claim 17, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a road segment type, (ii) a dimension of a road segment, (iii) a number of lanes corresponding to a road segment, (iv) a number of traffic directions supported by a road segment, (v) a width of a lane, (vi) a number of shoulders, (vii) a width of a shoulder, (viii) a road surface condition, (ix) a number of traffic signs, (x) a type of traffic sign, (xi) a number of traffic cameras, (xii) a type of traffic camera, (xiii) a number of traffic lights, (xiv) a number of crosswalks, (xv) a number of bike lanes, (xvi) a width of a bike lane, (xvii) a curvature, or (xviii) a combination thereof.” (Detailed Description, pp. 8, [0057]; “Some examples of geographic information include, but are not limited to, location information and attribute information. Examples of attribute information include, but are not limited to, information about characteristics of a corresponding location described by some location information: posted speed limit, construction area indicator (i.e., whether location has construction), topography type (e.g., flat, rolling hills, steep hills, etc.), road type (e.g., residential, interstate, 4-lane separated highway, city street, country road, parking lot, etc.), road feature (e.g., intersection, gentle curve, blind curve, bridge, tunnel), number of intersections, whether a roundabout is present, number of railroad crossings, whether a passing zone is present, whether a merge is present, number of lanes, width of road/lanes, population density, condition of road (e.g., new, worn, severely damaged with sink-holes, severely damaged with erosion, gravel, dirt, paved, etc.), wildlife area, state, county, and/or municipality. Geographic information may also include other attribute information about road segments, intersections, bridges, tunnels, railroad crossings, and other roadway features.” The system proposed in this application is able to use data from geographic databases to build static maps for the user. This is an example of the geographic data that can be used by the system to evaluate a section of a roadway for a frustration index.)
Regarding claim 18, Chintakindi discloses, “wherein the static map features associated with the plurality of locations comprises (i) a type of POI, (ii) a dimension of a POI, (iii) a position of a POI relative to a road segment, (iv) a type of landmark, (v) a dimension, (vi) a position of said landmark relative to one of the road segments, or (vii) a combination thereof.” (Detailed Description, pp. 9, [0061]; “For example, in accordance with aspects of this disclosure, a data source 104b may provide the computing device 102b with geographic information that is used to generate new roadway feature risk values in a database of risk values and/or update existing risk values; where the roadway feature may comprise intersections, road segments, tunnels, bridges, or railroad crossings. Attributes associated with roadways may also be used in part to generate risk values. The computing device 102b may use at least part of the received geographic information to calculate a value, associate the value with a road segment (or other location information), and store the value in a database format.” The system in this application is able to use data from different databases. This data can contain location information and roadway related features of the locations.)
Regarding claim 19, Chintakindi fails to explicitly disclose the elements of this claim. However, Zhao discloses, “wherein training the machine learning model on the static map features associated with the plurality of locations and the mood data associated with the plurality of locations further comprises training the machine learning model on the static map features and the mood data associated with the plurality of locations and dynamic features associated with the plurality of locations.” (Machine Learning Module Design and Training Data Collecting, pp. 4; "The foundation of the machine learning module is based on tourism interest big data. Text data that conforms to its function are collected and the valuable information is mined from the data. The valuable information is the training data that will be used in building the machine learning module. Through the process of data de noising, cleaning, integrating and grouping, etc., the training data is precisely processed. The valuable information data should be noted in text format and stored item by item in a database. Each item contains tourists' feature attribute information, and each feature attribute relates to one or more elements in the tourist site classification vector. The final output re suit of the machine learning module is the predicted descending order basic vector with the tourist interest tendency elements from highest to lowest. This system will determine a route for tourism based on many different factors. This will take in user information such as user preferences and the locations themselves. This system will take in static data such as landmarks or locations as well as dynamic data such as user wants and needs and/or financial constraints. It will use this information and use machine learning to determine an output route.)
Regarding claim 20, Chintakindi discloses, “wherein the mood data associated with the plurality of locations comprises (i) traffic data, (ii) weather data, (iii) event data, (iv) a time of day, (v) a day of a week, or (vi) a combination thereof.” (Detailed Description, pp. 7, [0052]; “The data sources 104b, 106b may provide information to the computing device 102b. In one embodiment in accordance with aspects of this disclosure, a data source may be a computer which contains memory storing data and is configured to provide information to the computing device 102b. Some examples of providers of data sources in accordance with aspects of this disclosure include, but are not limited to, insurance companies, third-party insurance data providers, government entities, state highway patrol departments, local law enforcement agencies, state departments of transportation, federal transportation agencies, traffic information services, road hazard information sources, construction information sources, weather information services, geographic information services, vehicle manufacturers, vehicle safety organizations, and environmental information services.” The system in this application is able to use data from different databases to evaluate a section of a roadway. Included in data from car accidents, this data would have location data along with data/time, etc.)
Regarding claim 21, Chintakindi discloses, “wherein determining mood data comprises determining mood data based on one or more visual aspects associated with one or more road segments associated with the plurality of locations based on past vehicle trips, one or more temporal elements corresponding to the one or more road segments of the plurality of the road segments based on past vehicle trips or a combination thereof.” (Detailed Description, pp. 2, [002]; “In some cases, one or more conditions experienced by the driver may have an effect on a level of frustration being experienced by the driver and may be included in a calculation of a road frustration index value for one or more road segments of a travel route. Such conditions may include, but not limited to, road conditions (e.g., potholes, standing water, turns, bridges, narrow lanes, darkness, etc.), a time of day, weather conditions (e.g., rain, fog, wind, storms, an angle of sunshine, etc.), environmental hazards (e.g., debris in the road, a threat of debris falling into the roadway, smoke from a nearby fire, etc. ), a particular human condition, and/or other people within the vehicle (e.g., a number of people in the vehicle, a noise level within the vehicle, a number of children being present, etc.), traffic flow, one or more traffic patterns, a traffic amount (e.g., heavy traffic), a time of day (e.g., night driving, rush hour, etc.), an event that may have an effect on traffic congestion (e.g., a concert, a sporting event, a political rally, etc.), pedestrian traffic (e.g., e.g., a crosswalk, a school zone, etc.), and the like. In some cases, the information may be gathered in near real-time, at time intervals during a trip, before a trip, after a trip, or the like.” This system is able to evaluate data from many different sources as stated above. This data includes visual data of the roadways and transient events. These features are used to generate a static map for the user. Each road section contains a frustration index determining a level of frustration a user might incur or from past user trips.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL MICHAEL GALVIN-SIEBENALER whose telephone number is (571)272-1257. The examiner can normally be reached Monday - Friday 8AM to 5PM.
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, Viker Lamardo can be reached at (571) 270-5871. 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.
/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147