DETAILED ACTION
Introduction
Claims 1-20 have been examined in this application. Claims 1 and 16 are amended. Claims 2-8, 10-15, and 17-20 are as previously presented. Claim 9 is original.
This is a final office action in response to the arguments and amendments filed 2/27/2026. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Office Action Formatting
The following is an explanation of the formatting used in the instant Office Action:
• [0001] – Indicates a paragraph number in the most recent, previously cited source;
• [0001, 0010] – Indicates multiple paragraphs (in example: paragraphs 1 and 10) in the most recent, previously cited source;
• [0001-0010] – Indicates a range of paragraphs (in example: paragraphs 1 through 10) in the most recent, previously cited source;
• 1:1 – Indicates a column number and a line number (in example: column 1, line 1) in the most recent, previously cited source;
• 1:1, 2:1 – Indicates multiple column and line numbers (in example, column 1, line 1 and column 2, line 2) in the most recent, previously cited source;
• 1:1-10 – Indicates a range of lines within one column (in example: all lines spanning, and including, lines 1 and 10 in column 1) in the most recent, previously cited source;
• 1:1-2:1 – Indicates a range of lines spanning several columns (in example: column 1, line 1 to column 2, line 1 and including all intervening lines) in the most recent, previously cited source;
• p. 1, ln. 1 – Indicates a page and line number in the most recent, previously cited source;
• ¶1 – The paragraph symbol is used solely to refer to Applicant's own specification (further example: p. 1, ¶1 indicates first paragraph of page 1); and
• BRI – the broadest reasonable interpretation.
Response to Arguments
Applicant's arguments, filed 2/27/2026, have been fully considered.
Regarding the arguments pertaining to the claim rejections under 101 (presented on p. 11-15 under the heading “II. REJECTION OF CLAIMES 1-20 UNDER 35 U.S.C. §101”), the arguments and amendments are not persuasive.
The arguments (p. 12-13) state that the claim is not directed to a mental process as it recites automatic extraction of features from imagery using machine learning functionality and image recognition functionality. The arguments (p. 13) cite McRO, Inc. v. Bandai Namco Games America Inc. in which the court emphasized that claims focused on a specific asserted improvement in computer animation. The arguments state that the claims of the present invention are a similar technological improvement, however the office respectfully disagrees. It is noted that the claims of the present invention (including image analysis for the purpose of vehicle navigation) are not analogous to computer animation, which is a field of generating and outputting synthesized imagery instead of evaluating real images. Additionally, the office submits that the claims of the present invention are not focused on “a specific asserted improvement” in these fields, because the claim merely mentions the field of machine learning and image recognition by name, as opposed to providing any detail or improvement in such techniques. Rather, the techniques are merely used to generate input data for the abstract idea.
The arguments (p. 13) further recite the added “monitoring” and “automatically recomputing” limitations in Claim 1, and state that these cannot be practically performed by the human mind. However, the office respectfully disagrees. The broadest reasonable interpretation of monitoring of data sources in real-time is merely receiving and evaluating updated data as it becomes available. The office submits that a person can perform this limitation mentally or manually, for example by receiving updates to current weather conditions at particular intervals and immediately recomputing the factors and danger rating. The ability of a processor to perform such functions faster is not determined to preclude the limitation from being able to be performed mentally and is not determined to integrate the judicial exception into a practical application or amount to significantly more as "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). The arguments (p. 13-14) further cite Thales Visionix Inc. v. United States, where the claims were determined not to be directed to an abstract idea because the mathematical equations were used with a particular sensor configuration to improve tracking accuracy. The arguments state that the claims of the present invention are similarly applying mathematical concepts in an architecture to improving navigation system behavior, however the office submits that the claims of the present invention are not analogous to a specific improved sensor configuration, and that the improvement is the decision-making of the abstract idea itself, and there is not determined to be an improvement in computer architecture or sensing or any other technological field based on the evaluation of additional elements in the claim (see the complete rejection below for more detail).
The arguments (p. 14) state that any abstract idea is integrated into a practical application as the claim improves computer functionality, as it requires modifying identification and recommendation of routes based on the first danger rating. The arguments state this alters the routing logic of the system. However, the consideration of a new danger rating to decide a new route is determined to be part of the abstract idea, as a person can decide, for example, to avoid a particularly dangerous segment. The office submits that this is part of the abstract idea as opposed to an additional element which can integrate an abstract idea into a practical application.
The arguments (p. 14) recite the case of Enfish, LLC v. Microsoft Corp., where claims were determined to not be abstract as they were directed to a specific improvement in the way computers operate, by solving a problem in the software arts. The arguments state that the claims of the present invention similarly modify navigation and changes the decision-making of the routing engine itself. However, the improvement in Enfish pertained to the use of a referential table for a computer database, which is not analogous to the present claims. The office maintains that using various factors and danger ratings to decide on routes is not a technological improvement and does not actually change the engine’s core function, it merely calculates routes using variable input data, which a person can do mentally or manually such as by considering different segment risks for times of day and different conditions.
The arguments (p. 14) further discuss Electric Power Group, LLC v. Alstom S.A., regarding the new limitations of machine learning / image recognition and monitoring and dynamic re-computation. However, the Electric Power Group case law is only relied upon regarding the additional elements of controlling a graphical user interface. Thus, the arguments are moot. See the complete rejection below regarding the evaluation of the new limitation of Claim 1.
The arguments (p. 14-15) further state that the arrangement of known components of the feature extraction and dynamically recomputed safety model to modify routes is a non-generic integration of components. However, the office submits that such a combination of components is well-understood, routine, and conventional in the art for example see US2017/0236013A1 at [0002-0005] regarding machine learning for vision in the field of self-driving vehicles (which necessarily includes updating of trajectory/route and dynamic re-computation based on new information). Therefore, the rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(101 Analysis - Step 1 - Statutory Category) Regarding Claims 1-20, the claims are directed to one of the statutory categories of subject matter as the claims recite a process, machine, manufacture or composition of matter.
(101 Analysis - Step 2A, Prong I - Judicial Exception) Regarding Independent Claim 1, the claim recites a method involving a computing device comprising a processor, and the method comprising:
executing, on the processor, instructions that cause the processor to perform operations, the operations comprising:
evaluating incident data associated with a first road segment to determine an incident trend for the first road segment;
identifying a set of factors associated with the first road segment, including automatically extracting one or more features from imagery of the first road segment using machine learning functionality and image recognition functionality to identify at least one factor of the set of factors;
calculating a first value by multiplying together a first subset of the set of factors that are indicative of the first road segment being dangerous, wherein an average severity of accidents along the first road segment, a road geometry factor, a dangerous slowdown factor, a real-time risk factor, a personal user risk, and a total accidents along the first road segment are selected from the set of factors as the first subset of the set of factors;
calculating a second value by multiplying together a second subset of the set of factors that are indicative of the first road segment not being dangerous, wherein a volume of traffic along the first road segment, a number of days since a last accident along the first road segment, and a construction improvement factor are selected from the set of factors as the second subset of the set of factors;
generating a first danger rating for the first road segment by dividing the first value by the second value, wherein the first danger rating is generated based upon a calculation of multiplying the first subset of the set of factors as the first value, a calculation of multiplying the second subset of the set of factors, and dividing the first value by the second value;
assigning the first danger rating to the first road segment;
monitoring one or more data sources for real-time changes to at least one factor associated with the first road segment and, in response to detecting a change to the at least one factor, automatically recomputing the first danger rating in real time based upon the change;
modifying identification and recommendation of routes to a destination location based upon the first danger rating such that route suggestions are generated using the first danger rating; and
controlling a graphical user interface of the computing device to (i) display a map illustrating a plurality of road segments, including the first road segment, associated with a set of routes to a destination location and (ii) provide access to causation factors for why the first road segment illustrated in the map is considered safe or dangerous as indicated by the first danger rating, wherein the controlling the graphical user interface to display the map comprises (i) displaying a first visual representation of the first road segment in the map with a first visual characteristic based upon the first danger rating of the first road segment associated with the causation factors and (ii) displaying a second visual representation of a second road segment in the map with a second visual characteristic based upon a second danger rating of the second road segment.
The limitations indicated in BOLD above, under their broadest reasonable interpretation, are an abstract idea of a mental process, capable of being performed in a human mind or manually, using pen and paper (see MPEP 2106.04(a)(2)(III)).
Particularly, a person is capable of mentally or manually performing a method comprising:
evaluating incident data associated with a first road segment to determine an incident trend for the first road segment (for example a person evaluating a printed or memorized table of incidents and times to determine a trend of when most incidents happen);
identifying a set of factors associated with the first road segment, including extracting one or more features from imagery of the first road segment to identify at least one factor of the set of factors (the person looking at imagery to extract features such as road grade, a blind crest/turn, or presence of construction cones, in order to identify a factor such as dangerous grade, or visibility issue, or presence of construction);
calculating a first value by multiplying together a first subset of the set of factors that are indicative of the first road segment being dangerous (the person multiplying mentally or manually, or alternatively the abstract idea of a mathematical operation of multiplication), wherein an average severity of accidents along the first road segment, a road geometry factor, a dangerous slowdown factor, a real-time risk factor, a personal user risk, and a total accidents along the first road segment are selected from the set of factors as the first subset of the set of factors (the person thinking of dangerous factors, or selecting the factors from the list based on judgment);
calculating a second value by multiplying together a second subset of the set of factors that are indicative of the first road segment not being dangerous (the person multiplying mentally or manually, or alternatively the abstract idea of a mathematical operation of multiplication), wherein a volume of traffic along the first road segment, a number of days since a last accident along the first road segment, and a construction improvement factor are selected from the set of factors as the second subset of the set of factors (the person thinking of non-dangerous factors, or selecting the factors from the list based on judgment);
generating a first danger rating for the first road segment by dividing the first value by the second value, wherein the first danger rating is generated based upon a calculation of multiplying the first subset of the set of factors as the first value, a calculation of multiplying the second subset of the set of factors, and dividing the first value by the second value (the person performing the multiplication and division mentally or manually, or alternatively the abstract idea of a mathematical operations of multiplication and division);
assigning the first danger rating to the first road segment (the person associating or memorizing or writing as an output the road segment and danger rating);
monitoring one or more data sources for real-time changes to at least one factor associated with the first road segment (for example the person observing or receiving a weather report for a current time, as the real-time risk factor, indicating changing conditions) and, in response to detecting a change to the at least one factor, automatically recomputing the first danger rating in real time based upon the change (the person performing the calculations again, for the danger rating, as soon as the updated information becomes available); and
modifying identification and recommendation of routes to a destination location based upon the first danger rating such that route suggestions are generated using the first danger rating (for example the person reconsidering and identifying and recommending a different route that avoids the first road segment when the danger rating exceeds some amount).
Thus, the claim recites an abstract idea.
Independent Claim 10 recites a device. The functions in Claim 10 which correspond to the method steps of Claim 1 are an abstract idea for the same reasons as presented above. Claim 10 additionally recites reducing the set of road safety data to create a reduced set of road safety data by determining and populating the reduced set of road safety data with percentages of road segments that correspond to values of danger ratings. This is a further function of the abstract idea of a mental process, as a person can simplify a map or table of data by categorizing segments into percentages of the segments with particular danger ratings. Alternatively, such limitation could be the abstract idea of a mathematical operation of statistical analysis. Thus, the claim recites an abstract idea.
Independent Claim 16 recites the same functions as Claim 1 and therefore also recites an abstract idea. Claim 16 additionally recites in response to determining that there is less than a threshold amount of incident data available for assigning a second danger rating to a second road segment: extracting features from imagery of the first road segment and the second road segment to determine whether the first road segment and the second road segment have a threshold amount of similar factors, and in response to the first road segment and the second road segment having the threshold amount of similar factors, assigning the first danger rating of the first road segment to the second road segment as the second danger rating. This is a further function of the abstract idea of a mental process, as a person can compare amount of data available with a threshold (such as manually comparing in a printed table), and can analyze images in memory or manually printed images to determine whether the first road segment and the second road segment have a threshold amount of similar factors by manually comparing and counting features for example, and in response to the first road segment and the second road segment having the threshold amount of similar factors, assigning the first danger rating of the first road segment to the second road segment as the second danger rating, such as the person associating the first danger rating to the second road segment, based on judgment. Thus, the claim recites an abstract idea.
(101 Analysis - Step 2A, Prong II - Practical Application) This judicial exception is not integrated into a practical application. The limitations indicated with underlining above are additional elements in the claim. That is, the additional elements in the claim are the method involving a computing device comprising a processor, comprising: executing, on the processor, instructions that cause the processor to perform operations, in Claim 1, and the extracting of image features “automatically” by using machine learning functionality and image recognition functionality (and similarly in Claim 16 executing image analysis using machine learning functionality to extract features), controlling a graphical user interface of the computing device to (i) display a map illustrating a plurality of road segments, including the first road segment, associated with a set of routes to a destination location and (ii) provide access to causation factors for why the first road segment illustrated in the map is considered safe or dangerous as indicated by the first danger rating, wherein the controlling the graphical user interface to display the map comprises (i) displaying a first visual representation of the first road segment in the map with a first visual characteristic based upon the first danger rating of the first road segment associated with the causation factors and (ii) displaying a second visual representation of a second road segment in the map with a second visual characteristic based upon a second danger rating of the second road segment in Claim 1 (and also Claims 10 and 16), and a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause the processor to perform operations in Claim 10, and the non-transitory computer-readable storage medium having stored thereon processor-executable instructions that when executed cause a processor to perform operations in Claim 16.
For the method involving a computing device comprising a processor, comprising: executing, on the processor, instructions that cause the processor to perform operations, in Claim 1, the computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause the processor to perform operations in Claim 10, and the non-transitory computer-readable storage medium having stored thereon processor-executable instructions that when executed cause a processor to perform operations in Claim 16, these elements are all recitations of generic computer components and their use, recited at a high level of generality. The claims do not provide an improvement in computer hardware or computing technology. Therefore, the claims act as mere instructions to “apply” the abstract idea using generic computer components as tools to perform the functions. This does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
For the extracting of image features “automatically” by using machine learning functionality and image recognition functionality in Claim 1, and executing image analysis using machine learning functionality to extract features in Claim 16, this is recited broadly, without any details of the particular machine learning or image recognition technique. Therefore, the claim merely generally ties the abstract idea to the field of image analysis / machine learning, and does not limit the claim or abstract idea in a meaningful way. Therefore, it does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
For the controlling a graphical user interface of the computing device to (i) display a map illustrating a plurality of road segments, including the first road segment, associated with a set of routes to a destination location and (ii) provide access to causation factors for why the first road segment illustrated in the map is considered safe or dangerous as indicated by the first danger rating, wherein the controlling the graphical user interface to display the map comprises (i) displaying a first visual representation of the first road segment in the map with a first visual characteristic based upon the first danger rating of the first road segment associated with the causation factors and (ii) displaying a second visual representation of a second road segment in the map with a second visual characteristic based upon a second danger rating of the second road segment in Claims 1, 10, and 16, this is determined to be insignificant extra-solution activity, because the displaying is merely data outputting (see MPEP 2106.05(g), examples of activities that the courts have found to be insignificant extra-solution activity include selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
Additionally, the ordered combination of additional elements and claim as a whole are not determined to integrate the abstract idea into a practical application as the ordered combination does not add anything already present when the elements are considered separately and merely recites input and output of data to/from a processor, at a high level of generality, for display and generally tied to the field of machine learning / image recognition.
(101 Analysis - Step 2B - Significantly More / Inventive Concept) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As above, the additional elements in the claim are the method involving a computing device comprising a processor, comprising: executing, on the processor, instructions that cause the processor to perform operations, in Claim 1, and the extracting of image features “automatically” by using machine learning functionality and image recognition functionality (and similarly in Claim 16 executing image analysis using machine learning functionality to extract features), controlling a graphical user interface of the computing device to (i) display a map illustrating a plurality of road segments, including the first road segment, associated with a set of routes to a destination location and (ii) provide access to causation factors for why the first road segment illustrated in the map is considered safe or dangerous as indicated by the first danger rating, wherein the controlling the graphical user interface to display the map comprises (i) displaying a first visual representation of the first road segment in the map with a first visual characteristic based upon the first danger rating of the first road segment associated with the causation factors and (ii) displaying a second visual representation of a second road segment in the map with a second visual characteristic based upon a second danger rating of the second road segment in Claim 1 (and also Claims 10 and 16), and a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause the processor to perform operations in Claim 10, and the non-transitory computer-readable storage medium having stored thereon processor-executable instructions that when executed cause a processor to perform operations in Claim 16.
For the method involving a computing device comprising a processor, comprising: executing, on the processor, instructions that cause the processor to perform operations, in Claim 1, the computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause the processor to perform operations in Claim 10, and the non-transitory computer-readable storage medium having stored thereon processor-executable instructions that when executed cause a processor to perform operations in Claim 16, for the same reasons as presented above, these elements are all recitations of generic computer components and their use, at a high level of generality, such that the claims act as mere instructions to “apply” the functions using a generic computer components as tools to perform the functions. This does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)). Additionally, such elements are well-understood, routine, and conventional in the art (see MPEP 2106.05(d) computer functions which are recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity include: ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)).
For the extracting of image features “automatically” by using machine learning functionality and image recognition functionality in Claim 1, and executing image analysis using machine learning functionality to extract features in Claim 16, for the same reasons as above, these elements are recited broadly such that the claim merely generally ties the abstract idea to the field of machine learning and image analysis, and does not limit the claim or abstract idea in a meaningful way. This does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).Additionally, such elements are well-understood, routine, and conventional in the art (see e.g. US2016/0379132A1 at [0004]).
For the controlling a graphical user interface of the computing device to (i) display a map illustrating a plurality of road segments, including the first road segment, associated with a set of routes to a destination location and (ii) provide access to causation factors for why the first road segment illustrated in the map is considered safe or dangerous as indicated by the first danger rating, wherein the controlling the graphical user interface to display the map comprises (i) displaying a first visual representation of the first road segment in the map with a first visual characteristic based upon the first danger rating of the first road segment associated with the causation factors and (ii) displaying a second visual representation of a second road segment in the map with a second visual characteristic based upon a second danger rating of the second road segment in Claims 1, 10, and 16, which was determined to be insignificant extra-solution activity, this is re-evaluated in step 2B and determined to be well-understood, routine, and conventional in the art (see e.g. US2002/0183924A1, [0006] it is well known to display a map with route information, different visual representations, and additional information, in the art of vehicle navigation).
Additionally, the ordered combination of additional elements and claim as a whole are not determined to amount to significantly more as the combination merely establishes input of data to a processor by broadly recited image recognition / machine learning and output to a display, and does not establish an inventive concept (see e.g. US2002/0183924A1 [0006] and US20170236013A1 at [0002-0005]).
Dependent Claims 2-9, 11-15, and 17-20, do not add limitations that integrate the abstract idea into a practical application or amount to significantly more.
Claim 2 recites the method of claim 1, wherein the controlling the graphical user interface comprises displaying a plurality of selectable options that are each associated with a suggested route to the destination location. This is further detail of the controlling of the graphical user interface which does not integrate the abstract idea into a practical application or amount to significantly more as it is determined to be insignificant extra-solution activity and well understood, routine, and conventional in the art (see e.g. US20020183924A1 [0002-0006]).
Claim 3 recites the method of claim 1, the operations comprising: evaluating the set of factors to identify a causation factor having a likelihood above a threshold of being a cause of incidents along the first road segment. This is a further detail of the abstract idea of a mental process, as a person can evaluate by comparing factors to threshold likelihoods. The claim does not add any new additional elements.
Claim 4 recites the method of claim 1, the operations comprising: in response to determining that a third road segment lacks a threshold amount of incident data for assigning a danger rating to the third road segment, comparing factors of the third road segment to factors of the first road segment; and in response to the factors of the third road segment having a similarity above a threshold to the factors of the first road segment, assigning a second danger rating to the third road segment based upon the first danger rating of the first road segment. This is a further detail of the abstract idea of a mental process, as a person can determine a lack of a threshold amount of provided information, can compare factors, and judge similarity, and associate a second danger rating to a third road segment by mental or manual association. The claim does not add any new additional elements.
Claim 5 recites the method of claim 1, the operations comprising: in response to determining that a factor of the set of factors associated with the first road segment has changed as a changed factor, updating the first danger rating based upon the changed factor. This is a further detail of the abstract idea of a mental process, as a person can decide to perform another iteration of the calculation steps to update a danger rating. The claim does not add any new additional elements.
Claim 6 recites the method of claim 1, the operations comprising: generating the set of routes from a starting location to the destination location for a trip to be traveled by a user; and assigning danger ratings to road segments of the set of routes, wherein the danger ratings are based upon at least one of factors of the road segments, incident data for the road segments, or driver attributes of the user. This is a further detail of the abstract idea of a mental process, as a person can generate routes by mentally knowing roads or manually on a map, and can assign the danger ratings (which per Claim 1 are based on at least one factor of the road segments) to the road segments that have been selected as part of the route. The claim does not add any new additional elements.
Claim 7 recites the method of claim 1, the operations comprising: generating the set of routes from a starting location to the destination location for a trip to be traveled by a user; and assigning danger ratings to road segments of the set of routes, wherein the danger ratings are based upon at least one of factors of the road segments, incident data for the road segments, or vehicle attributes of a vehicle driven by the user. This is a further detail of the abstract idea of a mental process, as a person can generate routes by mentally knowing roads or manually on a map, and can assign the danger ratings (which per Claim 1 are based on at least one factor of the road segments) to the road segments that have been selected as part of the route. The claim does not add any new additional elements.
Claim 8 recites the method of claim 1, the operations comprising: generating the set of routes from a starting location to the destination location for a trip to be traveled by a user; and assigning danger ratings to road segments of the set of routes. This is a further detail of the abstract idea of a mental process, as a person can generate routes by mentally knowing roads or manually on a map, and can assign the danger ratings to the road segments that have been selected as part of the route. The claim does not add any new additional elements.
Claim 9 recites the method of claim 1, the operations comprising: in response to the first danger rating exceeding a danger threshold, displaying a warning about the first road segment. The comparing of a danger rating to a threshold is a further function of the abstract idea of a mental process, able to be performed by a person. The display of a warning is an additional element in the claim but is broad data output for display and does not integrate the abstract idea into a practical application or amount to significantly more as it is determined to be insignificant extra-solution activity and well understood, routine, and conventional in the art (see e.g. US20020183924A1 [0002-0006]).
Claim 11 recites the computing device of claim 10, the operations comprising: predicting a likelihood that an accident will occur along the first road segment during a timeframe; and displaying the likelihood that the accident will occur on the graphical user interface of the computing device. The predicting is a further function of the abstract idea of a mental process, able to be performed by a person, such as by observation and judgment of historical accident rates. The displaying is an additional element in the claim but is broad data output for display and does not integrate the abstract idea into a practical application or amount to significantly more as it is determined to be insignificant extra-solution activity and well understood, routine, and conventional in the art (see e.g. US20020183924A1 [0002-0006]).
Claim 12 recites the computing device of claim 10, the operations comprising: performing image analysis upon imagery of the first road segment to identify a factor for inclusion with the set of factors associated with the first road segment. This is a further detail of the abstract idea of a mental process, as a person can evaluate a scene and decide what factors should be included based on observation and judgement. The claim does not add any new additional elements.
Claim 13 recites the computing device of claim 10, the operations comprising: performing image analysis upon first imagery of the first road segment to identify a first factor of the first road segment; performing the image analysis upon second imagery of a third road segment to identify a second factor of the third road segment; and in response to the first factor corresponding to the second factor above a threshold, assigning a second danger rating to the third road segment based upon the first danger rating of the first road segment. This is a further detail of the abstract idea of a mental process, as a person can evaluate imagery of two segments and identify or select factors, and decide correspondence that meets some criteria or threshold, in order to further associate danger ratings and segments. The claim does not add any new additional elements.
Claim 14 recites the computing device of claim 10, the operations comprising: evaluating autonomous vehicle data to identify a factor for inclusion with the set of factors associated with the first road segment. This is a further function of the abstract idea of a mental process, as a person can perform evaluation of received data on paper, which originated from an autonomous vehicle. The claim does not add any additional elements.
Claim 15 recites the computing device of claim 10, the operations comprising: generating a driving profile for a driver associated with the computing device based upon one or more trips traveled by the driver; and utilizing the driving profile to assign one or more danger ratings to road segments along routes of future trips to be traveled by the driver. This is a further function of the abstract idea of a mental process as a person can mentally memorize or write out a driver profile based on trip history, and use such data to personalize danger ratings (e.g. drivers with more or less risk associated with traffic amount, construction sites, or twisty road geometry) along planned trips. The recitation of the association with the computing device does not integrate the abstract idea into a practical application or amount to significantly more for the same reasons presented above with respect to the Independent Claims.
Claim 17 recites the non-transitory computer-readable storage medium of claim 16, the operations comprising: generating the set of routes from a starting location to the destination location for a trip to be traveled by a user; and assigning danger ratings to road segments of the set of routes, wherein the danger ratings are based upon at least one of demographic information of the user or demographic information of residents within a proximity distance threshold of the road segments. This is a further detail of the abstract idea of a mental process, as a person can generate routes by mentally knowing roads or manually on a map, and can assign the danger ratings to the road segments including further factors such as demographic information, that have been selected as part of the route. The claim does not add any new additional elements.
Claim 18 recites the non-transitory computer-readable storage medium of claim 16, the operations comprising: assigning danger ratings to road segments of the set of routes as road safety data; and reducing the road safety data to a reduced set of road safety data corresponding to percentages of road segments along the set of routes that correspond to values of danger ratings. This is a further function of the abstract idea of a mental process, as a person can assign danger ratings and simplify data such as sorting the segments into percentage categories. Alternatively the limitations are an abstract idea of a mathematical concept of statistical analysis. The claim does not add any additional elements.
Claim 19 recites the non-transitory computer-readable storage medium of claim 16, the operations comprising: identifying trends of drivers across demographic profiles as trend data; pre-populating a driving profile for a driver based upon a portion of the trend data corresponding to demographic profile information of the driver; and utilizing the driving profile to assign danger ratings to road segments along routes of future trips to be traveled by the driver. These are additional functions of the abstract idea of a mental process, as a person can identify trends in data by observation and judgment, and can write a driver profile with the trend data depending on demographic, to assign danger rating. The claim does not add any additional elements.
Claim 20 recites the non-transitory computer-readable storage medium of claim 16, the operations comprising: evaluating information corresponding to a planned road segment to be built to assign a danger rating for the planned road segment. This is a further detail of the mental process of evaluation of a road, whether theoretical or real. The claim does not add any additional elements.
Allowable Subject Matter
Claims 1-20 are rejected under 101, but would be allowable if amended to overcome the rejections under 101.
The following is an examiner’s statement of reasons for indicating allowable subject matter:
Regarding Independent Claims 1, 10, and 16, Patent U.S. 9,574,888 B1 (Hu et al.) teaches a method/device/ non-transitory computer-readable storage medium (see Figures 3, 5, 8:52-55, contextual risk knowledge pattern learner 310, part of route generation module 16, which 4:9-11 can be on the client system (4:57-67) using a processor), performing: evaluating incident data associated with a first road segment to determine an incident trend for the first road segment (see Figure 5, 9:1-40, a hash map is populated with accidents from a historical accident list (HAL) and a number of accidents can be associated with an evaluated segment (a determined trend)); identifying a set of factors associated with the first road segment (see 9:1-52, a number of accidents associated with the segment (accident type factors), as well as dynamic environmental attributes (environmental factors) associated with the segment); assigning the first danger rating to the first road (see 9:16-33, a route is plotted against the hash map and 9:52-54 in step 516 a risk level (danger rating) can be assigned for a particular segment/link); and displaying the first danger rating on a display of the computing device (see 4:49-56, Figure 9, the client device displaying risk level on a section of a route using the table and color code).
Published Application US2022/0042807A1 (Benericetti et al.) teaches a technique to calculate a danger rating (see [0032] a road safety score), based upon dividing a first value and second value (see [0032-0033] the road safety score for roadways (a segment) being a ratio of the number of harsh driving events to the number of general driving events).
KR-20180071178-A teaches a technique to calculate accident risk by dividing by traffic volume (see [0089-0092]).
However, the prior art does not disclose or render obvious a method/system/ non-transitory computer-readable storage medium with instructions for:
calculating a first value by multiplying together a first subset of the set of factors that are indicative of the first road segment being dangerous, wherein an average severity of accidents along the first road segment, a dangerous slowdown factor, a real-time risk factor, and a total accidents along the first road segment as selected from the set of factors as the first subset of the set of factors;
calculating a second value by multiplying together a second subset of the set of factors that are indicative of the first road segment not being dangerous, wherein a volume of traffic along the first road segment, a number of days since a last accident along the first road segment, and a construction improvement factor are selected from the set of factors as the second subset of the set of factors;
generating a first danger rating for the first road segment by dividing the first value by the second value, wherein the first danger rating is generated based upon a calculation of multiplying the first subset of the set of factors as the first value, a calculation of multiplying the second subset of the set of factors, and dividing the first value by the second value.
Applicant’s arguments and the amendments addressing the previous rejections under 103 (presented on p. 10-13 in the arguments filed 5/2/2022) are persuasive, and the prior rejections are withdrawn. The combination of limitations defining the particular inputs, and combination of multiplication and division functions to calculate the danger rating, combined with the particularly claimed parts integrated into the computing device is not found or made obvious by the prior art. The combination with the other claim limitations are neither anticipated nor made obvious by the prior arts on record. A search of foreign prior art and Non-Patent Literature was conducted; however, no relevant prior art was found.
As such the claimed subject matter of Claims 1, 10, and 16 would be allowable. The subject matter of Claims 2-9, 11-15, and 17-20 would also therefore be allowable as being dependent on Claims 1, 10, or 16.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/P.A./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669