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 .
Status of Claims
This action is in response to the claims filed on 04/01/2026. Wherein, claims 1, 5, 6, 9, 13, 14, and 16 are amended. Claims 1-20 are rejected.
Response to Arguments
Applicant's arguments, see REMARKS, filed 04/01/2026, with respect to the rejection of claims 1-20 under 35 USC §101, have been fully considered but they are not persuasive. Therefore, the previous rejections under 35 USC §101 are maintained.
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1-20 under 35 USC §103, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Cho et al.
With respect to claims 1-20, rejected under 35 USC §101, the Applicant argues:
The Office Action rejects claims 1-20 under a judicial exception to 35 U.S.C. § 101 because the claimed invention is allegedly directed to non-statutory subject matter. The amended claim expressly recites "extracting . .. end points or start points," "generating a polygon," "generating a filtered set of potential pickup locations . . . and removing . . . door points within the polygon," and then "determining ... a pickup location" from that filtered set. This ordered structure improves computational efficiency by constraining the dataset before model execution, allowing the system to evaluate only roadway-relevant candidates rather than all possible door points. By pre-filtering structured geographic data to eliminate unnecessary pickup candidates, the claims reduce the amount of data the computer system needs to process. As in Enfish, this reflects a specific improvement in how the computing system processes data, not an abstract idea performed on generic components.
“It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).” (MPEP 2106.05(a)(I))
“In contrast, limitations that confine the judicial exception to a particular, practical application of the judicial exception may amount to significantly more or integrate the judicial exception into a practical application. For example, in BASCOM, the combination of additional elements, and specifically “the installation of a filtering tool at a specific location, remote from the end‐users, with customizable filtering features specific to each end user” where the filtering tool at the ISP was able to “identify individual accounts that communicate with the ISP server, and to associate a request for Internet content with a specific individual account,” were held to be meaningful limitations because they confined the abstract idea of content filtering to a particular, practical application of the abstract idea. 827 F.3d at 1350-51, 119 USPQ2d at 1243.” (2106.05(f)(3))
Here, the filtering limitation is not confining the abstract idea of content filtering to a particular, practical application of the abstract idea in a way that is similar to that of BASCOM. Instead, the filtering step is merely generating a filtered set of location data based on comparison with other location data. This sort of filtering may be performed by a person with the aid of a pen and paper, e.g., drawing a map containing specific geographic data within a shape and then making a list of locations that fall within that shape. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (MPEP 2106.04(a)(2)(III))
The filtering limitation is not being applied to a particular, practical application like BASCOM. Instead, the step is performing a mental process using a generic computer as a tool, e.g., parsing and comparing data recited at a high level of generality. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Therefore, the Examiner finds these arguments unpersuasive and maintains the previous rejections under 35 USC §101.
With respect to the rejections under 35 USC §103, the Applicant argues:
Although Pao teaches that transport data "may [] be evaluated" to "suggest a target pickup location," or that a road segment "can be evaluated" and a predicted pickup location "may be adjusted" or "snapped" to the road segment, these teachings fail to describe, teach, or suggest the recited limitations. Indeed, Pao's general teaching regarding evaluating transport data fails to describe, teach, or suggest "extracting, by the one or more servers, end points or start points for one or more road segments which correspond to the door points." Neither does Pao teach "determining, by the one or more servers, a plurality of nodes from the end points or the start points for one or more road segments corresponding to the door points." Furthermore, Pao is silent as to "generating a polygon based on the plurality of nodes determined from the end points or the start points of the one or more road segments." In addition, Pao fails to teach "generating a filtered set of potential pickup locations by comparing the door points to the polygon and removing, from the door points, one or more door points within the polygon;" and "determining, utilizing a pickup location model to process the filtered set of potential pickup locations, a pickup location for the transportation request.
Examiner cordially disagrees with the argument that Pao fails to teach "determining, by the one or more servers, a plurality of nodes from the end points or the start points for one or more road segments corresponding to the door points."
Pao teaches that the system uses generated node, e.g., start and end points, and segment data, i.e., the length of segments between the nodes, that correspond to the door points, e.g., pickup and drop-off locations. As discussed in previously cited Col. 4, ln. 38-54, the system uses road node/segment data that is structured to include direction of travel, elevation, and is further associated with pickup/drop-off points. This information is then used to determine the most efficient route to the pick/drop-off locations.
With respect to the Applicants argument that “Pao is silent as to "generating a polygon based on the plurality of nodes determined from the end points or the start points of the one or more road segments."” one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Here, Pao discloses creating boundaries using a clustering technique based on the plurality of nodes determined from the end points or start points of the one or more road segments. Pao does not explicitly teach that the boundary includes a polygon, but Fig. 4a and 4b suggest that the boundaries could be a square, e.g., the square created by 412, 414, and 402. However, Colijn was cited as explicitly teach a boundary that includes a polygon. Thus, the combination of Pao and Colijn explicitly teach the above argued limitation.
Therefore, the Examiner finds this argument unpersuasive.
Applicant’s argument that “Pao fails to teach "generating a filtered set of potential pickup locations by comparing the door points to the polygon and removing, from the door points, one or more door points within the polygon;"” is unpersuasive due to the same clustering technique discussed above and cited in the previous rejection. By only providing potential pickup locations that are within the cluster, the system is generating a set of filtered locations based on the cluster boundary.
Therefore, the Examiner finds this argument unpersuasive.
Finally, the Applicant’s argument that Pao fails to teach “determining, utilizing a pickup location model to process the filtered set of potential pickup locations, a pickup location for the transportation request” is unpersuasive. The term “model” in this claim is extremely broad and any sort of data processing rule applied to the filtered set could be interpretated as a “model” in the context of the limitation. In Pao the system uses previous pickup locations and compares them to one another to determine which location should be moved. By applying previously used data the system as created a model and then uses that model to further analyze the clustered data.
Therefore, the Examiner finds this argument unpersuasive.
Although Colijn selects "predetermined locations within the threshold distance" and teaches that certain points "may be filtered from or not included in the set of suggested locations," this approach is fundamentally different than the recited claims. Indeed, selecting based on "threshold distances," as taught by Colijn, fails to teach "extracting, by the one or more servers end points or start points for one or more road segments which correspond to the door points" and "determining, by the one or more servers, a plurality of nodes from the end points or the start points for one or more road segments corresponding to the door points." Furthermore, Colijn's teaching regarding threshold distances fails to describe, teach, or suggest "generating a polygon based on the plurality of nodes determined from the end points or the start points of the one or more road segments." Furthermore, Colin fails to teach "generating a filtered set of potential pickup locations by comparing the door points to the polygon and removing, from the door points, one or more door points within the polygon;" and "determining, utilizing a pickup location model to process the filtered set of potential pickup locations, a pickup location for the transportation request.
As provided above, Pao teaches the entirety of the independent claims with the exception of “extracting, by the one or more servers end points or start points for one or more road segments which correspond to the door points” and that the filtering boundary is explicitly a polygon.
However, Colijn in Fig. 7, as previously cited, discloses a location filtering boundary which is explicitly a rectangle, e.g., a polygon. Therefore, the combination of Pao and Colijn discloses the entirety of the independent claims with the exception of “extracting, by the one or more servers end points or start points for one or more road segments which correspond to the door points.
However, upon further search and consideration a new rejection is presented below in view of Cho et al.
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.
Step 1
Claim 1 is directed towards a computer-implemented method. Claim 9 is directed towards a system. Claim 16 is directed towards A non-transitory computer readable medium comprising instructions.
Step 2A, Prong 1
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See the 2019 Revised Patent Subject Matter Eligibility Guidance.
In the instant application, independent claim 1 recites:
“…generating…door points for the transportation request that fall within a threshold of intersection points associated with the request location;
extracting…end points or start points for one or more road segments which correspond to the door points;
determining…a plurality of nodes determined from the end points or the start points for one or more road segments corresponding to the door points;
…generating a polygon based on the plurality of nodes determined from the end points or the start points of the one or more road segments;
….generating a filtered set of potential pickup locations by comparing the door points to the polygon and removing, from the door points, one or more door points within the polygon;
…determining, utilizing a pickup location model to process the filtered set of potential pickup locations, a pickup location for the transportation request…”
Independent claims 9 and 16 recite substantially similar limitations.
These claim limitations, when given their broadest reasonable interpretation, may be performed in the human mind. Therefore these limitations are abstract ideas and claims 1, 9, and 16 are directed to a judicial exception.
Step 2A, Prong 2
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it' ' (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See the 2019 Revised Patent Subject Matter Eligibility Guidance.
In the instant application, claims 1, 9, and 16 do not recite additional elements that integrate the judicial exception into a practical application of that exception. Claim 1 recites a server generically, i.e., “computing devices” (See ¶ [0050]) Claim 9 and 16 recites “at least one processor” generically, i.e., “a microprocessor” (See at least ¶ [0127]) Additionally, claims 9 and 16 recite “non-transitory computer readable medium comprising instructions” generically, i.e., “RAM, ROM, EEPROM, CD-ROM, solid state drives (SSDs) (e.g., based on RAM), Flash memory, phase-change memory (PCM), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.” (See at least ¶ [0129]) These combinations of elements merely describe a generic computer that is used as a tool to perform the abstract idea. These steps are not meaningful limitations on the judicial exception. The server, processor, and memory are recited so generically (no details whatsoever are provided other than that they are a server processor and memory) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Therefore, claims 1, 9, and 16 do not recite additional elements that integrate the judicial exception into a practical application of that exception.
Claim 1 further recites “receiving…a transportation request from a requester device…” and “providing…for display on the requester device, a pickup location interface including indication of the pickup location” The acts of receiving and displaying data are considered extra-solution activities. Therefore, these additional elements do not integrate the judicial exception into a practical application of that exception.
Claims 9 and 16 recite substantially similar limitations, therefore the same analysis applies to their limitations as well.
Step 2B
Finally, even when a judicial element is recited in the claim, an additional claim element(s) that amounts to significantly more than the judicial exception renders the claim eligible under §101. Examples that are not enough to amount to significantly more than the abstract idea include 1) mere instructions to implement the abstract idea on a computer, 2) simply appending well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well understood, routine and conventional activities previously known to the industry, 3) adding insignificant extra-solution activity to the judicial exception, and 4) generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to amount to significantly more than the abstract idea. Examples of generic computing functions that are not enough to amount to significantly more than the abstract idea include 1) performing repetitive calculations, 2) receiving, processing, and storing data, 3) electronically scanning or extracting data from a physical document, 4) electronic recordkeeping, 5) automating mental tasks, and 6) receiving or transmitting data over a network, e.g., using the Internet to gather data.
In the instant application, claims 1, 9, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this particular application, the same analysis above in determining whether the recited additional elements integrate the judicial exception into a practical application of that exception is applicable to determine if the additional elements amount to significantly more than the judicial exception.
Based on the above analysis, claims 1, 9, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 2, 10, and 17 recite additional abstract ideas that may be performed mentally, i.e., “…determining a first start node for a first road segment and a first end node for the first road segment; and determining a second start node for a second road segment and a second end node for the second road segment.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claims 3, 11, and 18, recite “…wherein generating the polygon based on the plurality of nodes of the one or more road segments comprises generating a polygon enclosing a region defined by the first start node, the first end node, the second start node, and the second end node. Which further defines an abstract idea identified above. However, the claim does not recite any additional elements and, therefore, does not recite any additional elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claims 4, 12, and 19 recite additional abstract ideas that may be performed mentally, i.e., “…removing the one or more door points based on determining that the region defined by the first start node, the first end node, the second start node, and the second end node encompasses the one or more door points.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claims 5 and 13 recite additional abstract ideas that may be performed mentally, i.e., “…dividing a traffic circle into a plurality of traffic circle road segments; and generating the plurality of nodes from a set of start points or end points of the plurality of traffic circle road segments.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claims 6 and 14 recite additional abstract ideas that may be performed mentally, i.e., “…identifying two parallel road segments from a digital map; and based on identifying the two parallel road segments, generating the polygon by generating a rectangular polygon from a set of start points or end points of the plurality of nodes for the two parallel road segments.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claim 7 recites additional abstract ideas that may be performed mentally, i.e., “…generating the filtered set of potential pickup locations by removing the one or more door points from a median area between the two parallel road segments that falls within the rectangular polygon.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claim 8 recites additional abstract ideas that may be performed mentally, i.e., “…the pickup location model is trained to generate pickup location scores from training door points and training pickup locations and determining the pickup location comprises utilizing the pickup location model to generate a plurality of pickup location scores for the filtered set of potential pickup locations.” In the instant application the claims recite the elements of their independent claims. However, as in their independent claims the elements are disclosed at a high level of generality. Therefore, the elements are no more than a generic computing element that is performing a generic computing activity. Thus, the claims do not recite elements that integrate the judicial exception into a practical application of that exception or amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-4, 6, 8-12, and 14-20, are rejected under 35 U.S.C. 103 as being unpatentable over Pao et al. (US 9,769,616 B1, “Pao”) in view of Cho et al. (US 2016/0076901 A1, “Cho”) and in further view of Colijn et al. (US 2019/0170520 A1, “Colijn”).
Regarding claims 1, 9, and 16, Pao discloses geohash-related location predictions and teaches:
A system comprising: (FIG. 7 illustrates an example block diagram 700 of a dynamic transportation matching system 130, in accordance with an embodiment of the present techniques – See at least Col. 16, ln. 41-43)
at least one processor; and (FIG. 11 shows an example computer system 1100, in accordance with various embodiments. In various embodiments, computer system 1100 may be used to implement any of the systems, devices, or methods described herein – See at least Col. 26, ln. 57-60; The computer system further contains a processing subsystem 1122 – See at least Col. 27, ln. 6)
a non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the system to: (Memory subsystem 1112 can include various types of memory, including RAM, ROM, flash memory, or other memory…As shown in FIG. 11,memory 1112 can include applications 1114 and application data 1110. Applications 1114 may include programs, code, or other instructions, that can be executed by a processor – See at least Col. 28, ln. 17-30)
receive a transportation request from a requester device indicating a request location; (At step 502, the dynamic transportation matching system receives a ride request from a requestor computing device. The ride request may include a request location (i.e., pick-up location) for the ride request that corresponds to GPS or other location data of the requestor computing device, a request time, a requestor identifier, a requestor computing device location, and/or any other relevant information associated with the ride request and/or requestor – See at least Col. 13, ln. 60-67)
generate door points for the transportation request that fall within a threshold of intersection points associated with the request location; (FIGS. 4A-4B illustrate example approaches for identifying prior transport data in a geographic location to be utilized by a dynamic transportation matching system, in accordance with an embodiment of the present techniques. In the example 400 of FIG. 4A, a requestor generates a transport request 404 at a particular location in a building 402 at the intersection of two streets 416-418, and having 65 two exits, one to each street 416-418 – See at least Col.11, ln. 59-67 The system utilizes distance threshold boundaries relating to the requested location.)
determine a plurality of nodes from the end points or the start points for one or more road segments corresponding to the door points; (Additionally, one or more embodiments may use road segment data, such as may be stored by a data structure 40 related to road segment system nodes associated with length. Direction, elevation, etc., and further associated with pickup/drop-off locations and distance traveled along respective nodes to get there. For example, road segment data having a particular direction in the geographical area 45 related to the request may be determined, and the directionality evaluated with respect to a navigation route to the requested destination. If a road segment within a threshold distance of a predicted pickup location has road segment data that is more efficient ( e.g., is in a direction headed 50 towards the destination, etc.), then the predicted pickup location may be adjusted ( or initially placed in some embodiments) to be adjacent (e.g., "snapped") to the appropriate road segment – See at least Col. 4, ln. 38-54)
generate a [] based on the plurality of nodes determined from the end points or the start points of the one or more road segments; (A sub-clustering approach in an embodiment uses a boundary 218 around the current request location 208 and/or the geographical area associated with the cluster. This boundary may be a uniform radius of a particular distance around the request location 208, or may be an irregularly-shaped boundary, depending on the embodiment. For example, "noisy" instances of prior transport data may not be valuable and/or relevant to a current request location 208; therefore, a boundary 218 is generated outside of which instances of prior transport data are disregarded, weighted less heavily, etc. A determination of a boundary 218 may be based on various factors, such as number of instances of prior transport data contained within, as discussed with regard to FIG. 3. In an embodiment, a boundary 218 is generated such that it includes certain road segments – See at least Col. ln. 9-32; Examiner notes that )
generate a filtered set of potential pickup locations by comparing the door points to the [boundary] and removing, from the door points, one or more door points within the [boundary]; (The system creates clusters of potential pickup locations. From this cluster, it further filters into sub-clusters, i.e., removing one or more points within a boundary – See at least Col. 7, ln. 53-Col. 8, ln. 9-44)
determine, utilizing a pickup location model to process the filtered set of potential pickup locations, a pickup location for the transportation request; and (In an embodiment, a set of instances of various types of prior transport data around a request location 208 is determined, such as within a boundary 218. These instances may then be evaluated with regard to each other in order to suggest a target pickup location for the current request location 208 – See at least Col. 9, ln. 4-9)
provide, for display on the requester device, a pickup location interface including an indication of the pickup location. (In an embodiment, a target pickup location may be determined based on movement of user-selectable locations in the past. For example, a potential target pickup location 216 may have been sent to a requestor in response to a prior request. In an embodiment, a requestor' s computing device receives modified transport information from the dynamic transportation matching system after a transport match is made; for example, the requestor's computing device may receive a potential target pickup location and display it in a manner that visually distinguishes it from the corresponding request location (e.g., a pin vs. a flashing blue dot), along with walking directions to the potential target pickup location – See at least Col. 10, ln. 13-25)
Pao does not explicitly teach extract end points or start points for one or more road segments which correspond to the door points. However, Cho discloses a method and apparatus of computing location of safe exit for moving range query in a road network and teaches:
extract end points or start points for one or more road segments which correspond to the door points; (The server computes range query results at the start node and destination node of each road segment, and com pares the range query results, thereby determining whether the range query results are identical to each other. In the road segment to which the query location q belongs, the road segment is segmented based on the query location q, and range query results for the resulting road segments are compared with each other. The server may find P, P and P as safe exits, as listed in Table 1. In this case, since a safe exit is not present between the node n and the query location q, the server extends a search range to nodes ns, n2 n and n7 adjacent to the node n, and compares range query results. A safe exit is not present between the node n and the node n, but the road network shown in FIG. 2 does not include a road network after the node n. Accordingly, a description of results obtained by extending the search range to nodes adjacent to the node n is omitted herein – See at least ¶ [0081]-[0082]; Here the server is extracting a start and end point of road segments, e.g., n1 to n2. These segments ultimately build a road network leading to a final location, e.g., n6 in Fig 1. The system takes these extracted end points and processes them to determine if the segment is a safe region for exiting that segment, e.g., to move to the next segment on the path.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao to provide for the method and apparatus of computing location of safe exit, as taught in Cho, to reduce a computational load. (At Cho ¶ [0014])
The combination of Pao and Cho does not explicitly teach that the boundaries are polygons. However, Colijn discloses determine pickup and destination locations for autonomous vehicles and teaches:
generate a filtered set of potential pickup locations by comparing the door points to the polygon and removing, from the door points, one or more door points within the polygon; (Fig. 7 illustrates a rectangular boundary, i.e., a polygon. The system may remove potential pickup locations within the identified boundaries – See at least ¶ [0062])
In summary, Pao discloses filtering sets of potential pickup locations based on a boundary, e.g., a circular or irregularly shaped boundary. The combination of Pao and Cho does not explicitly teach the use of a polygon as a boundary. However, Colijn discloses circular and rectangular boundaries and further teaches filtering out potential pickup locations within the boundaries.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao and Cho to provide for the determining pickup and destination locations for autonomous vehicles, as taught in Colijn, to provide for increases in the availability, safety, and usefulness of the services of autonomous vehicles. (At Colijn ¶ [0022])
Regarding claims 2, 10, and 17, Pao further teaches:
determining a first start node for a first road segment and a first end node for the first road segment; and
determining a second start node for a second road segment and a second end node for the second road segment. (According to an embodiment, a data structure (e.g., a portable location format) describing road segments may be generated that describes road segment nodes by such 35 factors as direction, elevation, distance, etc., such as in relation to instances of prior transport data – See at least Col. 13, ln. 32-37; Here, the system describes multiple segments, i.e., at least a first and second segment. The system further describes multiple nodes for each segment, i.e., at least a start and end node.)
Regarding claims 3, 11, and 18, Pao further teaches:
wherein generating the [boundary] based on the plurality of nodes of the one or more road segments comprises generating a [boundary]enclosing a region defined by the first start node, the first end node, the second start node, and the second end node. (In an embodiment, a boundary 218 is generated such that it includes certain road segments. For example, a request location 208 may be adjacent to roads. In the example 200 of FIG. 2, the request location 208 is adjacent Main Street and 2nd Avenue. The boundary 218 for a sub-clustering approach may be generated that includes road segments from both Main Street and 2nd Avenue, such as the intersection, as well as road segments of both streets that travel in opposite directions (i.e., the side of the street adjacent the request location 208 and the other side of the street, for a two-way street) – See at least Col. 8, ln. 30-44; Examiner notes that the boundary includes the segment and segments are defined by their nodes, therefore Pao teaches the above claim language.)
Regarding claims 4, 12, and 19, the combination of Pao and Cho does not explicitly teach, but Colijn further teaches:
wherein generating the filtered set of potential pickup locations comprises removing the one or more door points based on determining that the region defined by the first start node, the first end node, the second start node, and the second end node encompasses the one or more door points. (The set of suggested locations may include all of the predetermined locations within the threshold distance, as shown in FIGS. 7 and 8. Alternatively, the set may include one or more predetermined locations that are closest to the received location, up to some maximum value, such as 3 or more or less, within the threshold distance. For example, as shown in FIG. 9, points having locations within the set of suggested locations are shown as darkened circles and those not included have only a dark outline. Thus, while radius 820 and circle 830 identify a plurality of points, points 322 and 324 are not ones of the 3 closest to the map marker 810. In this regard, the locations of points 322 and 324 may be filtered from or not included in the set of suggested locations – See at least ¶ [0063])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao and Cho to provide for the determining pickup and destination locations for autonomous vehicles, as taught in Colijn, to provide for increases in the availability, safety, and usefulness of the services of autonomous vehicles. (At Colijn ¶ [0022])
Regarding claims 6 and 14, Pao further teaches:
identifying two parallel road segments from a digital map; and (One request 426 has a corresponding actual pickup location 426a across the street 418. By determining road segment 422, 424 data for the road 418, at least a direction 422a, 424a may be determined – See at least Col. 13, ln. 15-18; Examiner notes that segments 422 and 424 are parallel – See at least Fig. 4B)
based on identifying the two parallel road segments, generating the polygon by generating a rectangular polygon from a set of start points or end points the plurality of nodes for the two parallel road segments. (In an embodiment, a boundary 218 is generated such that it includes certain road segments – See at least Col. 8, ln. 33-34; In the case of a boundary including road segments 422 and 424, the boundary would be created based on identifying the two parallel road segments.)
The combination of Pao and Cho does not explicitly teach that the boundary is a polygon, however Colijn further teaches a polygon boundary.
[] generating the polygon by generating a rectangular polygon [] (As shown in Fig. 7, the boundary is a rectangle.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao and Cho to provide for the determining pickup and destination locations for autonomous vehicles, as taught in Colijn, to provide for increases in the availability, safety, and usefulness of the services of autonomous vehicles. (At Colijn ¶ [0022])
Regarding claims 8, 15, and 20, Pao further teaches:
wherein the pickup location model is trained to generate pickup location scores from training door points and training pickup locations and determining the pickup location comprises utilizing the pickup location model to generate a plurality of pickup location scores for the filtered set of potential pickup locations. (In the selected geohash, prior transport data for other people is analyzed to determine various instances of prior transport data corresponding to their previous requests. For example, 100 people in the building may have made numerous prior service requests where their request location was reported to be inside the building; in some embodiments, it is determined how close their prior request locations are to the current requestor's request location. Their actual pickups related to those requests are analyzed to make a prediction where an efficient pickup location around the (unfamiliar to the requestor) 30 building may be, based on other people's prior transport data. In some embodiment, additional signals may be utilized, such as relationship of the other people to the requestor (e.g., location, company, etc.), destinations of the other people, weather, time of day, etc. These signals, including the amount and/or type of prior transport data, may be assigned varying weights in evaluations to determine one or more predicted pickup locations.)
Claim(s) 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Pao in view of Cho and Colijn, as applied to claims 1 and 9, and in further view of Millington et al. (US 6,529,822 B1, “Millington”).
Regarding claims 5 and 13, the combination of Pao, Cho, and Colijn does not explicitly teach dividing a traffic circle into a plurality of traffic circle road segments; and generating the plurality of nodes from the plurality of traffic circle road segments. However, Millington discloses navigation system with zoomed maneuver instructions and teaches:
dividing a traffic circle into a plurality of traffic circle road segments; and (The traffic circle 90 includes a first road segment 92, a circle road segment 94 and an exit road segment 96 – See at least Col. 5, ln. 53-56)
generating the plurality of nodes from a set of start points or end points of the plurality of traffic circle road segments. (As described above, each of the segments 92, 94, and 96 include a beginning point B, an end point E, and a plurality of shape points S as illustrated in the expanded view of FIG. 4B – See at least Col. 5, ln. 57-61)
In summary, Pao discloses dividing the geographical area up into road segments and nodes. The combination of Pao, Cho, and Colijn does not explicitly teach that the roads include traffic circles. However, Millington discloses navigation system with zoomed maneuver instructions and teaches dividing up roads, specifically traffic circles, into segments and nodes.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao, Cho, and Colijn to provide for the navigation system with zoomed maneuver instructions, as taught in Millington, to provide clear and detailed instructions through the complex maneuver. (At Millington Col. 1, ln. 9-11)
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pao in view of Cho and Colijn, as applied to claim 1, and in further view of Lawson et al. (Illegal Stopping on Georgia Roadways, “Lawson”)
Regarding claim 7, Pao does not explicitly teach, but Colijn further teaches:
further comprising generating the filtered set of potential pickup locations by removing the one or more door points from [] within the rectangular polygon. (The scoring may be based on various factors that quantify the ease and/or difficulty of reaching the predetermined location by one or both of an autonomous vehicle and the user. Factors related to an autonomous vehicle may include, for example, the location of any autonomous vehicles available to pick up the user (if a pickup location), whether the vehicle would have to first pass the location (on the opposite side of the street) and turn around, whether the autonomous vehicle can currently reach the predetermined location (because access is temporarily prevented due to traffic or construction conditions), the availability of parking or places to pull over and wait at the predetermined location, as well as any other such factors – See at least ¶ [0068])
The combination of Pao and Colijn does not explicitly teach that the filtered areas include a median area between two parallel road segments. However, Lawson discloses illegal stopping on Georgia roadways and teaches:
[] a median area between the two parallel road segments [] (What are the Rules As To Where a Driver Can and Cannot Stop or Park a Vehicle? …In the area between roadways of a divided highway, including crossovers…)
In summary, Colijn discloses filtering out pickup locations via a weighting system. The weighting system allows for the removal of locations that do not have parking or places to pullover. The combination of Pao and Colijn does not explicitly teach that these locations include a road median. However, Lawson discloses illegal stopping on Georgia roadways and teaches that areas between a divided highway, i.e., a median, are illegal to stop at.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the geohash-related location predictions of Pao, Cho, and Colijn to provide for the laws, as taught in Georgia, to obey traffic and vehicle laws.
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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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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