DETAILED ACTION
Response to Amendment
The amendment filed 5/28/26 has been accepted and entered. Accordingly, claims 1, 3 and 6-20 are amended.
Response to Arguments
Applicant’s arguments with respect to the pending claims have been considered but are moot in view of the newly formulated rejection necessitated by applicant’s amendment.
However, at least one argument remains relevant to the current rejection.
With respect to the 35 U.S.C. § 101 rejection, Applicant merely asserts that the amendment renders the rejection moot and that any abstract ideas, if present in the claims are integrated into a practical application (Amend. 13). However, Applicant has failed to address with any particularity, reasoning or rationale as to why the additional subject matter renders the claims patentable. Merely pointing out that additional limitations were added does not amount to a separate patentability argument. Attorney arguments that are conclusory in nature, i.e., providing no further substantive explanation or evidence in support is afforded little weight. See In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997). See also Enzo Biochem, Inc. v. Gen-Probe, Inc., 424 F.3d 1276, 1284 (Fed. Cir. 2005) (“Attorney argument is no substitute for evidence.”). Furthermore, arguments of counsel cannot take the place of factually supported objective evidence. See, e.g., In re Huang, 100 F.3d 135, 139-40, 40 USPQ2d 1685, 1689 (Fed. Cir. 1996); In re De Blauwe, 736 F.2d 699, 705, 222 USPQ 191, 196 (Fed. Cir. 1984; Accord M.P.E.P. 2145. In addition, the arguments of counsel cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965); In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997) ("An assertion of what seems to follow from common experience is just attorney argument and not the kind of factual evidence that is required to rebut a prima facie case of obviousness.").
With respect to the 35 U.S.C. § 102(a)(1) rejection of independent claims 1, 10 and 17, Applicant merely asserts Farmer fails to disclose the amended claim limitations (Amend. 14-16), however, Applicant has failed to address with any particularity, reasoning or rationale as to why this is the case. Rather, Applicant merely provides a conclusory statement that amounts to no more than reciting the disputed limitations and generally alleging that the cited prior art references are deficient. Merely pointing out certain claim features recited in the independent claims and nakedly asserting that none of the cited prior art references disclose such features does not amount to a separate patentability argument. Attorney arguments that are conclusory in nature, i.e., providing no further substantive explanation or evidence in support is afforded little weight. See In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997). See also Enzo Biochem, Inc. v. Gen-Probe, Inc., 424 F.3d 1276, 1284 (Fed. Cir. 2005) (“Attorney argument is no substitute for evidence.”). Furthermore, arguments of counsel cannot take the place of factually supported objective evidence. See, e.g., In re Huang, 100 F.3d 135, 139-40, 40 USPQ2d 1685, 1689 (Fed. Cir. 1996); In re De Blauwe, 736 F.2d 699, 705, 222 USPQ 191, 196 (Fed. Cir. 1984; Accord M.P.E.P. 2145. In addition, the arguments of counsel cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965); In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997) ("An assertion of what seems to follow from common experience is just attorney argument and not the kind of factual evidence that is required to rebut a prima facie case of obviousness.").
Claim Rejections - 35 USC § 112
The rejection of claims 1-20 under 35 U.S.C. 112(b) has been withdrawn as a result of the amendment.
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.
1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Revised Guidance Step 2A – Prong 1
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Here, the claims recite the abstract idea of
monitoring, via one or more computing systems, updates from provider devices and requester devices corresponding to a plurality of transportation requests to determine a pickup transition time for each of a plurality of pickup locations by:
determining, from a provider device, a provider device arrival time at a pickup location corresponding to a transportation request from a requester device;
determining a departure time for the transportation request from the provider device or the requester device; and
comparing the departure time and the provider device arrival time to determine a pickup transition time for the transportation request;
selecting a subset of preferred pickup locations from the plurality of pickup locations based on determining that a subset of pickup transition times corresponding to the subset of preferred pickup locations satisfy a threshold transition time for accommodating autonomous vehicle provider devices; and
transmitting, via the one or more computing systems, one or more of the subset of preferred pickup locations to an autonomous vehicle navigation system for navigating autonomous vehicle provider devices to the one or more of the subset of preferred pickup locations.
Specifically, certain method of organizing human activity, including managing personal behavior or interactions between people following rules as well as a mental process.
The case law establishes the claim limitations reciting gathering and filtering or tailoring data based on relevance to an individual is an abstract idea. Cf. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 at 1313 (Fed. Cir. 2016) (“receiving e-mail (and other data file) identifiers, characterizing email based on the identifiers, and communicating the characterization – in other words, filtering files/e-mail –is an abstract idea”); see also M.P.E.P. 2106.04(a)(2), II (“Certain Methods of Organizing Human Activity”, Section D, example v, citing Symantec); Dealertrack v. Huber, 674 F.3d 1315 at 1333 (Fed. Cir. 2012) (“receiving data from one source, selectively forwarding the data, and forwarding reply data to the first source” constituted an abstract idea); M.P.E.P. 2106.04(a)(2), II (“Certain Methods of Organizing Human Activity”, Section A, citing Dealertrack).
For example, claim 1 manages an interaction between a human driver and a human user by matching a driver with a user and determining a location for them to interact using rules, i.e., see Spec. ¶¶ 27-31.
In addition, the above recited steps also fall within a second enumerated category, specifically “mental processes” since each of the above steps could alternatively be performed in the human mind or with the aid of pen and paper. This conclusion follows from CyberSource Corp. v. Retail Decisions, Inc., where our reviewing court held that section 101 did not embrace a process defined simply as using a computer to perform a series of mental steps that people, aware of each step, can and regularly do perform in their heads. 654 F.3d 1366, 1373 (Fed. Cir. 2011); see also In re Grams, 888 F.2d 835, 840–41 (Fed. Cir. 1989); In re Meyer, 688 F.2d 789, 794–95 (CCPA 1982); Elec. Power Group, LLC v. Alstom S.A., 830 F. 3d 1350, 1354–1354 (Fed. Cir. 2016) (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
Additionally, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource, 654 F.3d at 1375 (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”).
Here, a human could mentally monitor updates upon observing a provider and requester and mentally determine an arrival time, departure time and pickup transition time based on the observation. A human could further entirely mentally select shorter transition times as preferred.
Revised Guidance Step 2A – Prong 2
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)), i.e., “computing systems”; “provider devices”, “requester devices”, “processor”, “computer readable medium”, “servers” are merely broadly recited generic computing components.
In addition, limitations reciting data gathering such the monitoring step is directed to not only organizing human activity and using generic computing components to perform the abstract idea as noted above, but are also insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371–72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
Furthermore, the transmitting step is directed not only to organizing human activity and using generic computing components but also insignificant post-solution activity. The Supreme Court guides that the “prohibition against patenting abstract ideas ‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment’ or [by] adding ‘insignificant post-solution activity.’” Bilski, 561 U.S. at 610–11 (quoting Diehr, 450 U.S. at 191–92).
Revised Guidance Step 2B
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: “computing systems”; “provider devices”, “requester devices”, “processor”, “computer readable medium”, “servers” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A.); (see also, ¶¶ 95–98, 199-202 of the specification). See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Thus, these elements, taken individually or together, do not amount to “significantly more” than the abstract ideas themselves.
The additional elements of the dependent claims merely refine and further limit the abstract idea of the independent claims and do not add any feature that is an “inventive concept” which cures the deficiencies of their respective parent claim under the 2019 PEG analysis. None of the dependent claims considered individually, including their respective limitations, include an “inventive concept” of some additional element or combination of elements sufficient to ensure that the claims in practice amount to something “significantly more” than patent-ineligible subject matter to which the claims are directed.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field (e.g., the field of computer coding technology is not being improved); the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., “to an autonomous vehicle navigation system for navigating autonomous vehicle provider devices”, claim 1).
Revised Guidance Step 2A – Prong 2
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). This follows conclusion follows from the claim limitations which only recite a generic “non-transitory computer readable medium” outside of the abstract idea.
In addition, merely “[u]sing a computer to accelerate an ineligible mental process does not make that process patent-eligible.” Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012); see also CLS Bank Int’l v. Alice Corp. Pty. Ltd., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) (“simply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.”), aff’d, 573 U.S. 208 (2014). Accordingly, the additional element of a controller does not transform the abstract idea into a practical application of the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 20180328747 to Farmer et al. (Farmer)
With respect to claims 1, 10 and 17, Farmer discloses a computer-implemented method comprising:
monitoring, via one or more computing systems, updates from provider devices and requester devices corresponding to a plurality of transportation requests to determine a pickup transition time for each of a plurality of pickup locations by:
(i.e., computing systems shown in FIG. 6 and corresponding description including historical ride data 136C, location scoring system 610 and pickup location evaluation module 134A; ¶ 27, 84)
determining, from a provider device, a provider device arrival time at a pickup location corresponding to a transportation request from a requester device;
(i.e., provider device 150 arrival time in provider information 136B, Historical ride data 136C, location scoring module 612; ¶ 27 “a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride . . . ride history may be stored and used to generate different pickup location scores for a location”; 84)
determining a departure time for the transportation request from the provider device or the requester device; and
(i.e., provider device 150, requestor computing device 120, departure time in provider information 136B, requestor information 136A, Historical ride data 136C, navigation 136D, location scoring module 612; ¶ 27 “a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride . . . ride history may be stored and used to generate different pickup location scores for a location”; 84)
comparing the departure time and the provider device arrival time to determine a pickup transition time for the transportation request;
(i.e., historical arrival and departure times are used to determine historical transition times for a particular location, and is performed for a series of locations: ¶ 27 “a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride . . . ride history may be stored and used to generate different pickup location scores for a location”)
selecting a subset of preferred pickup locations from the plurality of pickup locations based on determining that a subset of pickup transition times corresponding to the subset of preferred pickup locations satisfy a threshold transition time for accommodating autonomous vehicle provider devices; and
(FIG. 9, 912, in the use case where at least one location does not have a pickup location score over threshold, a subset from a plurality of pickup locations is selected; the pickup location score threshold can be entirely represented by a transition time (delay time) -- ¶ 27 “the pickup location score may be based on . . . and/or a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride”; ¶ 28 use the pickup location score as a filter to ensure that any optimization of pickup locations is only performed on those locations that are of a sufficient quality. For example, pickup location scores for alternate request locations may be compared to a threshold pickup location score before being used in any pickup optimization process to ensure the location being optimized is at least a certain quantitative quality and will not result in a loss of time savings from the optimization due to a low quality pickup location”)
(¶ 29 “location score may be converted into a delay time”)
(¶ 84 “pickup location evaluation module 134A may be coupled to the ride history store 136C to determine the pickup location scores for the respective locations . . . aggregated data on pickup location scores of multiple locations, time delays associated with those locations”)
(¶¶ 102-103 “pickup location score may be based on . . . or a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . When the pickup location score for that pickup location meets that threshold value, the location may be identified as a suitable pickup location for the request. The pickup location score threshold may be used to filter the pickup request location”)
transmitting, via the one or more computing systems, one or more of the subset of preferred pickup locations to an autonomous vehicle navigation system for navigating autonomous vehicle provider devices to the one or more of the subset of preferred pickup locations.
(922, 924, FIG. 9 and corresponding description; Fig. 6 from ride matching system 130, bidirectional arrow provider computing device 150; FIG. 8, 806, YES, 822, 810-812, 814 YES, 816 and corresponding description; similarly, Fig. 9-10 and corresponding descriptions; ¶ 26 pickup location scores may be used to identify particularly good locations within a region, road, block, subblock, etc. for interactions between requestors and providers; 66 provider application generates navigation directions)
With respect to claims 2, 11 and 18, Farmer discloses
wherein selecting the subset of preferred pickup locations comprises utilizing an autonomous vehicle location filter to select the filtered subset, wherein the autonomous vehicle location filter indicates an area accessible to autonomous vehicles.
(¶¶ 39, 42 i.e., filtering out low pickup locations scores, 238A-238K, “238A and 238K . . . may be closed off due to construction . . . or inaccessible by providers”)
With respect to claim 3, Farmer discloses determining that the pickup transition time satisfies the threshold transition time for accommodating the autonomous vehicle provider devices by comparing the pickup transition time with the threshold transition time to determine a first transition classification for the transportation request, the first transition classification indicating that the pickup transition time is a long transition relative to the threshold transition time.
(¶¶ 39-41 large delay . . . reasonable delay . . . negligible delay; 43 pickup location scores may be converted into time delay metrics; cf., Spec. ¶ 61 transition classification . . . long transition . . . short transition; ¶ 64 three transition classifications, long, medium, short; 43 the pickup location scores may be compared to a threshold pickup location score value and as long as they meet that threshold value, the locations may be sufficiently fit)
(¶ 84 “ride history data store 136C may include aggregated data on pickup location scores of multiple locations, time delays associated with those locations”; 102 “the pickup location score may be based on . . . a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . or any other information associated with delay between a provider and a requestor that can be associated with a poor location for a pickup may be used in determining a pickup location score for a location”; 29 “the location score may be converted into a delay time that can be applied to an estimated travel time and/or estimated time of arrival for a request. For example, the average amount of delay time between a provider arriving at a request location and the matched ride starting . . . the converted delay time from the pickup location score may be incorporated into the pickup path optimization process to ensure that the alternate request location determination process incorporates the delay due to the quality of the pickup location”; 39 “The pickup location score threshold value may be determined based on historical data of previous . . . time to pickup”)
With respect to claim 4, Farmer discloses
determining an additional pickup transition time corresponding to the pickup location for an additional transportation request from an additional requester device;
comparing the additional pickup transition time with the threshold transition time to determine a second transition classification for the additional transportation request; and
determining a measure of threshold-length transition pickups1 for the pickup location based on the first transition classification and the second transition classification.
(¶¶ 28-30 “determine, track . . . measured quality scores of pickup locations based on previous ride history”; FIG. 2C depicts, for a given area/ curb segment, a standard of quality for the pickup location, which is based on a history of a length of transition time for historical transportation requests; i.e., 39-41 pickup location scores . . . the pickup location score threshold value may be determined based on . . . time to pickup, time from pickup to start of ride . . . historical numbers of successful pickups . . . large delay . . . moderate pickup location scores . . . may meet a pickup location score threshold but may not be excellent locations for interactions between providers and requestors . . . some delay . . . delay is minimal or reasonable . . . good pickup location scores . . . minimal . . . delay . . . minimal cancellations . . . pickup location scores may be determined using historical ride information associated with thousands of previous rides . . . poor pickup locations may have a history of matched rides . . . that resulted in . . . long delays; 43, 64)
(¶ 84 “ride history data store 136C may include aggregated data on pickup location scores of multiple locations, time delays associated with those locations”; 102 “the pickup location score may be based on . . . a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . or any other information associated with delay between a provider and a requestor that can be associated with a poor location for a pickup may be used in determining a pickup location score for a location”; 29 “the location score may be converted into a delay time that can be applied to an estimated travel time and/or estimated time of arrival for a request. For example, the average amount of delay time between a provider arriving at a request location and the matched ride starting . . . the converted delay time from the pickup location score may be incorporated into the pickup path optimization process to ensure that the alternate request location determination process incorporates the delay due to the quality of the pickup location”; 39 “The pickup location score threshold value may be determined based on historical data of previous . . . time to pickup”)
With respect to claim 5, Farmer discloses selecting the subset of preferred pickup locations by comparing the measure of threshold-length transition pickups corresponding to the pickup location with an additional measure of threshold-length transition pickups corresponding to an additional pickup location
(¶¶ 28-30 “determine, track . . . measured quality scores of pickup locations based on previous ride history”; FIG. 2C depicts, for a given area/ curb segment, a standard of quality for the pickup location, which is based on a history of a length of transition time for historical transportation requests; i.e., 39-41 pickup location scores . . . the pickup location score threshold value may be determined based on . . . time to pickup, time from pickup to start of ride . . . historical numbers of successful pickups . . . large delay . . . moderate pickup location scores . . . may meet a pickup location score threshold but may not be excellent locations for interactions between providers and requestors . . . some delay . . . delay is minimal or reasonable . . . good pickup location scores . . . minimal . . . delay . . . minimal cancellations . . . pickup location scores may be determined using historical ride information associated with thousands of previous rides . . . poor pickup locations may have a history of matched rides . . . that resulted in . . . long delays; 43, 64)
(¶ 84 “ride history data store 136C may include aggregated data on pickup location scores of multiple locations, time delays associated with those locations”; 102 “the pickup location score may be based on . . . a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . or any other information associated with delay between a provider and a requestor that can be associated with a poor location for a pickup may be used in determining a pickup location score for a location”; 29 “the location score may be converted into a delay time that can be applied to an estimated travel time and/or estimated time of arrival for a request. For example, the average amount of delay time between a provider arriving at a request location and the matched ride starting . . . the converted delay time from the pickup location score may be incorporated into the pickup path optimization process to ensure that the alternate request location determination process incorporates the delay due to the quality of the pickup location”; 39 “The pickup location score threshold value may be determined based on historical data of previous . . . time to pickup”)
With respect to claims 6 and 13, Farmer discloses
wherein the pickup transition time corresponds to a first time period and selecting the subset of preferred pickup locations comprises selecting a first subset of preferred pickup locations for the first time period, and further comprising: determining an additional plurality of pickup transition times corresponding to a second time period2; and selecting a second subset of preferred pickup locations corresponding to the second time period.
(¶ 27 “pickup location score may be determined through a variety of different methods using different types of information . . . pickup location scores may be time dependent such that a pickup location score for a particular location during one time ( e.g., morning commute) is different than during another time (e.g., afternoon). Accordingly, ride history may be stored and used to generate different pickup location scores for a location according to variables including time (e.g., morning, commute, afternoon, night, etc .), day (e.g., weekday, weekend, holidays, etc .)”)
With respect to claims 7, 14, and 20 Farmer discloses
determining a transportation mode corresponding to the transportation request,
wherein the transportation mode comprises at least one of a multi-passenger mode or a limited eligibility transportation mode3; and
selecting the subset of preferred pickup locations based on the subset of pickup transition times and the transportation mode.
(¶ 34 ride matching system 130 . . . requestor transport restrictions (e.g., pet friendly, child seat, wheelchair accessible, etc.) (FIG. 9, 912, in the use case where at least one location does not have a pickup location score over threshold, a subset from a plurality of pickup locations is selected; the pickup location score threshold can be entirely represented by a transition time (delay time) -- ¶ 27 “the pickup location score may be based on . . . and/or a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride”; ¶ 28 use the pickup location score as a filter to ensure that any optimization of pickup locations is only performed on those locations that are of a sufficient quality. For example, pickup location scores for alternate request locations may be compared to a threshold pickup location score before being used in any pickup optimization process to ensure the location being optimized is at least a certain quantitative quality and will not result in a loss of time savings from the optimization due to a low quality pickup location”) (¶ 29 “location score may be converted into a delay time”) (¶ 84 “pickup location evaluation module 134A may be coupled to the ride history store 136C to determine the pickup location scores for the respective locations . . . aggregated data on pickup location scores of multiple locations, time delays associated with those locations”) (¶¶ 102-103 “pickup location score may be based on . . . or a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . When the pickup location score for that pickup location meets that threshold value, the location may be identified as a suitable pickup location for the request. The pickup location score threshold may be used to filter the pickup request location”)
With respect to claims 8 and 15 Farmer discloses
determining a provider device rating associated with the provider device; and
selecting the subset of preferred pickup locations from the plurality of pickup locations based on the subset of pickup transition times and the provider device rating.
(¶ 36 ride matching system 130 . . . rating . . . or any other relevant information for facilitating the match and/or service being provided”; 41 pickup location scores may be determined using historical ride information associated with thousands of previous rides . . . additional criteria can be used . . . poor provider ratings) (FIG. 9, 912, in the use case where at least one location does not have a pickup location score over threshold, a subset from a plurality of pickup locations is selected; the pickup location score threshold can be entirely represented by a transition time (delay time) -- ¶ 27 “the pickup location score may be based on . . . and/or a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride”; ¶ 28 use the pickup location score as a filter to ensure that any optimization of pickup locations is only performed on those locations that are of a sufficient quality. For example, pickup location scores for alternate request locations may be compared to a threshold pickup location score before being used in any pickup optimization process to ensure the location being optimized is at least a certain quantitative quality and will not result in a loss of time savings from the optimization due to a low quality pickup location”) (¶ 29 “location score may be converted into a delay time”) (¶ 84 “pickup location evaluation module 134A may be coupled to the ride history store 136C to determine the pickup location scores for the respective locations . . . aggregated data on pickup location scores of multiple locations, time delays associated with those locations”) (¶¶ 102-103 “pickup location score may be based on . . . or a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . When the pickup location score for that pickup location meets that threshold value, the location may be identified as a suitable pickup location for the request. The pickup location score threshold may be used to filter the pickup request location”)
With respect to claims 9 and 16 Farmer discloses
determining a number of transportation requests corresponding to the pickup location; and
selecting the subset of preferred pickup locations based on the number of transportation requests corresponding to the pickup location and the subset of pickup transition times.
(¶ 39 “ride matching . . . pickup location scores . . . curb segments . . . historical number of successful pickups; 45; 69; 83; location scoring . . . historical data . . . record of requests from a particular location) (FIG. 9, 912, in the use case where at least one location does not have a pickup location score over threshold, a subset from a plurality of pickup locations is selected; the pickup location score threshold can be entirely represented by a transition time (delay time) -- ¶ 27 “the pickup location score may be based on . . . and/or a delay time measured between a time of arrival by the provider at a request location and a time of arrival of the requester to the provider for the start of the matched ride”; ¶ 28 use the pickup location score as a filter to ensure that any optimization of pickup locations is only performed on those locations that are of a sufficient quality. For example, pickup location scores for alternate request locations may be compared to a threshold pickup location score before being used in any pickup optimization process to ensure the location being optimized is at least a certain quantitative quality and will not result in a loss of time savings from the optimization due to a low quality pickup location”) (¶ 29 “location score may be converted into a delay time”) (¶ 84 “pickup location evaluation module 134A may be coupled to the ride history store 136C to determine the pickup location scores for the respective locations . . . aggregated data on pickup location scores of multiple locations, time delays associated with those locations”) (¶¶ 102-103 “pickup location score may be based on . . . or a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . When the pickup location score for that pickup location meets that threshold value, the location may be identified as a suitable pickup location for the request. The pickup location score threshold may be used to filter the pickup request location”)
With respect to claims 12 and 19, Farmer discloses
determine an additional pickup transition time corresponding to the pickup location for an additional transportation request from an additional requester device;
combine4 the pickup transition time and the additional pickup transition time to determine an expected pickup transition time at the pickup location; and
select the subset of preferred pickup locations by comparing the expected pickup transition time with a threshold transition time.
(¶¶ 28-30 “determine, track . . . measured quality scores of pickup locations based on previous ride history”; FIG. 2C depicts, for a given area/ curb segment, a standard of quality for the pickup location, which is based on a history of a length of transition time for historical transportation requests; i.e., 39-41 pickup location scores . . . the pickup location score threshold value may be determined based on . . . time to pickup, time from pickup to start of ride . . . historical numbers of successful pickups . . . large delay . . . moderate pickup location scores . . . may meet a pickup location score threshold but may not be excellent locations for interactions between providers and requestors . . . some delay . . . delay is minimal or reasonable . . . good pickup location scores . . . minimal . . . delay . . . minimal cancellations . . . pickup location scores may be determined using historical ride information associated with thousands of previous rides . . . poor pickup locations may have a history of matched rides . . . that resulted in . . . long delays; 43, 64)
(¶ 84 “ride history data store 136C may include aggregated data on pickup location scores of multiple locations, time delays associated with those locations”; 102 “the pickup location score may be based on . . . a delay time measured between a time of arrival by a provider at a request location and a time of arrival of the requestor to the provider for the start of the matched ride . . . or any other information associated with delay between a provider and a requestor that can be associated with a poor location for a pickup may be used in determining a pickup location score for a location”; 29 “the location score may be converted into a delay time that can be applied to an estimated travel time and/or estimated time of arrival for a request. For example, the average amount of delay time between a provider arriving at a request location and the matched ride starting . . . the converted delay time from the pickup location score may be incorporated into the pickup path optimization process to ensure that the alternate request location determination process incorporates the delay due to the quality of the pickup location”; 39 “The pickup location score threshold value may be determined based on historical data of previous . . . time to pickup”).
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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/KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667
1 No limiting definition provided. As best understood, the specification discussed “measure of threshold-length transition pickups” in terms of functionality, i.e., what it can do, rather than what it is, i.e., Spec. ¶ 35, something that “assesses the number of transition pickups that meet a certain standard or requirement”
2 Although no limiting definition is provided, the specification indicates this can refer to changing determinations based on context, i.e., where different transition times can be determined for different times of day. See Spec. ¶ 63.
3 Under a BRI, this term can include any factor that limits the pool of potential providers (Spec. ¶ 37).
4 Under a BRI, combining data, i.e., data1 and data2, in order to make a determination includes merely using both data1 and data2 in order to make the determination.
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