Detailed Action
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Status of Claim
This action is in reply to the action filed on 14 of May 2026.
Claims 1, 8, and 15 have been amended.
Claims 1-20 are currently pending and are rejected as described below.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/14/2026 has been entered.
35 USC § 102/103
Applicant’s arguments with respect to Claims 1-20 have been considered but are moot in light of new grounds of rejection.
35 USC § 101
Applicant asserts that none of these limitations sets forth or describes a mathematical relationship, formula, or equation. As the August 2025 Memorandum confirms that elements involving machine learning that do not themselves recite mathematical formulas or equations do not fall within the ''mathematical concepts'' grouping. The amended element is precisely such an element. The examiner respectfully disagrees. Computing under the broadest reasonable interpretation equates to calculating which falls within the abstract idea of a mathematical calculation. MPEP 2106.04(a)(2)(C) describes it in the following manner. “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation Therefore, the invention remains an abstract idea under 2A prong I.
The applicant asserts that the amended element also confirms that the claims cannot practically be performed in the human mind. Embedding an incoming data log entry and an arbitrary number of vehicle records into a shared latent vector space, computing proximity in that space, and outputting a confidence score for the match is not an operation that a person can practically perform mentally. The examiner respectfully disagrees. The human mind performs complex tasks and calculations, often in real-time, and is precisely the benchmark for all AI models. Inserting data into a model is something a human can do as well, while the computer is used as a tool to aid the method. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.
Applicant asserts that the claims recite a particular technical solution to the technical problem identified at Specification ¶22 associating an incoming data log entry with a specific vehicle in a fleet by applying a machine learning model that embeds the data log entry and vehicle records into a shared latent space and outputs a confidence score based on proximity. The Specification describes this as the operation of an ''identity resolution component'' that uses ''multimodal deep learning models that combine visual features from vehicle images with textual features from transaction records'' trained ''using a contrastive learning approach that learns to embed vehicles and transactions into a shared latent space." Specification at ¶23. The claims now reflect that improvement, just as the Appeals Review Panel in Ex parte Desjardins, Appeal 2024-000567 (PTAB Sept. 26, 2025), held was sufficient to integrate any abstract idea into a practical application. The Panel there cautioned examiners not to evaluate claims at ''such a high level of generality'' that any machine learning operation is dismissed as a generic algorithm, and instead to ask whether the claim language reflects an improvement disclosed in the specification. Here, it does. The examiner respectfully disagrees. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Mere automation of a manual process or claiming the improved speed or efficiency inherent with applying the abstract idea on a computer where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to transform an abstract idea into a patent-eligible invention. See MPEP 2106.04(a); MPEP 2106.05(a); MPEP 2106.05(f); FairWarning IP, LLC v. Iatric Sys., 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); Intellectual Ventures I LLC v. Capital One Bank (USA), 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Dejardins’ claims disclosed an improvement to the machine learning, not merely the use of machine learning which is in stark contrast with the instant application’s claims where they do not disclose how the machine learning improves the method, it merely applies the method via a processor (e.g. by a computer), from Desjardins:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. In summary, Desjardins discloses the improvement in the specification and the claim language reflects said improvement which is not what is happening in the claims of the instant application. There is no reflection of the improvement mentioned by the applicant. The instant application as the claims fail to integrate the abstract idea into a practical application. Considered as an ordered combination, the generic computer components of applicant’s claimed invention add nothing that is not already present when the limitations are considered separately. For example, claim 1 does not purport to improve the functioning of the computer components themselves. Nor does it affect an improvement in any other technology or technical field. Instead, claim 1 amounts to nothing significantly more than an instruction to apply the abstract ideas using generic computer components performing routine computer functions. That is not enough to transform an abstract idea into a patent-eligible invention. See Alice, 573 U.S. at 225-26.
Claim Rejections - 35 USC § 101
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 therefore, 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014). See MPEP 2106.03(II).
The claims are then analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 15 recites “a processor; and a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for: receiving, by the processor, a data log entry associated with a driver of a vehicle, the data log entry including a service provider location and a timestamp; identifying, by the processor, a vehicle associated with the data log entry, wherein identifying the vehicle comprises applying a machine learning model to the data log entry and a vehicle database, wherein the machine learning model embeds the data log entry and a plurality of vehicle records of the vehicle database into a shared latent space and outputs a confidence score for matching the data log entry to a vehicle record of the plurality of vehicle records based on proximity in the shared latent space; loading, by the processor from a location database, a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing, by the processor, an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry comprises applying a rule-based optimization algorithm to a historical service provider database; and transmitting, by the processor to a user device, a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry”. Claims 1 and 8 disclose similar limitations as Claim 15 as disclosed, and therefore recites an abstract idea.
More specifically, claims 1, 8, and 15 are directed to “Mathematical Concept” in particular “mathematical calculations” and “Mental Processes” in particular “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 2-7, 9-15, and 16-20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 8, and 15 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “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.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 8, and 15 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Further, the remaining additional element directed at receiving/transmitting data (italicized above) reflects insignificant extra solution activities to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Dependent claims 4-6, 11-13, and 18-20 recite additional elements “dashboard user interface”, “telematics device”, and “neural network”. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
With respect to step 2B, claims 1, 4-6, 8, 11-13, 15, and 18-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least ¶163 “the CPU 1202 may comprise a general-purpose CPU. The CPU 1202 may comprise a single-core or multiple-core CPU. The CPU 1202 may comprise a system-on-a-chip (SoC) or a similar embedded system. In some embodiments, a graphics processing unit (GPU) may be used in place of, or in combination with, a CPU 1202. Memory 1204 may comprise a memory system including a dynamic random-access memory (DRAM), static random-access memory (SRAM), Flash (e.g., NAND Flash), or combinations thereof. In one embodiment, the bus 1214 may comprise a Peripheral Component Interconnect Express (PCIe) bus. In some embodiments, the bus 1214 may comprise multiple busses instead of a single bus”.
As a result, claims 1, 4-6, 8, 11-13, 15, and 18-20 do not include additional elements, when recited alone or in combination, that amount to significantly more than the above-identified judicial exception (the abstract idea). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Claims2-3, 7, 9-10, 14, and 16-17 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness
Claims 1, 2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 11928738 to Estes et. al. (hereinafter referred to as “Estes”) in view of US 20240331235 to Smock et. al. (hereinafter referred to as “Smock”).
(A) As per Claims 1, 8, and 15:
Estes expressly discloses:
receiving, by a processor, a data log entry associated with a driver of a vehicle, the data log entry including a service provider location and a timestamp; (Estes Col. 33 Lines 47-56 in any event, as data 702 is received (block 706), the method 700 continues by processing the incoming data 702 for storage in a database 708 (block 710), which database 708 may comprise one or more databases 46. In one example, for each person, data is stored in the database 708 as a sequence of entries, where each entry includes (i) an identifier for a traveled segment, intersection, path, location, etc., (ii) a start time, (iii) an end time, (iv) a type (e.g., gig or non-gig) for the entry, if known, etc., and/or (v) an identifier for the gig-economy worker associated with the entry).
identifying, by the processor, a vehicle associated the data log entry…; (Estes Col. 32-33 Lines 64-1 further example behavioral data 702C includes, but is not limited to, the speed at which the gig-economy worker typically drives when performing gig-related activities; the type of vehicle that the gig-economy worker typically drives during gig-related or non-gig-related activities).
…wherein identifying the vehicle comprises applying a machine learning model to the data log entry and a vehicle database…; (Estes Col. 22 Lines 1-11 the risk model may utilize machine learning to adaptively determine risk scores for each driving behavior. Generally, machine learning may involve identifying and recognizing patterns in existing data (such as autonomous vehicle system, feature, or sensor data; autonomous vehicle system control signal data; vehicle-mounted sensor data; mobile device sensor data; and/or telematics, image, or radar data) in order to facilitate making predictions for subsequent data).
loading, by the processor from a location database, a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing, by the processor, an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry comprises applying a rule-based optimization algorithm to a historical service provider database; (Estes Col. 10 Lines 16-26 the digital map server 43 may store geocoded map data regarding locations and transit pathways through geographic areas, which map data may be used to determine effective distances or travel times between locations, as well as for determining optimal or alternative routes. The digital map server 43 may store geocoded map data regarding locations and transit pathways through geographic areas, which map data may be used to determine effective distances or travel times between locations, as well as for determining optimal or alternative routes. In some embodiments, the digital map server 43 may provide real-time or historical traffic data relating to one or more route segments within the geographic area, which may include indications of traffic congestion, traffic flow, construction, lane closures, road closures, accidents, blockages, or other traffic-related conditions).
transmitting, by the processor to a user device, a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry; (Estes Cols. 12, 16 Lines 46-50, 13-18 such gig work management application 236 may be configured to generate or present gig optimization recommendations or other gig optimization data to a user of the mobile computing device 110 (e.g., a gig-economy worker 17). Such gig-related information may include timing instructions or predictions, recommended travel routes, or delivery instructions. In some embodiments, the server 40 may provide sequential gig-related information at a plurality of stages of the gig, such as directions to a pick-up location followed by directions to a drop-off location).
Although Estes teaches systems and methods relating to improving the experience of gig-economy workers by inputting information about each vehicle in the fleet into vehicle database contains, it doesn’t expressly disclose machine learning embedding data into a shared latent space model v , however Smock teaches:
…wherein the machine learning model embeds the data log entry and a plurality of vehicle records of the vehicle database into a shared latent space and outputs a confidence score for matching the data log entry to a vehicle record of the plurality of vehicle records based on proximity in the shared latent space; (Smock ¶54, 106 the text encoder 402 may be trained with Contrastive Language-Image Pre-training (CLIP) as discussed in Radford, A. et al., Learning transferable visual models from natural language supervision…Specifically, CLIP is trained to map images and corresponding text descriptions into a shared latent space, where the distances between the embeddings correspond to semantic similarity. When the user input also includes an input molecular image, the output molecular image is identified by the machine learning model by proximity in the shared latent space to an encoding of the input molecular image and the encoding of the natural language text. Thus, the image embedding of the input molecular image as modified by the text embedding of the natural language text is used as the starting point in the latent space for image generation. One way this may be done is to generate a caption or textual description of the input molecular image. This caption is then modified by the natural language text provided by the user to create a new caption).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes’ data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and have the text encoder trained with Contrastive Language-Image Pre-training where CLIP is trained to map images and corresponding text descriptions into a shared latent space, where the distances between the embeddings correspond to semantic similarity of Smock as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes and have the image embedding of the input molecular image as modified by the text embedding of the natural language text used as the starting point in the latent space for image generation as taught in Smock.
Estes teaches a system in the abstract and a computer-readable medium at least in Claim 16.
(B) As per Claims 2, 9, and 16:
Estes expressly discloses:
comparing the timestamp of the data log entry with a plurality of predefined driving periods associated with the driver; determining, based on the comparison, a driving period that encompasses the timestamp of the data log entry, wherein the driving period is associated with the identified vehicle; (Estes Cols. 33-34 Lines 47-56, 10-15 in any event, as data 702 is received (block 706), the method 700 continues by processing the incoming data 702 for storage in a database 708 (block 710), which database 708 may comprise one or more databases 46. In one example, for each person, data is stored in the database 708 as a sequence of entries, where each entry includes (i) an identifier for a traveled segment, intersection, path, location, etc., (ii) a start time, (iii) an end time, (iv) a type (e.g., gig or non-gig) for the entry, if known, etc., and/or (v) an identifier for the gig-economy worker associated with the entry. In some examples, the gig or non-gig type of activity (or a likelihood of gig or non-gig activity) is determined using the behavioral data 702C. The classifier 53 utilizes, applies, or implements a machine-learning model 54 to process data stored in the database 708 to determine (e.g., estimate, generate, calculate, etc.) likelihoods that movements are gig-related. The machine-learning model 54 may utilize deep learning algorithms that are primarily focused on, for example, pattern recognition, and may be trained by processing example data).
(C) As per Claims 4, 11, and 20:
Estes expressly discloses:
wherein the geospatial visualization of the alternate data log entry is displayed within a dashboard user interface, the dashboard user interface displaying a plurality of service providers and associated data log entries; (Estes Col. 43 Lines 8-18 the representation may include a visual presentation of the gig optimization data outputs relative to input values, such as a tabular presentation of predicted profit for each a plurality of hours or a heat map showing the predicted profitability or waiting time for gigs at various locations within a geographic area. In some embodiments, a dashboard of gig optimization data may be presented to the user to make multiple decisions, such as a gig-economy platform to use and a location at which to offer gig-economy services).
(D) As per Claims 5, 12, and 18:
Estes expressly discloses:
receiving, from a telematics device associated with the identified vehicle, real-time vehicle data including at least one of a fuel level, a location, or a driver behavior metric; (Estes Col. 19 Lines 3-6 the set of data may facilitate mood detection in real-time or building an average mood profile for the gig-economy worker. The mood of a gig-economy worker may impact driving behavior).
predicting, using a second machine learning model, a future transaction based on the real-time vehicle data and the data log entry; (Estes Col. 34 Lines 33-37 machine-learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data 702.)
transmitting, to the user device, a proactive recommendation based on the predicted future transaction; (Estes Col. 43 Lines 52-59 the user input may be used to generate updated gig optimization data according to the previously identified gig-economy data models or additional gig-economy data models. In some embodiments, the user input may be used to generate one or more gig optimization recommendations for the user, which may be the same as or differ from previously generated gig optimization recommendations).
(E) As per Claims 6, 13, and 19:
Estes expressly discloses:
generating a message using a neural network, the message including a personalized feedback based on the alternate data log entry; transmitting the message to a messaging application installed on the user device; (Estes Cols. 55-56 Lines 63-48 the message provider device 1216 may transmit targeted recommendation data to the electronic device 1214 and/or external processing server 1215 through the network 1218. The recommendations are also more relevant than the static, general messages on conventional display devices because they are dynamically updated and targeted for the gig-economy worker and/or customer, and can be based upon travel routes extracted from the gig-economy applications).
(F) As per Claims 7, and 14:
Estes expressly discloses:
identifying, based on the data log entry and the vehicle location log, a driver behavior pattern associated with the driver; (Estes Col. 16 Lines 32-37 the insurance provider may use telematics and/or other data to monitor or identify driving behaviors of a gig-economy worker (e.g., gig-economy worker 17) during a gig).
comparing the driver behavior pattern with a plurality of historical driver behavior patterns associated with a plurality of drivers; (Estes Col. 20 Lines 57-62 the risk model may include pre-determined risk values for each driving behavior, the model may compare each driving behavior to a set of known driving behaviors of other gig-economy workers, and/or the model may utilize machine learning to adaptively determine risk scores for each driving behavior).
generating, based on the comparison, a driver-specific incentive to modify the driver behavior pattern; (Estes Col. 30 Lines 40-43 the method 600 may facilitate better driving habits, and the gig-economy worker's subsequent gig performance and overall profitability may be improved, by linking good driving behavior with monetary incentives).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 11928738 to Estes et. al. (hereinafter referred to as “Estes”) in view of US 20240331235 to Smock et. al. (hereinafter referred to as “Smock”) in further view of US 20250181593 to Niu et. al. (hereinafter referred to as “Niu”) in even further view of US 20240420045 to Garg et. al. (hereinafter referred to as “Garg”) and in even further view of US 20220084155 to Frederick et. al. (hereinafter referred to as “Frederick”).
(A) As per Claims 3, 10, and 17:
Although Estes in view of Smock teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose identifying alternate service providers within a radius, however Niu teaches:
identifying, based on the vehicle location log, a plurality of alternate service providers within a predefined radius of the service provider location; (Niu ¶52 the server 100 may communicate with the service provider device 170 to check the service provider's availability. In some embodiments, the server 100 may communicate with the service provider device 170 to aggregate information including, but not limited to, a location of the service provider, a type of services available, estimated fees, and other information, in order to produce the list of service providers within the delivery radius).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes in view of Smock’s data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and communicate with the service provider device to aggregate information including, but not limited to, a location of the service provider, a type of services available, estimated fees of Niu as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes in view of Smock and produce the list of service providers within the delivery radius as taught in Niu.
Although Estes in view of Smock and in further view of Niu teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose filtering alternate service based on vehicle type, however Garg teaches:
filtering the plurality of alternate service providers based on at least one of a fuel type, a vehicle type, or a driver preference; (Garg ¶36 user interface 200 may also allow a provider/administrator/requestor user to select a desired vehicle type such as a bus, a van, a short bus, a city bus, a truck, or any other type of fleet vehicle for the particular route).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes in view of Smock and in further view of Niu’s data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and have user interface also allow a provider/administrator/requestor user to select a desired vehicle type of Garg as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes in view of Smock and in further view of Niu and have a bus, a van, a short bus, a city bus, a truck, or any other type of fleet vehicle for the particular route as taught in Garg.
Although Estes in view of Smock in further view of Niu and in even further view of Garg teaches systems and methods relating to improving the experience of gig-economy workers, it doesn’t expressly disclose selecting alternate service provider based on cost optimization, however Frederick teaches:
selecting an alternate service provider from the filtered plurality of alternate service providers based on a cost optimization algorithm; (Frederick ¶24, 49 the transportation matching system can then analyze the set of requestor devices to generate a transportation group. For example, the transportation matching system can apply a cost function to a set of requestor devices to generate one or more transportation groups. More specifically, in some embodiments, the transportation matching system applies the cost function to identify transportation groups that minimize a cost metric subject to a constraint of satisfying each arrival deadline within the transportation group. the term “cost function” can include a computer-implemented algorithm that assigns costs to characteristics corresponding to a transportation group and selects requestor devices to join the transportation group based on the cost metrics (e.g., to minimize the cost metrics)).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Estes in view of Smock in further view of Niu and in even further view of Garg’s data gathering including the identification of type of vehicle that the gig-economy worker typically drives during gig-related activities and have the transportation matching system analyze the set of requestor devices to generate a transportation group of Frederick as both are analogous art which teach solutions to having the digital map server store geocoded map data regarding locations and transit pathways through geographic areas as taught in Estes in view of Smock in further view of Niu and in even further view of Garg and have the transportation matching system applies the cost function to identify transportation groups that minimize a cost metric subject to a constraint of satisfying each arrival deadline within the transportation groupas taught in Frederick.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is (571)272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao (Rob) Wu can be reached on (571)272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623
07/07/2026
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