DETAILED ACTION
The following is a Final Office Action in response to communications filed May 11, 2026. Claims 1–3 and 7–20 are amended, and claims 4–6 and 21 are canceled. Currently, claims 1–3 and 7–20 are pending.
Response to Amendments/Arguments
Applicant’s Response is sufficient to overcome the previous objection to claims 1, 4, and 13 for informalities. Accordingly, the previous objection to claims 1, 4, and 13 is withdrawn.
Applicant’s Response is sufficient to overcome the previous rejection of claims 1–20 under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Accordingly, the previous rejection of claims 1–20 under 35 U.S.C. 112(b) is withdrawn.
However, Applicant’s Response necessitates a new ground of rejection under 35 U.S.C. 112(b), and Examiner directs Applicant to the relevant explanation below.
Applicant’s Response is sufficient to overcome the previous rejection of claims 1–12 under 35 U.S.C. 101 as reciting software per se. Accordingly, the previous rejection of claims 1–12 under 35 U.S.C. 101 as reciting software per se is withdrawn.
With respect to the previous rejection of claims 1–20 under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive.
Applicant first asserts that the claims include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two. More particularly, Applicant asserts that the machine learning and data compression elements integrate the abstract idea into a practical application because the elements provide a technical solution to the technical problem disclosed in Applicant’s Specification. Examiner disagrees.
As noted by Applicant, MPEP 2106.05(a) requires “[a]n indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” Notably, MPEP 2106.05(a) expressly references unconventional technical solutions and technical improvements rather than general technical elements.
Here, Applicant’s Specification neither explains the details of an unconventional technical solution nor identifies technical improvements realized by the claim. Instead, Applicant’s Specification describes applying generically disclosed compression technology (See e.g., Spec. ¶¶ 35 and 86, wherein compression techniques are described generically) and generalized machine learning algorithms (See e.g., Spec. ¶¶ 48–51, wherein learning algorithms are generally disclosed) without disclosing any improvements in either compression techniques or machine learning. As a result, Applicant’s remarks are not persuasive because the technical elements do no more than generally link the use of the recited abstract idea to a particular technological environment without embodying any improvements in technology.
Under Step 2B, Applicant further asserts that the recited machine learning and data compression elements amount to significantly more than the recited abstract idea because the additional elements embody an unconventional combination of additional elements. Examiner disagrees. As noted above, Applicant’s Specification and claims disclose generalized machine learning and data compression technology. Further, neither Applicant’s Specification nor Applicant’s remarks disclose any technical improvements derived from the combination of machine learning and data compression elements. Instead, the technical features are described individually. As a result, Applicant’s remarks are not persuasive.
Accordingly, Applicant’s remarks are not persuasive, and the previous rejection under 35 U.S.C. 101 is maintained.
With respect to the previous rejections under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are moot in view of the updated grounds of rejection asserted below.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–3 and 7–20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 13 recite “the match” in the element reciting “notifying the subset of the service providers of the match”. There is insufficient antecedent basis for “the match” in the claims.
For purposes of examination, claims 1 and 13 are interpreted as reciting “notifying the subset of the service providers of [[the]] a match”.
Claim 1 further recites “the service provider” in the element reciting “notifying the service requester of the start acceptance”. There is insufficient antecedent basis for “the service provider” in the claim.
For purposes of examination, claim 1 is interpreted as reciting “notifying the service requester of the start acceptance … and receiving arrival and services completion confirmations from the subset of service providers
In view of the above, claims 1 and 13 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2–3, 7–12, and 14–20, which depend from claims 1 and 13, inherit the deficiencies described above. As a result, claims 2–3, 7–12, and 14–20 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 20 recites “the service providers” in line 3. However, claim 13, from which claim 20 depends, previously recites “onboarding … service providers and service requesters,” and claim 14 is amended to include two recitations of “the service providers and service requesters”. In view of claims 13–14, the claims recite “service providers and service requesters” as a single entity, such that there is insufficient antecedent basis for “the service providers” in claim 20.
For purposes of examination, claim 14 is interpreted as reciting “wherein the onboarding of the service providers and the service requesters includes collecting information about credentials, availability, or geographical location of the service providers and the service requesters”.
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
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 1–3 and 7–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–3 and 7–20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements reciting “onboarding service providers and service requesters”; “receiving a service request from a service requester of the service requesters and matching the service request to a subset of the service providers based on an analysis of the service request in relation to specific criteria to identify the subset of the service providers suitable for the service request based on the analysis”; and “notifying the subset of the service providers of the match and a start of services, receiving a start acceptance from the subset of the service providers, tracking geolocation data of the subset of the service providers after receipt of the start acceptance, notifying the service requester of the start acceptance and an estimated arrival of the subset of the service providers based on the geolocation data, and receiving arrival and services completion confirmations from the service provider.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people because the elements describe a process for matching service providers to service requests and managing performance of the service requests. Further, the elements above recite mental processes because the elements embody observations or evaluations that can be practically performed in the mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 13 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 13 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–3, 7–12, and 14–20 further describe the process for matching service providers to service requests and managing performance of the service requests and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 2–3, 7–12, and 14–20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computing environment comprising one or more processors and one or more memories, a machine learning algorithm, an element indicating that data “is compressed using lossless data compression techniques, and a step for “storing” data. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the algorithm and compression techniques do no more than generally link the use of the recited abstract idea to a particular technological environment; and the step for “storing” data is an insignificant extrasolution activity to the recited abstract idea. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 13 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 13 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claim 1.
Claims 7–8, 10–11, and 18–19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include “distributing a workload … across multiple servers or processing units” (claim 7), “employing data normalization and indexing … within a cloud-based data store” (claim 8), “implementing caching mechanisms” (claim 10), “synchronizing data … based on network conditions and data priority” (claim 11), GPS technology (claim 18), and a computing device (claim 19). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the computing device is a generic computing component that is merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 7–8, 10–11, and 18–19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–3, 9, 12, 14–17, and 20 do not include any additional elements beyond those included with respect to the claims from which claims 2–3, 9, 12, 14–17, and 20 depend. As a result, claims 2–3, 9, 12, 14–17, and 20 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a computing environment comprising one or more processors and one or more memories, a machine learning algorithm, an element indicating that data “is compressed using lossless data compression techniques, and a step for “storing” data. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea; the algorithm and compression techniques do no more than generally link the use of the recited abstract idea to a particular technological environment; and the step for “storing” data is a well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(II). Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claim 13 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 13 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above with respect to claim 1.
Claims 7–8, 10–11, and 18–19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include “distributing a workload … across multiple servers or processing units” (claim 7), “employing data normalization and indexing … within a cloud-based data store” (claim 8), “implementing caching mechanisms” (claim 10), “synchronizing data … based on network conditions and data priority” (claim 11), GPS technology (claim 18), and a computing device (claim 19). The additional elements do not amount to significantly more than the recited abstract idea because the computing device is a generic computing component that is merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 7–8, 10–11, and 18–19 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2–3, 9, 12, 14–17, and 20 do not include any additional elements beyond those included with respect to the claims from which claims 2–3, 9, 12, 14–17, and 20 depend. As a result, claims 2–3, 9, 12, 14–17, and 20 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–3 and 7–20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–3, 7, 9, and 12–20 are rejected under 35 U.S.C. 103 as being unpatentable over Klibanov et al. (U.S. 2021/0089997) in view of Li et al. (U.S. 2018/0108103), and in further view of Chen et al. (U.S. 2022/0114655).
Claims 1 and 13: Klibanov discloses a system for managing service provider lifecycles in a networked environment, comprising:
a computing environment comprising one or more processors and one or more memories storing processor-readable instructions therein, wherein the one or more processors are configured to access the one or more memories to execute the process-readable instructions to perform operations (See paragraphs 48–49; see also FIG. 3–4 and paragraphs 118–121), the operations comprising:
onboarding service providers and service requesters and storing data of the service providers and the service requesters (See FIG. 6A and FIG. 7A, wherein service providers and service requesters register for the service, and FIG. 6G and FIG. 7G, wherein profile data is displayed, thereby indicating that the onboarding information is stored);
receiving a service request from a service requester of the service requesters (See paragraph 29, wherein “the homeowner 105 uses the homeowner mobile device 110 via the network cloud 115 to submit a work request to the work assignment server”) and matching the service request to a subset of the service providers based on an analysis of the service request in relation to specific criteria to identify the subset of the service providers suitable for the service request based on the analysis (See paragraph 29, wherein “the work assignment server 120 helps the homeowner 105 locate a worker to complete the homeowner's job by associating the available driver locations 160 with the homeowner's work request to find an available driver in proximity to the requested work location”); and
notifying the subset of the service providers of the match and a start of services (See paragraph 29, wherein “the work assignment server sends a job notification to one of the available drivers in the list of available drivers 150 selected according to the available driver notification priorities”),
receiving a start acceptance from the subset of the service providers (See paragraph 29, wherein “upon accepting a job notification, one of the available drivers 150 becomes the scheduled driver”),
tracking location data of the subset of the service providers after receipt of the start acceptance (See FIG. 14 and paragraph 64, wherein “the processor 405 determining if the driver arrived to begin the job, based on location data”),
notifying the service requester of the start acceptance (See FIG. 6F, wherein service requesters are notified of acceptance) and an estimated arrival of the subset of the service providers (See paragraph 132, wherein the system may “send an electronic message comprising an indication of the assigned worker expected arrival time at the job beginning location”),
receiving arrival and services completion confirmations from the service provider (See paragraph 29, wherein “the scheduled driver 170 checks in at the homeowner 105 home 130 location by capturing the mailbox 175 optical code 180 with the mobile device” and wherein “the scheduled driver 170 captures the trash receptacle 125 optical code 185 with the mobile device 155 at the job ending location 140 near a collection location at the public street”),
wherein data related to the subset of the service providers matched to the service requester is transmitted to the service requester (See FIG. 6F, wherein service requesters are notified of acceptance; see also paragraph 132, wherein the system may “send an electronic message comprising an indication of the assigned worker expected arrival time at the job beginning location”). Although Klibanov implicitly discloses tracking the geolocation of the service provider (See FIG. 14 and paragraph 64, wherein “the processor 405 determining if the driver arrived to begin the job, based on location data” and paragraph 31, wherein “the driver mobile device 155 may include a GPS module”), Klibanov does not expressly disclose the remaining claim elements.
Li discloses tracking the geolocation data of the subset of the service providers after receipt of the start acceptance (See paragraphs 117 and 120–121, in view of paragraphs 32–33, wherein a provider vehicle is tagged and tracked on a display);
an estimated arrival of the subset of the service providers based on the geolocation data (See paragraphs 76 and 81, in view of paragraphs 32–33, wherein an estimated time of arrival is determined based on location and speed); and
wherein data related to the subset of the service providers is compressed using lossless data compression techniques before transmission (See paragraph 133, wherein driver status data is compressed before transmission using lossless compression).
Klibanov discloses a system directed to matching service providers to work assignments. Li discloses a system directed to matching service requests and vehicles. Each reference discloses a system directed to matching service requests to service providers. The technique of using geolocation monitoring is applicable to the system of Klibanov as they each share characteristics and capabilities, namely, they are directed to matching service requests to service providers.
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Li to the teachings of Klibanov would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate matching service requests to service providers into similar systems. Further, applying geolocation monitoring to Klibanov would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results. Although Klibanov discloses employing a machine learning algorithm (See paragraph 29, in view of paragraph 32), Klibanov and Li do not expressly disclose the remaining elements.
Chen discloses matching the service request to a subset of the service providers based on a machine learning algorithm configured to perform an analysis of the service request in relation to specific criteria, the machine learning algorithm configured to identify the subset of the service providers suitable for the service request based on the analysis (See paragraph 158, wherein machine-learning models match requesters to providers based on matching criteria).
As disclosed above, Klibanov discloses a system directed to matching service providers to work assignments, and Li discloses a system directed to matching service requests and vehicles. Chen discloses a system directed to managing a transportation ecosystem by matching requesters and providers. Each reference discloses a system directed to scheduling and fulfilling service requests. The technique of using machine learning to identify suitable providers is applicable to the systems of Klibanov and Li as they each share characteristics and capabilities, namely, they are directed to scheduling and fulfilling service requests.
One of ordinary skill in the art would have recognized that applying the known technique of Chen would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chen to the teachings of Klibanov and Li would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate scheduling and fulfilling service requests into similar systems. Further, applying machine learning for identification of suitable providers to Klibanov and Li would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claims 2 and 17: Klibanov discloses the system of claim 1, wherein the specific criteria comprises at least one of: proximity of the subset of the service providers to a requested service location (See paragraph 6, wherein workers are notified based on proximity to the job location), availability of the subset of the service providers during a requested service timeframe (See FIG. 6F, in view of paragraph 85, wherein workers register availability times), or credentials of the subset of the service providers (See paragraph 67, wherein workers are selected based on a star rating).
Claims 3 and 18: Although Klibanov discloses providing updates to the service requester and the service provider about the status of the service request (See FIG. 6F and FIG. 7E), Klibanov does not expressly disclose the remaining claim elements.
Li disclose wherein the operations further comprise providing real-time updates to the service requester and the subset of the service providers about a status of the service request (See paragraph 61, wherein the service request includes a real-time position of the passenger, and paragraph 66, wherein real time location status information for the vehicle is obtained; see also paragraphs 116–117 and 120–121, in view of paragraphs 32–33, wherein a requester and provider locations are tagged and tracked on a display).
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
With respect to claim 18, Li further discloses using GPS technology to monitor location (See paragraph 32).
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 7: Klibanov discloses the system of claim 1, wherein the operations further comprise distributing a workload related to data processing tasks associated with the operations across multiple servers or processing units, ensuring that no single device is overwhelmed (See paragraphs 31–32, wherein the environment may be realized in a distributed implementation such that devices “may delegate computation-intensive tasks to a host server to take advantage of a more powerful processor, or to offload excess work”).
Claim 9: Although Klibanov discloses sending notifications and updates (See citations above), Klibanov does not expressly disclose the remaining claim elements.
Li discloses wherein the real-time updates are sent in a compressed format that reduces a size of data packets associated with the real-time updates (See paragraphs 132–133, wherein real-time status updates are compressed before transmission).
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 12: Klibanov discloses the system of claim 1, wherein the operations further comprise analyzing historical data and usage patterns of the service requesters or the service providers to predict peak times and pre-allocate resources accordingly (See FIG. 15 and paragraph 65, wherein “the probability a specific trash receptacle may need to be transported in the near term may be recalculated in real time as nearby trash receptacles are transported in alignment with municipal collection schedules and historical work request data. In some examples, drivers may be notified with suggestions to temporarily position themselves in areas expected to experience higher than average predicted near-term demand, advantageously encouraging drivers to relocate toward areas with higher anticipated demand”).
Claim 14: Klibanov discloses the method of claim 13, wherein the onboarding of the service providers and service requesters includes collecting information about credentials, availability, or geographic location of the service providers and service requesters (See FIG. 6G and FIG. 7G, wherein profile data includes location and other details; see also paragraphs 85 and 89).
Claim 15: Klibanov discloses the method of claim 14, wherein the geographical location is used for matching the service request to the subset of the service providers based on proximity (See paragraph 6, in view of FIG. 7G and paragraph 85, wherein workers are notified based on proximity to the job location).
Claim 16: Klibanov discloses the method of claim 14, wherein the availability is used to match the service request to the subset of the service providers based on the availability of the subset of the service providers during a requested service timeframe (See paragraph 29, as above, in view of FIG. 6F and paragraph 85, wherein workers register availability times for receiving job notifications).
Claim 19: Klibanov discloses the method of claim 18, wherein the tracking enables providing the service requester with the estimated arrival of the subset of the service providers (See paragraph 132, wherein the system may “send an electronic message comprising an indication of the assigned worker expected arrival time at the job beginning location”), and
the arrival and services completion confirmations from the subset of the service providers are received through manual confirmations by the subset of the service providers via a computing device (See paragraph 29, wherein “the scheduled driver 170 checks in at the homeowner 105 home 130 location by capturing the mailbox 175 optical code 180 with the mobile device” and wherein “the scheduled driver 170 captures the trash receptacle 125 optical code 185 with the mobile device 155 at the job ending location 140 near a collection location at the public street”).
Claim 20: Although Klibanov discloses a machine learning algorithm (See paragraph 29, in view of paragraph 32), Chen more expressly discloses the recited elements.
Chen discloses wherein the machine learning algorithm is configured to learn and adapt over time to improve accuracy of matching service requests with the service providers based on the specific criteria (See paragraph 158, wherein the system employs machine-learning models for matching requestors and providers using matching criteria).
One of ordinary skill in the art would have recognized that applying the known technique of Chen would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Klibanov et al. (U.S. 2021/0089997) in view of Li et al. (U.S. 2018/0108103), and in further view of Chen et al. (U.S. 2022/0114655) and Shiely et al. (U.S. 2020/0090119).
Claim 8: As disclosed above, Klibanov, Li, and Chen disclose the elements of claim 4. Although Klibanov discloses data processing (See paragraph 30, wherein data is processed and analyzed), and Li discloses a cloud-based data store (See paragraph 133), Klibanov, Li, and Chen do not expressly disclose the remaining claim elements.
Shiely discloses wherein the operations further comprise employing data normalization and indexing associated with the operations within a cloud-based data store to streamline data retrieval and minimize storage overhead (See paragraphs 23, 26, and 82, wherein data is formatted and indexed in a data store for retrieval; see also paragraph 114, wherein storage may be a cloud-based storage).
As disclosed above, Klibanov discloses a system directed to matching service providers to work assignments, Li discloses a system directed to matching service requests and vehicles, and Chen discloses a system directed to managing a transportation ecosystem by matching requesters and providers. Shiely discloses a system directed to scheduling product delivery. Each reference discloses a system directed to scheduling and fulfilling service requests. The technique of using normalization and indexing is applicable to the systems of Klibanov, Li, and Chen as they each share characteristics and capabilities, namely, they are directed to scheduling and fulfilling service requests.
One of ordinary skill in the art would have recognized that applying the known technique of Shiely would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shiely to the teachings of Klibanov, Li, and Chen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate scheduling and fulfilling service requests into similar systems. Further, applying normalization and indexing to Klibanov, Li, and Chen would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 10: Klibanov, Li, and Chen do not expressly disclose the elements of claim 10.
Shiely discloses wherein the operations further comprise implementing caching mechanisms to store frequently accessed data locally on a client computing device, reducing a number of repeated queries to a server connected with the computing environment (See paragraphs 30 and 32, wherein caching mechanisms are utilized for storing scheduling and delivery information).
One of ordinary skill in the art would have recognized that applying the known technique of Shiely would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Klibanov et al. (U.S. 2021/0089997) in view of Li et al. (U.S. 2018/0108103), and in further view of Chen et al. (U.S. 2022/0114655) and Chan et al. (U.S. 2014/0289196).
Claim 11: As disclosed above, Klibanov, Li, and Chen disclose the elements of claim 4. Klibanov, Li, and Chen do not expressly disclose the elements of claim 11.
Chan discloses wherein the operations further comprise synchronizing data between entities based on network conditions and data priority (See Abstract and paragraphs 49 and 61, wherein files are synchronized between a user computing device and a vehicle computing device based on priority and network type/proximity).
As disclosed above, Klibanov discloses a system directed to matching service providers to work assignments, Li discloses a system directed to matching service requests and vehicles, and Chen discloses a system directed to managing a transportation ecosystem by matching requesters and providers. Chan discloses a system directed to scheduling product delivery. Each reference discloses a system directed to managing communications. The technique of using data synchronization is applicable to the systems of Klibanov, Li, and Chen as they each share characteristics and capabilities, namely, they are directed to managing communications.
One of ordinary skill in the art would have recognized that applying the known technique of Chan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chan to the teachings of Klibanov, Li, and Chen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate communication management into similar systems. Further, applying data synchronization to Klibanov, Li, and Chen would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to Applicant's disclosure:
MONTEIL et al. (U.S. 2020/0380629) discloses a system directed to matching transportation requests to transportation service providers.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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