DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s response to Office action was received on February 18, 2026.
In response to Applicant’s amendment of the claims, the corresponding prior art claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
In response to Applicant’s amendment of the claims, the corresponding 101 claim rejections, from the previous Office action, have been correspondingly amended, below in this Office action.
Regarding the 101 claim rejections, Applicant first argues that the claims are not directed to methods of organizing human activity under Step 2A, Prong 1. Applicant begins by stating certain alleged benefits flowing from the claims, such as addressing technical problems in delivery coordination systems, using neural networks to reduce computational waste, and improving resource efficiency. As an initial matter, Step 2A, Prong 1, is focused on determining if a claim recites an abstract idea, not specifically on determining whether the claims are directed to an abstract idea/judicial exception (which is the focus of overall Step 2A, Prongs 1 and 2). See MPEP 2106.04(II)(A), chart. Arguments directed to the technological/computing improvements consideration are relevant to Step 2A, Prong 2, and Step 2B, but not Step 2A, Prong 1.
MPEP 2106.04(II)(A)(1) states: “In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.” Note that the claim can also recite additional elements beyond the judicial exception (such as computer components), yet still recite a judicial exception in Prong One. Looking to representative claim 1, for example, we see a computer-implemented method which gathers and processes various pieces of data, and results in the ultimate generation of carrier instructions to address one or more delivery issues. This claim thus includes recitation of subject matter which falls under the abstract-idea subject-matter grouping of “Certain method(s) of organizing human activity”. For example, shipping/delivery is commonly a commercial activity (and claim 1 even recites “customer” and “retail service”). Furthermore, the claim manages relationships/interactions between the delivery service, the carrier/delivery person, and the consumer, each of which may involve humans. Finally, the instructions for the carrier may be viewed as managing the behavior of the carrier.
Although Applicant has placed some of the technological/computing improvement arguments under Step 2A, Prong 1, Examiner will now address those along with the other arguments under Step 2A, Prong 2, and Step 2B. Here is a list of such arguments, with Examiner’s corresponding responses:
a. the claimed invention addresses recognized technical problems in delivery coordination systems: Delivery coordination is not a technological field, but rather a business and/or organizational process. Also, the delivery coordination here falls within the judicial exception, and the 101 improvement consideration cannot be based on an improvement to the judicial exception alone. See MPEP 2106.05(a).
b. The neural networks reduce computational waste by intelligently filtering delivery events and generating communications only when necessary, thereby reducing processing load and network bandwidth consumption compared to alert-all approaches. This resource efficiency improvement is a technical benefit to the computing system itself, not merely to the delivery business process: The filtering here is part of the abstract idea; thus, an improvement to the filtering itself does not invoke the technological/computing improvement consideration (see above discussion). In addition, all of the claims are currently directed to the delivery context; therefore, it is difficult to characterize the claims as an improvement to computing resource usage efficiency, more broadly. The neural networks appear to be generically recited, and the claims do not appear to be improving machine-learning technology itself.
c. The neural networks perform integrated analysis of multiple data streams (location, weather, traffic, safety, profiles) in real-time to generate context-appropriate triggers -- a computational task that cannot be performed manually at the scale and speed required for modern delivery operations: MPEP 2106.05(f)(2) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” MPEP 2106.05(f)(2) further states: “Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.” Therefore, simply improving data processing results versus manual processing is not the type of improvement to invoke the 101 technological/computing improvement consideration. It does not appear that Applicant is improving the computing technology itself.
d. The system dynamically selects optimal communication channels based on profile settings and context, reducing failed connection attempts and network latency compared to sequential trial-and-error approaches: The communication selection procedure is part of the abstract idea. Thus, an improvement to the communication selection procedure itself does not invoke the technological/computing improvement consideration (see above discussion). Again, it does not appear that Applicant is improving the computing technology itself. For example, there is not an improvement in machine-learning technology, nor is there an improved type of communication hardware device.
e. Operations are performed by the system itself, not by humans, and cannot reasonably be characterized as rules for organizing human behavior: See the discussion of MPEP 2106.05(f)(2) above. Simply performing an abstract idea on generic computing components, without more, does not render eligibility to a claim reciting an abstract idea.
f. Independent claim 1 recites specific actions performed in response to delivery coordination tasks: Simply reciting specific actions does not automatically integrate an abstract idea into a practical application. For example, the specific actions themselves may be part of the abstract idea, at least in part.
g. Computational resource savings through intelligent event filtering that reduces unnecessary processing and network traffic is a technical improvement: See (b) in this list above.
h. Real-time multi-source data integration at scale that would be computationally prohibitive using sequential processing is a technical improvement: The particular data processing steps are largely part of the abstract idea. The underlying computing components are generic and not improved.
i. Automated cross-language communication that eliminates the computational overhead of routing through translation services while maintaining delivery schedule integrity is a technical improvement: The translation aspect (see Applicant’s claim 4, for example) is recited at a high level, and it does not appear that Applicant’s focus is machine translation but rather delivery management. Nor does it appear that how the translation is executed (routing, etc.) is Applicant’s claims’ focus.
j. Self-optimizing neural network architecture that progressively improves system accuracy without reprogramming is a technical improvement: This can be viewed as training a neural network model, which is an already-established part of the neural network field and not an improvement to machine-learning technology. Also, this full feature does not appear to be recited in claim 1 (for example).
k. Applicant argues that Applicant’s claims are eligible via comparability to the Thales decision: Examiner disagrees, because Examiner believes that Thales is distinguishable. Thales tracked motion of an object on a moving platform. An unconventional configuration of sensors was used, which resulted in improved accuracy. The decision concluded that the claims at issue in Thales were eligible. Applicant’s claims have at least two key differences from the subject claims in Thales. First, Thales featured an unconventional arrangement of devices in terms of the sensors. Examiner does not find any comparable “additional elements beyond any abstract idea” in Applicant’s claims because such additional elements are all generic computing components, even in combination. Second, the improved tracking accuracy in Thales can be viewed as a technological improvement. In contrast, any alleged improvements in Applicant’s claims appear to be either (1) improvements to judicial exception by itself; (2) improvements to a non-technical field; and/or (3) improvements that simply flow from using a generic computing component in its typical way and therefore do not invoke eligibility. Since Thales is distinguishable, Examiner does not find this argument to be persuasive.
l. Applicant argues that the claims are directed to an improvement for delivery coordination based on real-time contextual data using neural networks. Applicant continues that this improvement streamlines communication processes via automation, reduced carrier input to resolve delivery issues during the delivery to enhance carrier safety: Delivery coordination is not a technological nor a computing field, so the technological/computing improvement 101 consideration would not be invoked by that for eligibility. Real-time is a generic computing feature, and, while the claims use neural networks, they do not appear to improve machine-learning technology. (Simply using neural networks does not automatically represent an improvement in machine-learning technology.) Automation from the use of generic computing components, in and of itself, also does not invoke the 101 technological/computing improvement consideration for eligibility, as discussed above. Enhancing carrier safety, in and of itself, also is not necessarily a technological improvement (although it could follow from a technological improvement in some situations, though not here).
m. Applicant argues based on the McRO decision: Examiner finds McRO to be distinguishable from Applicant’s claims for at least the reason that the McRO decision found an improvement to a technological field in the form of computer animation. As discussed above, Examiner does not find a technological or computing improvement in Applicant’s claims.
Therefore, Examiner does not find Applicant’s 101 arguments to be persuasive.
Regarding the prior art rejections, Applicant argues that Tan does not disclose using a neural network to generate content that “indicates one or more issues of the scheduled delivery”. In response, note that the 103 rejection of claim 1 (for example) used the Lin reference for disclosure of “generating, content to be communicated via the communication medium, wherein the content indicates one or more issues of the scheduled delivery”. Therefore, Tan did not need to disclose “indicates one or more issues of the scheduled delivery”, because Lin provided this part.
Applicant argues that the Office has provided no rationale as to how the neural network of Tan could be modified to perform “this task”, and has provided no indication of how it would have been obvious to modify both the inputs and the outputs of the neural network of Tan to somehow combine it with the system of Lin to arrive at the claimed one or more second neural networks that generate content that indicates one or more issues of the scheduled delivery.
In response, Examiner provides a valid rationale-to-combine for Lin and Tan. For example, the current 103 rejection for claim 1 states:
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks feature for the same reasons its useful in Tan-namely, to perform tasks including natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, text generation, query processing, machine translation, chatbots, and the like. ( par.21) Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Note that this rationale corresponds to Rationale (A) in MPEP 2143.
Regarding Applicant’s concern about how Tan would be adapted to be combined with Lin, note that Lin is already used to disclose generating content indicating one or more issues of the scheduled delivery. For this part of the claim, we already have generating the particular content with Lin, so we just need “using the neural network”. Note how broadly claim 1 (for example) links in the neural network here; claim 1 does not recite much detail on how the one or more second neural networks are used to generate the content, mainly just that they are used to do so. Therefore, less detail on this is thus required from Tan, as well. Tan, paragraph [0029], states: “For example, the task may be to perform question-answering, text summarization, text generation, and the like based on information contained in an external dataset.” The ability to generate text is an example of a capability that would allow Tan to be used to generate the content in Lin in the combination of the two references.
Applicant also argues against the 103 rejections based on the amendments to the independent claims. Please see Examiner’s corresponding amendments to the 103 rejections, for Examiner’s addressing of such Applicant amendments. In particular, note Examiner’s addition of the Liu and Putra references to the 103 rejections.
Therefore, Examiner does not find Applicant’s arguments to be persuasive.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-4 are directed to a method (i.e., a process); Claims 5-12 are directed to a system (i.e., a machine); claim 13-20 is directed to a set of non-transitory computer-readable storage media (i.e., a machine). Therefore, claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claims 1, 5 and 13, substantially recites obtaining, from a delivery service, an indication that a carrier of a delivery service is to execute a scheduled delivery for a customer of a retail service; obtaining, from a delivery service, context information that indicates one or more events that are specific to the scheduled delivery; obtaining a plurality of carrier settings specific to the carrier and a plurality of customer settings specific to the customer; generating (or causing) a communication (to be established) between the carrier and the customer based, at least in part, on the plurality of carrier settings, the plurality of customer settings, and the context information; generating content to be communicated, wherein the content indicates one or more issues of the scheduled delivery, wherein generating one or more of the communication or the content comprises selecting one or more of the communication (from a plurality of communications) or the content to reduce time taken and/or carrier input to resolve the one or more issues; generating, one or more instructions for the carrier based, at least in part, on the content, wherein the one or more instructions are for the carrier to address the one or more issues; and providing, to the delivery service, the one or more instructions.
The limitations stated above are processes/ functions that under broadest reasonable interpretation (i.e., instructing the carrier to address delivery issues ) covers “certain methods of organizing human activity” (managing personal behavior or relationships or interactions between people and commercial or legal interactions and following rules or instructions). Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 5 and 13 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent).
The additional elements of (i) computer, real-time, communication medium, first neural network, second neural network, input, third neural networks, system, one or more processors; memory that stores computer-executable instructions, communication channel, transmit, a set of non-transitory computer-readable storage media that stores executable instructions, executed by one or more processors of a computer system, contact medium, are recited at a high-level of generality (See specification [0014] A contact medium refers to a software, method, channel, or devices through which communication or interaction takes place between individuals, software, neural networks, and/or organization [0029] software [0064-66] Communications module 350 may use one or more neural networks 352, performed by one or more processors (e.g., central processing unit (GPU), GPU, or any other hardware accelerators), to generate the virtual chat. [0099] content that is to be communicated via the communication channel (e.g., chat, call, smartwatch [0115] each server typically includes an operating system that provides executable program instructions for the general administration and operation of that server and includes a computer-readable storage medium (e.g., a hard disk, random access memory, read only memory, etc.) storing instructions that, if executed by a processor of the server, cause or otherwise allow the server to perform its intended functions [0117] user or client devices include any of a number of computers, such as desktop, laptop or tablet computers running a standard operating system, [0082], [0122]), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (as shown in Fig.1 and 9), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent), and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of: (i) computer, real-time, communication medium, first neural network, second neural network, input, third neural networks, system, one or more processors; memory that stores computer-executable instructions, communication channel, transmit, a set of non-transitory computer-readable storage media that stores executable instructions, executed by one or more processors of a computer system, contact medium, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination ( as shown in Fig.1 and 9), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea (e.g., using computers to communicate data). The dependent claims add the elements “mobile device”, “ phone call”, “ user interface” ,” audio message”, “transformer neural network”, “ telephone call”, “chat interface”, which fail to integrate the abstract idea into a practical application because merely invoking the generic components as a tool to perform the abstract idea or “apply it”. Therefore, the claims recite an abstract idea.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. Accordingly, the claims are not directed to significantly more than the exception itself. The dependent claims add the elements “mobile device”, “ phone call”, “ user interface” ,” audio message”, “transformer neural network”, “ telephone call”, “chat interface”, are recited at a high-level of generality ( see specification[0127] wherein the communication medium comprises at least one of chat, phone call, video, audio messages, or notification on a user interface. [0110] Examples of such client devices include personal computers, cellular or other mobile phones, handheld messaging devices, laptop computers, tablet computers,[0039-42] Communications module 118 may orchestrate digital communication methods, including but not limited to, phone calls, text messaging, as well as WhatsApp and Facebook messages, alongside the capability to dispatch notifications via applications. Communications module 118 may integrate diverse communication protocols and Application Programming Interfaces (APIs) ) ; therefore, they do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Therefore, the dependent claims are not eligible subject matter under § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 13, 16-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (Chinese Patent Reference No. CN 112329416 A, citations are to accompanying English machine translation)
As per claim 1, Lin teaches:
A computer-implemented method, comprising: obtaining, from a delivery service, an indication that a carrier of a delivery service is to execute a scheduled delivery for a customer of a retail service; ( see at least: [0114] In state 810, the processor 242 may retrieve an electronic record of an item from one of the DBs or memories in the distribution network 10 described above. In state 820, the processor 242 may determine whether an “out for delivery” scan has occurred. [0067] The clerk terminal 260 can be a device in a retail location, such as a post office or retail store. A clerk or employee can scan an item using the clerk terminal 260 when an item is sold, received, transferred from store inventory to a carrier, inducted, postage paid; paragraph [0070] (“The carrier terminal 210 can include a mobile delivery device (MDD), that includes a location circuit, such as GPS, and which can provide real-time location data of the MDD, the associated carrier, and distribution items.”))
obtaining, from a delivery service, real-time context information that indicates one or more events that are specific to the scheduled delivery;( [0116] When it is determined in state 830 that the delivery scan has occurred, the processor 242 may determine whether the delivery scan has been made by a predetermined local time in the area the recipient resides (state 840), [0094-96] [delivery scan data includes recipient, time, location to determine the delivery statues])
obtaining a plurality of carrier settings specific to the carrier and a plurality of customer settings specific to the customer; ( see at least: [0094] In state 520, the processor 242 may determine the location where an item delivery scan was made. When a carrier delivers an item, the carrier may perform an item delivery scan via the carrier terminal 210 shown in FIG. 2. The item delivery scan data may include information regarding the recipient, delivery time and/or delivery location (e.g., address), etc.)
generating, a communication medium, between the carrier and the customer based, at least in part, on the plurality of carrier settings, the plurality of customer settings, and the context information; generating, content to be communicated via the communication medium, wherein the content indicates one or more issues of the scheduled delivery; ( see at least: [0094-95], he processor 242 may compare the delivery scan location (determined in state 520) with actual destination coordinates of the item. The processor 242 may determine the actual destination coordinates of the item based on the electronic record retrieved in state 510. When there is a discrepancy between the two, this means that the item may not have been properly delivered to an intended recipient.[0096] even if the delivery scan location is not over a predetermined distance away, if the travel time is over a predetermined of time (e.g., due to traffic), the processor 242 may determine that the delivery scan location is over a predetermined distance away and move to state 550. [0098])
wherein the user is a carrier (paragraph [0055] (“Carriers may pick up items from the unit delivery facility 104b and deliver the items to the recipients 101.”); paragraph [0070] (most of paragraph); paragraph [0098] (most of paragraph))
generating, one or more instructions for the carrier based, at least in part, on the content, wherein the one or more instructions are for the carrier to address the one or more issues; ( see at least: [0098] In state 550, the processor 242 may send a notification to the carrier terminal 210 of the carrier who was intended to deliver the item. This communication may be sent immediately upon receipt of the incorrect scan, in order to allow the carrier to correct the issue as soon as possible. In some embodiments, the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action.)
and providing, to the delivery service, the one or more instructions ( see at least: [0080-81] When it is determined in state 330 that delivery issues are detected or found, the processor 242 may automatically generate an internal case file (state 340). The processor 242 may store the generated internal case file in one or more of the DBs or memories described above so that the internal case file can be accessed by one of the facilities 104 a-108 b.[0081] The internal case file may, for the missing, misrouted, delayed, etc. item. The above are merely examples of information that can be included in the internal case file, but other information that may help locate, identify, reroute, expedite, change service class, or take other action for the item can also be included. [instructions] )
Lin does not explicitly teach one or more first neural networks, one or more second neural networks, one or more third neural networks, However, this is taught by Tan ( see at least: [0047] The machine learning models include neural networks [0021-23] The model serving system 150 receives requests from the online system 140 to perform tasks using machine-learned models. The tasks include, but are not limited to, natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, and the like. In one or more embodiments, the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like [0029-30])
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks feature for the same reasons its useful in Tan-namely, to perform tasks including natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, text generation, query processing, machine translation, chatbots, and the like. ( par.21) Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
The modified Lin does not disclose wherein generating one or more of the communication medium or the content comprises selecting one or more of the communication medium or the content to reduce time taken and user input to resolve the one or more issues. Liu discloses wherein generating one or more of the communication medium or the content comprises selecting one or more of the communication medium or the content to reduce time taken and user input to resolve the one or more issues (third sheet, paragraph beginning with “In the embodiment of the invention”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Lin such that generating one or more of the communication medium or the content comprises selecting one or more of the communication medium or the content to reduce time taken and user input to resolve the one or more issues, as disclosed by Liu, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, Lin in view of Tan in further view of Liu teaches claim 1 as above. Lin further teaches:
wherein the communication medium comprises at least one of chat, phone call, video, audio messages, or notification on a user interface. ( see at least: [0131] message, [0020] send notification to a computing device of an intended recipient of the at least one item having the delivery issue.[0120] via call center)
As per claim 13, Lin teaches:
A set of non-transitory computer-readable storage media that stores executable instructions that, if executed by one or more processors of a computer system, cause the computer system to: ( see at least: abstract, [0121])
obtain information associated with a delivery to be executed by a carrier, ( see at least: [0114] In state 810, the processor 242 may retrieve an electronic record of an item from one of the DBs or memories in the distribution network 10 described above. In state 820, the processor 242 may determine whether an “out for delivery” scan has occurred. [0067] The clerk terminal 260 can be a device in a retail location, such as a post office or retail store. A clerk or employee can scan an item using the clerk terminal 260 when an item is sold, received, transferred from store inventory to a carrier, inducted, postage paid)
wherein the information comprises profile settings of the carrier and a recipient of the delivery; ( see at least: [0094] In state 520, the processor 242 may determine the location where an item delivery scan was made. When a carrier delivers an item, the carrier may perform an item delivery scan via the carrier terminal 210 shown in FIG. 2. The item delivery scan data may include information regarding the recipient, delivery time and/or delivery location (e.g., address), etc.)
generate a contact medium between the carrier and the recipient based, at least in part, on the information; generate content that is to be communicated via the contact, wherein the content indicates an issue associated with the delivery; ( see at least: [0094-95], he processor 242 may compare the delivery scan location (determined in state 520) with actual destination coordinates of the item. The processor 242 may determine the actual destination coordinates of the item based on the electronic record retrieved in state 510. When there is a discrepancy between the two, this means that the item may not have been properly delivered to an intended recipient.[0096] even if the delivery scan location is not over a predetermined distance away, if the travel time is over a predetermined of time (e.g., due to traffic), the processor 242 may determine that the delivery scan location is over a predetermined distance away and move to state 550. [0098])
identify, a procedure for the carrier to address the one or more issues indicated by the content; and provide the procedure to the carrier. ( see at least: [0098] In state 550, the processor 242 may send a notification to the carrier terminal 210 of the carrier who was intended to deliver the item. This communication may be sent immediately upon receipt of the incorrect scan, in order to allow the carrier to correct the issue as soon as possible. In some embodiments, the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action.)
Lin does not explicitly teach one or more first neural networks, one or more second neural networks, one or more third neural networks, However, this is taught by Tan ( see at least: [0047] The machine learning models include neural networks [0021-23] The model serving system 150 receives requests from the online system 140 to perform tasks using machine-learned models. The tasks include, but are not limited to, natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, and the like. In one or more embodiments, the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like [0029-30])
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks feature for the same reasons its useful in Tan-namely, to perform tasks including natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, text generation, query processing, machine translation, chatbots, and the like. ( par.21) Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
The modified Lin does not disclose wherein generating one or more of the contact medium or the content comprises selecting one or more of the contact medium or the content to reduce time taken to resolve the issue. Liu discloses wherein generating one or more of the contact medium or the content comprises selecting one or more of the contact medium or the content to reduce time taken to resolve the issue (third sheet, paragraph beginning with “In the embodiment of the invention”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Lin such that generating one or more of the contact medium or the content comprises selecting one or more of the contact medium or the content to reduce time taken to resolve the issue, as disclosed by Liu, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 16, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
provide a notification for the recipient and the carrier. ( see at least: [0098] the processor 242 may send a notification to the carrier terminal 210 of the carrier who was intended to deliver the item [0005] send notification to an intended recipient of the at least one item having the delivery issue. )
Lin does not teach chat interface however, this is taught by Tan ( see at least: [0021] The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like [0022] For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text) It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the chat interface feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 17, Lin in view of Tan in further view of Liu teaches claim 13 as above.
Lin does not teach, but Tan teaches the one or more third neural networks comprise a transformer neural network. ( see at least: [0021-22] the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like. In one or more embodiments, the language model is configured as a transformer neural network architecture. [0022]for a translation task, the transformer model may receive a sequence of input tokens that represent a paragraph in German and generate a sequence of output tokens that represents a translation of the paragraph or sentence in English. For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks and transformer feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 19, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
wherein the information indicates a location of a device associated with the carrier. ( see at least: [0007-8] in determining whether there is a delivery issue on an item, the processor is further configured to: determine a delivery scan location of the item in which a delivery scan on the item has been made; compare the delivery scan location with an actual destination coordinate of the item; and determine that there is a delivery issue on the item when the delivery scan location is over a predetermined distance away from the actual destination coordinate.[0078] a scan made in a location that is not consistent with the delivery plan,)
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (CN 112329416 A) in further view of Gabler (US10834253B1)
As per claim 2, Lin in view of Tan in further view of Liu teaches claim 1 as above. Lin further teaches:
obtaining, from a delivery service, an indication that the one or more issues ( see at least: [0052] The systems and methods can identify, based on scan information, location information, expected information, and the other item information, when an item is, for example, mis-routed, in an incorrect location according to a delivery plan, a scan is missed, and the like. The systems and methods can cause automatic investigation and corrective action, and can provide alerts to delivery resources)
Lin does not explicitly teach obtaining, from a delivery service, an indication that the one or more issues persist; generating instructions to initiate a phone call between the customer and the carrier using a mobile device. However, this is taught by Gabler ( see at least: abstract, During the delivery of the shipment, a delivery person may encounter a delivery issue, such as needing directions. The delivery person may call the temporary phone number and extension number and be connected to a user device. The delivery person may communicate the delivery issue, such as asking for directions to the user's residence, Col.3 Lines 1-16, the communication module may initiate a call to the phone number 444-4567 to establish communication between the delivery person's device and the user device. The delivery person may accept the call and communicate to the user about the delivery. For example, the delivery person may ask the user for directions to the user's residence or ask questions about what the user's residence looks like, such as color of the user's residence)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the calling feature for the same reasons its useful in Gabler -namely, delivery person may need to contact the recipient of the purchased item. For example, the delivery person may not be able to complete delivery of the ordered item due to a delivery issue . Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (CN 112329416 A) in further view of Chaudhari ( US 11423451)
As per claim 4, Lin in view of Tan in further view of Liu teaches claim 1 as above. Lin further teaches:
the plurality of carrier settings ( [0063-65] delivery plan [0049] The unit delivery facility also sorts and stages the items intended for delivery to destinations within the unit delivery facility's coverage area.)
Lin does not explicitly teach, but Tan teaches using a transformer neural network to generate a translation of the content ( see at least: [0021-22] the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like. In one or more embodiments, the language model is configured as a transformer neural network architecture. [0022]for a translation task, the transformer model may receive a sequence of input tokens that represent a paragraph in German and generate a sequence of output tokens that represents a translation of the paragraph or sentence in English. For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks and transformer feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Lin in view of Tan in further view of Liu does not explicitly teach generate a translation of the content based, at least in part, on language preferences indicated in the plurality of user settings. However, this is taught by Chaudhari ( see at least: Col.10 Line 63-65, Account management service 322 stores information associated with user accounts (e.g., buyer and/or procurement manager 106 accounts) Col.9 Line 58-62 , determines a localization and/or a preferred language of an intended recipient of the voice memo from an API of account management service 322 (e.g., by looking up the intended recipient's account in a database , Col. 22 Lines 3-19, Administrators may draft and submit the announcements, voice prompts, and/or voice memos in text form through respective user interfaces. The voice prompts and/or voice memos are stored in storage of voice memo manager 306 and can later be used by the voice memo localization manager 412 to translate the memo into a language preferred by the buyer according to preferences stored in account management service 322. For example, procurement manager 106 may enter the voice prompt in en_GB locale (Great Britain), but the buyer 104 may have de_DE (Germany) or fr_FR (France) as their preferred locale stored in account management service 322. The voice memo localization manager 412 uses the device-level and/or user-level preferences stored by account manager service 322 to translate the input voice prompts/memos into the desired language preferred by buyer 104. Col.line, The workflow generates the textual representation of the recommendation using the Neural Machine Translation (NMT) technique which uses a recurrent neural network).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the translation based on the profile setting feature for the same reasons its useful in Tan-namely, translates the voice memo into the language preferred by the intended recipient and/or into the predominant language or languages used at the location associated with the device. Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 20, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
The content ( see at least: abstract)
Lin does not explicitly teach translate, using a transformer neural network, the content to a language, however, this is taught by Tan ( see at least: [0021-22] the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like. In one or more embodiments, the language model is configured as a transformer neural network architecture. [0022]for a translation task, the transformer model may receive a sequence of input tokens that represent a paragraph in German and generate a sequence of output tokens that represents a translation of the paragraph or sentence in English. For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks and transformer feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Lin in view of Tan in further view of Liu does not teach translate the content to a language that is indicated in the profile settings. However, this is taught by Chaudhari ( see at least: Col.10 Line 63-65, Account management service 322 stores information associated with user accounts (e.g., buyer and/or procurement manager 106 accounts) Col.9 Line 58-62 , determines a localization and/or a preferred language of an intended recipient of the voice memo from an API of account management service 322 (e.g., by looking up the intended recipient's account in a database , Col. 22 Lines 3-19, Administrators may draft and submit the announcements, voice prompts, and/or voice memos in text form through respective user interfaces. The voice prompts and/or voice memos are stored in storage of voice memo manager 306 and can later be used by the voice memo localization manager 412 to translate the memo into a language preferred by the buyer according to preferences stored in account management service 322. For example, procurement manager 106 may enter the voice prompt in en_GB locale (Great Britain), but the buyer 104 may have de_DE (Germany) or fr_FR (France) as their preferred locale stored in account management service 322. The voice memo localization manager 412 uses the device-level and/or user-level preferences stored by account manager service 322 to translate the input voice prompts/memos into the desired language preferred by buyer 104. Col.line, The workflow generates the textual representation of the recommendation using the Neural Machine Translation (NMT) technique which uses a recurrent neural network).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the translation based on the profile setting feature for the same reasons its useful in Tan-namely, translates the voice memo into the language preferred by the intended recipient and/or into the predominant language or languages used at the location associated with the device. Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 5, 8-10, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Putra (US 20110087510 A1)
As per claim 5, Lin teaches:
A system, comprising: one or more processors; memory that stores computer-executable instructions that, if executed, cause the one or more processors to: ( see at least: abstract)
receive context information that is specific to a scheduled delivery to be executed by a carrier; ;( [0116] When it is determined in state 830 that the delivery scan has occurred, the processor 242 may determine whether the delivery scan has been made by a predetermined local time in the area the recipient resides (state 840), [0094-96] [delivery scan data includes recipient, time, location to determine the delivery statues])
identify, one or more trigger events associated with the scheduled delivery based, at least in part, on the context information and a plurality of settings particular to the carrier and a recipient of the scheduled delivery; ( see at least: [0094-95], he processor 242 may compare the delivery scan location (determined in state 520) with actual destination coordinates of the item. The processor 242 may determine the actual destination coordinates of the item based on the electronic record retrieved in state 510. When there is a discrepancy between the two, this means that the item may not have been properly delivered to an intended recipient.[0096] even if the delivery scan location is not over a predetermined distance away, if the travel time is over a predetermined of time (e.g., due to traffic), the processor 242 may determine that the delivery scan location is over a predetermined distance away and move to state 550. [0098])
cause a communication channel to be established between the carrier and the recipient based, at least in part, on the one or more trigger events; ( see at least: [0098] In state 550, the processor 242 may send a notification to the carrier terminal 210 of the carrier who was intended to deliver the item. This communication may be sent immediately upon receipt of the incorrect scan, in order to allow the carrier to correct the issue as soon as possible. In some embodiments, the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action.[0020] send notification to a computing device of an intended recipient of the at least one item having the delivery issue.; paragraphs [0094]-[0095]; paragraph [0096])
generate, content to be communicated via the communication channel, wherein the content indicates the issue associated with the scheduled delivery; ( see at least: see at least: [0094-95], he processor 242 may compare the delivery scan location (determined in state 520) with actual destination coordinates of the item. The processor 242 may determine the actual destination coordinates of the item based on the electronic record retrieved in state 510. When there is a discrepancy between the two, this means that the item may not have been properly delivered to an intended recipient.[0096] even if the delivery scan location is not over a predetermined distance away, if the travel time is over a predetermined of time (e.g., due to traffic), the processor 242 may determine that the delivery scan location is over a predetermined distance away and move to state 550. [0098])
generate, an instruction for the carrier based, at least in part, on the content, wherein the instruction is associated with the issue; and transmit the instruction to the carrier. ( see at least: [0098] In state 550, the processor 242 may send a notification to the carrier terminal 210 of the carrier who was intended to deliver the item. This communication may be sent immediately upon receipt of the incorrect scan, in order to allow the carrier to correct the issue as soon as possible. In some embodiments, the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action.)
Lin does not explicitly teach one or more first neural networks, one or more second neural networks, one or more third neural networks, However, this is taught by Tan ( see at least: [0047] The machine learning models include neural networks [0021-23] The model serving system 150 receives requests from the online system 140 to perform tasks using machine-learned models. The tasks include, but are not limited to, natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, and the like. In one or more embodiments, the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like [0029-30])
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks feature for the same reasons its useful in Tan-namely, to perform tasks including natural language processing (NLP) tasks, audio processing tasks, image processing tasks, video processing tasks, text generation, query processing, machine translation, chatbots, and the like. ( par.21) Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
The modified Lin does not disclose wherein causing the communication channel to be established comprises selecting the communication channel from a plurality of communications to reduce time to resolve the issue. Putra discloses wherein causing the communication channel to be established comprises selecting the communication channel from a plurality of communications to reduce time to resolve the issue (paragraph [0007] (“The incident notification to the identified one or more responders can be sent simultaneously to each of the identified one or more responders. Each of the plurality of delivery profiles includes predetermined contact paths including one or more of cell phone, email, work phone, home phone, pager, text messaging, and fax, and a predetermined number of contact attempts associated with each of the predetermined contact paths. For each of the identified one or more responders, the selected delivery profile can specify repeatedly sending the incident notification via one or more contact paths. Responsive to receiving indication of an incident notification delivery attempt to the responder, an associated status of the delivery attempt, an associated delivery attempt count, and an associated delivery attempt time can be recorded.”); paragraph [0019] (“In terms of safety, accurate information about the incident can be swiftly disseminated to field service personnel and by prompting them to respond through two-way communications. The ability of the control center to automate message creation and notification delivery can save time and can increase safety.”)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the modified Lin such that causing the communication channel to be established comprises selecting the communication channel from a plurality of communications to reduce time to resolve the issue, as disclosed by Putra, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
the content comprises one or more instructions to the recipient. ( see at least: [0005] notification to an intended recipient of the at least one item having the delivery issue. In the above system, the notification comprises one or more of an updated delivery date, an upgraded service class or an explanation of the delivery issue for the at least one item. )
As per claim 9, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
cause the system to generate the communication channel further include instructions that cause the system to provide ( see at least [0005] notification)
Lin does not explicitly teach a chat interface; however, this is taught by Tan ( see at least: [0021] The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like [0022] For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the chat interface feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
As per claim 10, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
wherein the context information indicates a location of a device associated with the carrier. ( see at least: [0007-8] in determining whether there is a delivery issue on an item, the processor is further configured to: determine a delivery scan location of the item in which a delivery scan on the item has been made; compare the delivery scan location with an actual destination coordinate of the item; and determine that there is a delivery issue on the item when the delivery scan location is over a predetermined distance away from the actual destination coordinate.[0078] a scan made in a location that is not consistent with the delivery plan).
As per claim 12, Lin in view of Tan in further view of Putra teaches claim 5 as above.
Lin does not teach, but Tan teaches the one or more second neural networks comprise a transformer neural network. ( see at least: [0021-22] the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like. In one or more embodiments, the language model is configured as a transformer neural network architecture. [0022]for a translation task, the transformer model may receive a sequence of input tokens that represent a paragraph in German and generate a sequence of output tokens that represents a translation of the paragraph or sentence in English. For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks and transformer feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Putra (US 20110087510 A1) in further view of Magliozzi ( US 20180131645 A1)
As per claim 6, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
feedback generated by the recipient as a result of transmitting the instruction to the carrier. ( [0118-119] the processor 242 can send a notification to the intended recipient to confirm delivery of the item, or can send notification of delay or an updated delivery time [0053] customer who experiences a delivery delay may request information from the distribution network as to why item is delayed. When a customer requests information, the systems and methods can provide tracking information, scan history, item location, and the like in an automated system for the customer)
Lin does not explicitly teach update the one or more third neural networks based, at least in part, on feedback generated by the user, however, this is taught by Magliozzi ( see at least:[0110-111] the chatbot can transmit the response to the user via a communications interface. The user can indicate the user's approval or disapproval of the response. For instance, the user may provide information indicating whether or not the response was helpful. Depending on the approval or disapproval, the NLP server can reward and update the language model. That is, if the user approves of a response, the NLP server rewards the language model for that response. In other words, the NLP server can update the language model to include or indicate a higher confidence value for that particular response. If the user disapproves of a response, the NLP server can update the language model to reflect the user's disapproval. In such scenarios, the chatbot can either search the knowledge base for synonyms and update the language model with the synonym or create an entirely new entry with a new answer to handle that particular question in the future [0007], [0039] the NLP server 110 includes a neural network to train the system 100 to determine responses to questions).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the updating the model based on the user feedback feature for the same reasons its useful in Magliozzi -namely, The approval can indicate an agreement by the user that the second response is an accurate response to the second query and the disapproval can indicate a disagreement by the user that the second response is the accurate response to the second query. The processor may update the model based on the approval or disapproval ( par.7) . Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Putra (US 20110087510 A1) in further view of Gabler (US10834253B1)
As per claim 7, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
receive an indication about the issue ( see at least: [0052] The systems and methods can identify, based on scan information, location information, expected information, and the other item information, when an item is, for example, mis-routed, in an incorrect location according to a delivery plan, a scan is missed, and the like. The systems and methods can cause automatic investigation and corrective action, and can provide alerts to delivery resources)
Lin does not explicitly teach receive an indication from the carrier that the issue persists; and initiate a telephone call between the recipient and the carrier. However, this is taught by Gabler ( see at least: abstract, During the delivery of the shipment, a delivery person may encounter a delivery issue, such as needing directions. The delivery person may call the temporary phone number and extension number and be connected to a user device. The delivery person may communicate the delivery issue, such as asking for directions to the user's residence, Col.3 Lines 1-16, the communication module may initiate a call to the phone number 444-4567 to establish communication between the delivery person's device and the user device. The delivery person may accept the call and communicate to the user about the delivery. For example, the delivery person may ask the user for directions to the user's residence or ask questions about what the user's residence looks like, such as color of the user's residence)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the calling feature for the same reasons its useful in Gabler -namely, delivery person may need to contact the recipient of the purchased item. For example, the delivery person may not be able to complete delivery of the ordered item due to a delivery issue . Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Putra (US 20110087510 A1) in further view of Chaudhari ( US 11423451)
As per claim 11, Lin in view of Tan in further view of Putra teaches claim 5 as above. Lin further teaches:
The content ( see at least: abstract)
Lin does not explicitly teach translate, using a transformer neural network, the content to a language, however, this is taught by Tan ( see at least: [0021-22] the machine-learned models deployed by the model serving system 150 are models configured to perform one or more NLP tasks. The NLP tasks include, but are not limited to, text generation, query processing, machine translation, chatbots, and the like. In one or more embodiments, the language model is configured as a transformer neural network architecture. [0022]for a translation task, the transformer model may receive a sequence of input tokens that represent a paragraph in German and generate a sequence of output tokens that represents a translation of the paragraph or sentence in English. For a text generation task, the transformer model may receive a prompt and continue the conversation or expand on the given prompt in human-like text.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the neural networks and transformer feature for the same reasons its useful in Tan-namely, to receive a prompt and continue the conversation or expand on the given prompt in human-like text ( par.22). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Lin in view of Tan in further view of Putra does not teach translate the content to a language that is indicated in the plurality of settings. However, this is taught by Chaudhari ( see at least: Col.10 Line 63-65, Account management service 322 stores information associated with user accounts (e.g., buyer and/or procurement manager 106 accounts) Col.9 Line 58-62 , determines a localization and/or a preferred language of an intended recipient of the voice memo from an API of account management service 322 (e.g., by looking up the intended recipient's account in a database , Col. 22 Lines 3-19, Administrators may draft and submit the announcements, voice prompts, and/or voice memos in text form through respective user interfaces. The voice prompts and/or voice memos are stored in storage of voice memo manager 306 and can later be used by the voice memo localization manager 412 to translate the memo into a language preferred by the buyer according to preferences stored in account management service 322. For example, procurement manager 106 may enter the voice prompt in en_GB locale (Great Britain), but the buyer 104 may have de_DE (Germany) or fr_FR (France) as their preferred locale stored in account management service 322. The voice memo localization manager 412 uses the device-level and/or user-level preferences stored by account manager service 322 to translate the input voice prompts/memos into the desired language preferred by buyer 104. Col.line, The workflow generates the textual representation of the recommendation using the Neural Machine Translation (NMT) technique which uses a recurrent neural network).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the translation based on the profile setting feature for the same reasons its useful in Tan-namely, translates the voice memo into the language preferred by the intended recipient and/or into the predominant language or languages used at the location associated with the device. Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (CN 112329416 A) in further view of Magliozzi ( US 20180131645 A1)
As per claim 14, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
feedback generated by the carrier as a result of providing the procedure. ( see at least: [0070] The carrier may manually input data to the carrier terminal 210 which can be transmitted to the carrier terminal DB 220 [0120] when the processor 242 determines that there is a delivery issue under the processes of FIGS. 3-8, or any other scenario, an item record can be updated to reflect the issue, or an issue record can be created and stored in the item information DB 230.[0098] the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action. In some embodiments, the carrier may indicate that the item was correctly delivered and that the location coordinate of the delivery was incorrect [ feedback])
Lin in view of Tan in further view of Liu does not explicitly teach update the one or more first neural networks based, at least in part, on feedback generated by the user, however, this is taught by Magliozzi ( see at least:[0110-111] the chatbot can transmit the response to the user via a communications interface. The user can indicate the user's approval or disapproval of the response. For instance, the user may provide information indicating whether or not the response was helpful. Depending on the approval or disapproval, the NLP server can reward and update the language model. That is, if the user approves of a response, the NLP server rewards the language model for that response. In other words, the NLP server can update the language model to include or indicate a higher confidence value for that particular response. If the user disapproves of a response, the NLP server can update the language model to reflect the user's disapproval. In such scenarios, the chatbot can either search the knowledge base for synonyms and update the language model with the synonym or create an entirely new entry with a new answer to handle that particular question in the future [0007], [0039] the NLP server 110 includes a neural network to train the system 100 to determine responses to questions).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the updating the model based on the user feedback feature for the same reasons its useful in Magliozzi -namely, the approval can indicate an agreement by the user that the second response is an accurate response to the second query and the disapproval can indicate a disagreement by the user that the second response is the accurate response to the second query. The processor may update the model based on the approval or disapproval ( par.7) . Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (CN 112329416 A) in further view of Mitchell (US20210092079A1)
As per claim 15, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
wherein the content is to indicate the issue. ( see at least: [0007], [0094-96])
Lin does not explicitly teach the content is a modification of what the recipient sent to indicate the issue. However, this is taught by Mitchell ( see at least: [0064] process 1000 may maintain a repository and/or utilize NLP and/or other suitable techniques to identify phrases and/or words that are ambiguous or unclear for a determination (at 1020) whether there is ambiguous language present. This determination may be made based on identified (e.g., from 1015) message characteristics. process 1000 may substitute (at 1060) the ambiguous language with the more defined language provided by the sending participant. [0017] the IMMS may determine substitute phrases and/or words by using natural language processing (“NLP”) and/or other suitable techniques to determine a syntactical meaning or intent of the message, and may use machine learning and/or other suitable techniques to determine phrases and/or words having a similar meaning, but in a more appropriate manner (e.g., where a message score for the replacement message does not exceed the message score threshold). The IMMS may recommend replacement language to the sending participant and request approval from the sending participant.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the modification to the content feature for the same reasons its useful in Mitchell-namely, determine phrases and/or words having a similar meaning, but in a more appropriate manner ( par.17). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin ( US 20210406823) in view of Tan ( US 20240303711) in further view of Liu (CN 112329416 A) in further view of Wang ( US11861546B1)
As per claim 18, Lin in view of Tan in further view of Liu teaches claim 13 as above. Lin further teaches:
update the profile settings based, at least in part, on feedback generated by either the carrier or the recipient as a result of providing the procedure to the carrier. (see at least: [0070] The carrier may manually input data to the carrier terminal 210 which can be transmitted to the carrier terminal DB 220 [0120] when the processor 242 determines that there is a delivery issue under the processes of FIGS. 3-8, or any other scenario, an item record can be updated to reflect the issue, or an issue record can be created and stored in the item information DB 230.[0098] the carrier can input via the carrier terminal 210 an acknowledgement and a correction of the action. In some embodiments, the carrier may indicate that the item was correctly delivered and that the location coordinate of the delivery was incorrect [ feedback by the carrier ])
Lin does not explicitly teach update the profile settings based, at least in part, on feedback generated by the user. However, this is taught by Wang ( see at least: Fig.4, 406 database corresponds profile setting, Col. 21 Line 50-51, the database 406 can include previous delivery information 430 and customer feedback 432. Col.23 line 20-44, the user interface 420 provide fields for deliverer feedback, deliverer input to the local algorithms 414 and the global algorithms 416, deliverer overrides, deliverer confirmation of drop-off location/package identification, and other deliverer workflows associated with package placement verification. The user interface 420, configured to identify whether the package has been placed within an approved delivery location or within a rejected delivery location. Col.3 Line33-44 , one or more customers can provide location information associated with the delivery location including indications of appropriate or approved drop-off locations and inappropriate or disapproved drop-off locations for packages. Further, the location information, customer indications regarding the drop-off locations, delivery results (e.g., customer received package, package was delivered not received, customer reported a complaint with the delivery, etc.), and other delivery information can be associated within a database [profile settings]. The location information and delivery information associated with the delivery location can be analyzed and utilized to confirm package placement at the delivery location and ensure that packages are effectively delivered to customers. Col.7 Lines 1-23)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine updating the profile settings based on user feedback feature for the same reasons its useful in Wang-namely, determine whether the drop-off zone associated with the package 106 within the delivery environment 104 matches or is within one of the one or more drop-off locations (e.g., unsafe drop-off locations, rejected drop-off locations, approved drop-off locations, etc.) associated with the delivery location ( Col.7 Lines 1-23 ). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Santo, US 10572852 B2 (software application for the automated drop-off and pick-up of a service item at a service facility).
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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/NATHAN ERB/Primary Examiner, Art Unit 3628
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