DETAILED ACTION
The following NON-FINAL Office Action is in response to Applicant’s Remarks filed on 02/18/2026.
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/18/2026 has been entered.
Status of Claims
Claims 1-20 were previously pending and subject to a final Office Action mailed 11/18/2025. Claims 1, 9, and 17 were amended. Claims 1-20 are currently pending and are subject to the non-final Office Action below.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 7-10, filed 02/18/2026, with respect to the 35 U.S.C. 101 rejections of Claims 1-20 have been fully considered and are not persuasive.
Applicant argues that the claims recite an improved method of using machine learning techniques to automate responses to roadside assistance queries without the need for human intervention. Examiner respectfully disagrees that the claims recite an integration into a practical application.
To clarify “automating responses to roadside assistance queries without the need for human intervention” is directed to the abstract idea of organizing human activity - commercial interactions or business relations between a business providing roadside assistance services and a user.
The one or more machine learning models which have “specific training” on the historical conversation data and subset of categories of the plurality of categories are merely indicating a field of use or technological environment in which the judicial exception is performed.
Although the additional element limits the identified judicial exceptions “processing”, “analyzing”, and “generating”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e. execution by one or more trained machine learning models) and thus fails to add an inventive concept to the claims.
Additionally, Examiner notes Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Similarly, Applicant’s “specific training” of the one or more machine learning models do not provide a technological improvement.
Applicant also argued “an improvement in the technical field of machine learning processing”. Examiner respectfully disagrees as improvements in “identifying a query category with an ongoing roadside assistance request” and “automating a response to the query” are not actually improving machine learning processing but are instead improving the abstract idea itself. See MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Regarding Applicant’s Step 2B arguments, Examiner respectfully disagrees as addressed previously, the machine learning limitations are indicating a field of use and the “improved method” are an improvement in the abstract idea.
Accordingly, the 35 U.S.C. 101 rejection of Claims 1-20 have been maintained.
35 USC § 103
Applicant’s arguments, see pages 11-14, filed 02/18/2026, with respect to the 35 U.S.C. 103 rejections of Claims 1-20 have been fully considered and are not persuasive.
Examiner relies upon the combination of Leise and new reference Liao to teach the amended limitations.
Leise was relied upon to teach the context of the ongoing roadside assistance request, the roadside service providers, etc. as the user may have been involved in a car accident and the Leise system offers to assist with finding a vehicle repair facility or arranging for a tow truck to pick up the user’s vehicle (“processing”). Leise in figure 5 and Col. 20 Line 44-61 teaches a machine learning module may identify patterns in the user’s or other user’s previous inquiries and identify requested actions (“analyzing”) based on the patterns.
Leise also teaches in Col. 10 Line 51 to Col. 11 Line 48 the machine learning module learns the most likely insurance related information for responding to the requested action and the set of rules for identifying the most likely insurance-related information (“query response”).
See also figure 3C and Col. 17 Line 12 to Col. 18 Line 36 specifically Col. 17 Line 43-58 requested action learned by machine learning module and the requested action is associated with a set of rules stored in the rules database.
Liao teaches the one or more machine learning models trained on the historical conversation data to analyze natural language communications of the text communication to identify a category of a query and the one or more machine learning models trained on a subset of categories.
Further Liao teaches that the intent may be “asking for the status of a refund” (para. 67-69) and the chatbot may be applied in a variety of use cases. For example, hospitals (para. 93), retail establishment (para. 32), large company (para. 32), etc.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent Claims 1, 9, and 17 recite the limitation of “analyzing, using the one or more machine learning models trained on the historical conversation data and based on the identified plurality of categories, natural language patterns of the text communication to identify a category of the query, wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query, wherein each of the one or more machine learning models are trained on a subset of categories of the plurality of categories”.
The closest description of the limitation is within the following paragraphs of the specification:
Paragraph 16 “In an implementation, the chatbot model 120 is a machine learning model. The chatbot model 120 may have instructions that direct and/or cause the chatbot computing platform 106 to train, maintain, and deploy the one or more chatbots 122 using the chatbot model 120 to execute the techniques, as discussed in greater detail below. The one or more chatbots 122 may each correspond to a unique chatbot model 120, which may be trained as an expert on a particular topic (e.g., queries related to a submitted roadside assistance request).”
Paragraph 17 “The chatbot model 120 may be built from historical conversation data stored, of example, at the one or more databases 110. In this implementation, the chatbot model 120 leverages historical conversational data relating to user queries relating to roadside assistance requests to identify a category that the query belongs to.”
Paragraph 12 “The systems and methods described herein use natural language processing to determine an intent of the user for roadside assistance services. The determined intent is used to submit a roadside assistance request”.
Thus, the specification supports the machine learning models trained on historical conversation data (para. 17) and a subset of categories of the plurality of categories (para. 16). The specification also supports using natural language processing (para. 12) and the chatbot leveraging historical conversation data to determine an intent or category of the user’s query (para. 17).
The specification does not support matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query.
Accordingly, the limitation of “wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query” is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. Dependent claims 2-8, 10-16, and 18-20 inherit the rejection as they do not cure the deficiencies of the independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-8 are directed to a method, Claims 9-16 are directed to a system, and Claims 17-20 are directed to one or more tangible non-transitory computer-readable storage media. Thus, all the claims fall within one of the four statutory categories of invention.
Step 2A Prong 1
Independent Claim 1 recites the limitations of: identifying a plurality of categories based on historical conversation data corresponding to user queries associated with roadside assistance requests; processing a query in a text communication…, wherein the query is associated with an ongoing roadside assistance request submitted…,; analyzing, … and based on the identified plurality of categories, natural language patterns of the text communication to identify a category of the query, wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query… ; generating, …a query response based on the identified category by retrieving, in real-time, information associated with the ongoing roadside assistance request … and automatically formatting the query response for transmittal …; and sending the query response …
Independent Claim 9 recites the limitations of: identify a plurality of categories based on historical conversation data corresponding to user queries associated with roadside assistance requests; process a query in a text communication … wherein the query is associated with an ongoing roadside assistance request submitted …; analyze, … and based on the identified plurality of categories, natural language patterns of the text communication to identify a category of the query, wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query…; generate, … a response based on the identified category by retrieving, in real-time, information associated with the ongoing roadside assistance request … and automatically formatting the response for transmittal …; and send the response …
Independent Claim 17 recites the limitations of: identifying a plurality of categories based on historical conversation data corresponding to user queries associated with roadside assistance requests; processing a query in a text communication …, wherein the query is associated with an ongoing roadside assistance request submitted …; analyzing, … based on the identified plurality of categories, natural language patterns of the text communication to identify a category of the query, wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query…; generating, …, a response based on the identified category by retrieving, in real-time, information associated with the ongoing roadside assistance request … and automatically formatting the query response for transmittal …; and sending the response …
Organizing Human Activity
The limitations of Claims 1, 9, and 17 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (managing personal behavior or relationships or interactions between people or commercial/legal interactions). Specifically, commercial interactions or business relations between a business providing roadside assistance services and a user in light of Paragraph of 2-3 Applicant’s specification details that users who experience vehicle failures have to interact with businesses to arrange for roadside assistance and paragraph 12 details that a chatbot is used to determine an intent of the user for roadside assistance services and the intent is used to submit a roadside assistance request. Thus, it is further evident that the claims are directed towards organizing commercial interactions/business relations between a business and a user.
Accordingly, the limitations above recite a judicial exception (an abstract idea that falls within the organizing human activity grouping) and the analysis must therefore proceed to Step 2A Prong 2.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, Claim 1, Claim 9, and Claim 17 recite the additional elements of a user device, a roadside service provider system, a chatbot computing platform, one or more processors, one or more tangible non-transitory computer-readable storage media, and a computing system. Each additional element is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP2106.05(f).
Further, the roadside service provider system “wherein the roadside service provider system is associated with one or more roadside service providers that provide roadside assistance in response to the ongoing roadside assistance request” may amount to generally linking the use of the judicial exception to a particular technological environment or field of use. Specifically limiting the application of the commercial interaction/business relation to the field of use of businesses such as a roadside service provider system that is associated with one or more roadside service provides who provide assistance to the user. See MPEP2106.05(h).
Additionally, the claims recite one or more machine learning models “trained on the historical conversation data” and “wherein each of the one or more machine learning models are trained on a subset of categories of the plurality of categories” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element limits the identified judicial exceptions “processing”, “analyzing”, and “generating”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e. execution by one or more trained machine learning models) and thus fails to add an inventive concept to the claims.
Accordingly, the additional elements do not integrate the abstract idea into a practical application, whether individually or viewed in an ordered combination, because mere instructions to apply the exception using a generic computer component and field of use does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a user device, a roadside service provider system, a chatbot computing platform, one or more processors, one or more tangible non-transitory computer-readable storage media, and a computing system to perform the limitations noted above amount to no more than mere instructions to apply the exception using a generic computer component.
Again, the roadside service provider system “wherein the roadside service provider system is associated with one or more roadside service providers that provide roadside assistance in response to the ongoing roadside assistance request” may amount to generally linking the use of the judicial exception to a particular technological environment or field of use. Specifically limiting the application of the commercial interaction/business relation to the field of use of businesses such as a roadside service provider system that is associated with one or more roadside service provides who provide assistance to the user.
Similarly, the one or more machine learning models “trained on the historical conversation data” and “wherein each of the one or more machine learning models are trained on a subset of categories of the plurality of categories” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element limits the identified judicial exceptions “processing”, “analyzing”, and “generating”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e. execution by one or more trained machine learning models).
None of the steps/functions of Claims 1, 9, and 17 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, thus, the analysis does not change when considered as an ordered combination. Even when considered in combination, the additional elements of Claims 1, 9, and 17 amount to no more than mere instructions to implement the abstract idea on a computer and field of use which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, claims 1, 9, and 17 are ineligible.
Dependent Claim 2, 11, and 18 specify where the text communication is received via text message or an online chat interface. Such a limitation amounts to insignificant extra-solution activity of pre-solution data gathering similar to “receiving or transmitting data over a network” which is a computer function the courts have recognized as well-understood, routine, and conventional. See MPEP 2106.05(d).
Further, the limitation may be considered as limiting the application of the abstract idea to text communication received via text message or an online chat interface. See MPEP 2106.05(h) “For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).”
Dependent Claim 3 and 16 specifies that the user device is a computing device or a mobile computing device. The computing device/mobile computing device is recited at a high level of generality and amounts to no more than mere instructions to implement the abstract idea on a computer.
Dependent Claims 4, 10, and 20 merely specify further when the category cannot be identified, transferring the text communication to a live agent. Thus, further narrowing the abstract idea identified above.
Dependent Claims 5-8, 12-15, and 19 merely specify further what the category is related to and what the query response is based on. Thus, further narrowing the abstract idea identified above.
Nothing in dependent claims 2-8, 10-16, and 18-20 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Accordingly, claims 1-20 are ineligible.
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.
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.
Claims 1-3, 5-6, 8-9, 11-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Leise et al. (US Patent No. 10,387,963) in view of Liao et al. (US2021/0295203).
As per independent Claim 1, Claim 9, and Claim 17,
Leise teaches a computer implemented method comprising:/ a system comprising: a chatbot computing platform comprising one or more processors configured to: (figure 1 and Col. 7 Line 46 to Col. 8 Line 11 where the avatar generation server receives user input and transmits a response to the user input; see also Col. 1 Lines 51-59 and figure 3A-3C where a call agent avatar functions as a chatbot)/ one or more tangible non-transitory computer-readable storage media storing computer- executable instructions for performing a computer process on a computing system, the computer process comprising: (figure 5 and Col. 19 Line 60 to Col. 20 Line 2 non-transitory computer readable memory and processor of the avatar generation server)
identifying a plurality of categories based on historical conversation data corresponding to user queries associated with roadside assistance requests (Col. 9 Line 37 to Col. 10 Line 50 where in Col. 9 Line 37-40 the grammar module may call upon the machine learning module to learn additional requested actions or a most likely requested action based on the text input and Col. 10 Line 3-22 machine learning module may learn frequent behavior of the user based on the user’s insurance-related inquiries; Col. 10 Line 51 to Col. 11 Line 48 where in Col. 11 Lines 6-26 the machine learning module uses as training data (previous requested actions, example information provided in response to requested actions, and a set of rules); Col. 18 Line 53 to Col. 19 Line 5 machine learning module may identify the requested action and the insurance related information as the correct action and information, the machine learning module may update the model to increase the likelihood that the requested action and information are selected in the future based on the user input; Col. 20 Line 17-61 specifically 44-61 machine learning module may identify patterns in the user’s or other user’s previous inquiries and identify requested actions based on the patterns – for example, the machine learning module may associate two requested actions with the insurance related inquiry thus when the user later inputs the same inquiry the machine learning module may identify the two requested actions to perform)
processing a query in a text communication from a user device using one or more machine learning models (figure 3A and Col. 15 Lines 14-46 where the user provides an insurance related inquiry; figure 3C and Col. 17 Line 12 to Col. 18 Line 36 where in Col. 17 Line 32-56 the user Jack may have been in a vehicular accident and asks “What should I do with my vehicle?” which is transcribed into text and used as input, and then based on the text input, a requested action is identified (“the requested action… may be learned by the machine learning module”); see also figure 5 and Col. 20 Line 3-16)
wherein the query is associated with an ongoing roadside assistance request submitted to a roadside service provider system, wherein the roadside service provider system is associated with one or more roadside service providers that provide roadside assistance in response to the ongoing roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; see also Col. 7 Lines 1-11 and Col. 6 Lines 24-51 avatar application voice recognition module may be activated automatically upon detecting a vehicle crash and prompt the user with questions such as “do you need assistance?”, “would you like me to find the nearest vehicle repair facility?”, and “can I schedule a tow truck to pick up your vehicle?”)
analyzing, using the one or more machine learning models and based on the identified plurality of categories, patterns of the text communication to identify a category of the query (Col. 8 Line 37 to Col. 9 Line 23 specifically Col. 9 Line 20-23 where the grammar module identifies a category of the inquiry and then identifies a requested action that matches with the category; Col. 15 Lines 47-54 where the text input is transmitted to the server which identifies a requested action based on the text input; see also figure 5 and Col. 20 Line 17-61 specifically 44-61 machine learning module may identify patterns in the user’s or other user’s previous inquiries and identify requested actions based on the patterns; Col. 9 Line 31 to Line 40 where the grammar module is determining the requested action based on the text input)
generating, using the one or more machine learning models, a query response based on the identified category by retrieving, in real-time, information associated with the ongoing roadside assistance request from the roadside service provider system and automatically formatting the query response for transmittal to the user device (Col. 5 Lines 40-46 and Col. 7 Line 46 to Col. 8 Line 11 where the server may access a vehicle crash database to determine cost of repair, recommended repair facilities for one or several sets of impact characteristics, estimated repair durations, etc. and a static response database which stores portions of responses to user’s insurance related inquiry based on a requested action – for further examples of how the server formulates a response to the user’s inquiry, see Col. 9 Lines 4 to Col. 11 Line 48; Col. 15 Lines 47-54 where the server generates a text and audio response to the requested action; see also figure 5 and Col. 20 Line 62 to Col. 22 Line 11 specifically Col. 21 Line 60 to Col. 22 Line 11 response is generated; figure 3C and Col. 17 Line 12 to Col. 18 Line 36 specifically Col. 17 Line 43-58 requested action learned by machine learning module and the requested action is associated with a set of rules stored in the rules database; Col. 10 Line 51 to Col. 11 Line 48 where in Col. 10 Line 51-59 the machine learning module learns the most likely insurance related information for responding to the requested action and the set of rules for identifying the most likely insurance-related information and in Col. 11 Lines 6-26 the machine learning module uses as training data (previous requested actions, example information provided in response to requested actions, and a set of rules); see also Col. 18 Line 53 to Col. 19 Line 5)
sending the query response to the user device (Col. 5 Lines 46-61 where the server transmits the call center avatar and the response to be displayed on the user’s client device; Col. 6 Line 52 to Col. 7 Line 26 where the response displayed to the user include follow up questions (arrange for a tow truck), repair estimates, nearest repair shop, directions, etc.; Col. 15 Line 52-54 where the response is displayed; see also figure 5 and Col. 22 Line 12 to 36)
Leise does not teach, but Liao teaches:
using the one or more machine learning models trained on the historical conversation data (Para. 37 training a chatbot and para. 38, 91 machine learning system to analyze historical data to develop a list of intents – for example, natural language processing on conversations; para. 54 training the chatbots on historical conversation data; see also para. 104)
natural language patterns of the text communication to identify a category of the query, wherein the analyzing includes matching the natural language patterns of the text communication to patterns learned from the historical conversation data to identify the category of the query (para. 22 chatbot receives utterance, analyzes the utterance by detecting an intent that matches a stored intent; para. 28-30 in para. 28 chatbots recognize the intent of an utterance (match content of utterance to stored intent) by using classifiers; para. 43 once list of intents is developed, chatbot can be trained to recognize the intents within a user utterance; para. 47 natural-language analysis on utterances to organize them by topic – “when does the store close” and “what is the store’s address” may be respectively categorized as “store hours” and “store location”; see also para. 67-70 and para. 78-81 where in para. 81 each intent may have a set of historical utterances associated with it)
wherein each of the one or more machine learning models are trained on a subset of categories of the plurality of categories (para. 76 variety of chatbots each trained for a different use case – human resources, product return, product issues, reservations, etc.; para. 24-27 where in para. 24 granularity of intents that a chatbot recognizes can vary from chatbot to chatbot; para. 43 training data for a particular classifier may take the form of a set of utterances and a record of whether the utterance contains the intent for which the classifier is being trained; para. 31-36 where in para. 34 design chatbot to have high granularity with product return and low granularity with pricing topics; para. 55 and 60 analyze utterances to create intent hierarchy or list of macro topics “returns”, “replacements”, “refunds”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Leise invention with Liao with the motivation of increasing the accuracy of the category identification as well as reducing the amount of processing resources.
See para. 104 “a customized intent hierarchy for a chatbot may be periodically analyzed once the chatbot has been deployed. For example, a chatbot-training system may continue to monitor a chatbot while the chatbot is communicating with end users. That chatbot-training system may collect that chatbot's real-world conversation data and add the utterances from that conversation data to the list of historical utterances with which the chatbot's intent hierarchy was originally trained. The chatbot-training system may then retrain the chatbot with the updated data”.
See also para. 35 “Because the amount of resources required to operate a chatbot can increase drastically as the granularity of the chatbot increases, designing a chatbot with high granularity only on topics that the chatbot is expected to be able to converse with specificity can result in significant resource savings. In other words, designing a chatbot with very precise granularity (sometimes referred to herein simply as precisely designing a chatbot or designing a chatbot with high precision) significantly reduce the amount of storage and processing resources required to operate a chatbot”.
As per dependent Claim 2, Claim 11, and Claim 18,
Leise/Liao teaches the method of claim 1, the system of claim 9, and the one or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing the computer process on the computing system of claim 17.
Leise further teaches:
wherein the text communication is received via one of a text message or an online chat interface (Col. 5 Lines 31-46 where the server receives text input corresponding to an insurance-related inquiry from a user’s client device; Col. 6 Lines 24-51 where the application may transcribe voice input into text input; see also figure 3A-3C and corresponding description specifically Col. 15 Line 22-54)
As per dependent Claim 3,
Leise/Liao teaches the method of claim 1.
Leise further teaches:
wherein the user device is a computing device (Figure 1 and Col. 6 Lines 4-16 where the client device includes mobile devices such as a tablet computer, cell phone, wearable computing device, etc.)
As per dependent Claim 5,
Leise/Liao teaches the method of claim 1.
Leise further teaches:
wherein the category is related to a status update of the previously submitted roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; see also Col. 7 Lines 1-11)
As per dependent Claim 6,
Leise/Liao teaches the method of claim 5.
Leise further teaches:
wherein the query response is based on information retrieved relating to the status update (figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 7 Line 46 to Col. 8 Line 11 where the server may access a vehicle crash database to determine cost of repair, recommended repair facilities for one or several sets of impact characteristics, estimated repair durations, etc. and a static response database which stores portions of responses to user’s insurance related inquiry based on a requested action – for further examples of how the server formulates a response to the user’s inquiry, see Col. 9 Lines 4 to Col. 11 Line 48)
As per dependent Claim 8,
Leise/Liao teaches the method of claim 1.
Leise further teaches:
wherein the category is related to information for a service provider (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; Col. 17 Lines 32-55 where Jack asks “What should I do with my vehicle?” and the grammar module determines that the requested action is to find the optimal vehicle repair facility for repairing the vehicle and estimate the cost of vehicle damage; figure 3C and Col. 17 Lines 12-31 where the response is “Your vehicle can be repaired today at John’s Repair Shop on 123 Main Street. The estimated repair cost is $3000”; Col. 17 Lines 56 to Col. 18 Line 36 where the set of rules include determining the highest ranking vehicle repair facility)
As per dependent Claim 12,
Leise/Liao teaches the system of claim 9.
Leise further teaches:
wherein the category is related to a status update of the roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; see also Col. 7 Lines 1-11)
As per dependent Claim 13,
Leise/Liao teaches the system of claim 12.
Leise further teaches:
wherein the response is based on information retrieved relating to the status update (figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 7 Line 46 to Col. 8 Line 11 where the server may access a vehicle crash database to determine cost of repair, recommended repair facilities for one or several sets of impact characteristics, estimated repair durations, etc. and a static response database which stores portions of responses to user’s insurance related inquiry based on a requested action – for further examples of how the server formulates a response to the user’s inquiry, see Col. 9 Lines 4 to Col. 11 Line 48)
As per dependent Claim 15,
Leise/Liao teaches the system of claim 9.
Leise further teaches:
wherein the category is related to information for a service provider responding to the roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; Col. 17 Lines 32-55 where Jack asks “What should I do with my vehicle?” and the grammar module determines that the requested action is to find the optimal vehicle repair facility for repairing the vehicle and estimate the cost of vehicle damage; figure 3C and Col. 17 Lines 12-31 where the response is “Your vehicle can be repaired today at John’s Repair Shop on 123 Main Street. The estimated repair cost is $3000”; Col. 17 Lines 56 to Col. 18 Line 36 where the set of rules include determining the highest ranking vehicle repair facility)
As per dependent Claim 16,
Leise/Liao teaches the system of claim 9.
Leise further teaches:
wherein the user device is a mobile computing device (Figure 1 and Col. 6 Lines 4-16 where the client device includes mobile devices such as a tablet computer, cell phone, wearable computing device, etc.)
As per dependent Claim 19,
Leise/Liao teaches the one or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing the computer process on the computing system of claim 17.
Leise further teaches:
wherein the category is related to one of a status update of the roadside assistance request, a cancellation of the roadside assistance request, or information for a service provider (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; figure 3C and Col. 17 Lines 12-31 where the response is “Your vehicle can be repaired today at John’s Repair Shop on 123 Main Street. The estimated repair cost is $3000”; Col. 17 Lines 56 to Col. 18 Line 36 where the set of rules include determining the highest ranking vehicle repair facility)
Claims 4, 7, 10, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leise et al. (US Patent No. 10,387,963) in view of Liao et al. (US2021/0295203) as applied to claims 1, 9, and 17 above, further in view of Kuo et al. (US2023/0222316).
As per dependent Claim 4, Claim 10, and Claim 20,
Leise/Liao teaches the method of claim 1, the system of claim 9, and the one or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing the computer process on the computing system of claim 17.
Leise further teaches:
wherein if the category cannot be identified (Col. 9 Lines 31-40 where if the grammar module cannot determine a requested action based on the text input, the grammar module provides follow up questions to the user or uses a machine learning module to learn the most likely requested action based on the text input – further example in Col. 9 Line 41-Col. 10 Line 22; similar embodiment in Col. 20 Lines 35-61)
Leise/Liao does not teach, Kuo teaches:
wherein if the category cannot be identified, [the method further comprising:/ the one or more processors is further configured to:/the computer process further comprises:] transferring the text communication to a live agent (para. 30 text data such as utterances; para. 4 “exchanges of chat messages” also referred to as utterances and an issue that the customer would like to address is referred to as an intent; para. 18 the system may determine an intent of the customer based on one or more utterances (an inquiry); para. 25 and 44 when the model fails to predict an intent of the customer, a human agent is used to determine the intent of the customer; figure 5 and para. 57-65 where in para. 64-65 the human agent assists; para. 70 “As shown in FIG. 7, when it is determined that the intent is not determined/predicted by the prediction models 206 and 208 based on an utterance (e.g., the utterance 602), the utterance 602 may be transmitted to a human agent, and also to an intent generation module 702”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Leise invention with Kuo with the motivation of improving the accuracy of the category/intent determination.
See Kuo para. 4 “However, as utterances provided by customers during online chat sessions can be drastically different from languages normally used by people in a formal writing, automatic prediction of an intent of the customer based on utterances (e.g., using a natural language intent predictor) can be challenging. Thus, there is a need for developing an advanced intent predictor for predicting intent based on utterances and providing a mechanism for integrating the advanced intent predictor into an online chat robot system” and para. 25 “Thus, after determining the intent of the customer, the human agent may select the selectable element on the chat client corresponding to the determined intent. The online chat system may then use the one or more utterances and the intent determined by the human agent to generate a new training data set for re-training the first model and/or the second model to further improve the performance of the first model and/or the second model”.
As per dependent Claim 7,
Leise/Liao teaches the method of claim 1.
Leise further teaches:
wherein the category is related to a status update of the previously submitted roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; see also Col. 7 Lines 1-11)
Leise/Liao does not teach, but Kuo teaches:
wherein the category is related to a cancellation of the previously submitted request (para. 15-17 where the customer may wish to cancel an order or a payment; para. 48 the user may want to cancel a past transaction; figure 4 and para. 49-56 where in para. 51 the possible intents include an intent to obtain information about a product or a service, an intent to cancel a transaction, an intent to obtain information about a transaction, an intent to inquire about a status of a transaction, etc.; para. 52 the model associates the intent to cancel a transaction with the keywords of “cancel”, “remove”, etc.)
Examiner noting that Leise teaches determining the user’s “intent” within their inquiry and the Kuo modification allows for the user’s intent to be the cancellation of a past transaction. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Leise invention with Kuo with the motivation of improving the accuracy of the category/intent determination. See para. 4 and 52.
As per dependent Claim 14,
Leise/Liao teaches the system of claim 9.
Leise further teaches:
wherein the category is related to a status update of the roadside assistance request (Col. 4 Line 62 to Col. 5 Line 16 where insurance related inquiries are questions such as when the customer’s vehicle will be ready from a vehicle repair facility, steps to take after a vehicle crash, recommended repair centers for repair the customer’s vehicle after a crash, etc.; figure 3C and Col. 17 Lines 12 to Col. 19 Line 12 – specifically Col. 17 Lines 12-51 where the user Jack Lang asks “What should I do with my vehicle?” and the server may respond with navigation directions, estimated repair costs, or an arrangement for a tow truck to pick up Jack’s vehicle and transport it to a vehicle repair shop; Col. 12 Lines 33-41 server identifies the current status of a vehicle repair process including the amount of time remaining in the process; Col. 10 Lines 3-22 where the user may ask about the status of vehicle repair on several occasions; see also Col. 7 Lines 1-11)
Leise/Liao does not teach, but Kuo teaches:
wherein the category is related to a cancellation of the request (para. 15-17 where the customer may wish to cancel an order or a payment; para. 48 the user may want to cancel a past transaction; figure 4 and para. 49-56 where in para. 51 the possible intents include an intent to obtain information about a product or a service, an intent to cancel a transaction, an intent to obtain information about a transaction, an intent to inquire about a status of a transaction, etc.; para. 52 the model associates the intent to cancel a transaction with the keywords of “cancel”, “remove”, etc.)
Examiner noting that Leise teaches determining the user’s “intent” within their inquiry and the Kuo modification allows for the user’s intent to be the cancellation of a transaction. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Leise invention with Kuo with the motivation of improving the accuracy of the category/intent determination. See para. 4 and 52.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Douglas, Jr. (US Patent No. 10,499,190)
Clark (US Patent No. 10,943,463)
Wang (US2023/0245651)
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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628